Insights Blog

Is Generative AI a Game-Changer for Self-Service BI?

According to Dresner’s 2023 Self-Service Market Study, a significant majority (57%) of respondents view self-service BI as critical or very important to their business, demonstrating a clear link to overall BI success. However, despite this recognition, only 25% of the same group report being very successful in implementing self-service BI. This gap between aspiration and reality highlights the ongoing challenges with user adoption and effective use of these tools. Could Generative AI (GenAI) unlock the full potential of self-service BI and democratize data access by empowering users to analyse data and uncover insights without requiring extensive technical expertise?

GenAI is reshaping expectations

The rise of GenAI, particularly Large Language Models (LLMs), has revolutionized the way users interact with information. Tools like ChatGPT demonstrate the ease of asking open-ended questions and receiving comprehensive, conversational responses on diverse topics. This intuitive approach has fuelled interest in applying GenAI to BI stacks, envisioning conversational interfaces that simplify data exploration and analysis.

The integration of GenAI and self-service BI (which we’re calling GenAI on BI) offers huge potential on a number of fronts.

  • Natural Language Understanding: LLMs boast extensive vocabularies, understanding natural language nuances beyond technical terms and commands. This surpasses the search and NLP capabilities in current BI tools, enabling users to interact with data using plain language rather than complex queries.
  • Democratization of Data Insights: GenAI makes data exploration accessible to non-technical users, reducing reliance on expert assistance. It also streamlines workflows for experienced analysts by minimizing code requirements. Ultimately, GenAI-on-BI lowers barriers to entry, promoting broader data utilization and insights generation.

GenAI on BI: an early-stage market

Despite its potential, GenAI-on-BI remains in its early stages, with acknowledged limitations. One notable challenge is GenAI’s habit of exhibiting “hallucinations” and introducing errors by accumulating inaccuracies. While LLMs are versatile in handling tasks such as text generation, content classification, and question-answering, they are currently less reliable when it comes to “pure” BI use cases, requiring advanced mathematical operations or time-series analysis for example. This raises concerns about the trustworthiness of generated insights, a crucial factor for successful self-service BI adoption.

Similar to many data and analytics initiatives efforts, GenAI-on-BI will remain subject to data and analytics governance, regulatory, and data security considerations for the foreseeable future. The absence of proper guardrails and robust governance frameworks could expose businesses to potential risks and ultimately erode trust in the generated insights. Implementing clear data quality standards, transparent audit trails, and responsible AI practices will be crucial to building trust in the generated insights and unlocking the true potential of GenAI-on-BI.

Despite these challenges, the GenAI-on-BI market is rapidly evolving. Major vendors like Tableau, Microstrategy, and Veezoo are actively integrating and enhancing related AI technologies into their BI stacks. They’re focused not only on finding ways to address limitations, improve the querying and analysis user experience, but also on enriching conversational output and making insights available for different user types.

Where to start with GenAI on BI?

This rapid innovation highlights the immense potential of GenAI-on-BI, but to truly capitalize on it, a robust data and analytics foundation is also critical. This requires:

  • Scalable and Cost-Effective Architecture: A semantic layer facilitating LLM comprehension, coupled with governance and security controls, forms the foundation for a scalable and secure GenAI-on-BI ecosystem.
  • Proof-of-Concept Approach: Starting with focused trials helps test and refine the value proposition for specific use cases. Early wins with tangible results build momentum and pave the way for wider adoption.
  • Fast and Fluid Data Access: Self-tuning capabilities for interactive querying and a high-performance data platform are essential to enable continuous, seamless data exploration.
  • Data Governance and Security: As with any BI initiative, data governance and security remain paramount. Without robust controls, potential business risks and loss of trust in insights can arise, hindering data democratization efforts.

By prioritizing data accessibility, infrastructure, data governance and a “test-and-learn” approach, organisations can navigate the exciting (and sometimes challenging) world of GenAI-on-BI.

If you want to learn how to start with AI for self-service BI the easy way and unlock a new era of data-driven insights and innovation, then Exasol is the solution. Get in touch with us today to begin your journey.

Helena Schwenk