Start with decisions, not charts: how to design KPIs in Power BI?
The most common mistake is to build a dashboard that is “pretty” but not operationally useful – lots of KPIs that do not lead to any decisions. In Power BI, it is worth starting with a simple question: what decision will we make differently when the indicator changes? If there is no decision, the measure is unnecessary.
The second step is to arrange KPIs in a logical tree: strategic goals → process KPIs → operational metrics for teams and changes. This allows you to build navigation in Power BI from the management view (result) to the “on the floor” or “on the process” view (cause).
In practice, operations also need consistent definitions – one definition of lead time, one of case closure, one of error – otherwise the team spends time arguing over numbers. Microsoft Power BI supports this order well, as DAX measures and metadata descriptions can serve as the “single source of truth” in an organization.
Examples of KPI → decision links (working well in Power BI):
• Lead time increases → change priorities / WIP limit / eliminate bottlenecks
• Percentage of exceptions increases → analyze causes / improve input data / standardize
• Right First Time decreases → adjust quality control / training / automation of validation
5 dimensions of process efficiency that are worth showing in a single Power BI cockpit
In operations, it is impossible to optimize only one parameter – improving speed can worsen quality, and cutting costs can affect SLAs. That is why the five-dimensional model works best in Power BI: time, cost, quality, flow, and resources.
This structure allows you to quickly see the trade-off: whether shortening the cycle time increases rework, or whether “full occupancy” of the team generates queues. In operational practice, not only is the average important, but also stability – two processes may have the same average completion time, but one will be predictable, and the other will have a “tail” of difficult cases. Power BI allows you to clearly show percentiles (P50/P75/P90), the standard deviation of cycle time, distributions, and heat maps. This allows operations to work on facts rather than on averages, which often mask the problem. Ultimately, the goal is for a single screen to answer the question: is the process fast, cheap, high-quality, and predictable – and where is it losing momentum?
Data for operations: how to prepare an “event log”?
Power BI will show as much as it gets – that’s why process analytics should start with events, not charts. The most useful structure is the so-called event log, i.e., a record of process events for each case (order, job, ticket, batch).
This allows you to calculate stage times, detect bottlenecks, exceptions, loops, and rework. In practice, an event log consists of data from operating systems (ERP/WMS/MES/CRM/ITSM), quality records, data from readers or forms – and Power BI can integrate this into a single data model.
The key is a consistent case identifier and consistent status meanings. Without this, time and flow metrics will be “floating.” In many companies, the greatest improvement comes only after organizing the dictionary of stages and reasons for exceptions – then the dashboard begins to show the real causes, not noise. If the organization is in a transitional stage, the event log can even be built from files and stabilized over time in the target warehouse/lakehouse.
The minimum standard fields that “trigger” operational analytics in Power BI are:
• Case ID (order/request/case ID)
• Step/Activity (stage name)
• Timestamp (event time; often start/stop)
• Resource (team/line/position)
• Outcome/Reason (result and reason for exception/error)
KPIs that deliver the fastest results in Power BI: bottlenecks, exceptions, rework
In operations, it is not “standard” cases that cost the most, but exceptions and corrections. That is why Power BI should highlight operational friction indicators, not just totals and averages. The touch time vs. wait time metric is very effective, showing how much real work is involved in the process and how much waiting in line. This approach is often eye-opening, as it turns out that the problem is not the speed of work, but priorities and queues. Another category is the rework rate – the percentage of cases returning to the previous stage, which is almost always a “double cost.”
In addition, Microsoft Power BI is also great for showing the structure of exceptions: top 5 causes, weekly trend, share of total time, and costs. It is also worth considering stability: if cycle time variance increases, the process loses predictability, and SLAs begin to depend on luck. And most importantly: each metric should be drill-downable to the stage, team, change, and segment (product/customer/channel), because only then do KPIs lead to action.
How to design an operational dashboard in Power BI so that it becomes a control tool?
An operational dashboard is not a presentation – it is supposed to be a cockpit. For this reason, in Power BI, it is worth designing it in layers:
• monitoring (what is happening),
• diagnostics (why),
• response (what now).
The first screen should show daily/shift KPIs, SLA status, queue sizes, and risk signals. The second level is diagnostics: process stages, bottlenecks, causes of exceptions, heat maps by changes/teams, and cycle time distributions. The third level is a drill-through to the list of cases, because operations must be able to “catch” specific issues and intervene.
Microsoft Power BI gives you an advantage here: interactions, filters, tabs, and the ability to create role-based views (shift leader vs. operations manager vs. controlling). In practice, it is worth setting tolerance thresholds and alert logic: not every KPI drop is critical, but SLA violations and growing queues should be visible immediately. The whole process should end with a simple answer: where is the problem, how big is it, and what needs to be changed today.
Microsoft Power BI in operations departments – does it really make sense?
In operations, companies that can react faster than the problem grows win. When used well, Power BI turns process data into an advantage: it allows you to spot deviations earlier, make decisions based on facts, and consistently reduce losses that are often “invisible” in the daily rush – minor delays, corrections, exceptions, and manual workarounds. This is not a project “for the report,” but an investment in predictability: more stable SLAs, lower unit costs, better quality, and easier scaling. The most important business effect? Operations stop putting out fires and start managing the process consciously – with control, priorities, and a clear impact on the company’s results.