Self-Service BI: AI-based Business Intelligence

Self-service business intelligence (self-service BI) systems represent an ensemble of tools and platforms that allow users to perform quick and effective data analyses, generate real-time insights, and perform data-driven decision-making without having a deep technical understanding of the subject. Self-service BI revolutionizes the field of business intelligence and data analysis by allowing users to perform deeply detailed analyses of complex datasets in natural language without needing to write complex SQL queries. 

This article sheds some light on the field of self-service BI by explaining what it is, how it works, how organizations can benefit from it, and the challenges faced by the developers to design and develop such systems. It also highlights the reliance of self-service BI on generative AI and its future. This article will help those in the field of data analysis and business intelligence to prepare for self-service BI.

Summary of key self-service BI concepts

What is business intelligence?

Business intelligence (BI) collects, analyzes, and visualizes data to help organizations make data-driven decisions. Over time, BI tools have evolved in their capabilities as they have become able to answer increasingly more complex and important questions for analysts: 

• “What happened?” Descriptive analytics is used to visualize historical and real-time data to identify trends and relationships to answer this question. For example, a graph may show that traffic from paid advertisements increased by 15 percent annually.
• “Why did this happen?” Diagnostic analytics shows relationships among datasets to answer this. For example, a company collecting customer data might like to understand how its customer demographic correlates with subscription cancellations.
• “What might happen next?” Predictive analytics takes the process further by enabling forecasting. In manufacturing, predictive analytics can be used to predict when a piece of equipment may malfunction based on historical data.
• “What should we do next?” Prescriptive analytics is the culmination of earlier analytics processes and is used for business strategy. An example would be detecting and flagging suspicious banking activity to identify bank fraud and recommend a cause of action.

What is self-service BI?

Self-service business intelligence (BI) tools put the power of analytics into the hands of more users by enabling them to explore and analyze the data without having deep technical expertise. This brings the culture of data literacy within the organization as it allows users to confidently leverage analytics to perform decision-making. Its overall effect leads to data democratization as users with little to no deep technical expertise can perform hypothesis testing, make data-driven decisions, and accelerate business growth.

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Key requirements for self-service BI

Here are some of the key requirements for self-service BI to deliver on all of its objectives.

Understanding the meaning of data

A self-service BI system needs to understand the meaning of the data to provide insights like the above example. Traditional BI tools enabled users to quickly visualize data by building dashboards containing charts. The development of these tools focussed on making it easier to plot data in different ways. The tool did not require any understanding of the data being plotted, and it was up to the user to assign meaning to the X or Y axis and numbers on a chart. The primary purpose of these tools was to help with data visualization.

Modern self-service BI systems like WisdomAI use a context layer that learns from metadata associated with the raw data and analyzes enterprise-specific content so the system can interpret business jargon. The context layer provides clarity and standardization in responses to natural language queries. When users submit queries like “What are monthly sales in the last quarter,” the semantic layer processes them into the appropriate SQL format and returns the results to the user. 

Interactive interfaces

Traditional BI was capable of building both visualizations and dashboards, but that was a hefty job that required a lot of data churning and accurate query writing. In traditional BI, interactivity was built upon existing dashboards and only enabled users to filter visualizations based on predefined parameters. Creating or updating dashboards was a specialist task performed by dedicated teams.

Modern self-service BI empowers users across all levels of technical expertise to generate, analyze, and perform question answering over data using natural language queries without writing down complex SQL queries and having deep technical expertise.

Natural language input

Modern self-service BI tools provide natural language processing capabilities that allow users to query data conversationally. This basically removes the need to write complex SQL queries and helps users receive relevant insights immediately from questions issued in natural language, such as: “What were the sales figures last quarter?” This type of interface lowers the barrier to performing the data analysis and makes it much more reachable to non-technical users within an organization. 

