Business Intelligence Archives

Logistics Analytics: How to Achieve Smarter Supply Chains in 2025

Posted on June 10, 2025September 14, 2026 by Artsyman Lizaveta

Key highlights

In 2025, advanced logistics analytics is picking up speed with AI at the helm. Companies like Amazon and DHL are automating insights, slashing delays, and adapting on the fly.
From predictive ETA and smart warehouse slotting to lane-level forecasting and automated returns, logistics analytics is solving T&L’s biggest challenges with measurable ROI.
Fragmented systems, legacy tech, and talent gaps can block your progress with analytics adoption.

People in the logistics industry know better than anyone how even small disruptions like a road closure on a secondary route can ripple through the entire supply chain, leading to empty shelves and failed SLAs. That’s why data analytics in logistics is mission-critical — it helps anticipate such issues before they wreak havoc.

In 2025, logistics analytics has become smarter than it’s ever been. With advanced AI in tow, it enables companies to create supply chains that think and match the market’s dynamics autonomously. But these smarts come with unique complexities.

What is logistics analytics?

Logistics data analytics allows companies to collect, analyze, and interpret data to gain the intelligence necessary to optimize costs, improve efficiency, and inform decision-making across all operations.

Historically, logistics data analysis came in four flavors, including descriptive, diagnostic, predictive, and prescriptive analytics. These days, however, such classification has gone out of style, as current logistics heavyweights run on analytics solutions that blend multiple approaches.

For example, AWS Supply Chain suite doesn’t separate between the types and offers an integrated AI analytics platform with real-time dashboards for tracking package movements, warehouse inventory, etc., demand forecasting models, dynamic warehouse picking schedules and delivery route optimization, and other tools.

Why ignoring advanced data analytics in logistics is a fast track to failure

By 2032, the global supply chain analytics market is expected to surpass $32 billion — almost a threefold surge from $11.08 billion in 2025. It’s easy to see why things are taking off that fast. Advanced analytics has become a GPS for T&L companies, and without it, they’re flying blind.

Manual data analysis brings critical operations to a grinding halt

If a supply chain and logistics team relies on a patchwork of spreadsheets, documents, and Industry 3.0 systems, they are doomed to a lifetime of manual errors and delays. Customer service reps can waste hours reconciling critical data only to send an irrelevant response to the wrong customer. Perishable cargo goes to waste because temperature logs are buried in someone’s inbox.

Due to the lack of insight into thousands of nodes, teams also spend the majority of their time firefighting. Inventory updates get recorded days after demand shifts, reactive spot-market purchases trigger markups — every hour of reactive management is multi-million, self-inflicted damage.

No real-time visibility, no effective risk management

Without advanced transportation logistics analytics, companies have no ears and eyes to spot or predict a delay before it turns into a costly escalation. This lack of foresight also means acting based on historical data or, in the worst-case scenario, uncovering the problem well after customers complain.

One of our global manufacturing clients experienced it for themselves. Their legacy solution frequently failed to detect delays early enough, meaning issues were often uncovered too late in the delivery process (sometimes by their own customers) leaving little time to respond or re-route. The new system allows their teams to monitor shipments as they happen and inform customers before issues escalate. As a result, the fallout from late deliveries has been significantly reduced, while customer satisfaction — preserved, even in the face of unexpected disruptions.

Market twists can’t be handled without timely data-backed insights

Black swans throw a wrench into the way logistics companies operate. Without a data-driven heads-up, T&L businesses are caught up in rapid and sometimes extreme swings in supply and demand alongside limited transportation resources.

For instance, during the pandemic, one of our clients experienced a stark 7x increase in freight lead times. A solution that forecasts port closures and capacity shortages in real-time could’ve staved off this scenario, so the company made a strategic decision to build one with our team to anticipate and mitigate similar incidents in the future.

Transportation and logistics analytics help companies cushion the blow of such systemic shocks. By unifying historical and real-time data across multiple sources, analytics tools uncover risks in their tracks, allow companies to simulate multiple scenarios, and help businesses regroup way ahead of the market.

The path towards a net-zero supply chain is data-driven

Holding a logistics company to its GHG commitment, emission standards regulations, and customer demand for greener shipping requires a thorough understanding of Scope 3 emissions. But when businesses grapple with 10,000+ products and an army of suppliers, the low-carbon transition becomes a far-fetched goal unless there are advanced analytics tools in the mix.

Data analytics in logistics and supply chain management makes sure companies can track carbon emissions and identify GHG-friendly suppliers. Additionally, this visibility lets teams optimize delivery routes for fewer GHG output, avoid breakdowns before they become a high-emission catastrophe, and bake in GLEC, DEFRA, or EPA standards into every operation.

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12 use cases of logistics analytics in the T&L industry

From reducing transportation costs to improving inventory management and achieving the perfect last mile, here are twelve high-impact logistics analytics use cases transforming supply chain operations end to end.

1. Tariff scenario modeling

The current tariff environment is anything but predictable, so it ushers in a lot of volatility into landed costs. Tariff simulators and AI-driven modeling enable businesses to apply hypothetical tariff changes, quantify margin impacts, and understand the operational trade-offs early on.

For example, companies can run Monte Carlo simulations to model the combined impact of potential tariffs on imported components and use decision tree analysis to determine an optimal response strategy in this case.

2. Predictive ETA estimation and AI-based network re-routing to minimize delays and empty miles

Like the rest of the industry, one of our clients often faced unexpected increases in fuel costs due to unforeseen delays and empty backhauls. To counter this challenge, many companies — our client included — resort to AI-based routing tools to optimize multi-stop and multimodal networks.

More optimized networks lay the foundation for ETA precision, which is then enhanced with AI/ML models to take into account dynamic, real-time conditions like traffic or weather. This also solves the issues of empty backhauls: for our client, network optimization led to a 64% reduction in empty miles and a 23% trimming in drivers’ mileage.

3. Lane-level demand forecasting and capacity allocation for freight efficiency

Around 43% of truckloads are going about partially empty. The origins of this deadweight are often traced back to an imbalance between supply and demand across lanes. Logistics analysis tools give companies data driven insights into the freight demand at the lane level and help predict how it’ll flex based on seasonality or market shifts.

With this granularity of insight, companies can dispatch the right number of trucks and trailers per lane and reduce the number of deadhead miles. Moreover, advanced technologies, like mixed deep learning models, can predict lane speeds with surgical accuracy by capturing spatiotemporal traffic patterns. This allows CAV networks to make lane-selection decisions in real time.

4. Continuous AI-driven warehouse slotting for high-throughput order fulfillment

When it comes to high-volume fulfillment, one-time slotting is not enough, so companies resort to AI and analytics to dynamically arrange storage units. Working in tandem with IoT, smart slotting optimization tools feed on real-time order data, SKU velocity, and storage limitations to strategically house items where they’re needed most.

This living layout can update hourly, allowing warehouses to reshuffle inventory closer to the picker location. For example, Walmart’s AI-driven fulfillment system organizes inventory by department and groups palletized loads, allowing the ecommerce giant to get products onto shelves at its more than 4,700 stores faster.

5. Automated order grouping and route planning to minimize travel time

Almost two-thirds of global shoppers want their orders delivered within 24 hours. However, delivering that fast requires getting all ducks in a row, including smart order bundling and perfectly timed route planning.

Analytics-driven agentic systems can take on this challenge by automatically grouping orders according to delivery locations, windows, and vehicle capacities. They can also constantly fine-tune routes based on real-time traffic, weather, and order-priority data, so that the order ends up in the right location and within the requested time window.

6. Machine learning-based demand forecasting and multi-echelon inventory optimization

Accurate demand forecasting is a non-negotiable for lean supply chains and a heavy lift for companies with traditional tools. By analyzing historical sales data, seasonal trends, market shifts, and other variables, advanced analytics tools can predict future demand at a product, location, or time-period level.

Some ecommerce titans, like Amazon, for example, take it up a notch and pair demand forecasting with multi-echelon inventory optimization. This way, companies can optimize inventory levels across multiple tiers and set buffer stocks across their layered fulfillment networks.

7. Workforce and robotic system planning aligned to predicted inbound and outbound volumes

When there’s an upcoming Black Friday sale, Cyber Monday, or a generally high-demand season, logistics operations highly depend on operational efficiency, specifically, effective workforce and robotic system planning. Data input, like historical order volumes and upcoming promotions, enables analytics solutions to predict inbound and outbound flows to help companies handle the spike.

Based on the actionable insights, a warehouse can ramp up robot deployment during a sales night, fit in extra night shifts for holiday rushes, or set conveyor belts at 50% speed during low-demand periods.

8. Real-time equipment health monitoring and predictive maintenance

Changing tires too late or letting overheated conveyor components go unnoticed can easily equate to people getting hurt and shipments getting delayed. Together with IoT sensors, predictive maintenance constantly keeps tabs on forklifts, tires, and other equipment and creates real-time health scores.

Based on the score, the system can predict failures up to 72 hours in advance and self-schedule maintenance workshops to fix the issue.

That’s exactly how our client, a European cold-chain logistics provider, avoided $850,000 in potential downtime. Our predictive maintenance system scheduled a work order 68 hours before the conveyor’s score dropped to 62/100.

9. Dynamic traffic-aware last-mile routing based on real-time prioritization rules

As the most variable leg of the supply chain, last-mile delivery accounts for over 50% of total shipping costs. This variability can be chalked up to traffic, including urban congestion, road closures, accidents, and other circumstances.

Analytics-powered systems leverage real-time data, such as GPS feeds, traffic congestion levels, and road restrictions, to dynamically reroute delivery vehicles. FedEx’s Global Delivery Prediction Platform also factors in street-level geography, package-level data, and updates like delays and detours.

10. Unified multi-carrier visibility with predictive exception management

Shippers working with a bunch of carriers have to jump between tracking systems just to get a snapshot of their shipment whereabouts. Unified data platforms bring data feeds from all carriers under one roof, so that shippers can access the entirety of shipment data from a single dashboard.

For one of our clients, we’ve also combined multi-carrier visibility with smart allocation rules, enabling the system to self-assign shipments to the optimal carrier based on destination, cost, service level, and historical reliability. Layered with predictive exception management, this platform also flags shipments at risk of delays and missed SLAs.

11. Geozone-specific delivery capacity planning and dynamic driver allocation

Area-based delivery planning is one of the most high-value and often underrated transportation analytics use cases. These solutions break down delivery areas into smaller zones and then predict the order volume for each zone depending on the time or certain products.

Logistics analytics makes sure that companies don’t over- or underallocate drivers and vehicles in any given zone. With real-time analytics integration, companies can also assign gig drivers from crowdsourcing platforms to pick up the slack of immediate delivery needs.

12. Return logistics optimization through pattern analysis and route scheduling

Many T&L businesses work in reverse, with an average manufacturer spending around 9% to 15% of total revenue on return logistics, according to UPS. Logistics analytics helps cut those costs by giving detailed breakdowns of returns by product types, customer segments, regions, or seasons.

Say, a certain SKU consistently gets returned in a specific metropolitan area. Seeing that, the system can suggest pick-up route tweaks so that returns from the same areas or of similar product types are lumped together. Some systems also allow customers to self-schedule in-home returns within pre-set geozones to make returns more convenient for both sides.

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Well intentions that won’t pay off: what blocks logistics companies from leveraging advanced analytics

Although many logistics companies are eager to tap into advanced analytics, they often see their projects hit structural and operational roadblocks that can’t be overcome by enthusiasm or investment alone.

Logistics data remains trapped in isolated, disconnected systems

78% of supply-chain executives say their companies still run a hodgepodge of systems for inventory, ordering, logistics, and planning. It means that the ERP, TMS, WMS, and partner data are locked behind standalone software, creating a fragmented view that stonewalls advanced analytics.

The solution to that fragmentation lies in data consolidation — creating data warehouses that house unified data and setting up API integrations for seamless data flows between systems.

Legacy systems can’t support modern analytics demands

Outdated systems run on stale data formats, rigid architectures, and batch-focused workflows. They can’t handle sensor data, they lack role-based access control, and they don’t have cloud-native compatibility by default. In simple words, they aren’t built for that sub-second intelligence advanced analytics is aiming for.

Unless modernized with APIs, middleware, edge gateways, or overhauled completely, legacy systems can never cover the needs of competitive analytics solutions.

Logistics teams lack internal data science expertise

According to a global research study, the lack of internal expertise is the third most cited barrier to technology implementation. Logistics data analytics is no exception — no amount of analytics can fix bad inputs and T&L companies need data scientists to prep those inputs for AI models.

As on-site data science talent is often too expensive or limited to secure, many logistics companies turn to third-party data analytics partners to bridge the talent gap.

How *instinctools can help with adopting logistics analytics

Marrying logistics and analytics is not just about tools. To make analytics work for your T&L business, you need a solid data foundation and analysts who speak both data and supply chains. As a data analytics partner of 25+ years, *instinctools brings in tech experts who understand both and can take over the end-to-end process:

Data preparation — performing automated cleansing, normalization, and feature engineering to make sure your logistics data is high-quality.
Data integration/consolidation — setting up ETL pipelines that bring disparate data sources onto a centralized control tower.
ML algorithms implementation and fine-tuning — developing or customizing machine learning models for predictive analytics.
Visualization — building interactive, straightforward dashboards for real-time monitoring and insight exploration based on BI tools.

