Paulina Jedrzejewska shares how Power BI self-service analytics can improve data trust, user confidence, model performance and decision making.
Most organizations do not struggle because they have too little data.
They struggle because they have too many reports, too many calculations and too many versions of the same truth.
One team works with a Power BI dashboard. Another downloads the data into Excel. A third creates its own calculations. When leaders finally meet to discuss performance, the first question is often not, “What should we do next?”
It is, “Which number is correct?”
During a recent conversation on The Executive Outlook, Power BI consultant and self-service analytics specialist Paulina Jedrzejewska explained why this problem continues to appear and how organizations can solve it.
Her view is practical. Power BI self-service analytics should not mean giving every employee complete freedom to create separate models, calculations and reports.
It should mean building a trusted data foundation that people can use without waiting days for the BI team.
The goal is not to create more dashboards.
The goal is to help people reach reliable answers faster.
Paulina’s journey with Power BI began while she was still a university student.
The company where she worked had just started introducing Power BI and she became one of the first people there to test it.
At first, the experience was difficult.
She already knew SQL, but Power BI and DAX followed a completely different logic. Calculations changed depending on context, and it took time to understand why the platform behaved the way it did.
But as Paulina continued learning, something changed.
She realized that she was good at solving Power BI problems. She could understand what was happening inside a model, find the cause of an issue and create a solution.
Her interest grew even stronger when she moved into consulting and discovered that she could use C sharp scripts to automate repetitive tasks inside Power BI.
That discovery changed the way she viewed the platform.
Power BI was no longer a reporting tool. It became a way to solve business problems, simplify complex work and help people use data with greater confidence.
She rarely needs to convince an organization that data is important.
Most companies already use data in some form. They may rely on spreadsheets, operational reports, system exports, dashboards, or manually prepared presentations.
The harder conversation is about why they need a proper business intelligence structure.
Why should everyone look at the same data?
Why should a company invest in clean semantic models, shared definitions and one trusted source of truth?
The answer becomes clear when different teams calculate the same number in different ways.
Finance may define revenue one way. Sales may use another definition. Marketing may work with a third number.
Each team may believe its calculations are correct.
The meeting then becomes a debate about data instead of a discussion about business action.
A trusted Power BI structure removes this confusion. It gives people a common starting point and allows leaders to make decisions with more confidence.
She also believes that one message will not work for every stakeholder.
A finance leader may care most about accuracy and control. An operations leader may need speed. A marketing team may want more freedom to explore information.
A good consultant must understand what matters to each person and explain the value of analytics in a way that connects with their work.
One of the most important lessons Paulina has learned is that consultants must do more than listen.
They must also observe.
When business users are asked what data they need, a common answer is, “All of it.”
They may genuinely believe they need every record. A user might need to compare the performance of one product ten years ago.
But that does not mean a report should load millions of products and ten years of detailed data every time it opens.
The real requirement becomes visible when data teams sit with users and watch how they work.
What information do they check every morning?
Which questions do they ask when performance changes?
Do they begin with a summary and then move into detail?
How often do they need historical records?
By observing these habits, BI teams can make better decisions about what information should be imported, what should remain available through Direct Query and what level of detail belongs in each model.
This approach also prevents companies from building heavy systems based on unclear requests.
The strongest analytics solutions are not designed around all the data a company owns.
They are designed around the decisions people need to make.
She sees Power BI self-service analytics in three stages.
In the first stage, there is no self-service. The BI team controls everything. It prepares the data, creates semantic models, builds reports and handles every small change.
This gives the company control, but it also creates delays. Business users must wait for the BI team whenever they need a new view, calculation, or report.
In the second stage, there is completely self-service. Business departments create their own models, reports, calculations and analysis.
The BI team mainly manages access, permissions, capacity and administration.
This gives users freedom, but it can quickly create several versions of the truth. Teams may duplicate data, define measures differently and build models that become expensive or difficult to manage.
She believes the strongest approach sits in the middle.
In this model, the BI team builds and manages trusted semantic models. It handles relationships, measures, business definitions, quality and performance.
Business users connect to those models and create their own reports, subscriptions and ad hoc analysis.
This structure gives users freedom without removing governance.
It also reflects what people are already doing.
In almost every organization, employees continue to export data into Excel even when official reports exist. They do this because fixed reports cannot answer every question.
A strong self-service environment gives them the flexibility they need while keeping them connected to trusted data.
Semantic models do not remain small forever.
New tables have been added. New measures have been created. Users request more calculations, filters, field parameters and special conditions.
Each change may appear harmless.
Over time, however, the model becomes larger, slower and more difficult to understand.
The performance problem often grows quietly. By the time users notice the model is slow, it may already need major repair.
Paulina’s advice is clear.
Do not begin with one semantic model for everything.
Instead, identify different business use cases and create focused models around them.
She shared an example of a client where the team built a smaller overview model. It included finance, marketing and other business information, but the data was kept at a higher level.