Natural language interpretation

Beyond enabling natural language input, self-service BI needs to help users glean insights from data more quickly. Self-service BI systems should synthesize insights and generate narratives from multiple data sources and charts. Traditionally, BI systems have relied on the data analyst to provide this narrative. Generative AI is being used as the foundation of modern self-service BI systems and enables natural language output. The example below shows a self-service BI system summarizing the sales leads funnel data displayed in chart form. 

Collaboration features

Collaboration and insight sharing are essential for effective decision-making while working in a team. Self-service BI platforms often comprise features that promote collaboration and real-time analytics sharing, such as data annotation tools, data sharing capabilities, and even a provision to be integrated with productivity and collaboration platforms such as MS Teams and Slack. These collaborative features allow critical analytical insights to be shared among cross-functional teams and ensure swift communication within the organization. The context layer underpins these collaboration features by adapting in real-time, leveraging user interactions to enhance and refine business terminology and user intent.

Challenges of building a self-service BI system

Some of the challenges of building, using, and maintaining self-service BI systems are as follows.

Data integration challenges

To build a robust self-service BI system, it is of the utmost importance to integrate different data sources seamlessly in the form of on-premises databases, cloud storage, and third-party APIs. Since each data source has a different structure and format, there are issues when it comes to data accessibility and data compatibility. Resolving compatibility issues remains critical to creating an environment that allows accurate and meaningful data analysis.

Creating rich metadata

When there are large volumes of data, it is a task to create rich metadata, which helps BI systems by providing the context for the data on which it is acting. The development of a semantic layer that defines meanings and relationships across different datasets ensures accurate querying and data interpretation. Traditional semantic layers may not be suitable for this task; for more, refer to this article on the topic.

Building a disambiguation layer

Complex table names, column names, and ambiguous value representations within the tabular data can hinder data analysis. For instance, if similar data is present within different database tables, it might cause ambiguity. Similarly, an LLM prompt from a user may not be able to resolve the user's query to a particular table or column due to this ambiguity, which leads to the generation of incorrect queries at times. Read more here.

Schema selection refers to the selection of the relevant tables and columns from a database to return the relevant data. An enterprise’s data warehouse is likely to contain 1000s of different tables with overlapping information between tables. An AI system needs to be able to distinguish relevant tables and data depending on a users’ prompt. In contrast, prompt efficiency refers to writing precise prompts that contain clear and unambiguous instructions for the LLM to perform a task.

An efficient selection of schema and prompt optimization protects against the risk of high latency and ensures that the user gets timely responses from the system. A BI system must be able to navigate its way through complex databases by choosing the most relevant contextual schemas, so it can generate precise prompts for data analysis. Read more here.

Learning user preferences

One of the most critical challenges lies in ensuring that the self-service BI system can remember user interactions and allow follow-up queries. This is critical to the functioning and effectiveness of a self-service BI system as it allows users to build on previous insights generated by the system without having to repeat the instructions repeatedly, enhancing the system's efficiency and improving the user experience. For example, different users may attribute different meanings to terminology like “price cohorts” or “large customers”. The system needs to learn these user preferences and remember the definitions.

Feedback and learning for deterministic responses

A feedback mechanism must be added to a BI system so that its outputs can be refined over time. This can be achieved by different frameworks—such as keeping a separate database to store user interactions—but setting up a feedback loop that first churns out the relevant information from the conversation history and updates the knowledge base accordingly on the fly comes with an additional cost. 

Data security (guardrails)

As organizations become more and more data-centric, data security and privacy are becoming extremely critical to prevent unauthorized access and accidental exposure of sensitive information, which could have both financial and social repercussions. This is why, before setting up a self-service BI system, it is of paramount importance for an organization to have a robust data security apparatus in place. This may involve setting up different policies, permissions, and role-based access controls for users across the organization. This represents an additional cost for the cybersecurity apparatus within the organization.

Multi-modal interactions

For any BI system, generating clear, unambiguous, and actionable visualization has been a challenge for a long time. This is because it is quite complex to design a system that can interpret a natural language query and generate visual data based on it in real time. This requires a deep technical understanding of the concepts of NLP and computer vision, which, in turn, increases the cost of building such a system. 