With ISO-certified processes in place, we also make sure that your data infrastructure is based on watertight data governance frameworks to promote the quality, consistency, and compliance of your logistics solution.

Summary

Rising customer expectations and perennial supply chain disruptions have put an unprecedented strain on transportation and logistics companies. Advanced analytical techniques help T&L businesses stand up to those challenges with demand forecasting, route optimization, dynamic last-mile routing, and predictive maintenance.

But smarter analytics starts with smarter data. And that’s where most T&L companies get stuck. Siloed data sources, legacy tech, and the shortage of internal tech talent make advanced analytics near-impossible to implement. If you too are experiencing similar roadblocks or generally need an advanced analytics tool for your T&L company, *instinctools is ready to help.

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Focus On Results Not Process: Augmented Analytics Guide

Posted on February 28, 2025September 7, 2026 by Artsyman Lizaveta

Key highlights

Augmented analytics takes business intelligence to the next level by automating data analysis and generating insights.
AI-powered analytics solutions transform how organizations interact with data, making insights accessible, intuitive, and action-oriented.
To fully leverage augmented analytics, businesses must ensure data quality, implement strong governance, and build an AI-ready culture.

Data is now like air. It’s all around us. Every manager, front-line employee, and business user needs to be able to breathe it in, break down its composition, and leverage it to inform their decision making. Most importantly, they need to do it fast and organically to capitalize on fleeting opportunities.

Augmented analytics makes it easier for data scientists to nail transformational discoveries — while also accelerating decision intelligence for everyone, without a data background. With 50% of business decisions predicted to be augmented or automated by AI agents by 2027, the early adopters stand to reap the biggest rewards.

What is augmented analytics and why is it a step above traditional analytics?

Augmented analytics is the type of advanced data analytics that builds on artificial intelligence and machine learning to democratize business insights, take on busywork from data science teams, and provide tailored suggestions to users based on their roles, preferences, and past behaviors. Augmented analytics also automatically generates such complex capabilities as forecasting and model building.

Unlike conventional analytics, augmented analytics tools don’t just follow instructions. They go out of their way to anticipate your needs based on the contextual and behavioral cues pulled over time and offer you the insights you never knew you needed. This transformative approach to business intelligence stems from the following enabling technologies:

Machine learning — as the core engine for augmented analytics, machine learning sets the overall framework for intelligent automation, allowing such tools to autonomously level up based on new structured and unstructured data. Machine learning is also where advanced analytics functions like predictive analytics come from.
Conversational AI — the combination of natural language processing (NLP) and natural language generation (NLG), enhanced by detailed prompt-engineering, enables analytics solutions to build on internal and external context (RAG), turn complex data into a clear, concise narrative digestible for anyone, and go out of their way to offer insights proactively.
Automation — the final piece of the puzzle that enables these solutions to handle routine tasks across the data analytics lifecycle, such as data preparation, cleaning, and integration.

Augmented analytics grows up: agentic analytics

Augmented analytics is an ever-evolving field that has lately collided with AI agents, programs capable of autonomously performing tasks on behalf of a user or another system. While augmented analytics is focused on enhancing human decision intelligence, agentic analytics tools aim to proactively identify problems, generate solutions, and even take action based on the insights — and do so with no or minimal human intervention.

Also, agentic analytics solutions can actively go out and tap external environments beyond their initial training set to handle complex, multi-step analytics tasks.

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Watch augmented analytics software in action

Your CRM system, accounting software, ERP platform, and other business software jot down real-time actions and changes. They are awash with data that could help you save money and boost profits. But that’s not happening because traditional analytics tools are a tough lift for everyone but its savvy users. AI augmented analytics fixes that.

Automating data management

To become insights, your data — that comes from a variety of sources — needs to be consistently formatted and cleaned. But if it comes in wildly different forms and shapes, your data analytics team will spend days scrambling to understand the structure before they can get down to analysis.

One of the biggest benefits of augmented analytics is that it introduces automations throughout the entire data management lifecycle:

Collecting data from multiple, sometimes complex sources, automatically identifying different data types and relevant attributes within datasets, including outliers.
Cleaning and preparing the data (filling in missing values, identifying outliers, removing duplicate records, etc.).
Indexing and clustering data (semantic indexing, patterns/trends/segments identification).
Revealing hidden connections in datasets, generating hypotheses from these connections, and automatically building predictive models.

Democratizing access to data

When strategizing their efforts, every company grapples with three core questions: What happened? Why? What’s next? Classical analytics with its learning curve makes finding those answers, let alone easily accessible ones, a challenge. Not every person on your team can boast the knowledge of statistical or analytical methods — or SQL commands.

Augmented data analytics allows users to ask questions in plain English instead of writing a complex query. The NLP engine inside acts as a translator and intermediary, making data insights more intuitive, approachable, and accessible to everyone. In simple words, the interaction boils down to the user asking the tool, “What were our sales in Q3?” and the system breaking down the stats in response.

Giving recommendations to users

Another reason why big data and augmented analytics tools are billed as the new era of insight generation is because they speak your industry semantics and can broadly track user behaviors. It means that over time, they sync with the user’s chain of thought and proactively suggest actionable insights, metrics, and insights that matter most to them and their work, curated into a custom digest.

Users might not ask explicitly, but augmented analytics solutions anticipate their needs and surface relevant information before they even ask. It’s less about asking the right question and more about the platform already knowing what you might need.

Visualizing data

Augmented analysis systems don’t just explain the why behind certain trends. If married with multimodal systems, they can also visualize data on demand, choosing visualizations that best communicate the key insights. Not only that, but visualizations in this case can incorporate deeper insights across multiple modalities, such as images of popular products and snippets of customer reviews.

With augmented analytics, you can also endlessly get into the nitty-gritty details of your data with no pre-defined drill paths or data aggregation required.

Breaking down the what, why, and how with no effort

100% automated insights, 0% busywork — that’s likely the biggest benefit you can gain from augmented analytics. It redefines the way companies approach analytics, making the whole process hassle-free. Users can simply ask the system for insights and let it determine the best approach to dissect data.

Whether it’s forecasting, goal seeking, scenario analysis, or any other data task, augmented analytics takes the heavy lifting from a human and returns insights in a ready-to-use form. And if the system comes across any complementary data, it’ll surface it, too.

What augmented analytics can do that BI cannot

Essentially, traditional BI tools empower users to analyze data, while augmented analytics tools empower users to understand it, with or without the necessary technical background.

	Augmented analytics platforms	Business intelligence platforms
User proficiency	Accessible to business users with no coding or statistical knowledge needed	Requires the involvement of technical experts (analysts, data scientists)
Data exploration	Enables free-form data exploration with natural language processing and AI-powered guidance	Relies on pre-defined reports and dashboards
Insights generation	Automated discovery of insights, patterns, anomalies, and trends	Manual, time-consuming process
Data preparation	Automated data preparation and cleansing	Extensive data modeling and preparation required
Output format	Static reports, charts, and dashboards	Interactive visualizations, narratives, and recommendations

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The mechanics of augmented analytics tools

The foundation of augmented analytics is a complex orchestra of interconnected components that parlay into a holistic system, uniting data, technology, and human understanding. It all begins with a robust data integration layer that connects a myriad of structured and unstructured data sources, including SQL/NoSQL databases, CRMs, ERPs, streaming data sources, IoT devices, external systems, and more. The system retrieves the relevant data points and transforms them into a consistent and usable format on its own.

Augmented analytics relies on AI and machine learning to automate data exploration, build predictive models, and generate deeper insights. Based on the insights and predictions, the system can recommend an optimal decision path — or even automate decisions based on the analysis if it’s operating within well-defined and repetitive scenarios.

Who can benefit from augmented analytics?

Regardless of the use case, augmented analytics capabilities do not aim to replace human judgment — they amplify it. In this case, the entire paradigm is being shifted: instead of dealing with the tedium of data collection, data analysis, and insight explanation, humans become strategic supervisors.

Business users like marketing teams can shorten the time-to-insight, improve their data literacy by tapping into insights previously locked behind technical expertise, and focus on taking action. For example, marketers can monitor social media sentiment along with website traffic to see how their campaign is faring.
Instead of requesting a report from other departments, executives can ask their augmented analytics platform about sales trends, conversion rates, and campaign performance.
Data analysts can leverage augmented analytics features to reduce the iteration loop in their data analysis activities, automating time-consuming tasks like data prep, model building, and report generation.

One of our clients, a wealth management firm, spent months exploring the reason behind quarterly customer churn. Our developers built a custom augmented analytics solution that allows the VP of wealth management to peer into the trends on their own and give the data analytics team more time to focus on higher-value initiatives like strategic recommendations. The time-to-insight reduced from months to days, and data preparation time decreased from weeks to minutes.

Augmented intelligence — augmented challenges?

Although augmented analytics ushers in a new era of accessible data insights, it brings in new challenges, too. Some of the challenges are conditional and purely mechanical, while others occur due to the technology’s ongoing evolution and refinement.

Maintaining data quality

The accuracy and strategic potential of your augmented analytics solution hinges on the accuracy and reliability of the underlying data. But the sheer scale and variety of such data requires a significant manual effort from companies to cleanse, tag, and enrich it.

Although the lion’s share of tagging and cleansing is automated, data teams still need to actively participate in the process — like developing tagging and cleansing strategies to deal with the subjective nature of data such as customer sentiment or product reviews.

Privacy and bias concerns

As data analytics transitions from fact-finding to conversation, companies enter the grey zone of data ethics. More underlying data means more potential biases, higher risks of data inference and profiling, as well as expanded vulnerability surface.

Augmented analytics companies should take a multi-faceted approach to address the ethics risks that includes robust data governance frameworks, bias detection and mitigation strategies, and human-in-the-loop oversight. Adversarial conversational AI solutions can also improve the robustness of augmented analytics solutions against malicious attacks.

Expensive conversion processes

Although companies get an AI data-savvy assistant, this capability comes with a hefty subscription fee. Cloud infrastructure, increasing networking costs, and the dedicated effort required to enable augmented analytics capabilities, although not overweighing the ROI, calls for substantial money injections. So does the ongoing need for LLM recalibrating, model retraining, and system updates.

Good news is that companies can at least bring down the cost of solution development and model retraining. For example, augmented analytics vendors can build off pre-trained models and leverage AutoML tools to reduce development time and costs. MLOps tools like AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform can automate the entire model retraining workflow.

Non-determinism of the model

Less of a challenge, more of a consideration, the non-determinism of an augmented analytics solution occurs when the same input leads to different results each time. It doesn’t inherently cause less accurate insights, but may jeopardize the credibility of the solution and difficulties in reproducibility.

When non-determinism is not intentional, it could signal a bug or default in the system. In other cases, varying results crop up due to contextual changes, real-time updates, or stochastic algorithms at the solution’s core.

New roles and capabilities

Sometimes, augmented analytics projects go off track simply because companies fail to build up their own capabilities or repurpose necessary skills. Whether it’s a lack of AI and machine learning capabilities, solid data infrastructure, or even the meager organizational capacity, the result is often the same: companies abandon the initiative without unlocking “alpha”.

Solutions with this level of promise require companies both to supplement existing AI and data roles with skills like DataOps, vector database development, and others — and acquire new talents such as AI ethics stewards, prompt engineers, or unstructured data specialists.

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How to put your best foot forward with augmented analytics

Whether you’re in for quick wins or want to lay a foundation for broader-scale transformation, augmented analytics necessitates a strategic roadmap.

Customizing models

To make sure you can bet your bottom dollar on your augmented solution, you need to train the underlying LLM or SLM on your data grounds and fine-tune it through prompt engineering. Keep in mind that however advanced, this analytics type is only as good as the data it’s fed. Ensure the foundational data is clean, consistent, accurate, and reflects the up-to-date business context.

Establishing data governance and compliance strategies

Augmented analytics tools can benefit any industry, even compliance-heavy ones like finance and healthcare, provided data governance considerations have been properly addressed. Role-based access control, comprehensive data security strategy, metadata management, and a clear data lineage framework are crucial for building trust in the insights generated.

Since many regulations push for transparency in AI systems, organizations also need to set up XAI techniques to make the model’s decision-making process more understandable and traceable.

Treating augmented analytics as an engineering discipline

If you value long-term success, treat augmented analytics as an engineering discipline, not just a business project with defined budgets and timelines. Focus on a robust and easily scalable foundation with modular components that can be reused for other use cases and support the multitude of business needs over the long term.

Embracing a culture of continuous improvement will enable your team to enhance the solution over time and easily calibrate it to your evolving business needs.

Promoting AI-enabled, but data-literate culture

Although augmented analytics bestows users with unmatched self service capabilities, the final decision is on humans. Keeping the potential non-determinism of the augmented analytics technology in mind, it’s important to instill a data-literate culture where your teams can challenge AI insights instead of just going with them.

This level of discernment can only stem from an AI-ready organizational culture, supportive of change management — including training and upskilling, stakeholder engagement, and augmented analytics advocacy team to navigate the human side of the innovation.

AI is the new BI

Regardless of economic headwinds, companies that can swiftly translate data into insights will not just survive, they will tower above others. This makes augmented analytics technology by far one of the most salient capabilities for high performers.

