Generative AI: A Key Driver in the Evolution of Self-Service Analytics

In the past, organizations used traditional dashboards, reports and data visualizations to explore data, which were static snapshots of key metrics at a specific moment. Business users faced challenges when analyzing real-time data or exploring emerging trends, leading to missed opportunities and insights. In addition, these static dashboards provided a high-level overview of key metrics and lacked the detail to answer specific in-depth questions using NLP (Natural Language Processing). For instance, imagine a sales report showing month-wise sales figures for different product categories. Although these reports are helpful and provide a high-level overview of key metrics, what if a user wants to know the “why” behind the sales figures or why sales dropped for a specific category?

Additionally, the traditional analytics tools used then required a strong understanding of data structures, querying languages (SQL) and statistical methods. All business users didn’t have this level of technical expertise, so they had to rely on IT teams to extract insights from complex datasets, which usually resulted in delayed insights and increased backlog burdens for IT teams. Soon, organizations realized that business users should be empowered to take the call themselves whenever they need to drive data-driven decision-making instead of going to IT teams to get these answers.

According to a recent market study by Dresner Advisory Services in 2024, end-user self-service ranks 13th of 63 technologies and initiatives strategic to business intelligence and 55% of respondents say self-service BI is critical or very important for organizations’ success. However, organizations still struggle to implement self-service analytics and provide democratized data access to enterprise-wide users.

Rise of Generative AI to Revolutionize Self-Service Analytics

With the advancement of technology, Generative AI today is emerging as a new way to implement self-serve analytics, empowering users to analyze data without requiring technical expertise. The rise of LLMs has revolutionized how users interact with information. Users now just have to ask questions in plain language to get answers. For instance, if a user is trying to understand sales data but doesn’t know complex data analysis techniques, Gen AI acts like a helpful assistant that understands the questions asked in plain English, such as why are sales dropping in the north region? The algorithms can analyze the data and provide a comprehensive explanation, making data exploration accessible to everyone.

However, despite Gen AI’s potential to democratize data access, there are a few notable challenges while using it for data exploration, such as biased outputs, lack of context, inability to trace data lineage and poor data governance. Organizations need to implement a semantic layer that can act as a translator to overcome these challenges, bridging the gap between Gen AI’s knowledge and an organization’s specific terminology.

Kyvos Semantic Layer Enables Secure and Accessible Self-Service Analytics

Kyvos semantic layer standardizes business terms and metrics for users across the enterprise, allowing businesses to leverage their data effectively. To ensure data is secure, the layer acts as a front door between all the user queries and data. The platform also offers a three-tiered security architecture that enforces row and column-level data protection, enabling role-based access control and preventing unauthorized access or data leaks. Additionally, the layer tracks data origin and usage history and conjoins it back to model outcomes, creating a clear audit trail.

Kyvos augments the power of a semantic layer with Generative AI capabilities for democratized data access to a wide range of users. Data analysts and power users generally use languages like SQL or MDX to analyze data, but the complexity and steep learning curve of these languages become a barrier for non-technical users.

With Kyvos’ KPI designer assistant, users of all skill levels can interact with data in a natural, conversational way using NLP without actually learning these complex languages. They only need a basic understanding of key performance indicators (KPIs). The assistant leverages this tentative knowledge and its built-in intelligence to find the exact match within the semantic model and convert the user’s natural language questions into MDX expressions. Think of it as a calculator designed explicitly to define KPI formulas. The user can then further edit and refine these expressions to gain even greater control over their data exploration.

Additionally, Kyvos provides a Query Playground that enables business users to fire queries directly to a semantic model. It can be imagined as a search engine to get answers to ad hoc queries by asking questions in plain language and get answers through NLQ, eliminating the need to write SQL or MDX queries to get insights.

Future of Self-Service Analytics: Powered by Gen AI, NLP and NLQ

Generative AI and its associated technologies like NLP and NLQ are transforming the landscape of self-service analytics. These technologies are empowering users with natural language capabilities, making data exploration more accessible and intuitive than ever before. This shift towards democratized data access is precisely what Kyvos semantic layer helps organizations to achieve. It brings self-service analytics within everyone’s reach by democratizing data access and equipping users of all skill levels to become data heroes. This involves building trust in data, encouraging collaboration and ensuring responsible data exploration to make data-driven decisions and gain a competitive edge in the market.