Role of generative AI in self-service BI

The introduction of generative AI within self-service BI represents a paradigm shift in how organizations foster efficiency, accessibility, and leveraging data analytics for quick decision-making. Generative AI reduces the barrier to entry for non-technical users by automating complex analytics processes within organizations.

Generative AI can translate user intent into precise SQL queries, bridging the gap between natural language and SQL query writing. This allows even non-technical users to access and retrieve data in a timely manner without deep technical expertise.

Retrieval augmented generation (RAG) allows organizations to train genAI agents on their own data, allowing users to perform quick and accurate contextual querying on their data at a huge scale. Generative AI enhances the overall effectiveness of self-service BI through the following transformative capabilities.

Using AI to build your data model

One example of a modern tool is WisdomAI, which is revolutionizing the field of self-service BI with the help of generative AI. Its platform allows users to query complex databases from multiple sources and generate interactive visualizations to discover trends and patterns within data without having deep technical expertise. 

The figure above explains the self-improvement cycle of a genAI-powered self-service BI system. It begins with ingesting data from various data sources into the system, followed by adding different users. As the data is fed in, the system builds an understanding of the data and its relationships. At the early stage of data ingestion and understanding, a human-in-the-loop approach is used to disambiguate table and column names and improve the system’s understanding of business jargon As the system learns from user interactions, it can start suggesting to users a set of relevant questions to ask about the data. As users ask more questions, it keeps on learning and continuously improves its performance in terms of behavior and recommendations.  

Automated insights and proactive intelligence

Generative AI makes self-service BI capable of establishing proper workflows by allowing users to put in custom prompts according to their needs and uncovering hidden trends and patterns within the data by providing actionable insights with minimal human intervention. It can detect unnoticed anomalies and correlations, providing deeper insights into operational and strategic areas. Generative AI can generate deep insights tailored to specific business contexts, thus saving time and enhancing decision-making accuracy.

Self-service BI systems will be even more proactive, being capable of anticipating user needs and providing valuable insights before they are requested. This will be made possible by AI capabilities that will continuously monitor data streams and identify anomalies that it finds relevant to the user's objectives. 

Data exploration and interactive learning

AI-driven recommendations from generative AI enable normal users to explore data intuitively by recommending relevant ways to visualize their data in terms of charts, graphs, different metrics, etc., based on their behavior. Gen AI adoption allows the creation of interactive visualizations based on the underlying data returned from a natural language query. In turn, this provides a much more personalized user experience while performing the data analysis.

Generative AI-powered self-service BI systems are highly interactive systems that are capable of adapting to user preferences over time. With each conversation, these systems can refine their user understanding regarding priorities and communication styles.

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Prompt chaining and AI-driven personalization

Generative AI is able to connect multiple prompts or queries as well as insights sequentially to generate complex results. By connecting multiple queries and insights, AI streamlines complex analyses into cohesive narratives, enhancing the storytelling capabilities of self-service BI. Users across the organization can share these narratives, promoting collaborative decision-making and further streamlining the process of generating accurate results. 

Generative AI-powered BI tools have personalization as their key focus area. These tools are able to learn user preferences and user behavior and, based on them, tailor fit and customize the user experience. For instance, a self-service BI system powered by generative AI is able to recommend insights, datasets, and visualization on the basis of a user's past interactions.

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Last thoughts on self-service BI

With the ever-growing demand for quick data analysis and the availability of data in huge quantities, self-service BI tools and platforms have become the need of the hour for almost every organization in the world. There are a lot of organizations that are still stuck on traditional methods of data analysis, but it's time for them to move up to self-service BI to enhance productivity.

The future of self-service BI will be shaped by systems that are intelligent, capable of evolving in accordance with user behavior, and able to cater to the ever-growing demand for quick, data-driven decision-making.