Why Self-Service Analytics is Still a Struggle and How to Fix It

Self-service analytics was introduced to make data more accessible, reduce the pressure on analytics teams, and enable faster, data-informed decision-making.

For many organisations, it is also a strategic move to scale analytics capability without growing headcount. Whether it’s through dashboards or AI agents, the goal is the same: empower business users to get information independently.

Yet the reality often falls short. Tools are underused, data is misinterpreted, and users revert to spreadsheets. These challenges will persist, even as AI-driven tools evolve, if the fundamentals remain unaddressed.

The issue is not the technology. The real barriers are people, processes, and trust in data. If employees do not understand how to use the information, what it means, or how to ask the right questions, if solutions are not aligned with business needs, or if teams do not trust the information, then self-service will never deliver the impact it promises.

In my experience, there are five common reasons self-service analytics falls short in practice. Addressing these is key to unlocking its full potential.

1 - More Dashboards Do Not Equal More Insights

Dashboards have become the default solution for self-service, but building dashboards is the easy part. Organisations often assume that simply increasing access to dashboards will help users answer their questions. Many dashboards are underused, misunderstood, or misaligned with what people need to know.

A user might open a dashboard, see a set of charts or metrics, and not know what they’re looking at or what decision they’re meant to make. Without context or clarity, data is just noise.

The result is often hundreds or even thousands of dashboards within an organisation, but very few are actively used to drive decisions.

To move forward, dashboards need to do more than visualise data. They need to guide decision-making by aligning with business questions, providing explanations, and giving users the confidence to act.

To get started, organisations often need to take stock of what reporting exists today. A focused audit can help identify what’s being used, what’s duplicated, and where there are gaps or misaligned metrics. Our Insights & Reporting Health Check helps uncover these issues and creates a plan to streamline reporting, align metrics, and improve access to meaningful insights.

 2 - The Data Literacy Gap: Tools Are Not Enough

Giving people access to data does not mean they know how to use it. Many employees are not confident working with numbers, and without training, it’s easy to misinterpret information or ignore it altogether.

True self-service requires more than just user-friendly tools. It depends on whether people know how to ask the right questions, interpret trends, challenge assumptions, and tell stories with data.

Organisations that invest in self-service must also invest in education. This includes tailored training that empowers employees to engage with data in their day-to-day roles. Not just a one-off dashboard walkthrough, but ongoing support that builds real capability.

Our Data Academy is designed to help teams at all levels become more confident with data, from asking the right questions to telling compelling stories with insights.

3 - The Disconnect Between Business and Data Teams

Self-service also breaks down when business and data teams are not working together effectively. IT or analytics teams may build dashboards based on a brief, but if they don’t fully understand how it will be used, the final product often misses the mark.

At the same time, business users may struggle to articulate what they actually need. This leads to a cycle of rework and frustration, with neither side getting what they want.

What’s often missing is a structured way of working. How are requests captured? How is prioritisation handled? How do teams check they’re on the right track before building a full solution? How do we check that we are on the same page?

Self-service works best when business and data teams collaborate from the start, with clear processes for communication, feedback, and iteration. It is not just about delivery, it is about working together to solve real problems.

4 - Data Governance: If Users Do Not Trust the Data, They Will Not Use It

Even with great tools and training, self-service will fail if people do not trust the data.

This is one of the most common reasons self-service initiatives lose momentum. Different teams may report different numbers for the same metric, or the data may be out of date, inconsistent, or poorly defined.

When users encounter conflicting reports or lack clarity on where the data came from, confidence quickly erodes.

A strong data governance framework is the foundation of self-service. This includes consistent definitions, clear ownership, high data quality, and the ability to trace data back to its source. Without this, users will hesitate to make decisions based on what they see, and self-service becomes another broken promise.

If you're unsure where to start, our Governance Health Check helps assess your organisation’s current data governance and management practices. It identifies areas of risk, pinpoints opportunities for improvement, and delivers tailored recommendations to build confidence in your data and support safe, scalable self-service.

5 - Moving Beyond the Spreadsheet Mentality

Despite all the investment in BI tools, many employees still default to spreadsheets. And it's no surprise. Excel is familiar, flexible, and fast.

If dashboards or AI tools feel clunky or confusing, people will naturally go back to what they know.

To shift this behaviour, self-service tools need to be intuitive, relevant, and seamlessly integrated into existing workflows. It should be easier to use a dashboard than to export the data and start over in Excel.

Designing for real business users, not just data professionals, is key to adoption. The goal is not just access to data, but enabling people to make better decisions, faster.

How to Get Your Self-Service Back on Track

Self-service analytics is not just about technology, it is about people. The tools will keep evolving, from dashboards to AI agents, but unless users are confident, engaged, and working with trusted data, the outcomes will stay the same.

To succeed with self-service, organisations need to focus on three key areas:

1. Investing in data literacy so employees feelconfident using data
2. Improving collaboration between business anddata teams to ensure solutions are relevant and usable
3. Strengthening governance to ensure data isconsistent, trustworthy, and understood

These focus areas give organisations a practical starting point to improve adoption, reduce friction, and unlock more value from their data investments.

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Not sure where to start with all of this? That’s exactly what we’re here for!

Our Data Accelerator Workshop is a great first step to assess where you're at, clarify your priorities, and uncover quick wins that set you up for self-service success.