AI-boosted business intelligence will simplify query generation and empower data analysts to delve deeper into data analysis by producing comprehensive reports and visualizations.
Until recent times, business intelligence and analytics were automated, but that last mile leading to the decision-maker’s screen often required a quant or analyst to organize and make sense of the incoming information. That meant real-time data analysis was never truly real time, of course. While trained quants and analysts will always be essential, generative AI may help do the job of teasing out and developing insights at blazing speeds.
However, business and IT leaders need to carefully weigh the benefits versus the costs of ramping up the necessary technology resources that can support AI-boosted real-time business intelligence.
“Generative AI’s impact spans the entire spectrum of data-driven decision-making,” writes Pan Singh Dhoni, data science lead at Five Below Inc., in a recent paper. “From providing tangible mockups to expediting analysis and development processes, this technology is poised to redefine the efficiency and efficacy of business intelligence.”
The incorporation of generative AI into business analytics could also deliver impressive productivity gains for data and analytics teams, he adds. “This amplification can span a spectrum of functions, encompassing data ingestion, analysis, testing, and reporting. By automating these processes, generative AI bolsters the efficiency of data-centric tasks, contributing to swift and agile decision-making.”
See also: How to Make Generative AI Work for Industry
Swift, yes, but at what cost? Decision-makers need to weight these costs if interested in putting AI-boosted business intelligence in place. “While the technology bears immense potential, its operational framework often necessitates the use of high-performance GPU machines, entailing associated costs,” Dhoni cautions. This requires that decision-makers “meticulously assess the balance between investment in generative AI and the resultant returns on investment.”
The business side also needs to be closely involved with the process to correctly assess the potential value of generative AI, while bearing in mind that it will also change many ROI equations. “Generative AI’s pervasive influence transcends multiple dimensions,” says Dhoni. “It propels marketing strategies and customer experiences, aligns with existing tools, enhances productivity across various stages, prompts cost/benefit analysis, and redefines business requirements.”
For data analysis, it brings a shift in how analysts approach their tasks. For example, SQL query
generation against databases, the prime method of insight extraction, may be easier to configure. “Crafting precise and efficient SQL queries often demands meticulous syntax and logical structuring,” Dhoni illustrates. “Generative AI tools automatically generates SQL queries based on specified criteria. This not only expedites the query formulation process but also minimizes the likelihood of human errors.”
AI-boosted business intelligence will do much more than simplify query generation, of course. “These tools empower data analysts to delve deeper into data analysis by producing comprehensive reports and visualizations,” says Dhoni. “The automated creation of sample reports, accompanied by insightful data analyses and meticulously organized tables, streamlines the analytical process. By integrating these AI-driven capabilities, data analysts can allocate more time to interpretative tasks and strategic insights, rather than getting bogged down by the intricacies of report creation.”
As a result, business intelligence itself will never be the same, “fundamentally transforming how business partners engage with data-driven decision-making processes.,” says Dhoni. AI-boosted business intelligence also means faster sign-offs on mockups from stakeholders. “Analysts can adeptly translate these mock-ups into sophisticated reports, foretelling the trajectory of insights before their formalization.”