It focused on recent years, weekly analysis and category level performance.
This model helped users understand the overall business picture and quickly identify unusual results.
The team then created separate models for finance, inventory and marketing. These models contained more detailed data.
When a user notices an unusual result in the overview, they could move into the relevant detailed model and investigate it at the product or product variant level.
This approach kept the overview fast while still providing access to detailed information.
The models were also adjusted according to real user needs.
For example, inventory analysis sometimes requires financial information. Instead of forcing users to move between models constantly, the team added selected finance columns and measures to the inventory model.
The aim was not to create perfect technical separation.
The aim was to build something useful, fast and easy to maintain.
A semantic model created for self-service must remain simple, flexible and reliable.
Business users will often use fields in ways the developer did not expect. They may combine measures with different categories, apply unusual filters, or create reports for new situations.
She believes the information should still make sense.
If a measure only works in one specific context, but users can apply it anywhere, the model may produce false information.
The report may look professional while presenting the wrong conclusion.
That is one of the biggest risks in self-service analytics.
BI teams must therefore design measures carefully, use clear names and build models that behave consistently.
The user should not need deep technical knowledge to understand what a field means.
Consistency across models is also important.
When semantic models follow the same structure, naming rules and organization, users can move between them more easily.
A familiar structure reduces confusion and improves adoption.
Power BI is easy to begin using.
A person opening it for the first time can create a basic report within minutes.
But that simplicity can also create problems.
Someone can build a report without understanding model relationships, DAX context, visual design, or performance.
The report may look correct while hiding serious errors.
For her, training and support are essential parts of Power BI self-service analytics.
Power users need to understand how to build useful reports. Consumers need to know how to read and question the information. Developers need standards for semantic models. Administrators need to understand permission, governance and capacity.
Giving users access without training does not create self-service.
It creates unmanaged activities.
People become confident when they understand the tools and know where to get help.
Formal training is useful, but questions continue after the session ends.
She often creates an internal Power BI community inside the organization.
It may begin as a Teams channel where users can ask questions and share solutions.
It can also include regular meetings where employees discuss new features, problems and lessons.
At first, She usually leads these sessions. She shows new Power BI features, answers questions and helps users solve issues.
Over time, the community begins to change.
Other people start sharing what they have learned. They demonstrate reports they created. They explain how they solved a problem. They begin answering questions from colleagues.
Eventually, the community no longer depends completely on the consultant or BI team.
That is a sign of success.
Self-service analytics should not create permanent dependence on one expert.
It should build enough knowledge inside the organization for people to support each other.
She openly admits that report design is not her strongest area.
Her strength lies in building semantic models and solving technical problems.
The self-service approach allows work to move to the people best suited for it.
The BI team can focus on the trusted data foundation. Business users can focus on reports that support their own needs.
She also recommends using automation when building models.
Calculation groups, scripts and user-defined functions can reduce repetitive work. They can help developers create measures faster and make sure different models follow the same structure and naming rules.
Automation saves time, but its greater value is consistency.
For leaders managing analytics across several teams, consistency makes governance, maintenance and future changes much easier.
One of the hardest situations in Paulina’s consulting career happened when she was still a junior consultant.
A large company had one enormous semantic model. Over time, it had grown into what she described as a monstrosity.
Two other consultants had been managing it, but they left within a few weeks of each other.
She was left alone with the model.
Soon after they left, it began to collapse.
New errors appear almost every day. Much of the model used Direct Query and the error messages gave little useful information about the real problem.
It took her around one month just to stabilize the system.
Once it was working again, she told the company that small fixes were no longer enough.
The entire Power BI architecture had to be rebuilt.
The rebuilding process took around eight months.
It involved meeting different stakeholders, understanding what each team needed, deciding what could remain and rebuilding trust in the new structure.
The technical work was difficult, but the change management was even harder.
Users were used to finding everything inside one model. They worried that the new structure would remove important data or produce different results.
Paulina and the team had to prove that users would still have the information they needed and that the numbers remained accurate.
The experience taught her a great deal about debugging, model design and stakeholder communication.
It also confirmed an important lesson.
It is easier to begin with several focused models than to divide one massive model after users have become dependent on it.
Paulina’s story is about Power BI, but the larger lesson is about leadership.
More access does not automatically create better decisions.
Access without structure creates confusion. Freedom without governance creates inconsistent numbers. Technology without training creates risks.
Successful Power BI self-service analytics requires balance.
The BI team must create a trusted foundation. Business users must have enough freedom to explore and answer their own questions. Models must remain focused, understandable and reliable.
Most importantly, organizations must pay attention to how people work.
The strongest analytics environment is not the one with the most data, the largest model, or the greatest number of dashboards.
It is the one that helps people reach the right answer without losing trust along the way.
Watch the full conversation on The Executive Outlook to hear more from Paulina Jedrzejewska about Power BI, semantic models, self-service analytics and the lessons she has learned from solving complex data challenges.
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