Augmented analytics democratizes the analysis process
When people can ask questions about data in plain language, analysis becomes more accessible. However, this does not diminish the analyst’s role: someone still needs to monitor which data can be used, which definitions apply, and how reliable a conclusion is. So the business analyst’s job isn’t going away, but it is changing significantly. Business intelligence is evolving from dashboards and periodic reports—which are primarily retrospective—to proactive analyses that are partly driven by the work of AI agents. AI identifies patterns, outliers, and trend breaks; it can visualize them and even suggest possible causes and next steps. This is often referred to as augmented analytics.
However, AI can only provide reliable answers if the definitions of KPIs are established very carefully and centrally. They must be applied consistently and be verifiable. The greater the role of automation becomes in the analysis process, the more important it becomes for the business analyst to establish a centrally managed semantic layer (governed semantic layer) in which these definitions are documented and applied.
The Business Analyst as Guardian of the Analysis Process
Formulating the question for AI also requires precision. The business analyst is tasked with pointing this out to AI users and guiding them. A good prompt is helpful, but not sufficient. It is more important to first consider which decision-making processes the organization wants to improve, for whom, with what margin of error, and within what constraints.
The role of the business analyst is shifting further away from analyzing data themselves toward verifying the output of AI. A plausible-sounding AI analysis is not proof unless the sources, the time frame, missing data, and possible alternative explanations are verified. In addition, legislation surrounding data use has become much stricter, giving the business analyst the responsibility to verify which datasets the AI agents are permitted to use and how the output is logged. In some cases, a human review is even mandatory.
Asking BI questions in plain language makes the analysis process more accessible: it seems as though every employee can now get started on their own. However, this also creates the risk of divergent and even contradictory interpretations of the AI output. It therefore remains the business analyst’s responsibility to act as an interpreter and provide context where necessary, so that employees can make the right decisions.
Augmented analytics makes analysis faster and more accessible, but the business analyst ultimately determines what the results are really worth.
