According to Gartner, nearly half of finance executives see self-service data and analytics as a driver of employee productivity. Despite this, organizations have struggled to deliver on the promise of enabling all users to independently explore and analyze data. The challenge isn’t technical — we have more data and better tools than ever before. Rather, it’s about bridging the fundamental gap between business users and meaningful data.

The emergence of GenAI has opened new possibilities for self-service analytics, but simply connecting business data to GenAI models isn’t enough — and could even be counterproductive without the right foundation. Success requires a thoughtful approach that addresses three critical elements:

• Ensuring the explainability of responses

• Ensuring every self-serve interaction with data is properly governed

• Making data accessible through intuitive user interfaces

Building explainability for all users

While it’s relatively straightforward to generate answers by connecting GenAI models to data, providing numbers without context renders these answers non-actionable for business users. As GenAI becomes increasingly integral to analytics, true explainability becomes paramount. This explainability should mirror the experience of working with a trusted analyst who walks you through their methodology, showing how they arrived at each calculation and where the data originated.

This level of transparency requires comprehensive data lineage — the ability to trace data back to their source systems and understand the calculations involved. For instance, when a system reports revenue figures, users should be able to see not just the final number but also understand which systems the data came from (like CRM or ERP), what formulas were applied, and what business rules (such as discount calculations) were considered.

Taking it a step further, different users need different views of this explainability. While technical analysts might require detailed SQL code and complete data lineage for validation, business users often prefer higher-level explanations focused on metrics and formulas. Users’ needs are rarely one-dimensional, a marketing manager might need procurement data access for events, while a finance analyst might require both detailed technical information and business-friendly summaries for board presentations. Achieving true self-serve analytics requires recognizing these dynamic needs, and providing the right level of explainability at the right time.

Implementing effective governance 

Data governance stands at a critical inflection point. The traditional governance frameworks were built for a simpler time, relying on rigid rules and checklists when data volumes were manageable and systems were static. Current exponential data growth and dynamic systems have rendered these conventional approaches practically obsolete. Traditional governance simply cannot handle the scale, complexity, and real-time nature of today’s data.

This challenge intensifies as organizations expand self-service capabilities throughout their workforce. With more teams independently analyzing data, organizations must balance democratizing data access with maintaining consistent definitions across the board. Without significantly improved governance frameworks that can adapt to this new reality, organizations risk creating multiple versions of truth – for instance, finance reports Q4 revenue at $10.2M using bookings data while sales shows $8.7M based on cash receipts​​. 

The implications of this governance gap become more severe as organizations connect to additional data sources and systems. Each new integration introduces potential variations in how metrics are calculated and interpreted, creating a compound effect that threatens data reliability. Without proper documentation and controls, these inconsistencies don’t just persist – they propagate through systems over time, creating a web of conflicting definitions and calculations that becomes increasingly difficult to untangle. This “definitional drift” undermines the benefits of self-service capabilities that organizations are trying to enable.

The solution lies in implementing comprehensive audit trails and metadata management that go beyond simple access logging. Modern governance systems need to maintain detailed lineage of every insight, capturing not just who accessed what data, but how metrics were calculated, which definitions were used, and what context was applied. This approach ensures that analyses remain traceable and reproducible — critical capabilities when organizations need to verify the accuracy of important business decisions or regulatory reports.

Effective governance in the age of GenAI-powered analytics also requires deterministic systems built on pre-defined business context. By establishing clear, immutable definitions and relationships within the data, organizations can ensure that self-service analytics tools consistently interpret and apply business rules, regardless of how users phrase their questions. This preventative approach to governance helps maintain data consistency while still enabling the flexibility and speed that makes self-service analytics valuable.

Visualizing data for better insights

Although users may be motivated to explore data independently, they often don’t know where to start or how to translate their business questions into meaningful queries. This “blank prompt syndrome” occurs because users are presented with technical interfaces that obscure the underlying business logic and relationships within their data.

Rather than exploring data through traditional tables and views, it’s more intuitive for users to visualize data relationships as interactive mind maps that represent business concepts and their connections. This allows users to see how different aspects of their business interconnect and relate to one another.

Visual ontologies can serve as intuitive guides, showing users the logic hidden within their data and helping them formulate more meaningful questions. When users can see that sales data connects to geography, demographics, and pricing, they naturally ask more sophisticated questions that lead to deeper insights. The visualization of these relationships helps users grasp the full context of their data landscape.

Conclusion

The path to effective self-service analytics requires addressing three interconnected challenges: full explainability at every level, implementing proper governance, and providing intuitive representations of data. By focusing on these aspects organizations can utilize the full potential of self-service analytics safely and empower their users to make better, faster, data-driven decisions. The technology exists – what’s needed now is a thoughtful approach to implementation that puts users first while maintaining the integrity and security of enterprise data.

About the Author:

Inna Tokarev Sela is the founder and CEO of illumex and brings two decades of experience leading large-scale initiatives of leveraging data for AI. Before founding illumex, Inna was VP of AI at Sisense and Senior Director of Machine Learning at SAP. She led the product and GTM for cloud and machine learning, built AI-driven platforms within the organizations, and established data science departments.