Business Intelligence Challenges Our Experts Faced… and Overcame

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.

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.

— Alex Obolensky, Head of BI unit at *instinctools

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.

— Dan Sommer, Senior Director of Market Intelligence at Qlik

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.

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.

Alex Obolensky, Head of BI unit at *instinctools

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. 

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.