Evolution of Analytics: Use Cases of Gen AI in Business Analytics

Genie AI
8 min readFeb 23, 2024

In the rapidly evolving landscape of enterprise technology, generative AI has emerged as a transformative force, particularly within the realm of data analytics. As we stand on the brink of a new era, the impact of generative AI on businesses is undeniable, with an overwhelming majority of companies integrating AI tools by 2024.

Within the vast expanse of the data analytics market, a critical need for transformation has become evident. The inefficiencies in enterprises have prompted a revolutionary shift, and the convergence of generative AI and advanced data analytics is poised to address these longstanding challenges.

Highlighting the magnitude of the problem, a survey by Dimensional Research and Fivetran in 2020 revealed alarming statistics. A staggering 86% of data professionals reported outdated data, while 90% encountered unreliable data sources in the preceding 12 months. Additionally, a concerning 67% of senior managers expressed discomfort in accessing or utilizing data from their tools, indicating a pervasive trust issue within the existing framework (Deloitte, 2019). Compounding this, a report from Seagate Technology in 2020 disclosed that a substantial 68% of organizational data goes unused, emphasizing the urgency for a solution that transcends traditional approaches.

The struggle to harness the full potential of data has been a persistent challenge, prompting organizations to explore various avenues for improvement, hence incorporating Gen AI.

In this article, we invite you to accompany us on a journey through the step-by-step development of Gen AI within the realm of data analytics. So, grab a cup of tea, settle in, and join us in unraveling the unfolding narrative of evolution, as envisioned by the Genie AI team — the conversational data analyst that goes beyond traditional approaches to help access company data with Natural Language.

History of the Data Analysis

Analytics has consistently served as the cornerstone of informed decision-making in businesses. Over the years, methodologies and tools have undergone evolution, with each phase bringing us closer to more intuitive and tech-friendly ways of engaging with data.

Here are just a few important milestones in data analysis development that, if not educating you thoroughly, will undoubtedly pique your curiosity about the development of business intelligence tools — tools that you have likely used in your work, personal life, or even in your business.

Early BI Tools: The Foundations

The journey commenced with the introduction of early Business Intelligence (BI) tools. These tools were groundbreaking, providing the initial structured approach to data analysis and reporting. However, their complexity often necessitated specialized skills for navigation and interpretation, limiting accessibility to broader business teams.

Let’s take the example of one of the earliest BI tools, IBM Cognos (1969): IBM Cognos is a pioneering BI tool introduced in 1969. It marked a significant step in the evolution of data analysis by providing organizations with a structured approach to handling data. It allowed users to extract, transform, and load (ETL) data, as well as generate reports for more informed decision-making.

However, due to its intricate nature, using IBM Cognos often required specialized skills, making it more accessible to a limited group of individuals within business teams. Despite the fact that this early tool served as a foundational element, influencing the development of later BI tools that aimed to simplify the data analysis process and broaden accessibility, it itself became highly insufficient in the modern world and created further demand for the new wave of Gen AI in analytics that would solve the problem.

Web-Based Applications: Expanding Access

The advent of web-based applications marked a substantial leap forward. These platforms made analytics more accessible, allowing users across various business units to access insights without requiring deep technical expertise. Despite this progress, interaction with data remained predominantly static, confined to predefined reports and dashboards. One of the best examples of web-based applications of the later development is Tableau (2003): Tableau, introduced in 2003, is a prime example of a web-based application that played a pivotal role in expanding access to analytics. It revolutionized the way users interacted with data by providing a dynamic and visually intuitive platform. Tableau allowed users across different business units to explore and visualize data without the need for extensive technical expertise.

Despite the static nature of early BI tools, Tableau’s innovative approach allowed for more interactive and customizable data exploration. Users could now go beyond predefined reports and dashboards, gaining the flexibility to tailor their analyses according to specific needs. Tableau exemplifies how web-based applications contributed to breaking the constraints of static data interaction, fostering a more dynamic and user-friendly environment for data analysis.

Self-Serve Cloud-Based Applications: Empowering Users

The emergence of self-serve, cloud-based applications represented a paradigm shift. These platforms empowered users to create customized reports and dashboards, significantly enhancing flexibility and personalization. Businesses could now tailor their analytics to specific needs, fostering a more data-driven culture across all levels. Despite these advancements, a common limitation persisted: the interaction with data remained primarily through static dashboards and reports, struggling to easily adapt to the dynamic nature of business questions and explanations.

