Setting up a Business Intelligence Data Strategy

Setting up the Foundation

Not all challenges are technical

Business Intelligence is an important aspect of modern organizations, but it's not just about technology. The implementation of an effective business intelligence strategy requires a multi-disciplinary approach, encompassing a range of technical, organizational, and cultural aspects. One of the key challenges of setting up a business intelligence strategy is that many of the challenges are non-technical, and these are often overlooked.

Step 1. Defining organizational goals

One of the most important non-technical challenges of setting up a business intelligence strategy is goal alignment. Without clear goals and objectives, it can be difficult to know where you are, where you’re going, how fast you’re moving there, and how to focus your efforts.

Without these goals defined, yes, you might have beautiful charts with lots of numbers and data, but without a “Why”.

There are two types of measurable goals in the FORCE methodology: OKRs (Objectives and Key Results) and KPIs (Key Performance Indicators).

While OKRs are a more comprehensive goal-setting framework that includes both the objective and the specific results that need to be achieved to reach that objective, KPIs are specific metrics used to track the performance of specific business objectives. It’s common to use OKRs to define more abstract, strategic objectives and KPIs to track the performance of operations.

Each data-driven initiative should also have associated OKRs or KPIs so we can track how successful it is, following the FORCE guidelines.

Step 2. Single Source of Truth

A "single source of truth" is a critical component of an effective business intelligence strategy because it helps ensure that all data used in the analysis is consistent, accurate, and reliable. In other words, a single source of truth refers to the practice of having a designated repository where all data is stored and managed in a consistent and standardized manner.

When a business intelligence strategy lacks a single source of truth, it's easy for different parts of the organization to use different data sources, resulting in inconsistent data that can lead to incorrect conclusions and decisions. For example, if one team uses a spreadsheet for a certain analysis, while another team uses a different spreadsheet, the two spreadsheets may contain different data, making it difficult to compare and analyze the information accurately.

Having a single source of truth ensures that everyone in the organization is using the same data and that this data is up-to-date, consistent, and accurate. This enables organizations to make more informed decisions based on accurate and consistent data, and ultimately, helps drive better business results.

While the organization’s Single Source of Truth might not be a Data Warehouse, which can be the best data storage system for a BI tool to consume from, solutions can be built to either use the existing data storage solution or to create a synchronized data warehouse using extract, transform and load (ETL) processes.

When you define your Single Source of Truth, make sure you’re considering the basic rules of data governance and management. You should define who is responsible for what, and consider implementing data validation, data profiling, and data cleansing processes to improve data quality.

Step 3. Don’t focus too much on data quality, too soon

When applying business intelligence, it's important to focus on data quality because high-quality data is essential for making informed decisions. However, it's also important not to focus too much on data quality too soon, as this can slow down the implementation of business intelligence and create roadblocks.

In the early stages of a business intelligence project, it's more important to get started with collecting and organizing data and establishing the processes and systems needed to support ongoing data collection and management. Once these steps are in place, it's possible to focus on data quality and to fine-tune the data as needed. Focusing too much on data quality too soon can result in an over-engineered and complex solution that is difficult to implement, maintain, and use effectively.

By starting with a focus on the foundational elements of business intelligence, it's possible to lay the groundwork for a successful implementation that can be scaled and refined over time. As the project progresses and data quality becomes more important, it's possible to focus on it more deliberately, and to make any necessary changes to ensure that data quality is consistently high and supports informed decision making.

Step 4. Selecting the right BI tools

When implementing a business intelligence strategy, it's important to understand that the success of the tool depends as much on its technical aspects as it does on its non-technical aspects.

The tools should be driven by the users, not just the technical teams who set them up. If you want everyone in your organization to be able to use the tools effectively, it's important to choose tools that are user-friendly.

If you choose a very advanced tool, it may require a lot of training and education to use, causing friction within the organization. If you are reading this guide, that’s exactly the opposite of what you should be looking for. The goal is to make the implementation of business intelligence as easy and straightforward as possible, so go with simple tools anyone can use and understand.

Step 5. Meaningful Visualizations

Effective visualizations are an essential component of a solid business intelligence foundation.

While data collection and analysis are important, the ultimate goal of BI is to assist organizations in making better decisions.Visualizations play an important role in this process by assisting in the interpretation of complex data and the effective communication of insights.

Effective visualizations help in the reduction of large amounts of data into clear and concise visual representations. This is especially important when dealing with large amounts of data or communicating complex ideas to a diverse audience.

Visualizations can help organizations in identifying patterns and trends in data, as well as areas where improvements can be made. One of the primary advantages of effective visualizations is that they can improve data comprehension.

It may be easier to understand the relationships between different data points and to identify patterns and trends when data is presented visually. This can help to make data more accessible to a broader audience, including those who aren't necessarily data experts.

Another advantage of powerful visualizations is that they can help with decision-making.

Visualizations, by presenting data in a clear and concise manner, can assist decision-makers in quickly identifying key insights and making more informed decisions. This is especially important in high-stakes or fast-paced environments where decisions must be made quickly.