Despite its imminent benefits, augmented analytics is more than just flipping a switch. The technology itself is only part of the equation. The real challenge lies in beefing up the necessary technical skills and updating the organizational AI governance roadmap.

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FAQ

What is augmented data analysis?

Augmented data analysis is the type of analytics enhanced by AI and ML technologies to automate various aspects of the analytical process. These technologies make augmented analytics work better than any other type of analytics, enabling more valuable insights, faster data preparation, better data discovery, and automated predictive insights.

What is an example of augmented analytics?

Examples of augmented analytics include automated demand forecasting based on the user’s natural language queries, such as “Predict the demand for our flagship offering next quarter, based on the upcoming marketing campaign”. Other augmented analytics examples include smart data profiling, automated data integration, automated data discovery, and more.

How to Create a Business Intelligence Strategy? | Expert View

Posted on January 18, 2023September 7, 2026 by Artsyman Lizaveta

What should be the first step of your BI journey? Choosing a tool? Collecting data? Not so much. It’s a comprehensive business intelligence strategy that will help you move toward advanced analysis.

While BI can make your reporting faster and graphs more sophisticated, even without a proper plan, you still will be deprived of a holistic view of how to use the technology to your maximum benefit. As you probably know, the devil is in the details. That’s why, not to let them slip away, imperiling your whole BI initiative, you need to follow particular business intelligence implementation steps.

In this article, we analyze real-life cases of our clients who have leveraged a BI strategy despite being at different stages of BI implementation. Some were new to using business intelligence and wanted to ‘make it right’ from the very beginning, others had been already using BI tools, but mostly intuitively, without any specific plan. Nevertheless, all of them could see how much the quality of data analysis improved with proper strategizing.

If you don’t want to fall by the wayside, here are the reasons to care about business intelligence strategy

Business intelligence isn’t only about creating flashy presentations. The potential and value of the technology are much broader, and they can be unlocked through a proper business intelligence plan. With a BI strategy, you can tackle data issues more efficiently, build a holistic, well-integrated system, and ensure it remains to function properly.

Saving time and money. Acting on a whim is fraught with costly mistakes. No one wants to waste money on features that, in the end, no employee will need or buy licenses for 100 employees if the system will be used only by 20 of them. A BI strategy allows you to think through such things in advance, saving time and money.
Adopting advanced risk management. You minimize the likelihood of losing time and money by analyzing each step in detail. Furthermore, with such an approach, you’ll be able to detect weak spots and bottlenecks earlier and fix them right away.
Building end-to-end data analytics across your organization. A well-thought-out BI strategy empowers you to break data silos between departments and connect all your data sources to get end-to-end analytics. Such an approach allows you to track processes throughout the organization, which means you can spot problems in time and make decisions backed up by accurate and up-to-date data.

A well-designed and nuanced BI strategy helps to significantly revamp internal business processes, which, in turn, has a positive impact on the quality of service or product you provide your customers.

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3 areas you can’t ignore when building a BI strategy

To create a robust business intelligence strategy, you should take care of vision, people, and processes — paying equal attention to each element and their interrelationships. Let’s investigate them in more detail.

Vision

Before adopting any technology, you need to ask yourself a couple of fundamental questions, such as “What is its practical value for our organization? What do we want to achieve thanks to it?” The answers to them will help you outline an action plan on BI implementation or optimization.

To build a data-driven culture in your organization, you should also review how you handle your data. Rather than thinking of it as raw material for analytics, treat it as a product with a real return on investment.

People

When creating a BI implementation strategy, you shouldn’t discount your employees and their skills. Otherwise, you’ll waste your budget and time adopting the technology, which will be sabotaged by people who got used to working differently.

Therefore, take into account who will interact with a BI tool to provide suitable dashboards for each decision-making level. For example, an employee responsible for machinery maintenance, data analyst, and CEO require different types of dashboards.

Check here to learn more about operational, analytical, and strategic dashboards.

Another point to keep in mind about your employees is their technical background. Creating a business intelligence strategy and roadmap for a tech-savvy company isn’t the same as building it for an organization that isn’t on close terms with digital technologies.

When we understand the level of users’ technical expertise and the processes they are involved in, we can create custom dashboards for each role instead of one general dashboard for all company processes with a mind-boggling number of filters. This expands the range of BI users from executives and analysts to rank-and-file managers and employees.

Process

This side of the BI strategy is about setting up the technology implementation process. For this, you should think about employing a Chief Data Officer (CDO), define a project budget, consider security and compliance questions, and identify KPIs to track the effectiveness of the BI blueprint and technology adoption.

Also, it’s crucial to take care of knowledge transfer from your technology partner. Therefore, pay attention to establishing a BI competency center. BICC is your in-house team that will make the self-use of the system truly comfortable for non-tech savvy employees and handle minor adjustments such as dashboard configuration.

With this approach, you make users more advanced, and as a result, increase the speed of change and the efficiency of working with the BI system. You’ll also become less dependent on your technology partner and only turn to them for major modifications such as connecting new data sources to the system, visualizing data on new business processes, and more.

The final result of the work on the process area of a BI strategy is the development of a BI roadmap. It’s a document that consistently describes the particular steps necessary for implementing BI, project milestones, deadlines, and KPIs to evaluate your progress.

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Data profiling: a fundamental step you should take

Before loaded into a BI system, your data must be checked for quality and consistency. This is what data profiling is about.

Data quality. Poor data quality is the reason for a myriad of business problems, such as inaccurate financial forecasts, regulatory issues, lost customers, reputational damage, etc. If you don’t take care of it in the initial data analysis stage, dealing with low-quality data will drain your staff’s time — and that’s before we mention irrelevant results. Statistics show that covering repetitive problems related to data quality may take up to half of the employees’ working hours.
Data consistency. Duplication of data in different systems can reflect suboptimal business processes, wherein employees manually and in an uncoordinated way enter the same information in two different systems. As a result, input errors and incomplete matching inevitably occur. Instead, the rule of a single entry point for any data should work, and then the systems should only exchange it rather than create a copy.
Data classification. This is needed when data comes from a variety of sources. It can be your data lake, ERP, or traffic from your site, to name a few. In addition to the source, you should consider data structure (structured or unstructured) to properly classify data, as it simplifies determining the update frequency for each data profile.

When profiling data, you may uncover that some of it isn’t updated as often as needed for effective decision-making.

Consider that you don’t necessarily have to strive for real-time updates. Usually, you only need such things when dealing with financial markets. However, if you own an e-commerce business and your logistics system and ERP are synchronized only once a day, there might be a situation when the product has arrived at the store but it isn’t displayed on the site. Thus, you risk losing customers because of the insufficient frequency of data updates.

We suggest doing continuous data profiling. Leverage automation to speed up and simplify the process.

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Architecture and toolset selection

When picking BI tools, you need to choose those that will allow you to ingest, store, process, analyze, and visualize data easily.

1. Ingestion

Ingesting data implies taking raw data from primary sources without transforming it. You need to choose an appropriate method of data ingestion.

Real-time processing. Once the ingestion software recognizes the data piece, it downloads the data in your data lake or warehouse as a separate object.
Batching. With this approach, data ingestion software collects data, groups it based on criteria or a schedule, and then sends it to the data storage in batches.
Micro batching. This is a subtype of batch processing. The difference is that the batches are smaller.

Data ingestion software depends on the type of data you process, the data sources you use, and the speed you need to access the data. Apache Kafka, Azure Stream Analytics, and Amazon Kinesis are the most notable players in the data ingestion tools market.

2. Storage

This is the point where you should identify where your data will be stored. Several options are available. We’ve already covered data lake and warehouse differences when discussing building a solid data infrastructure.

Moreover, you should determine which of your data is ‘hot’ and which is ‘cold’ if you want to save on storing data that you don’t need on hand all the time. Both on-premise and cloud storages offer options for hot and cold data. For instance, hot data that has to be easy and fast to assess can be stored on the solid-state (SSD) drivers and in-memory (RAM), and cold archival data can be kept on optical disks. There’s also warm data that is used not as often but isn’t archived, like the five-year-old sales data you need every few years for a cut-off point. It can be stored on hard disk drivers (HDD).

3. Processing

It’s impossible to directly connect heterogeneous data sources and a data warehouse where information has to be cleared of errors, structured, and classified. You’ll need a bridge, an ETL tool that processes the raw data and unifies it in three steps.

Extract. The tool retrieves data from your data sources, such as spreadsheets, legacy systems, CRM, ERP, analytics, etc.
Transform. All extracted data is analyzed to identify duplicates and delete them, form new columns or split them, etc. After that, the data can be standardized – filtered, sorted, and verified.
Load. The data goes into the repository or analytic software.

The difference between ETL and data ingestion is that there is a data transformation step in the case of ETL.

As long as the ETL process plays the first fiddle in providing high-quality data analysis, choosing a proper tool becomes a crucial undertaking. The decision has to be based on multiple factors, such as your use case (a cloud solution or on-premise one, the necessity of real-time updates, etc.), maintenance specifications, scalability, built-in integrations, and costs.

Struggling to find an ETL tool to enhance your data processing?

4. Analytics and visualization

Defining an analytical toolset is the next step in your BI strategy. According to the Gartner Magic Quadrant, you should pay attention to three leaders in the field of data analytics – Power BI, Tableau, and Qlik. The choice of the most appropriate tool needs to be guided by your requirements and limitations.

Present architecture. It isn’t mandatory to implement a separate BI solution. Analytics can be built into your existing applications to speed up decision-making and its accuracy. Moreover, embedded analytics and immediate access to data encourage users to rely on data more in their everyday tasks.
Current technology stack. If your organization already uses Microsoft products, choosing Power BI and other infrastructure tools from the Microsoft stack is a more reasonable approach.
Range of users and tasks. Tools for a startup and a corporation with 3,000 users will be different. The latter will probably need an open-source solution to eliminate licensing costs or arrangements with the vendor for a special licensing plan and discounts. Whereas a scaling startup can definitely consider other options.

By thoughtfully assembling a toolkit at this stage, you can empower each employee to be a data hero. Here are some examples of dashboards for rank-and-file staff and C-suite team members.

Operational dashboards for employees from different departments include detailed real-time information.

And strategic dashboards for senior-level management include key metrics across the whole organization.

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How we helped a large retailer to increase a turnover by 9%. Spoiler alert: it’s about a logical BI strategy

A well-developed BI implementation strategy empowers you to leverage the technology entirely. Here’s a BI strategy example that allowed one of our clients, a vending machine retailer, to make more accurate decisions faster, keeping up with their business growth trajectory.

The existing solution was inefficient in terms of scalability:

“We never completely realized that we have so much unused data. Only about half of all the data we had was used to make decisions”, says the company’s product and customer experience director.

So how were the carefully designed strategy and the precise BI roadmap developed?

During the Vision phase, we found that business intelligence could improve several company processes:

Finding lost sales
Detecting low-margin contracts
Monitoring the technical condition of vending machines in real-time

Additionally, the client needed an intuitive tool without limits on how much data they could process. And although Power BI is the most user-friendly tool, it also has a limit of 3,500 data points. Therefore, because of the client’s data volume requirement, we chose Qlik, which has no rigid limitations in the number of data points.

After implementing BI software in accordance with the strategy worked out in advance, the client reduced the number of lost sales by 30%, renegotiated low-margin contracts, and minimized vending machine downtime as much as possible. The confluence of these results led to a 9% increase in total client turnover in half a year.

For more details on the aforementioned project

download the full case study

Unleash the full potential of a BI system

Ideally, a strategy is developed before any steps are taken to implement the solution. But what about organizations that have already managed to implement the technology themselves, even picked some low-hanging fruit, but then realized that the capabilities of BI could extend much further?

At some point, users who are not new to BI understand that they can’t unlock the full potential of the technology without a proper business intelligence strategy and roadmap.

A BI strategy for organizations that already use the technology will include the same basic steps — just like for beginners. It’s just as vital for them to keep the Vision, People, and Process in mind, take care of data quality, reconsider BI tools, etc. However, another thing to pay attention to is emerging. Organizations with self-implemented BI have to constantly extinguish fires that inevitably occur, such as handling backlogs and dealing with issues that popped up after BI adoption and can’t be shelved for later.

One of our customers implemented Power BI for employees of all departments and levels. But over a year, they realized that they were not using all the capabilities of the tool. Therefore, we took action. Working in two directions while establishing the business intelligence strategy, we:

Launched in-depth research on the system’s architecture, features, and limitations. It’s a mandatory step to adjust the data storage architecture to the needs of the system’s end users so that employees at any organizational level can independently retrieve the data they need from the data storage and use this information to create customized reports.

Simultaneously with this large-scale process, we worked with the client’s current tasks.

Helped cover ongoing tasks. The customer also had clearly defined tasks, but their in-house BI team was too small and not skillful enough to handle the workload. We tapped into these activities. This way, the client got the reports they needed faster, and we got to know the system’s architecture and the people on the client side so we could pass the knowledge to them.

