# Business intelligence: You're losing money > BI built on unreliable data leads to waste. The 5 pitfalls that hold back data maturity and how to avoid them before choosing a tool. Source: https://www.erathos.com/en/blog/business-intelligence-are-you-losing-money Em português: https://www.erathos.com/blog/business-intelligence-are-you-losing-money Published: 2026-07-11 Category: Business Intelligence & Analytics ![Chart showing the gap between BI investment and results due to lack of data maturity](https://cms-media.erathos.com/6i3NbC3bgvvWpQbEUwxNIluJI-1.png) BI is neither a dashboard nor data visualization software. The practice of business intelligence encompasses processes, technologies, and people to enable knowledge management. Many companies invest heavily in data without realizing they are wasting resources. They believe they are doing everything right, but end up caught in traps that stall their evolution toward data maturity. We have highlighted 5 traps and barriers you might be stuck in right now while trying to transform data into assets: - Data quality - Multiple sources of truth - Confusing reporting with analytics - Treating BI as a closed-scope project - Misalignment with strategy ### 1. Data quality Beyond data structuring and cleansing, information quality is closely tied to company processes and culture. Often, organizational data is of poor quality because employees aren't guided on the importance of filling out certain fields within a system, or they lack a process with incentives to do so, ending up leaving them blank. This gets worse when all control relies on spreadsheets, without an ERP or CRM system to enforce a minimum baseline process. The practice of _business intelligence_ helps identify certain anomalies during analysis, but it's crucial that this drives the development and improvement of organizational processes for data collection, something many companies end up ignoring. They choose to just "filter out" inconsistent data, or worse, they perform analysis and make strategic decisions based on information that does not reflect reality. ### 2. Multiple sources of truth Another very common issue in companies growing their business intelligence capabilities is the lack of standardized information and metrics. What does this mean? Within some organizations, each department has its own filters for a given metric, resulting in misalignment across departments. Imagine a supermarket chain where store managers analyze revenue minus expenses for that specific store to calculate operational profit, claiming, let's say, a 10% net margin. However, corporate leadership calculates 7% for the exact same metric for that store because they include corporate overhead allocation in the results, whereas the store manager does not take this into account. A second example is update schedules. Frequently, executives demand real-time data, even when there is no business need for it. Standardizing update schedules helps maintain alignment, ensuring everyone is looking at data from the exact same point in time, such as d-1 (as of yesterday). In practice, this problem usually has a simple technical root cause: each department extracts data from the original source however they can, at different times, using different logic. Centralizing ingestion in a single place, with a standardized update schedule and a clear lineage of when each data point arrived, is what transforms "each department with its own numbers" into a single source of truth that everyone queries. This is exactly the problem we detail in [Data centralization without a dedicated team](/blog/centralizacao-de-dados-sem-time-dedicado). ### 3. Confusing reporting with analytics Reports serve to track key company performance indicators. They are used to monitor the performance of the organization, departments, and people. In other words, they are always backward-looking. Analytics is the process of exploring data to extract deeper insights, with the goal of understanding and improving business performance. The former provides information on what happened and raises questions; the latter seeks to answer them. Both are essential. ### 4. Treating BI as a closed-scope project A business intelligence team must ensure data quality, which demands process and culture improvements. This means BI is a knowledge management practice focused on continuous improvement. Moreover, it involves both reporting and analytics, that is, tracking metrics and answering questions. The business questions and pain points that practices like _analytics_ aim to solve are rarely static, which requires continuous evolution from the company. In other words, BI is an ongoing 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 constantly changing. ### 5. Misalignment with strategy We already know that _business intelligence_ is a continuous 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 journey that must have clear priorities and roadmaps. This, in fact, connects directly with [data governance](/blog/governanca-de-dados): without clear roles regarding who owns what data and without traceability of how data flows through the company, it is practically impossible to maintain the strategic alignment this trap demands. Only then will data drive real impact in the organization. ## Frequently Asked Questions about BI Traps **Which of these traps is usually the easiest to solve first?** Multiple sources of truth. Unlike cultural change or strategic alignment, this is a technical issue with a straightforward solution: centralizing data ingestion in one place with a standardized update schedule. **Does a BI tool solve data quality issues by itself?** No. A BI tool displays the data, but it doesn't fix internal processes or standardize sources. If the incoming data is already inconsistent, the dashboard will only make that inconsistency more visible, not solve it. **How do I know if my company is falling into these traps?** A clear sign is when two people from different departments present the same metric with different numbers in the same meeting. Another is when most of the data team's time is spent reconciling spreadsheets instead of answering business questions. **Where should we start to fix this?** By diagnosing the 5 traps described in this article, prioritizing the one causing the most pain right now. In most cases, solving data source centralization already reduces a significant amount of friction across the other four. ## What's next? Diagnose your organization keeping the 5 points mentioned in this post in mind. Then, build a clear action plan to escape your current traps. Make these 5 points explicit to your team; this will help prevent you from falling into other traps in the future, but be sure to redo this assessment periodically. Don't forget: developing an organization's data maturity involves data quality, culture, processes, people, strategic alignment, and, of course, technology. It is a journey, but it is well worth it. [Create your free Erathos account](https://app.erathos.com/signup?slug=blog&button=cta&utm_campaign=bi_are_you_losing_money) and solve the multiple sources of truth trap by centralizing your data before choosing your next BI tool.