The perfect example of it is Microsoft Power BI (2013): Microsoft Power BI, launched in 2013, stands out as a prime example of a self-serve, cloud-based application that has empowered users in the realm of data analytics. With its user-friendly interface and integration with cloud services, Power BI allows users to create customized reports and dashboards effortlessly.

Microsoft Power BI has significantly enhanced flexibility and personalization in data analysis. Users can connect to various data sources, transform data, and visualize insights in real-time. The cloud-based nature of Power BI enables seamless collaboration, making it easier for businesses to tailor their analytics to specific needs and cultivate a more data-driven culture across all organizational levels.

Casing Efficiencies with Stagnant Dashboards

In the realm of self-serve, cloud-based applications embraced by businesses, a recurring challenge loomed large — the prevalence of stagnant dashboards. While robust platforms like Microsoft Power BI granted users the ability to craft tailored reports and dashboards, these visualizations often succumbed to inertia over time. Users grappled with the confines of predefined layouts, struggling to keep pace with the dynamic evolution of business inquiries.

The constraint of stagnant dashboards bore down on the efficacy of data analysis. As the data landscape underwent rapid transformation, organizations grappled with the imperative of keeping their dashboards both relevant and up-to-date. Access to data insights remained ensnared in friction, confined to technical team members well-versed in the intricacies of data structures. The hunger for real-time insights and the agility of ad hoc analysis grew paramount in the pursuit of well-informed decision-making.

The Emergence of Generative AI and Real-Time Ad Hoc Analysis

Cue the arrival of generative AI and Real-Time Ad Hoc Analysis (RAG). By 2024, the integration of generative AI tools into data analytics platforms sparked a revolution in the approach businesses took towards data analysis. Fueled by sophisticated machine learning algorithms, these tools bestowed upon users the ability to generate dynamic, real-time insights from their data.

Generative AI, seamlessly woven with RAG, emancipated organizations from the shackles of stagnant dashboards. This technological marvel empowered users to engage dynamically with their data, fashioning on-the-fly analyses finely tuned to the nuances of specific questions at hand. This pivotal shift marked a departure from dependence solely on predefined reports, allowing businesses to glean invaluable insights in real-time. Hence, analytics emerges as the quintessential use case for generative AI, undoubtedly standing as the most potent application in the data analytics arsenal.

The Revolution of Generative AI in Data Analytics

Generative AI not only addressed the challenges posed by stagnant dashboards but also ushered in a new era of data analysis. These AI-driven tools could understand natural language queries, interpret user intent, and generate responses that were not predefined but dynamically generated based on the context of the inquiry.

The ability of generative AI to adapt to evolving business needs, coupled with its capacity to learn and improve over time, made it a game-changer in the data analytics space. As businesses leveraged these capabilities, decision-makers gained access to a more intuitive and conversational approach to data analysis, democratizing the use of analytics tools across various levels of expertise within an organization.

The Conversational Era of Analytics

With generative AI leading the way, the new era of analytics became conversational. No longer bound by rigid dashboards or complex queries, users can engage with their data in a natural, conversational manner. This shift in interaction transformed the way organizations approach decision-making processes, making data analysis more accessible and collaborative.

Conversational analytics, powered by generative AI, allowed users to ask questions, seek insights, and explore data seamlessly. The technology understood the context, learned from user interactions, and continuously improved its ability to provide relevant and meaningful information. The conversational era of analytics marked a significant step forward in making data-driven decision-making a more inclusive and user-friendly process within organizations.

The conversational era can be illustrated by Genie AI (2023): Genie AI is the conversational data analyst that goes beyond traditional approaches by connecting to structured databases and parsing data using natural language processing. Its primary mission is rooted in the democratization of analytics, catering to companies of diverse scales and industries. It serves as a transformative entity, embodying a pioneering force in rendering analytics universally accessible.

Functioning as a virtual data assistant available at all times, Genie AI mitigates the necessity for protracted periods dedicated to mastering intricate SQL protocols. This strategic elimination of a significant learning curve ensures that individuals, regardless of their technical acumen, can effortlessly harness the analytical capabilities embedded within the platform. Genie AI thus becomes instrumental in dismantling entry barriers to analytics utilization within business frameworks.

The adoption of Genie AI not only fosters inclusivity but also engenders heightened efficiency in decision-making processes. By facilitating intuitive and readily comprehensible data analyses, it contributes to a more streamlined and effective approach to business decision-making.

As we move forward into this new era, the synergy between generative AI and conversational analytics promises to redefine how businesses harness the power of data, unlocking unprecedented insights and fostering a culture of continuous improvement and innovation.


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