How Power BI Consultants Turn Data Into Business Strategy

In many companies, analytics begins with a sense of chaos. Data is scattered across ERP, CRM, Excel, and marketing platforms. The numbers don't match, duplicates appear, and the answers to simple management questions vary each time. The business sees the metrics but doesn't understand which ones to rely on.

How can all this data be assembled into a single logical picture and transformed into a basis for decisions? The answer is simple: hire Power BI consultants. At a minimum, they will help you organize disparate sources, build a unified data logic, and transform metrics into a clear basis for management decisions.

In this article, we will examine the role of Power BI consultants in data strategies: what business issues they work with, what tools they use, and how this looks in practice in a case study from the financial sector and e-commerce.

What business issues does a Power BI consultant address?

A Power BI consultant works with the management aspects of the business. That is, those for which management expects clear and unambiguous answers to make decisions.

In particular, these are questions about:

• Sources of results—what exactly generates profit: which departments, areas, or specific products bring in margin, and which create hidden losses.
• Reasons for deviations—what causes changes in indicators: sales volumes, operating expenses, and customer behavior.
• Process efficiency—at what stage do delays, time, or resource overruns occur, and how does this affect the implementation of plans.
• KPI performance—what factors actually influence the achievement of goals and what management levers produce the predicted effect.
• Decision scenarios—how the result will change with different management actions: budget adjustments, price revisions, resource reallocation, or priority changes.

The consultant translates these issues into clearly defined indicators, calculation logic, and analytical models. This allows businesses to see the causes and consequences of changes in indicators and assess the impact of management decisions even before they are implemented.

What tools does a Power BI consultant use, and how do they work with them?

Power BI is a strategic tool for working with data. It is a platform for combining information from different systems, building analytical models, and presenting indicators in the form of dashboards—a visual visualization.

The work begins with selecting KPIs and metrics that correspond to specific business goals. The consultant determines which indicators have managerial value for different levels of responsibility and builds an analytics structure for specific roles.

The same set of data is presented differently depending on the tasks, for example:

• For the CEO, a generalized picture of financial results, key performance indicators, risks, and growth points is formed.
• For the operations manager, the analyst details the processes, deviations from the plan, and areas of direct influence.

In this format, analytics works as storytelling through data. The consultant builds a sequence of indicators and visualizations so that management decisions are derived from the data in a logical and transparent manner.

The next stage is the transition from analytics to strategy. Consultants help:

• Identify sustainable trends and potential risks.
• Model development scenarios using what-if analysis.
• Find key growth drivers and assess their impact on the outcome.

In this logic, analytical tools become the basis for discussing decisions and strategic scenarios.

Process turning data into strategic insights with Power BI examples

In this section, we will look at how the process of transforming data into strategic insights works in practice. We will present two case studies from the financial sector and e-commerce. Each example shows how analytics helps to see the real picture of the business and determine priority actions.

Financial sector: participants, roles, and key challenges

Let's take a look at who works in the financial sector. These are banks, insurance companies, financial and credit organizations, investment and management companies, and fintech services. Within these businesses, financial analysts, controllers, BI analysts, FP&A teams, and managers who make decisions based on reporting work with data on a daily basis.

Now let's see what answers analysts most often look for on specialized forums and in professional communities:

• How to forecast delinquency and default probability for a loan portfolio, rather than recording debt after the fact?
• How to explain the growth of NPLs and understand which products or customer segments are driving it?
• How to conduct stress testing of a financial portfolio and assess the impact of changes in interest rates, exchange rates, or macroeconomic factors?
• How to calculate IFRS 9 or CECL with transparent logic that can be reproduced and explained to auditors and regulators?
• How to control liquidity and maturity gaps (ALM), including LCR and NSFR indicators?
• How to break down Net Interest Margin by product and channel and understand what exactly affects its change?
• How to identify anomalous transactions for fraud and AML by agreeing on rules between the risk team, finance, and operational departments?

In solving such issues, the role of Power BI consultants is to show where exactly the cause of the problem is hidden in the data. For example, let's take a credit case at a fintech company, which was shared with us in an interview by an outsourcing company.

Case study of a financial company

A fintech startup working with online lending noticed an increase in delinquencies of 30+ days from 4.2% to 6.0% at the end of the third quarter of 2024. At the same time, the total loan portfolio amounted to approximately USD 18 million, and the pace of lending remained stable. The team saw the issue but did not understand what was causing it.

The consultant collected data from the loan issuance platform, CRM, and payment accounting system and analyzed it in terms of issuance dates, products, and scoring rules. The analysis indicated that more than 55% of new delinquencies were attributable to loans issued in June–July 2024, when the startup simplified its scoring model to accelerate growth. These loans accounted for only 22% of the portfolio but constituted the bulk of the problem debt.