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A comprehensive BI strategy allows you to increase the odds of your BI project’s success

Without elaboration on the Vision, People, and Process areas, you can’t accurately choose the solution architecture or the most powerful toolset for your tasks. Therefore, it’s better to take a holistic approach to implementing a data analytics solution by developing a BI strategy. Also, keep in mind that your BI project doesn’t end with BI deployment. It’s a long-lasting initiative. Your BI software has to evolve constantly as your external and internal conditions change, and new processes, systems, and data appear. To make these adjustments smooth, you need a business intelligence strategy. Without a robust BI strategy that is periodically adapted to the current state of things, it will be much harder to figure out how to move forward.

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The Wild West of Open Source Business Intelligence

Posted on January 16, 2023September 7, 2026 by Artsyman Lizaveta

Long gone are the days when the market of business intelligence was at the mercy of proprietary BI tools. In 2023, top-flying companies and startups opt for open source business intelligence to supercharge their business resilience.

The data shows that public BI is on the rise due to its usage among Fortune 500 companies. Nearly all of the most profitable businesses use open source software, and the tech community is heavily backing this trend. On GitHub, there are more than 140 million open source projects.

The high rate of adoption is a result of the advantages that open source BI offers, such as agility, cost effectiveness, and personalization. Nevertheless, like any other technology, it has some potential issues that need to be examined. In this article, we will provide an overview of the risks as well as share the examples of where open source solutions are most suitable.

Three pillars of open-source BI tools

Although non-proprietary software has made significant inroads into enterprise systems, it’s still wrapped in myths. Below, we outlined the main differentiators of open-source BI solutions or OSBI, that separate it from traditional, closed products.

An avid developer community makes business intelligence open source

Transparent BI software is backed by a large community of enthusiasts. These communities don’t have a corporate hierarchy and allow each developer to make their contribution to the open source code. Contributions may include custom extensions, security patches, and others.

Open source BI tools are free. But not completely

Unlike commercial products, public business intelligence software doesn’t charge any license fees to access its core functionality. However, these tools may still bill an additional amount for other add-ons or lack some functionality. That’s why Apache Superset has drill-down limitations in data visualization tools.

OSBI is not synonymous with free commercial products

Some popular business intelligence companies roll out a free version of their canned BI software. However, a license-free model or a trial period does not make these freebies open-source. Users face different kinds of constraints in free versions of commercial software, for example, Tableau Public.

The bright side of open-source business intelligence

Non-proprietary solutions are treasured by thousands of companies and tech-savvy individuals. Just like for-sale products, public business intelligence guides its users into taming huge datasets and generating critical insights.

Here is a breakdown of other unique benefits open source BI software has in store.

No license fees

Open source business intelligence platforms require little to no upfront investment since they are accessible with no licensing. In most cases, OSBI products offer the core modules at no cost, while additional features can be unlocked for an affordable fee. For example, you can get an infinite number of reports and dashboards, yet your data storage and data connectors will be limited.

If you already have an in-house team of developers, they can build the needed functionality on top of core modules with no additional costs. In any case, open-source software doesn’t incur the overheads of commercial licensing and offers an affordable takeoff for small and medium businesses.

A dedicated community

According to StackOverflow, open source solutions often topple or are on par with closed source software in terms of quality. The high quality of open source software is courtesy of a large developer community that is collectively working to enhance the solution.

A strong and mature community, in turn, translates into a number of other benefits, including:

Faster response to market trends;
Faster bug resolution;
More consistent release cycles, which equal regular updates;
Better reaction to security issues;
The diversity of ideas with no bias or lopsided vision.

For companies, it means that it’s not that difficult to find experienced developers. In some cases, enthusiasts can even chip in with a free custom feature for your unique case.

No vendor lock-in

Statistics show that 62% of companies use open source software to avoid vendor lock-in. Open source BI allows businesses to use an optimum set of tools to fit their unique footprint with no price hikes. Most importantly, you don’t need to pay for a package solution from the vendor. Instead, you can get the best of the platform and combine it with the tech stack your company uses.

Instead of being shackled by one technology, you can test your options and decide on the most optimum one. It also means that you can keep pace with the new transformational trends without costly migration or switchover.

Freedom of choice

Non-proprietary systems put you in a favorable position to hand-pick each component of your BI solution. While many commercial BI products focus on specific fortes, such as ETL pipelines or interactive dashboards, open sources tend to excel as a solid, full-fledged BI solution. This is a direct result of strong community support and a regular rhythm of new features and updates.

Thus, your developer team can take out each system component and enhance it or combine it with other functionality. For example, at *instinctools, we use data integration studio and Apache Airflow to set up a custom-built ETL pipeline. At the visualization stage, our BI engineers manipulate visualization components with hand-coded libraries such as D3.js to add funnels, pivot tables, and other custom representations or open source BI tools like Redash or Metabase.

All the benefits mentioned above naturally flow into the power of customization. As developers have access to the core code, they can make changes on demand to better suit their needs. Unlike a closed system that locks users in, open-source allows them to adapt and modify the code to meet a particular need or application.

Therefore, coders can tweak and twist open source software for a unique fit — be it functionality or design — to make it a natural part of any type of operating system for any application.

More flexible integration options

The adaptability of open-source tools allows for integration tolerance to support personal data processing needs. It means that you can seamlessly embed the OSBI solution into your enterprise system, even if it doesn’t consist of open-source components, with no disruption or system changes. Thus, if the rest of your business ecosystem is open source (CRM, CMS, ERPs, etc), you are free to plug them into your open source BI tool to create a blended single platform of data excellence.

Proprietary software, on the contrary, tends to cover a specific set of integrations that may limit your business intelligence. Power BI, for example, sits within the Microsoft ecosystem and integrates naturally within Microsoft products, including Excel, Azure, Access, and others. For instance, we’ve already discussed that you can marry Power BI with Excel to benefit from both tools at once.

Support

Timely and quality support is a must for any business intelligence software, be it commercial or free. However, OSBI tools add a bit more confidence to the business owners if the latter faces an issue or needs a quick walk around.

Issues are resolved even faster when you have skilled developers at your service.

Security

The secrecy of your code doesn’t guarantee its security. Instead, making the backend available for the public exposes it to a thousand vigilant eyes. As a result, community involvement accounts for responsible vulnerability disclosures that would take longer to detect if the code was closed.

Moreover, open-source BI software gets faster patches and updates when a severe vulnerability is detected. On average, it may take a couple of days until the vulnerability is eliminated. Open source also fares better for compliance and internal security policies since it’s fully customizable for any regulation.

The dark side of open source BI

Open-source business intelligence may still come at a cost. Below, you’ll find the main downsides of such projects.

Requires experienced developer talent

Ready-to-go technology doesn’t free you from the skill investment. To make the most of open-source business intelligence, you need trained developers who can find their way around it and make it work for your benefit. Improving data quality before loading it into the BI system, adjusting the system to your business framework and maintaining it is also knowledge-intensive and mandates an experienced developer pod.

If you’re looking for a vetted team of developers that can keep your system up and running, *instinctools provides open-source BI services for companies of all sizes. Drop us a line to get a ready-to-go team of BI experts.

Total cost of ownership can be higher than you expected

Until your BI platform is bug-free and fault-tolerant, it doesn’t need any effort from your side. But once you’re facing an issue, your company is left on its own to resolve it. Therefore, while being free, open BI may still require investment to adjust the infrastructure or eliminate a bug. Introducing extensions is also on your payroll.

Basic and hard-to-use interfaces

Finally, public software is purpose-driven. While it isn’t a bad thing, the user experience of open solutions may not keep up with its functionality. Open source business intelligence doesn’t have a team of UX/UI experts to polish its look. Instead, each community member contributes to the software, which makes it a patchwork, rather than a singularity.

To improve user experience, businesses can add new design elements or other strategies. However, these modifications will incur additional costs.

This is how we handled an open-source BI project for one of our clients

For 25+ years, our team at *instinctools has been helping global businesses introduce custom data analytics solutions that target their unique needs. Open source BI software is what adds an extra individual touch to our business intelligence services and allows our engineers to sculpt a unique business intelligence infrastructure.

One of our clients, an agency of the federal government, was looking to build a custom business intelligence infrastructure. However, a Qlik-based solution, which had been initially offered, didn’t meet both the technical and budget requirements. A ready-made analytics platform required significant upfront investment and an in-house IT department to manage the solution. Moreover, a commercial solution didn’t cover the user’s needs and experience at a given destination.

To eliminate these challenges, our BI engineers suggested a get-started BI infrastructure that covers the individual needs of our client. Our team came up with the following plan to accommodate the data needs of this client:

Postgres – as an open-source data storage;
ClickHouse – as an open-source data storage system for large data analysis;
Apache Airflow – for building a robust ETL pipeline and orchestrating the ETL workflow;
Python – as a core data processing technology for an ETL pipeline;
Redash – as the main data analysis and visualization tool for in-house data scientists;
A custom-made application that generates reports for other user groups.

The open-source component in this custom BI setup helps our client to manage and maintain the solution with no additional effort. It also means that our client can easily find a team of engineers to support the infrastructure.

Who benefits from open-source BI the most?

Although open data intelligence solutions seem like a go-to option for all verticals, some industries and user groups will reap more benefits from integrating them into their ecosystem.

SME and start-ups

Virtually any small business is looking to avoid financial drains to optimize its sales revenue. Public BI software helps SMEs and startups get value for money and is easy on their budgets. As small businesses usually have limited in-house tech support, community support of OSBI is also the wisest approach to troubleshooting.

Consulting agencies

Software consulting companies are among the regular users of BI open source. The latter allows companies to speed up development for their clients. As consulting agencies also have experienced developers on-site, they can easily adapt the solution to individual requirements.

Companies operating on legacy software

For some organizations, the hassle of software migration isn’t worth the trouble as they are more focused on their business than on the tech components. Typically, it’s the government or finance verticals that rely on a tried-and-true infrastructure. Thus, no matter how edgy the solution is, it won’t bring them any ROI or business value.

Open-source analytics tools, on the contrary, will still help them adapt to the new reality with no switchover or much budget allocation.

Organizations that seek automation and connectivity

Companies that may benefit the most include those using Internet-of-Things-connected devices, autonomous vehicles, and consumer products as well as retail, manufacturing, and industrial applications. Overall, any industry that needs a consistent and 360° data view can easily integrate the OSBI into the existing infrastructure.

Summing up, public BI strategy can benefit anyone who has specific data needs but lacks either time or money to build a full-fledged platform.

Your shortcut to data supremacy

Staying on top of your data is a mandate in 2023 to make informed business decisions. But despite a widespread opinion, business intelligence doesn’t have to be a pricey undertaking. Public BI platforms enable companies to drive data efficiency without large investments or huge technical dedication.

Being a go-to option for SMEs and startups, open-source BI solutions can both invite data consciousness at the early stages of a BI journey or become a highly customizable business asset for existing infrastructure.

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Power BI vs Excel: which tool fits your business needs

Posted on January 5, 2023September 7, 2026 by kostyn.gricuk

Often, companies that analyze data with Excel spreadsheets consider this tool the best option for this task. Power BI fans, on the other hand, are sure that their favorite rocks. To make the right decision on which Microsoft product to choose for your company, you need an unbiased view of them both.

This article encompasses a very detailed comparison of the two business analytics tools. The insights are based on the facts of the fast-growing big data market size.

So, if your company faces the issues connected with the efficient processing of big data and is looking for an option of how to cope with data volume growth, keep reading this article and find the best solution to meet your needs.

Power BI vs Excel: A comprehensive comparison

This section analyzes the difference between the two reporting tools based on the exact tasks you manage in your ordinary working life. This will help you understand the Power BI advantages over Excel and vice versa, depending on what you need, and consider it in your BI strategy.

Multiple sources input

Task: you need to connect multiple data sources for automating data inputs in real time.

Power BI vs Excel comparison: both reporting tools can address this issue almost with the same efficiency. For this, they use Power Query and get data from almost anywhere in real time.

However, for the process automation and exclusion of manual inputs, Microsoft Power BI uses Direct Query, whereas Excel has data connectors.

As for the ease of the data entry process, it is similar in both cases. The advantage of Excel, though, is the possibility to use unstructured data sets from unstructured sources and their manual or macros-driven processing afterward. However, people looking for Power BI benefits over Excel sometimes say that it’s not effective enough for steady dataflows, although it may be helpful for the operational work of the back office.

Data transformation

Task: you need to automate the data conversion from one format or structure to another for data processing at a later date.

Power BI vs Excel comparison: both business analytics tools efficiently handle this task thanks to Power Query. The latter allows for about 350 data transformation types. For instance, you get data from SQL Server, CSV file, OData, or any other data sources and convert them into a format convenient for data processing in Power BI or Excel. Power Query will do this transformation for you automatically.

Big Data Performance

Task: you need to analyze big data and build pivot tables for better data performance.

Power BI vs Excel comparison: both tools claim they work with millions of data rows, but Power BI still benefits over Excel in this task. *instinctools’ analysts who are experienced in using both Microsoft Power BI and Excel deny the two have a similar efficiency level.

According to the tests we’ve run, Excel effortlessly handles about 200-300 thousand rows, with one Excel sheet covering a bit more than one million rows. If you want to process more, you need to compile data from different sheets, which is time-consuming and not very convenient at all. Meanwhile, Power BI is more efficient when it comes to a data volume of up to 50-100 million rows, which is 100-200 times more than you can do in Excel.
— Dzianis Yushkevich, Senior Business Intelligence Analyst, at *instinctools.

