Microsoft Power BI
Power BI Business Central – How to Transform ERP Data into Real Business Decision Support
2026-03-18
Today, investment projects are among the most complex areas of management within organizations. Their success is no longer determined solely by whether the project was completed on time and within budget, but above all by whether it actually delivered the expected business value. PMI emphasizes that a modern assessment of project success should combine the execution perspective with the business impact perspective, rather than being limited solely to the classic “iron triangle.” In a 2024 PMI study, 48% of projects were rated as successful, 40% as yielding mixed results, and 12% as failures, which shows how many organizations still struggle to translate capital expenditures into measurable outcomes. At the same time, projects that combined strong execution with genuinely useful outcomes achieved the highest perceived effectiveness. This is precisely why investment project analytics must encompass not only cost but also value, risk, progress, variances, and decision-making scenarios. Power BI is particularly useful in this area because it allows you to combine financial, scheduling, and operational data into a single, cohesive reporting environment.
In the context of Power BI HubSpot integration, it is important to understand that a CRM system is not an analytical environment — it is an operational one. This means its primary purpose is to support the daily work of sales and marketing teams, not to deliver comprehensive management-level analytics.
Power BI adoption rarely fails because the tool itself “doesn’t work.” Most often, the problem lies elsewhere: reports do not fit into the daily work rhythm of users. They are too complex, the data is often inconsistent or outdated, and the report can take too long to load—as a result, people return to Excel. This is important because BARC research shows that on average only about 25% of employees actively use BI tools, and in large organizations even about 16%. The good news, however, is that adoption is “designable.” If we approach reporting as a product (with UX, audience segmentation, usage measurement, and iteration), Power BI becomes an everyday work tool rather than a monthly “PDF for a meeting.” How to make such a change? We’ll give you some tips!
Just a few years ago, reporting in many organizations ended with the analysis of historical data. We reviewed last month’s sales results, the previous quarter’s margin, or the level of operating costs. The problem is that such an approach answers only one question: what happened?
In many organizations, Power BI still primarily serves as a tool for historical reporting, showing what has already happened. However, this type of data analysis is increasingly insufficient in environments where business decisions need to be made quickly, based on current and forecasted information.
Operational efficiency most often “slips away” quietly: queues grow, exceptions increase, and rework begins to eat up the team’s time. In such conditions, Power BI should not be just a tool for monthly reporting, but an operations control center—a place where you can see deviations, their causes, and priorities for today and the coming days. The key change is that the report should not only answer the question “how much have we done,” but also show “where the process is slowing down, why, and what needs to be changed.” A well-implemented data analysis system, such as Microsoft Power BI, allows you to translate the discussion about “feelings” into data: stage times, exceptions, process stability, team workload, and the risk of SLA violations. What’s more, when data is fed in regularly, the operational dashboard becomes a tool for everyday work: for change leaders, coordinators, operations managers, and those responsible for process improvement. And that means a real impact on unit costs, quality, and predictability of delivery. Let’s take a closer look at this topic.
Power BI offers a wide range of built-in visuals that, in many cases, allow organizations to analyze data quickly and effectively. However, as organizations grow and data complexity increases, there are scenarios where standard charts no longer meet real reporting needs.
