As the dust settles on the initial hype and hoopla surrounding generative AI (GenAI), organizations have entered a phase of pragmatic exploration with this exciting new technology. Businesses are discovering substantial and practical use cases of integrating artificial intelligence into their IT applications. For BI, in particular, a focused and systematic incorporation of GenAI has the potential of yielding a higher level of business insights that may significantly uplevel the overall decision-making process.
It is firmly established now that the convergence of GenAI and BI is not just a fleeting trend, but rather a strategic imperative for an organization towards driving data-driven excellence. Delving deeper into some of the areas that are transformed by GenAI, especially when integrated with BI, is fodder for decision makers to explore and apply within their particular businesses. We explore a few areas here and highlight how together they make a very potent combination.
Data insights exploration by asking questions in natural language: GenAI powered by large language models (LLMs) is designed to understand and generate content very similar to humans. When added to BI, it becomes a powerful natural language interface between users and the data layer, facilitating interactive queries, answering questions, generating reports, summarizing and providing recommendations.
Traditional query systems required users to have a technical understanding of data structures and query languages. This created a barrier between business users and insights that they would find useful from their unique perspective. GenAI lets them pose questions in natural language. With its ability to understand context, nuances and intent in business queries, LLM transforms natural language into precise data requests. This enhances the efficiency and effectiveness of BI tools and contributes to a more intuitive and inclusive data exploration experience.
Traditional BI tools are primarily designed with pre-formatted reporting rather than exploring data to derive insights in a self-serve manner. With a chat-based question-answering system, users with no technical expertise can delve deeper into the data and receive real-time responses. Moreover, with context sensitivity, GenAI remembers the previous questions and answers in a session, hence has the ability of adding more value upon the previous content and insight.
Individually tailored analytics: Imagine logging into your BI system and being greeted by a dynamic dashboard already populated with insights curated based on your unique interests. Generative AI makes this a reality by leveraging historical interactions, data exploration patterns and the key performance indicators (KPIs) that matter most to individual users. It predicts the type of questions they might ask and preemptively generates answers.
This predictive capability is akin to having a data-savvy assistant who not only understands a user’s information needs but also proactively prepares the information they seek. While exploring different facets of data, GenAI continues to adapt its predictive analysis. With each interaction, it refines its understanding of an individual’s needs, fine-tuning its anticipatory responses.
The most significant aspect here is the presentation of summaries in natural language. Instead of charts, graphs and spreadsheets, decision makers are presented with concise, coherent narratives that encapsulate the trends, anomalies and noteworthy observations within the data. This natural language summary transforms raw data into meaningful insights that can be readily grasped and acted upon.
Automated Data Modeling:
A significant challenge in extracting accurate insights from today’s complex multi-data source systems lies in the technicalities of data modeling. The process of building a comprehensive data-to-analytics pipeline involves tasks such as connecting diverse data sources, implementing transformations and establishing intricate relationships between datasets. This necessitates expert involvement and a deep understanding of data architecture.
When GenAI foundation models are trained with targeted instruction and data ecosystem design, a substantial portion of the data modeling process can be automated. Users are empowered to seamlessly generate the code or scripts required for data transformation by articulating their intentions in natural language which is a significant improvement over the traditional dependence on coders and analysts.
Consider the scenario where a user needs to combine customer purchase history with marketing campaign engagement data to unearth valuable insights. A simple user instruction like “Combine customer purchase data with marketing campaign engagement data and identify correlations” may now do the job with automatic generation of the necessary script to achieve the desired outcome.
Enriched Semantics: A robust semantic layer is the cornerstone of an effective BI implementation. Encompassing database column descriptions, synonyms, relationships and various attributes, it provides a deeper context and understanding of data. With enriched metadata, a semantic layer unlocks the true value of data, making it more accessible, meaningful and usable for users.
The process of populating the semantic layer has been a labor-intensive manual endeavor, requiring substantial time and expertise. With GenAI, this process of extracting and enriching can be significantly automated by ingesting sample metadata and contextual information. The time-consuming task of manually curating metadata attributes, which was a significant bottleneck, could progressively give way to a more agile and dynamic approach.
Holistic Data Mining: Data has evolved beyond the confines of traditional structured formats. It now encompasses a blend of both structured and unstructured information. Social media, emails, multimedia content and textual content are a rich source of human expression, opinions and sentiments.
Holistic data analysis necessitates harnessing both dimensions—structured and unstructured—for a comprehensive understanding of operations and the voice of the customer. BI vendors are looking at using GenAI to pioneer text mining capabilities. This equips them with the power to analyze unstructured data, particularly lengthy strings and open-ended responses. With their vast linguistic capabilities, the LLMs that GenAI encompasses hold the potential to decipher and interpret textual nuances with remarkable accuracy.
Conclusion:
The intersection of Generative AI and Business Intelligence is one of immense promise and potential. As per a 2023 McKinsey report, the increase in GenAI’s ability to understand natural language has created the potential to automate 60-70% of current work activities.
The synergy between language understanding and data analytics is forging a new frontier where human intent meets data seamlessly. AI-driven automation empowers data exploration, making insights accessible to all stakeholders. From automated data modeling to personalized analysis, metadata enrichment and text mining, Generative AI’s impact on BI is profound and it still remains an evolving landscape.