Another drawback of Microsoft Excel is that the Power Pivot data model is not available in all the Excel versions. Apart from building pivot tables as the standard Excel does, Power Pivot creates the more sophisticated data models, with filtering data, renaming columns while importing data, and much more.

Data refresh

Task: you need to update your data regularly to support its relevance.

Power BI vs Excel comparison: both reporting tools allow for automatic and scheduled data refreshing. Although some sources say that Excel automates data updates only with VBA, the pre-scheduled data refresh is also available.

When it comes to VBA usage, it cannot automate the data refresh process entirely because sometimes it will require a user to confirm certain steps. Apart from the inconvenience, the key stopper here is that users without basic knowledge of VBA cannot do this effectively.

Data modeling

Task: you need to do data calculations.

Power BI vs Excel comparison: in Power BI, the only option for data modeling and calculations is to use data analysis expressions (DAX). This is a library encompassing functions and operations that can be used for building expressions and formulas. In Microsoft Excel reports you can operate both DAX and standard Excel formulas, which is easier for people not acquainted with DAX.

Data visualization

Task: you need to visualize datasets for a better understanding of trends and fluctuations.

Power BI vs Excel comparison: the visualization toolset is very similar in Power BI and Excel. Users highlighting Power BI advantages over Excel may not know that the extra modules and add-ons can be used in Excel, too. Excel even has a module for building advanced business diagrams. Although both data analytics tools utilize the functionality of Power Query (to automate the processing of raw data), Power Pivot (to handle big data), and DAX (to make advanced calculations), Power BI forces users to get acquainted with this toolkit, whereas Excel does not.

Generally, Power BI is like a combination of Excel and Access in one application, but with more powerful options for data processing and a broad portfolio of ready-to-use visualization solutions. Power Query is also integrated better into Power BI thanks to its more powerful internal memory and processing.
— Dzianis Yushkevich, Senior Business Intelligence Analyst, at *instinctools.

If you are looking for a tool with better interaction opportunities, Microsoft Power BI will come in handy here, too.

When it comes to diagrams, Power BI benefits over Excel because it is more convenient in use, there is less room for error, and since the diagram is connected to the dataset directly, the transformation of data into visual happens almost instantly.
— Dzianis Yushkevich, Senior Business Intelligence Analyst, at *instinctools.

Flexible analytics

Task: you need to aggregate data in different ways depending on the task.

Power BI vs Excel comparison: Power BI automates the reporting process and can work with multiple dataflows. Meanwhile, Excel allows for versatile advanced modeling and solving non-trivial analytic tasks. Taking a helicopter view, Power BI utilizes matrix tables, decomposition trees, insights, and key influencers. But, only in Quick Insights are there up to 32 insight cards.

As for Microsoft Excel, you can create dashboards and easily analyze data with Pivot Tables, using formulas anywhere you want, entering data, or applying visualization templates.

Data security

Task: you need to share your reports inside or outside your organization, and still prevent any hijacking associated with the fraudulent activities of third parties.

Power BI vs Excel comparison: generally, Power BI is considered a more secure tool than Excel because of its highly secure password access and restricted access to roles.

Mobile reports

Task: you need to access reports on a smartphone.

Power BI vs Excel comparison: Power BI benefits over Excel reports in this question because Power BI allows for much easier interactions with its mobile-driven version. As for Excel, although it claims to have the same functionality as the desktop version has, actually, it is only convenient for the report reviewing. If you’d like to make any on-the-go changes, Power BI is a better option for any mobile device.

Cost

Task: you want to choose a cost-efficient tool for your data analysis.

Power BI vs Excel comparison: since Power BI and Excel use different approaches to pricing, it’s better to calculate the total cost of each tool. Power BI has two plans, Pro and Premium. The Pro plan for $9/user/mo allows for a 1GB model size limit, 10GB/user data storage, and standard Power BI functionality. However, if you need paginated reports, advanced AI, dataflows, or application lifecycle management, you need to choose one of the Premium options, for $20/user/mo or at a flat rate of $4,995/capacity/mo, which is more relevant for an enterprise-scale level organization. More detail on the plan difference is available here.

Microsoft Excel comes at a flat rate of $135 if you buy a standalone version or $6.99/mo if it is a part of Office 365 Suite.

What are Power BI benefits over Excel?

To summarize the benefits of Power BI, let’s use the following list:

It processes larger data volumes.
It has more functionality for data visualization.
It automates the processing of data coming from multiple sources.
It assures higher data security.
It is more convenient to use on a mobile device.

Power BI and Excel: the perfect combination of both

If you are still undecided about whether to switch to Power BI, you can benefit from using both tools at once. Although Excel will remain your main tool, you can connect Power BI for some of its functionalities. For instance, you can build Power BI dashboards with multiple workbooks in a single view.

Also, you can easily discover insights with Power BI reports.

Self-service data visualization. Source: Microsoft

In addition, you can enrich your analytics and maintain the flexibility you need.

When is the right time to move forward?

It’s time to start thinking about Power BI refresh data from Excel if your data volumes are growing and you need an efficient tool that can cope with such datasets. This migration will let you process big data, use data on the go, and make your reports more secure. If you don’t know where to start or how to tune up the data analysis process in your company, turn to *instinctools — we’ll provide you with an efficient and cost-saving Power BI service.

How is business intelligence changing the retail industry?

Posted on January 5, 2023September 7, 2026 by kostyn.gricuk

The customer is always right. That’s the first rule of the retail industry, isn’t it? But how do you know what your customer wants, even before they do, and keep them satisfied? After all, customer demand is constantly evolving with new trends emerging every day. That’s where business intelligence (BI) comes in. BI is a small tool for analyzing your business and getting you the results you need. So, how is business intelligence changing the retail industry? And, what can we expect from the future of smart retail?

What is business intelligence?

In a nutshell, business intelligence is a powerful tool used by companies to understand their target customers and market better. Otherwise known as BI, this software takes raw data and turns it into valuable information. This is then transformed into actionable insights that lead to future business decisions.

To understand the value of BI for the retail businesses today, we just need to ask one simple question: which of the following is an example of technology’s impact on the wholesale/retail trade industry?

Amazon
Blockbuster

We’ll give you a hint. One is a company with a market cap of over 1.5 trillion USD, and the other is a nostalgic memory from the past. The difference? One was future-focused, the other looked only at the current market. The results speak for themselves.

Today’s business intelligence tools provide companies with new opportunities and allow them to take advantage of data not only to predict current sales but to see future potential, trends and understand customer demand on a deeper level. But how does BI work and why is it so effective?

Fuelled by data: how would the retail industry use business intelligence?

One does not simply BI and win. Reaping its benefits of a BI tool implementation can only be achieved when the application of business intelligence in the retail industry forms an integral part of the company’s overall BI strategy.

From product to inventory management, sales, marketing, and beyond, business intelligence solution should underline everything. It helps develop that closer customer connection essential to dominating the marketing and predicting future trends. Below are some of the crucial techniques that BI has at its core.

Data analysis visualization. This is all about how you see your data. Data analysis visualization presents data in dashboards and uses customized metrics relevant to your retail company to make informed decisions quicker and based on facts. Moreover, BI tools can be embedded into your existing applications to empower users with immediate access to data.

Reporting. When the bottom line is finance, making sure your business is in the black is vital. BI tools for reporting gather data from all over your company and process it to allow better data analytics and reporting and financial decision-making with a cool head and rational mind.

Predictive analytics. How do you know a strategy will work? The truth is you can’t, not 100%. However, with business intelligence, you can make an evidence-based decision to drive business further. BI allows you to analyze data and make a reasonable guess at the latest trends and customer behaviors that drive sales and impact the company’s future growth.

But, most of all, all that data tells you one important thing – all about your customer and the market. This is what those statistics really mean.

Data tells meaningful stories: benefits of business intelligence in the retail industry

Your customer is an individual. They are not a statistic. Not a market segment. And not a group. In today’s market, they expect to be treated as such. Brands that can tap into personalization and good customer service can deliver 5.7 times more revenue than competitors.

But how do you know who your customer is and what they desire? This is where business analysis steps up to the plate. It allows retail companies to absorb mass amounts of data about their business and customer data – from sales to market trends, consumer behavior and needs, and more – and using BI transforms it to tell meaningful stories.

It is these stories that help you connect with your company’s bottom line – your customers. After all, isn’t how good you think your product or service is. It is how good your client thinks your product or service is.

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Here are the six advantages of business intelligence in the retail industry

Instead of just witnessing how business intelligence is changing the retail industry, be an active player in the process. With the technology in place, your employees will be able to rely on meaningful information when making decisions, uncover pain points in your business processes in the early stages, gather data about your customers from heterogeneous sources to create deeply personalized offers, and identify the most appropriate way to connect with each consumer.

Improve your decision-making

Data isn’t just numbers. It’s behavior. By engaging data in a meaningful way, businesses can better understand their market and capitalize on it. Utilizing all that big data and transforming it into easy-to-digest actionable steps allows retail companies to plan and strategize more effectively based on actual market need, not presumed ideals.

Optimize your processes

No business strategy is perfect from the start. What’s important is to identify and eliminate those errors early on. BI solutions empower companies to continuously assess how their business operations run to gain operational efficiency. By identifying pain points early on, these can be quickly eliminated, and improvements made.

Know your customer

Business is driven by market demand. If no demand exists, what you have is a hobby, not a business. BI tool allows you to gather data about your customers, like never before. And this applies whether you are a brick and mortar store or an online platform.

Here’s an example of a visualization of a customer’s decision-making process that helps you simulate customer-switching and estimate the likelihood of switching from one need state to another.

BI tools enable your business to take both structured and instructed data and transform it into actionable insights. These tell not only about your sales process and any issues but who your customer is. This allows you to optimize for them and their needs, increase customer engagement or predict customer churn and mitigate the impact of this process.

Personalize to perfection

As we said, consumers now expect businesses to interact with them on an individual level. This is crucial to client satisfaction. After all, a satisfied customer is four times more likely to refer a friend and five times more likely to make a repeat purchase. But, can a business do so without jeopardizing profit?

Business Intelligence, of course. This solution helps you to identify areas within your business and customer journey that can be personalized, and cost-effectively. For example, it may be the purchase process on your app or how your staff interacts with clients.

Keep your staff in-the-know

Henry Ford once said, “The only thing worse than training your employees and having them leave is not training them and having them stay.” One of the key benefits of business intelligence is helping your staff better understand your clients and their needs.

Empowered with knowledge, your team can better trouble-shoot issues, adapt to customer needs and behaviors, and, thus, increase customer satisfaction level and improve sales. For example, this could be as simple as allowing shoppers to shop independently or providing assistance. Knowing that your shoppers prefer home delivery over pick up or having an option to do so. Or even as complicated as offering recommendations based on previous choices.

Connect the right way

Sales and marketing probably make up a significant chunk of your budget. That’s why it’s vital that they’re effective. No longer are sales and marketing campaigns merely targeted to customer segments or around special occasions, such as the holidays. Your sales and marketing need to directly connect with an individual customer as 96% of consumers expect a seamless shopping experience across channels.

BI empowers your retail business, first to understand what makes your customer tick, and utilize it in a meaningful way to connect on their level. What platforms do they use? Where do they shop? And could will they know about your business? Saving you time, money, and allowing you to plan effectively.

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So then, how is business intelligence changing the face of the retail industry?

This is retail, but not as we know it. Data is driving everything, and only those who can lasso the great bull of ones and zeros can hope to control the market. Smart retail is here to stay. It is data-driven, efficient, and, most of all, customer-focused.

In the digital world, the bottom line of business is the ability to adapt, be future-focused, and personal. And the tool to achieving it is business intelligence. BI is changing how retail is done, making it forward-facing and customer-focused in a way unlike ever before.

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How to Make Use of Financial Business Intelligence? (Non-) Obvious Benefits Revealed

Posted on January 4, 2023September 7, 2026 by Artsyman Lizaveta

What finance departments certainly have an abundance of is data. It comes from everywhere: spreadsheets, invoices, journal entries, and whatnot. A real treasure, right? Unfortunately, a lot of data by itself does not guarantee that you will be able to unlock all its hidden potential without sacrificing operational efficiency. Forrester reports that up to 73% of enterprise data goes unused for analytics. So what can be done to change the status quo and leverage data from heterogeneous data sources for getting actionable insights and your business growth? Financial Business intelligence addresses finance leaders’ needs for dynamic modeling, holistic data analysis, and accurate planning.

What is so wrong with spreadsheets?

Many CFOs are still not ready to trade Excel for BI tools in finance, guided by the logic of not touching a system that already works. But does it? Spreadsheets have been used for keeping records and conducting data analysis for so long that it’s not even questioned whether or not they are worth it.

However, there are plenty of rationales why you should no longer rely on moss-grown solutions for financial reporting and consider tapping into new alternatives, such as business intelligence software.

One of the main reasons for this is susceptibility to human errors. According to various studies, 9 out of 10 spreadsheets contain mistakes, This is not surprising at all, given manual data entry. Even something as small as a misplaced decimal point can lead to huge accounting distortions.

Another problem with spreadsheets is that they provide a static view of historical data, so putting them to work across complex and dynamic processes is quite a useless idea. A holistic business vision cannot fit into dry financial statements and end-month reports – translating them into actionable insights is a painful undertaking.

