Self-Service Analytics: The pitfall of poor input and blind interpretation

21 Apr 2025

Self-service analytics (SSA) allows selected employees to build dashboards, generate reports, and even ask AI questions without relying on a central BI team. It sounds like the perfect setup: faster insights, more autonomy, and less pressure on data departments.

But when users are not trained to handle this freedom, things can go wrong. Poorly framed questions, careless interpretation of AI-generated outputs, or unconscious bias can all lead to flawed insights. And flawed insights lead to poor decisions.

Self-service analytics is a powerful enabler, but only if users are properly trained and prepared for their role in the analytics process.

The risks of untrained use

1. Faulty insights caused by incorrect data usage

Self-service analytics gives users the freedom to make their own data selection. That gives a lot of freedom, but also responsibility. If someone is unfamiliar with the data model, definitions, or data field origins, they may misuse the data without realizing it.

Consider a clothing store owner who wants to know how many items have been sold recently. He selects an order table, applies filters, and creates some visualizations. However, the table only reflects the number of orders, not the number of products per order. It also includes returns. The result appears accurate, but the underlying figures are completely off.

If you don't know what you're measuring, you're probably measuring the wrong thing. Understanding the semantic layer, data definitions, and how filters are applied is essential for building reliable dashboards.

2. Poorly framed questions lead to misleading outputs

When GenAI is integrated into self-service analytics, it makes data analysis more accessible than ever. Users can ask natural language questions and instantly receive visualizations or insights. But this ease of use comes with risk. AI responds to exactly what is asked, not what is meant.

For example, a manager asks the BI assistant, “What are our best-performing products?” Within seconds, they see a report with impressive growth figures and charts. It looks convincing, so it is shared and used to support decisions. However, there was no specified time period, no filter by region, and no definition of what “performing” actually means. Is it based on revenue, profit, or something else?

The AI did its job, but the input was too vague. Without training in how to ask a good question – also known as prompt engineering - users may develop a false sense of certainty. What looks trustworthy on the surface can be misleading in practice.

3. Misinterpretation: data is not absolute truth

Whether users create visualizations themselves or use GenAI, the resulting insights are never fully objective. AI tools and BI platforms can reveal patterns with incredible speed, but they do not explain or contextualize those patterns. As a user, you have to understand and do this yourself.

A user may notice a sudden drop in revenue and immediately conclude that a specific region or team is underperforming. They consider taking corrective action. A trained analyst, on the other hand, would ask whether the change is seasonal, whether there is data delay, or whether a shipment was simply delayed.

Without context, there is a high risk of treating generated insights as absolute truth. In reality, insights only gain value when someone interprets or questions them in the right way.

4. Invisible bias and assumptions

Many BI tools and AI systems are trained on historical data, which is not always neutral. If users don’t understand this background, they may unknowingly reinforce outdated patterns.

Imagine an HR dashboard that predicts promotion likelihood based on past trends. If historical promotions favored a specific profile, the system would consider that profile to be the “right” one. Without awareness, these biases are repeated and amplified.

Users unfamiliar with how AI is trained or how datasets are constructed often accept these results at face value. Instead, they should be asking critical questions about how such outputs are generated.

The solution: Training and empowerment

Even the best technology is only as effective as the people using it. Self-service analytics only truly delivers value when users understand what they are doing, how the tools work, and when they need to think critically. The key is to invest in your users: their knowledge, their critical thinking skills, and their confidence in working with data. Only then can you use self-service analytics as a strategic tool and prevent it from becoming a risk.

Data literacy as a foundation

Not everyone needs to become a BI specialist, but a basic understanding of data is essential. What does a KPI really measure? When is a trend significant? How do you read a visual? Training users in these concepts prevents dashboards from being misread based on gut feeling.

Awareness of AI and its limitations

AI tools provide rapid answers, but they are neither flawless nor objective. Users must understand that AI works with historical patterns, which has its limitations and can contain biases. Training helps them view AI outputs as suggestions, not as definitive answers.

Asking the right questions

With AI-driven BI tools, the input determines the outcome. Users need to learn how to formulate questions clearly, precisely, and with the right context. Training in prompt engineering is critical, as it is the key to producing reliable output and actionable insight.

Ongoing support and knowledge sharing

A single training session is not enough. Establish recurring knowledge sessions and close collaboration between users and central BI developers. This fosters a culture where questions and improvements are encouraged and normalized.

Building effective dashboards

Creating a dashboard is one thing. Building one that is understandable, reliable, and usable for end users is another. What do you show? What do you leave out? How do you keep it clear and focused? Most mistakes don’t come from the data itself, but from poor visualization or confusing layout.

It is essential to educate users on dashboard design principles. At Orange Business we can help with this, for example through our Dashboard Design Workshop, where teams learn how to design dashboards that are clear, targeted, and compelling.

Want to know more?

Every dataset tells a story. At Orange Business, we help you bring those stories to life. Whether you need support designing dashboards, building data visualizations, or training your teams—we create solutions tailored to your challenges. Together we ensure that your organization has reliable insights that you can build on with confidence.

Curious about how self-service analytics or data visualization can work for your organization? Get in touch and let’s explore the possibilities.