Additionally, it became apparent that the average size of such loans was USD 2,500–4,000, and the share of customers without a previous credit history exceeded 45%. The insight was that the increase in delinquency was not related to the behavior of all customers or market conditions, but to a specific decision in the scoring logic.

Based on this, the fintech company revised its risk assessment rules for this segment from September 2024, maintaining growth rates but reducing risks in new issuances.

E-commerce case study: how to link marketing to real income

Let's look at another example. An e-commerce company sells products through its website and works with several channels of attraction: Google Ads, Meta Ads, email marketing, and organic traffic. Marketing costs are rising, but management does not fully understand which investments are actually paying off.

The main questions for management are as follows:

• Which marketing channels generate revenue, not just traffic?
• Where exactly in the funnel is value being lost?
• Why is the increase in advertising expenses not leading to a proportional increase in sales?

The work begins with compiling data from advertising platforms, web analytics, and sales systems into a single analytical model. The analysis is then built around the entire customer journey — from the first click to the purchase and repeat orders.

As a result, it becomes clear, for example, that some brand campaigns on social media with high click-through rates and reach have a low contribution to revenue. While search campaigns and email channels are more effective at the conversion and repeat sales stage.

This analysis allows businesses to shift their management focus and understand which marketing channels are best to invest in.

When to engage a Power BI consultant

You don't turn to a consultant when you “need a dashboard,” but when you need clarity in your data and decisions. This often makes sense in the following situations:

• The numbers from different systems don't match up. Finance, sales, and operations teams work with different metrics, and there's no single version of the truth.
• There are reports, but no answers to management questions. The data is available, but it doesn't explain why the results are changing and what actions are affecting them.
• The business is growing, but analytics can't keep up. New products, channels, or segments are emerging, and the current analysis model no longer reflects the real picture.
• Decisions are made based on assumptions. Hypotheses are discussed without verifying the data or based on isolated pieces of information.
• Metrics need to be agreed upon between teams. KPIs are formally defined, but are calculated differently and do not allow for comparison of results.
• The complexity of the business is increasing. Data is distributed across multiple systems, and manual work with Excel is no longer scalable.
• The consequences of decisions need to be seen in advance. There is a demand for scenario analysis and understanding of how changes in one indicator impact others.

In such situations, a consultant helps to build a logic for working with data so that analytics supports management rather than simply recording indicators.

Choosing a consulting company with Power BI: practical criteria

If analytics is already being used for management decisions, the choice of a consulting company determines the future logic of working with data. At this stage, it is worth focusing on an approach that allows you to work with Power BI as a tool for analysis, modeling, and strategic decisions, rather than as a separate element of reporting.

To evaluate a potential partner at the outset, it is worth paying attention to the following criteria:

• Focus on management issues. A company that operates at the strategic level discusses requests in terms of their impact on margins, risks, financial results, and development scenarios. It is this approach that allows analytics to be used for decision-making rather than for recording indicators.
• Working with analytical models rather than individual reports. A sign of mature expertise is attention to data structure, calculation rules, and connections between sources. This is the basis for scalable analytics that retains value as the business grows.
• Integration of data from different systems. Experience working with ERP, CRM, financial, and operational systems shows whether a company is capable of building a unified data logic and eliminating gaps between teams.
• Ability to work at the intersection of roles. A company that focuses on management decisions knows how to work with finance, analysts, operational teams, and management, ensuring a common understanding of data and metrics.
• A transparent approach to cases and solutions. It is important that the team can explain not only the result, but also the path to it: what hypotheses were tested, what limitations were considered, and how insights emerged.
• Willingness to work with complex and “imperfect” data. Real business data almost always contains inaccuracies and limitations. Experience is demonstrated by the ability to work with such conditions and gradually improve the quality of analytics.
• A partnership approach to analytics development. A consulting company focused on long-term cooperation helps adapt analytics to new tasks, changes in business, and increasing data complexity.

Incidentally, this is precisely the approach taken by the consulting company Cobit Solutions, whose expert advice and case studies we used in preparing this material.

Conclusion

In this article, we have covered the entire process of working with analytics: from chaotic data in different systems to the process of turning data into strategic insights with Power BI examples. We have shown how, through a coordinated data model, clear metrics, and the right cuts, analytics begins to answer management questions rather than simply displaying indicators.

The case of the fintech company clearly showed that key insights come not from the tool, but from the approach: the ability to ask the right questions of the data, combine different sources into a single logic, and interpret the result in a business context. This is precisely the role of consulting—to help businesses see the causes, not just the consequences.

If analytics is already being used in your company, it is worth taking a critical look at it:

• Does it respond to real management requests?
• Does it allow you to work with scenarios?
• Does it provide a common picture for different teams?

Choosing a consulting company in such a situation means choosing the logic by which the business will continue to work with data. And the sooner analytics begins to support strategic decisions, the greater the value it brings in the long term.