The constant changes in the financial landscape leave businesses with no choice but to quickly assess these alterations and act on them. In these conditions, ignoring a wealth of data stored in your enterprise systems is not just unwise but can be devastating. The question is can you take advantage of the raw data locked in spreadsheets? Skimming through hundreds of pages of traditional reports is hardly a good way to gain control over your numbers.

In addition, manual number crunching takes too much time, which, along with taking up your employees’ precious time, leads to the inability to make informed decisions quickly.

Employees might spend 3-5 days a month filling out some crazy Excel spreadsheets. And then, there’s versioning, which, after changing a single digit or moving working files to other shared folders, or after sharing a final presentation file with some links to the working files, involves running through a dozen other files to make sure everything fits. But what if the specifics of your business require you to make well-informed decisions more often than on a monthly or even weekly basis? At the same time, no one is immune to force majeure, which is not scheduled and calls for immediate actions.

Finally, spreadsheets do not correlate with the financial planning process making it hard for the finance office to get a clear perspective of the current situation and to take forward-looking actions.

Conducting plan vs actual analysis, calculating variance, extrapolating data – all this is very challenging to do in Excel. It’s enough to change the slightest detail for the whole thing to collapse instantly. In addition, it’s incredibly inconvenient. If you use Pivot tables, they tend to overlap, so it’s hard to imagine what they will look like in fact. No wonder adding data for new analysis or reporting periods in Excel files and fine tuning all Pivot tables and charts become utterly discouraging.

The explanations above as to why spreadsheets hinder the work of the finance department are already sufficient to lean toward BI in finance. This technology is capable of outstanding things. Keep reading to find out more about how it drives finance and accounting tasks and what exactly makes Business Intelligence so attractive for financial services.

Behind the pretty pictures: a detailed look at the benefits of financial Business Intelligence

There’s an opinion that BI is first and foremost a way to present data. Yet, outstanding data visualization is far from everything financial Business Intelligence tools can offer. To put it clearly, the visuals are just a nice-to-have feature in comparison to another kind of new opportunities and value this technology brings to your organization.

If it was just a matter of visualization, it wouldn’t be the sticking point at all. For example, Excel’s modeling capabilities allow presenting charts and graphs too. However, compared to Business Intelligence applications in finance, its limits, such as data size restrictions, poor collaborative functions, and others, are quite significant.

What finance departments cry out for is something faster, something ‘click-of-a-button’ to get updates and predictive analytics in next to no time, something like BI tools for finance. When you implement technology using a thoughtful strategy, what can you expect to gain?

1. Deep stakeholder engagement

The incomparable advantage of BI financial services is that they provide a genuine stakeholder involvement in the “process”, namely long-term financial planning, budgeting, forecasting, and comparison with reality. For instance, if a branch manager has a clear understanding between an input (certain indices, plans for sales volumes, etc.) and an output, it allows for more effective conversations within the department. With self-service BI tools, like Power BI, Tableau, or Qlik, that Gartner mentions among the leaders in the field of data analytics, questions that arise in meetings are addressed and answered immediately. There is no need to go back to the analysts to prepare another report and postpone the discussion until the next time.

Moreover, access to BI solutions via mobile devices and tablets also boosts the business leaders engagement – making financial data analysis more accessible; anytime, anyplace.

2. Time saved. A lot of it

Long gone the time when companies had no other alternatives than to use several Excel files, matching and filtering them manually to get a comprehensive picture of data. Now, the alternative is here and it’s Business Intelligence in finance, which provides incomparably faster report preparation and, thus, operational efficiency. Instead of almost a week to compile the data, it takes no longer than a leisurely cup of coffee and frees up several full days for intellectual work.

Couple that with the fact that there are some businesses where you can’t afford to wait until the end of the month to make a decision. For example, if you’re dealing with fast-moving consumer goods, to survive, you have to keep your finger on the pulse and an eye on your competitors. Otherwise, your analytics are pointless.

BI allows you to see and quickly grasp operational data such as, let’s say, the results of marketing campaigns. The contribution turned out to be twice as much as the investment? Perfect! All you have to do is to analyze exactly what caused this and move forward in the same direction. Whereas an in-the-red campaign serves as an obvious sign to change the strategy or, at least, make some adjustments. There are also such things as tracking bad-selling items, or low-margin ones. It’s difficult to do this in Excel, especially if you have many product lines and ten, fifteen, twenty thousand positions in the assortment.

3. Consolidated location for your data

Imagine, how much more value a finance department can bring by analyzing the data and offering solutions, rather than spending eons on scrambling for the information on operations, supply chain, HR, procurement, sales and, well, you name it. Thanks to a full-scale BI system, financial data from all over the company and beyond – speaking of Big Data – is gathered in one place.

There are companies that have to pull raw data from a whole set of disparate systems, which in itself is a pain, not to mention that some of these systems might be outdated. Complete re-engineering of legacy software will take years and cost millions. So working with data is handled in any way possible: somebody uses FTP to transfer files, others upload CSV files into certain folders… But what if the folder is changed or the IT team rebooted the server? No wonder the output is messed up. Meanwhile, a well-designed, full-fledged BI system helps to organize this data correctly with a data warehouse or data lake and an ETL process in one place.

With Business Intelligence for a finance department, employees get the capability to quickly incorporate new data into the data warehouse and interact with all the necessary information in one location without having to search for additional data. Imagine, you can have all your data sources such as the web, SQL Server, Excel, and flat files funneled into a pre-configured solution, where this data is stored, processed, and visualized.

There’s no longer a need to juggle dozens of open tabs and separate reports to tell a story. Whatever data you want to put together, finance business intelligence software allows you to do it without a hitch. For more details, it’s just enough to drill down into the source report.

4. Self-sufficiency

The problem might not even be bringing data from different sources together. You may have a data warehouse and an in-house development team dedicated to helping users with managerial reporting. But that still won’t keep you out of going through all the circles of corporate IT hell while drawing up the terms of reference as many times as it takes for your needs to be understood, and you may still end up with something that doesn’t fully work. BI in finance, on the other hand, helps you unlock the full potential of your data – the kind you never knew you had – without involving the IT department.

5. Effective financial management

By providing robust financial business analytics wrapped in dashboards, the BI tool makes financial management more efficient on all fronts.

Cash flow management

Cash flow is crucial in non-ideal conditions in which current business has to function – when one crisis is followed by another. Some clients ask for extended terms, and others just slow down payments… With finance business intelligence, you will have visibility into your cash flow and make accurate forecasts to improve its health.

Oftentimes, companies go bankrupt not because they are unprofitable, but because they have a cash deficit. Cash flow management is the top priority for any CFO. In this way, all businesses that work to order and where the production cycle is long enough are unlikely to be able to do without BI financial services.

For example, with the Cash Overview report in Power BI, you’ll get a grasp of inflows and outflows in system currency, total bank balance and balance by legal entity, today’s actual vs forecasted balance, and more. Meanwhile, cash flow forecasting itself allows you to go into more detail and get daily forecast summaries and actionable data insights.

Revenue management

The mission-critical questions like what, when, and who to sell as well as what price to set can be answered more easily and accurately when the data is collected, interpreted, and visualized by a financial BI system.

Finance business intelligence offers a 360-degree analysis of pricing, inventory, promotional campaigns, and product launches. BI-driven revenue management helps businesses from a wide array of industries keep the lights on and reach target profitability. Take, for instance, hotel chains that use finance BI software to determine optimal room rates, forecast reservation, staffing needs, inventory levels, and other resources to avoid problems with procurement and staffing during off-seasons and peak seasons.

Expense management

For any organization that wants to stay in the business, it’s important to pay the bills on time, which can’t be done without proper expense management. But what makes it so? BI, among other things, is designed to keep track of the company’s expenses. It enables businesses to replace piles of receipts and spreadsheets with insightful dashboards to gain a holistic view of staff spending, ensure compliance with expense policies, and track T&E trends.

The decision to use special software for managing company’s costs and strategic planning is getting even more effective when bolstered with scheduled dashboards and reports email delivery. These tactics help managers keep an eye on their employees, set up alerts and notifications to manage expenses in a timely manner, and connect BI tools to your organization’s expense, billing and online travel reservation systems.

6. Accurate forecasting

Thanks to Business Intelligence applications in finance, you can bring the plan and the fact together on one level of business logic, in a single tool. And if this tool is also able to show what a fact consists of and why it is like that; it’s priceless.

Let’s say you set up a business unit, so you had to buy extra SAP licenses, or a new production site was added, which entailed additional infrastructure. After a while, it all becomes forgotten – you can, of course, dig up the budget presentation for the required period, but it’s much more convenient, reliable, and faster just to click on the cell with the number, where there’ll be a note with the name of the person who added it and the date when it was done.

If the plan differs from the fact, you can do a drill-down to see what the discrepancies are due to. The data becomes available: you can do both synthesis and analysis without having to look for some emails, going through the tables, or calling a controller.

7. Faultless reporting

The thoughtless manual task of copying and pasting numbers is error-prone and can undermine trust in your financial data. Fortunately, all this heavy lifting can be done for you by financial Business Intelligence. Utilizing BI software for direct data access and report automation saves you time, money, and, most importantly, reputation as a specialist, which “takes decades to build and five minutes to lose”.

Besides, BI brings more credibility to data by differentiating between good and bad data. Proper analysis is only possible when data is of decent quality. That’s why it’s not enough to simply gather information. The ETL process, which lies in the core of BI systems, is designed to ensure good quality of data in the warehouse through standardization and removing duplicates.

In addition, data health can be maintained by conducting regular audits or working with reliable data experts who know exactly which steps to take to optimize data quality.

8. Visibility of entire organization

What a CFO needs most is a one-stop-shop for all financial information. Armed with calibrated dashboards tracking financial data across the entire company, C-suites can drill down into the reporting to find answers to any financial-related questions. How do the costs of producing similar units at different enterprises compare? Are our production volumes different compared to the same period last year? What are the drivers of the variances? How does our performance correspond to forecast and budget? What are the slow-moving items that should be cut out of the supply chain? Speaking of which, BI does a great job covering that part of the business.

Now, answering these questions is child’s play. And, on top of that, it has become much easier to explain the correlation between the numbers to non-financial workers.

9. Consumable data

Financial business analytics has never been as crystal-clear as BI technology has made it. Hidden patterns and trends, non-obvious connections, unnoticed opportunities, and underestimated threats – all come to the surface instantly when reflected on a dashboard. In addition to presenting complex concepts in a digestible format, robust data visualization democratizes data and communicates insights throughout the organization by determining why it is performing in a certain way and what you can do to improve numbers.

This way, BI tools for finance are an indispensable constituent of any modern company for analyzing financial KPIs against business performance and presenting a dynamic view of market trends, product interest and customer sentiment to highlight future strategic opportunities.

How does Business Intelligence for a finance department look? An overview of a dashboard example

After having undergone the transformations, the data can be grouped according to the custom pre-built finance report packages, such as CFO dashboard, Event briefing, Benchmarking, etc. Below we’ve prepared an example of the Finance performance dashboard, that summarizes the 10 most relevant KPIs on a single screen.

The top row shows profit, sales, quantity sold, and the number of orders in the current year against the previous year. The second row illustrates profits and sales by category, while the third one displays sales by margin, as well as the profits and margins of various subcategories of goods.

To have a closer look and to gain a better understanding, users can drill down on data by clicking the relevant areas, such as sales. This action brings up a specific report that shows the granular details for the KPIs and drivers. The report opens right away and provides a root-cause analysis of the information shown in the dashboard.

It’s not only the head of finance that can benefit from these dashboards. Other specialists with the finance department (e.g. financial controllers) can utilize them to gain insights in their focus areas.

The BI solution can be also supplemented with Artificial Intelligence (AI) and Machine Learning (ML) technologies to further analyze data and succeed at prescriptive analysis.

Get a head start with BI financial services

All the benefits that financial Business Intelligence brings to organizations, in fact, can be combined into one – a competitive advantage. Being able to automate number crunching, store, and process data in a singular place, and present it in a format understandable to all stakeholders, you save a huge amount of time on deep and holistic analysis and ensure decision-making is driven by data rather than by gut feelings. And this will certainly allow you to feel more comfortable in the market and act confidently, while those who are reluctant to embrace the power of business intelligence software will be flying blind.

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Business Intelligence Challenges Our Experts Faced… and Overcame

Posted on January 4, 2023September 7, 2026 by Artsyman Lizaveta

The importance of BI solutions is becoming indisputable. Statistics show that 26% of all companies have already adopted BI, and around 33% will do it this year. However, despite the fast growth of the trend, the path from getting started to leveraging its full potential still has some bumps along the way. A great number of business intelligence challenges stem from a system’s limitations, intricate business requirements, human factors, and whatnot.
How to make BI software work for your organization? Our in-depth understanding of incorporating BI solutions into different industries and corporate levels enables us to articulate business intelligence issues and give practical advice on their resolution.

Stepping into the age of action comes with certain BI challenges

Business intelligence isn’t confined to the big guys anymore. Companies of all sizes can now turn disparate data into a plan of action. However, action-oriented data analytics is premised on multiple precursors, each being a potential challenge for early BI adopters.

