Business intelligence: You're losing money

BI is not a dashboard or a data visualization tool. The practice of business intelligence encompasses processes, technologies, and people for knowledge management.

Many companies invest heavily in data without realizing they are wasting resources. They think they are doing everything right, but end up stuck in traps that block their progress toward data maturity.

We have listed 5 traps and barriers you may be stuck in right now while trying to turn data into assets.

• Data quality.

• Multiple sources of truth.

• Confusing reporting with analytics.

• Understanding BI as a fixed-scope project.

• Misalignment with strategy.

1. Data quality

Beyond data structuring and cleaning, information quality is related to company processes and culture. In many cases, organizational data is low quality because employees are not guided on the importance of filling certain fields in a system, or there is no process with incentives to do so, so those fields are left blank.

This gets worse when all control is based on spreadsheets, without an ERP or CRM system that requires at least minimal process discipline. The practice of _business intelligence_ helps identify certain anomalies during analysis, but it is important that this leads to the development and improvement of organizational processes for information collection—something many people end up ignoring.

They choose to simply “filter out” what is inconsistent, or worse, they run analyses and make strategic decisions based on information that does not reflect reality.

2. Multiple sources of truth

Another issue often found in companies developing their business intelligence capabilities is the lack of standardization in information and KPIs. What does that mean?

In some organizations, each department has its own filters for a given KPI, resulting in misalignment across departments. Imagine you have a supermarket chain where store managers analyze revenue minus expenses for that specific store to determine operating profit—let’s say they get a 10% net margin.

But corporate leadership arrives at 7% for the same KPI for that store because they include corporate cost allocation in the result, while the store manager does not.

A second example is refresh windows. Directors often ask for real-time data, even when it is unnecessary. A standard refresh window helps maintain alignment, as it ensures everyone is seeing data from the same point in time, such as d-1 (up to yesterday).

3. Confusing reporting with analytics

 Reports are used to track companies’ key indicators. They are used to monitor the performance of the organization, departments, and people. In other words, they are always looking at the past.

Analytics is the process of exploring data to extract more meaningful insights in order to understand and improve business performance. The first provides information about what happened and raises questions, while the second seeks to answer them. Both are essential!

4. Understanding BI as a fixed-scope project

A business intelligence team needs to ensure data quality, which requires improvements in process and culture. This means BI is a knowledge management practice with continuous improvement. In addition, it involves reports and analytics—that is, tracking metrics and answering questions.

These business questions and problems that practices such as _analytics_ seek to solve are unlikely to repeat in the same way, which requires constant company evolution. In other words, BI is a continuous process. Never stop trying to answer your questions using data and analytics.

Do not assume a single project will satisfy all your business needs—they are always changing!

5. Misalignment with strategy

We already understand that _business intelligence_ is an ongoing organizational challenge. This means long-term thinking is essential. Therefore, all business intelligence efforts must be aligned with the organization’s vision and strategy. It is a long process that must have clear priorities and plans.

Only then will data generate real impact in the organization.

What now? 

Run a diagnostic of your organization based on the 5 points mentioned in this article. Then create a clear action plan to get out of your current traps. Make these 5 points explicit to your teams—this will help prevent you from falling into another trap in the future—but do not stop revisiting the diagnostic regularly.

Don’t forget: developing an organization’s data maturity involves information quality, culture, processes, people, strategic alignment, and of course, technology. It is a long process, but it is worth it :)