Self-service analytics works when literacy and governance move together

Nathi Dube, Director: PBT Innovation at PBT Group

I have seen self-service analytics thrive and stall. The difference is rarely the tool, but rather whether the organisation is mature enough to use it, and whether the data has been curated in a way that business people actually understand. If you expose a technical schema and call it “self-service,” you will build a white elephant. If you expose a clear semantic layer and data products that fit how teams think and talk about their work, adoption follows.

Where self-service lands nicely, the benefits are immediate. Business users already understand the meaning behind their KPIs. Put a familiar interface in their hands, such as Power BI, and they move faster, make decisions sooner, and stop queuing for extracts from IT. The combination of domain knowledge plus an approachable tool surface is powerful, provided the underlying solution was architected with self-service in mind.

The most significant gaps I encounter are not in dashboards, but rather in data quality, lineage, and access clarity. That is why governance matters. Too often, it is treated as a blocker. In practice, good governance is an enabler. It defines who may see what, makes the path from source to consumption traceable, and ensures privacy rules are applied consistently (including hiding sensitive elements where appropriate). When stewardship is active from ingestion through to exploitation, quality improves because it is being watched all the way through the pipeline.

Do not forget literacy

Data literacy is the other side of the coin. We are already seeing pockets of excellence. These are teams that can read, interpret, and communicate their data in context to make decisions within their domain. Self-service works best when those teams take ownership of the data they generate and use, and when the platform brings them closer to that data, rather than pushing them away from it. Literacy turns access into good judgment.

Governance and literacy are not parallel projects, but they work hand in hand. As organisations mature, both grow together through clearer definitions, better lineage, stronger privacy practices, and, in step, a workforce that is more capable of using data responsibly. Treat it as an iterative journey, not a big-bang rollout.

If you are looking for practical steps, here is what I recommend based on what works in the field:

• Start with awareness. Put data literacy on the agenda for everyone who touches data, not just analysts.
• Set up a governance function. Give it the mandate to define frameworks, enforce access, and maintain lineage so the data exposed to business is traceable and trustworthy.
• Invest in training and the right partners. Short, focused enablement, combined with specialist support, accelerates setup and adoption.
• Pick a pilot “hub.” Select a business unit with a strong appetite for data and leadership support. Iterate there, prove value, and expand.
• Design for re-use. Expose a semantic layer that other teams can pick up; share models where it makes sense, so you do not keep reinventing the wheel.

AI is part of the process

AI is already changing the experience. Many platforms now embed assistants that let users query data or author reports in natural language. That lowers the barrier to entry and will accelerate adoption. We are heading toward a world where using AI inside your analytics tool is as routine as using the tool itself, for instance, an assistant that fades into the background while people do their work. Some organisations are even starting to expect staff to use AI on a daily basis.

One caution: self-service is often promised at project kick-off and then forgotten once delivery pressure mounts. Keep it visible, continue training people, and empower them with access to well-designed, well-governed data. Do that, and you’ll get the efficiency gains self-service was meant to provide, with precision and responsible use built in.