1. Integrating data from different source systems

BI only makes sense when it can collate and analyze data from multiple sources to present the end user with some solid ground for insights. Otherwise, what’s the point? However, the numerous data sources BI software has to connect to — from a plethora of databases and business apps to big data systems — increase the risk of telling the wrong story.

From the start, it might not seem to be a problem as long as in-built ETL processes allow ready-made BI platforms to directly connect to various data sources and transform data for their own use. As fast and attractive as it sounds, the built-in ETL is not omnivorous. Although some specialized connectors are constantly being finalized for new source systems, a medium to a large organization will at some point face scaling, performance, and maintenance issues if they exclusively use Power BI Data Flows as the ETL tool and DWH storage.

First, working with raw, unstructured data increases the complexity and number of datasets which makes reporting more time-consuming. If the report integrates data from different sources, the same logic cannot be easily applied to another dataset. Secondly, with multiple versions of the truth across different datasets, the odds of discrepancies across the reporting system are high. Third, if your data amounts to millions of rows, the built-in ETL won’t be able to handle it, leading to slow report responsiveness.

The most rational solution in this situation seems to be the setup of a single repository, where data would be pre-aggregated and stored in a structured way — a data warehouse. By eliminating the confusion within your data, it contributes to the creation of a single version of the truth. Among other significant benefits central repositories bring is the possibility of historical data analysis and faster report preparation. Data warehouse technology allows dealing with an ever-growing amount of data sources without making you spend more on your BI tool maintenance.

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2. Data quality issues

Data quality is one of the major challenges of business intelligence and stumbling blocks to achieving BI goals, namely, making the right, valuable decisions. Human errors, duplicated and invalid data, and inconsistent data formats do not allow you to acquire any worthwhile insights and can provoke wrongheaded actions on top of it.

A proper data management strategy helps tackle data quality issues. To put it into a nutshell, it deals with the data collected or generated by the company to ensure better decision-making.

Data architecture is a crucial data management component that plays a vital part in delivering high-quality information. Let’s say, a company has multiple sales channels, it’s a nice idea to merge all the information generated by them at the data warehouse level, from which it can be further distributed to different reports, after passing certain clearance algorithms defined by the business rules.

Data modeling is another thing you can’t neglect while trying to make your data eligible for analysis. For example, a visitor to your website, a participant in a survey you’ve conducted, and your client can be one person. However, you might have them presented in different roles in different systems, even if it’s the same entity. That’s why, to avoid data redundancy it should be decided which system (CRM, ERP, etc.) to assign this entity to.

Data management strategy is largely an administrative activity. At the same time, the technical part of building a proper, well-thought-out solution architecture must not be discounted. Work on the strategy should begin with a diagram of all the company’s data flows. Determine the source systems you have, where data is generated and consumed, what entities you have and where they are stored, and then decide on how to implement it technically.

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3. Lack of data talent

Skill shortage is among other common business intelligence problems that impede data analytics efforts. In 2020, the United States faced a scarcity of data science talent — companies failed to fill around 250,000 positions. The 2022 Tech Hiring Survey also defined data science as a skill for which demand far outstrips supply.

It is exacerbated by the looming talent crisis across the entire hiring market, changing demographics, and the ‘Great Resignation’, compacted by what could be another recession. Without dedicated skills, companies fail to make effective use of BI analytics, set up data warehouses for baseline information, or establish the required level of data literacy in general.

To grapple with the talent crunch, companies tend to leverage outsourced expertise. A dedicated BI team makes up for an entire suite of in-house data experts and helps businesses validate their data initiatives fast and with no hiring hassle.

4. Bad data visualization

The quality of your data and analytics processes tends to steal all the glory. However, the design of your BI dashboards is just as important to communicate complex data to the decision-makers and turning critical insights into action.

If data isn’t presented and argued in a compelling way, it is ignored, or trumped by opinion. The value of having an argument and crafting a story component should never be underestimated.

Lack of interactivity, the inability to pull near real-time data, rigid templates, and even the wrong choice of color may lead to potential challenges in implementing the dashboard. To emphasize proper data values, companies should employ highly customizable dashboards with broad personalization capabilities to meet the unique needs of the organization.

The right choice of dashboard type can also whip your BI management in shape. Analytical dashboards provide a comprehensive overview of crucial data, while operational dashboards include real-time updates relevant to a specific department. The strategic type delivers a rundown on the essential KPIs to the executives.

5. Choosing the right software

Selecting the right BI tool is half the battle when it comes to tackling business intelligence implementation challenges. According to TrustRadius, Tableau, Qlik Sense, and Microsoft Power BI are the leading business intelligence platforms with the largest market shares. But which one clicks with your unique needs? Let’s touch on the main difference between the three.

Criteria	Power BI	Tableau	Qlik Sense
Popularity (users)	Over 5 million users	Over 220,000 data scientists	38,000+ customers
Data sources	130+ data sources	Connects to nearly any data repository	100+ data connectors
Data visualization	Custom dashboards, embedded analytics, extensive data visualization tools	Custom visualizations, rich and interactive dashboards, embedded analytics	Unique “associative” data engine, custom visualizations
Cloud compatibility	AWS cloud	AWS, Google Cloud Platform, Microsoft Azure, Alibaba Cloud	Amazon (S3, EC2, RDS, Redshift, EMR), Azure, and Google.
Customer support	Included for Power BI Pro customers	Free	Free
Advanced features	Natural Language Processing, Machine Learning integration, predictive analysis	Enhanced data visualization functions, predictive analysis and forecast, augmented analytics	Smart visualizations and analytics, AI and machine learning analytics

As you see from the table, choosing between those three tools is like choosing between Audi, BMW, and Mercedes, about the same quality packed into a slightly different exterior.

However, in the case of a large-scale adoption, even subtle differences begin to play a role. License type, roles and permissions, discount allocation, and other factors have to be taken into account to optimize your BI experience.

Moreover, generic commercial solutions may not always suffice your visualization needs. For example, B2C startups are better off with open-source BI solutions due to the high analytical needs and the absence of licensing burdens. In some cases, companies opt for a custom BI tool for branded design.

If you’re struggling to choose your BI option, our engineers can recommend the right BI solution based on your infrastructure and unique project needs

6. Low adoption levels of BI among employees

After all the money, time, and effort you’ve invested in your analytics software, it still might not work because users won’t accept it. Low adoption levels within organizations remain one of the leading BI problems. If you want a freshly deployed BI tool to be used not only by analysts or data scientists, make sure it is user-friendly, and not intimidating.

Besides, employees frequently show justifiable — from their standpoint — resistance to new software. Such a BI aversion is entirely understandable as people whose main task has been to manually bring together the company’s analytics are afraid that reporting automation will put them out of the job. That’s why they need to be convinced otherwise. Employees who can embrace business intelligence challenges and opportunities will become more valuable assets to the company as they won’t longer have to waste tons of time on number crunching or worry about the risk of making a mistake. Instead, they will analyze the information from top to bottom and communicate the result of this analysis to their managers.

The subtle art of dealing with managerial issues related to BI implementation and mindset transformational practices requires even more precision when it comes to Excel. People’s loyalty to this tool should not be ignored. Numbers tend to speak louder than words. Use them to show your employees how beneficial a BI tool can be in terms of saving their time. For instance, it takes financial controllers one or two days to handle ad-hoc requests, three-five days to prepare for monthly meetings, and around three weeks to summarize the year’s results. With the BI system, all reporting is done automatically, at a click of a button.

The main goal is to show a person how to use the dashboard to answer their questions and explore the available data quickly and efficiently. Trends are way easier to spot when data is adequately visualized rather than scattered across spreadsheets.

Yes, we know that Excel is not just about tables, and you can build charts in it, too, but you won’t be able to interact with them on the spot. In a dashboard, you can click on a single segment and see the information you need right away, while in Excel it will take you more time to do the same thing.

An efficient way to deal with business intelligence implementation challenges and an essential component of proper change management designed to soothe users’ pain is staff training. That’s why users shouldn’t be left in the lurch after the system’s deployment. Find BI implementation partners who will prepare the documentation on the new processes and arrange wide-scope training aimed not only at teaching how to work with the software but also at increasing general tech literacy.

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Navigating the path of business intelligence

Failure rates for data science projects loom large at 87%. It attests to the fact that a solid data analysis and visualization architecture cannot be strategized on a hunch unless you want to end up with sporadic and incomplete insights. Awareness, planning, and expertise will help you avoid most BI problems. Below, we’ll touch upon the three pillars of a successful business intelligence strategy.

Define what problem you want to solve

The application area of business intelligence is immense and spans virtually every business operation. Therefore, you should start small by identifying the scope of analysis and linking it with correlating metrics and reports. The reports, in turn, should revolve around a specific selection of KPIs, internal or external, to measure and analyze an organization’s data and improve on it. A BI consulting partner can advise you on the relevant metrics and validate your scope of analysis.

Transform the organization’s mindset with proper change management

As users segue from fragmented tools to an integrated BI system, you should have the correct transformational methods in place to eliminate inertia and promote system acceptance. Establish stable and transparent communication flows, bring leaders from different lines together, and run workshops and training to cultivate collaborative data flows and seamless knowledge sharing for accurate business insights.

Choose a reliable consulting partner

A single unified data warehouse and consistent data strategy lay the ground for fast and accurate data analysis. Without these precursors, your insights will be isolated in data silos, locked within departments as missed opportunities. To avoid data failures, secure an experienced BI consulting team to establish a robust data infrastructure, manage data governance, and connect your data warehouse with the right BI tool.

Hit pay dirt with business intelligence

Data-driven decision-making is no longer an option; it’s a mandate for business longevity and competitiveness. Business intelligence is what nurtures a data-fuelled approach and allows companies to turn their data into action.

BI-enabled vigilance is a collective result of the right data strategy, a unified IT architecture, and a consistent adoption cadence. If even one piece is missing from your BI puzzle, your company will be short-sighted when it comes to data, failing to make the right decision. Enlisting the support of data and BI professionals will help you put your puzzle together and overcome common business intelligence challenges.

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When Data Goes Bad: How To Improve Data Quality?

Posted on January 3, 2023September 7, 2026 by Artsyman Lizaveta

There’s a clear connection between the quality of your data and the efficacy of organizational decisions. The idea of ‘garbage in, garbage out’ is relevant here. When companies fail to understand how to improve data quality (DQ), it can backfire in a huge way. Fixing issues caused by bad data can take away up to 25% of their annual revenue. Furthermore, it can disrupt their progress toward digital transformation.

How to turn this loss into a profit and leverage data quality as a competitive advantage that will reshape your position among rivals and boost your data analytics? We’ve listed common issues you may face while dealing with data and outlined a data quality strategy.

Six possible issues you may face on your way to improve data quality

Data has particular quality characteristics – completeness, validity, uniqueness, consistency, timeliness, and accuracy. There are a number of issues related to them. Bad DQ results in:

Data silos. According to McKinsey, multiple data lakes and warehouses with no common data model are one of the top challenges at the enterprise level. Even if you have only one warehouse, analyzing data becomes troublesome when your data is scattered across multiple enterprise systems.
Human errors. When customers or employees make typos such as writing “Minesota” instead of “Minnesota” when entering information manually, you get data that doesn’t represent reality.
Duplicated data. When one employee enters customer data into your CRM, and another records the same customer data into another system, you end up with duplicates. If they are not completely identical, then there is a problem: which one is reliable?
Invalid data. The analysis doesn’t make sense if you get just any data instead of the data you need. An example of this error is when the name field is filled with surnames. Imagine yourself having a whole table of Smiths when you need to determine which of your regulars deserves a personal discount.
Missing values. If you want to build a data-driven culture, missing data is unacceptable for statistical procedures. If some obligatory fields aren’t filled out, you can’t analyze the data and take action. For instance, if you are collecting data on the age and gender of your buyers in a customer satisfaction survey, some of them might not reveal their gender if only “female” and “male” options are offered. This may be related to young people identifying themselves as non-binary, queer, etc.
Inconsistent data formats. You may feel like you’re going through hell when having to handle dates entered in European and US styles.

High-quality data makes data governance easier. And if you can confidently manage data, you can confidently manage the whole company. That’s why raising DQ is one of the top priorities for the next 6-12 months for 91% of organizations. If you are still undecided about how soon you should start fixing your DQ, this is your sign to not put it off until tomorrow.

How to mitigate data quality issues: embrace state-of-the-art technologies

Before answering the question: how to improve data quality, you need to figure out how to improve data management first. Focus your attention and budget on the adoption of new technologies. There are at least two possibilities to facilitate your data quality enhancement journey:

Take advantage of automation to eliminate human errors. For instance, adopting robotic process automation (RPA) frees your employees from monotonous, repetitive operations, erases the possibility of human error, and lowers the cost of processing data by up to 80%. For example, with RPA, you can simplify data entry, data profiling, etc. The technology allows you easily convert all dates into one format, verify the absence or presence of the data, its actuality, as all these actions can be reduced to a clear algorithm performed by a bot. Besides, in highly regulated industries such as healthcare, automation improves compliance with numerous protocols (HIPAA, PSQIA, GDPR, etc.) and, thus, helps to create a better customer experience.
Leverage Business Intelligence (BI) to have a comprehensive view of the quality of your data. You have to regularly evaluate your data to ensure that the information is still reliable for business operations.

