How to Create a Business Intelligence Strategy? | Expert View

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.

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.

— Alexandr Obolenskiy, Head of BI department, *instinctools

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.

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. 

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.  

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. 

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. 

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.

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.