Cooperation with experienced BI analysts is key. They help you figure out which questions you need to answer, what story you want to tell with your data, and create a custom dashboard based on that information.

A generic dashboard can show the extent to which the data meets data quality requirements. According to Gartner, tracking data quality metrics helps improve them by 60%.

You can also provide your data scientists and engineers with more granular dashboards that visualize the stories of issues underlying major data quality problems.

Use BI consulting services to decide where to start your data quality improvement journey and identify appropriate technologies to help you along the way.

How to develop a robust data quality improvement strategy

One-off initiatives and ad-hoc actions treat the symptoms, not the disease. You need long-term strategic adjustments to empower your staff with advanced analytics at all organization levels. That’s why, before jumping into a DQ initiative create a data quality strategy (DQS). We’ve listed six vital elements of it.

1. Do an inventory of your data and describe the issues

Developing a common vision of data quality for employees from different departments is essential. To achieve it, answer basic questions such as: How much data do you have? What types of data do you collect and store? How many errors are there in the data? What kind of errors are these?

2. Develop your requirements and objectives

At this stage, you should identify the stakeholders of the future data quality improvement process. The more experts that can evaluate the data from different perspectives, the more accurately you can define the DQ requirements and aspirations for your organization and the best practices to improve data quality.

It may turn out that your company needs dedicated employees who will assess the quality of data according to key parameters – the data stewards. They are responsible for what data you keep in your organization, enforce internal rules on how data can be used and track the movement of the data inside the company. A data steward’s mission is to coordinate all the business processes and decisions that arise from your DQS.

Don’t forget to set an approximate timeline for implementing a data quality improvement plan, as it depends on the scale of your organization.

3. Set priorities for different data sets

Working on the quality of customer data and the company’s internal data simultaneously is great. But if your budget is limited, you need to choose the improvement of which data is the priority for your business success and growth. By enhancing the quality of the data related to the customers’ personal information, you can personalize their experience and increase customer experience and satisfaction. However, revamping the organization’s internal data can bring you just as much benefit. Having high-quality data about your staff, you can fully reveal the potential and talents of your employees and uncover how to optimize the processes within a company.

4. Select technologies and tools to improve data quality

Given the sheer number of offerings for data collection, data cleansing, etc. on the market, it turns out to be time-consuming and tricky to compare their features, licensing costs, payment options, etc. Consider that if you are burdened with outdated software, the task gets more complicated as you may need to modernize it.

Adoption of new technologies and tools may require more inside-out knowledge than was initially expected, so choose tech partners who are an old hand at handling data issues.

5. Identify the roles and responsibilities for stakeholders

At this stage, you settle on the tasks assigned to the data stewards, data engineers, business analysts, executives, etc. For the boat of your data quality improvement strategy to sail smoothly, you need many hands rowing in the same direction. A data steward can track data quality standards across the organization and in particular projects, business analysts prioritize tasks from the perspective of business benefits, and C-suite members make final decisions about what actions should be taken.

6. Set KPIs to evaluate the progress

What degree of data quality do you want to achieve in six months, in a year? How much time can it take your employees to correct errors of different types? To what extent do you expect to reduce them? An experienced business analyst can help you determine the realistic KPIs for your organization.

When the time period you’ve designated as a benchmark has passed, analyze achieved results, review your data quality improvement strategy, and modify it if necessary.

The draft of your data quality improvement plan may look like this.

Clean up the way for accurate data analysis and genuine insights

The quality of the data you process determines how valuable the insights will be. In some way, without advanced analytics, an organization is deprived of the future, at least one, that is bright and prosperous.

You can partially and temporarily solve burning data quality issues by adopting modern technologies and best practices. But it’s like putting out a fire in one room when an entire building is engulfed in flames. Creating a data quality improvement plan is a surefire way to pinpoint what to do with your data to enhance its quality, how to do it, who is in charge of the process, and track the progress to analyze when you can achieve an expected outcome.

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FAQ

Why is it important to improve data quality (DQ)?

“No money, no honey,” they say. This rule also works for the information – you can’t expect more than run-of-the-mile insights if your raw data is of low quality. Improving DQ should be a top priority for any organization as data is a competitive advantage that can influence your position among competitors.

How can we improve poor data quality?

You should evaluate the data from different perspectives to outline the ways to improve data quality. Start with the basic characteristics such as completeness, validity, uniqueness, consistency, timeliness, and accuracy. Involve employees from different departments to ensure that the initial data quality analysis and future expectations related to it are the same for all stakeholders.

What are the steps for improving data quality?

To solve the problem in a holistic way, you need to develop a data quality improvement strategy. It includes analyzing and describing your actual DQ issues, developing your requirements and objectives, setting priorities for different data sets, choosing technologies and tools to improve data quality, identifying the roles and responsibilities for stakeholders, and setting KPIs to evaluate the progress.

What is a data quality strategy?

Data quality improvement strategy is your plan on who, what, how you interact with your data to enhance its quality and when you can expect the results. It’s also an indirect way to promote a data culture in your organization since employees from different departments are engaged in the strategy’s development.

Whenever, Wherever: Keep On Top of Your Data With Mobile BI

Posted on September 8, 2022September 7, 2026 by Artsyman Lizaveta

From tech-heavy reporting projects to dedicated analytics tools, business intelligence has come a long way to becoming an industry standard among companies. However, despite the present proliferation of BI, businesses still struggle to seek out analytics excellence. The isolation of traditional BI tools continues to pose challenges, resulting in a mere 24% of data-driven organizations.

But why limit business intelligence to a desktop version, when your employees can leverage advanced analytics literally in the palm of their hand? Just like fitness or financial trackers, mobile business intelligence can seamlessly support existing data pathways and produce actionable insights on the go.

What is mobile business intelligence?

Mobile BI is software that extends desktop business intelligence applications and makes analytics accessible on a mobile device. Mobile BI software allows users to access and evaluate business metrics, and reports from smartphones and tablets.

Stepping into the age of action: ultimate benefits of mobile BI

Driven by increasing Bring-Your-Own-Device workplace practices and mobile computing, mobile business intelligence has experienced unbelievable growth over the last few years. In 2021, the mobile BI market size stood at over $11 billion. By 2030, it is expected to rack up over $38 billion. Let’s see which advantages of mobile business intelligence have secured their pride of place among adopters.

Real-time data access on the go

Traditional business intelligence solutions tie BI users to tools that are specifically allocated to corporate environments. When away, decision-makers cannot access valuable data, which hampers proactive response. Conversely, mobile BI provides the ability to make fast, well-informed decisions even when you are away from your desk, which is especially vital in industries such as finance and healthcare.

Offline access to data is among other differentiating mobile business intelligence benefits. Since mobile devices have wide in-memory caching capabilities, all data visualizations can be explored with no internet connection provided it’s a native mobile app.

Increased team collaboration

Since mobile BI directly reaches the hands of BI users, it ensures easier knowledge-sharing. On-the-go data analytics also matches well with growing remote practices, making sure each employee is in sync with other remote team members.

Mobile BI also allows for permanent, intermediary, or temporary access, which keeps your controls on par with desktop enterprise software.

Rich user experience

Supreme user experience is among other core benefits of mobile business intelligence. While smartphones come with inherent constraints such as small screens, tablets can pick up the slack as user-friendly mobile business intelligence solutions. Tablets make the best of both mobile design and desktop design, offering portable, always-on, and touch-capable analytics.

Compared to static desktop reports, portable BI solutions also usher in more interactivity, allowing the user to zoom, pinch, and swipe their way through insights. Moreover, native mobile applications can tap into device-specific features such as GPS, calendar integration, and QR-code scanning to expand analytical capabilities.

Maximized value of BI adoption

According to mobile business intelligence trends, CEOs that make data-driven decisions are 77% more likely to cultivate a company’s success. Cloud and big data mobile business intelligence confers accurate governing capabilities to other levels of the business hierarchy.

It means that a larger number of employees can have access to high-quality data, which facilitates the digital transformation of the company, gives the whole organization a vantage point, and increases operational efficiency.

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Who can benefit from mobile BI the most?

Businesses of all types and sizes can benefit from mobile business intelligence applications. However, according to the Mordor Intelligence report, the telecommunications and the software tech industries stand to gain the most benefits due to the presence of large corporations like Apple, Facebook, IBM, and Google.

Beyond the software market, retail, healthcare, and manufacturing are among other major players in the world of portable BI. Around 47% of retail respondents attest to the growing importance of mobile BI applications with 38% of healthcare providers sharing the enthusiasm. High adoption rates of mobile BI apps in these industries stem from increased demand for rapid data processing and high volumes of operational data.

For example, retailers can use mobile augmented data analytics for on-the-move warehouse management, customer profiling, or store layout optimization. Healthcare providers use data analytics to support clinical decision-making, provide more personalized treatments, and increase healthcare accessibility for rural areas. Ultimately, a mobile BI strategy can both support the bottom line and boost customer satisfaction.

On a broader level, mobile analytics can support the following application matrix:

Fraud and security management;
Sales and marketing optimization;
Predictive asset maintenance;
Risk and compliance management;
Customer relationship management;
Supply chain analytics, and others.

Which roles are among those likely to be adopters of mobile BI solutions?

As for corporate levels, each employee — from decision makers to employees — can leverage all the necessary data without the hassle of corporate back and forth.

Executives

Executives and senior-level managers spend a lot of time either in meetings or traveling for business, away from their desks and enterprise software. Having mission-critical information always on hand helps upper management explore data on business performance from any part of the world and make data-driven decisions, be it high-level forecasts or current business operations.

Field workers

Field engineers and technicians have lots of projects on their plates — from company infrastructures such as railways and utility stations to customer sites. On-demand and on-the-go access to unified data analytics from all facilities helps field workers better prioritize tasks and keep an eye on prospective and current customers. Moreover, mobile analytics can be amplified with sensor and GPS data to schedule predictive maintenance activities or optimize routes.

Line managers

Supervisors and team leaders can benefit from mobile data visualizations to get handy insights about staff, production lines, and ongoing operational activities. Lightweight visibility also helps first-line managers to respond proactively to any emergency and make important decisions faster and smarter.

Sales representatives

Equipped with decision management and predictive analytics capabilities, a mobile BI tool can provide the latest sales information such as deal status or prospects to help salespeople prepare for the meeting or run analyses in any setting. All-in-one business intelligence mobile can also give salespeople a head start in customer data before any sales call.

Sales representatives in the retail sector, for example, can rely on a portable BI solution to predict the profitability of a new store without close supervision from the head management. In this case, the salesperson can calculate and select the correct location for new stores on the go, based on various parameters, including geography, demand, and others. If the input parameters meet the predefined benchmark, the BI solution greenlights the new location. Otherwise, the solution notifies the salesperson of the low profitability of the new sales point.

Any flies in the ointment? Yes, several. But they can be extracted.

Just like any technology, mobile business intelligence has a flip side. In this case, the core challenges of deploying portable analytics resonate with desktop BI. This is why accurate forecasting, data governance, and reporting need to be balanced against data security and quality followed by end-user training and cultural shifts.

However, mobile business intelligence software also poses some specific challenges that arise from the portable nature of analytics and mobile devices. With the right tech expertise though, you can make mobile work to your advantage and overcome general BI constraints.

Security issues

The smaller your device is, the easier it gets to misplace or forget it. Mobile devices also tend to get stolen or hacked, which increases the risk of a data breach. On average, hackers get their hands on 6.85 million accounts every day.

When away, mobile users can also expose their devices to data leaks through non-secure channels such as public Wi-Fi.

To keep hackers at bay, companies should embed robust data encryption, two-factor authentication, and biometrics-based access into portable enterprise devices. Mobile hardware should also meet both industry and internal security standards to ensure a consistent security policy on all company-owned devices.

Poor design

Although mobile BI is a natural extension of your desktop analytics software, a mobile version shouldn’t blindly replicate the desktop experience. You can keep the user experience consistent on both platforms, but wrap your solution into a mobile-focused experience.

To recreate a typical mobile user flow, we recommend:

Including app notifications and alerts;
Employing voice recognition to access certain options hands-free;
Equipping your applications with in-app guidance prompts;
Tapping into native touch controls, including swiping, pinching, zooming, and others.
Integrating the application with other communication channels to boost team collaboration;
Embedding in-app analytics for further enhancements.

It might seem that achieving this kind of native-like user experience is a costly undertaking. However, a mobile BI version for a specific platform doesn’t require many resources compared to a standalone solution with multi-platform compatibility.

Neglecting the needs of your audience

By default, hand-held devices and desktop applications cannot deliver the same level of functionality. Therefore, you should adjust your mobile application according to the screen size and mobile constraints. While the main BI solution can provide visibility into data-rich charts and detailed reports, the portable solution should be optimized to display key metrics and alerts as well as share data via messages and emails.

Mobile BI apps: the power of BI, as and when it’s needed

As the world is quickly moving towards mobile, enterprise software also steps in to deliver better access and data visibility through mobile business intelligence. Mobile BI applications combine the analytical capabilities of desktop data processing and portability of mobile devices to equip companies with on-the-go data usage, risk management, and forecasting.

Although mobile business intelligence brings unique advantages to the table, its flexibility can play against you if handled irresponsibly. You should pay due diligence to necessary safeguards and user interfaces to eliminate risks and maximize mobile benefits.

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