AI-powered business intelligence will simplify query generation and enable data analysts to dig deeper into data analysis by producing comprehensive reports and visualizations.
Until recently, business intelligence and analysis were automated, but the last mile to the decision maker’s screen often required a quantitative or analyst to organize and make sense of incoming information. This of course meant that real-time data analysis was never truly real-time. Although trained quants and analysts will always be essential, Generative AI can help do the work of discovering and developing ideas at lightning speed.
However, business and IT leaders must carefully weigh the benefits against the costs of increasing the technology resources needed to support real-time business intelligence powered by AI.
“The impact of generative AI spans the entire spectrum of data-driven decision-making,” writes Pan Singh Dhoni, head of data science at Five Below Inc., in a recent report. paper. “From providing tangible mockups to accelerating analysis and development processes, this technology is poised to redefine the efficiency and effectiveness of business intelligence.”
Integrating generative AI into business analytics could also generate impressive productivity gains for data and analytics teams, he adds. “This amplification can cover a spectrum of functions, encompassing data ingestion, analysis, testing and reporting. By automating these processes, generative AI boosts the efficiency of data-centric tasks, contributing to fast and agile decision-making.
See also: How to make generative AI work for industry
Fast, yes, but at what cost? Decision-makers must weigh these costs if they want to implement AI-powered business intelligence. “Even though the technology has immense potential, its operational framework often requires the use of high-performance GPU machines, which has associated costs,” warns Dhoni. This requires decision-makers to “meticulously assess the balance between investment in generative AI and the resulting returns on investment.”
Businesses also need to be closely involved in the process of properly assessing the potential value of generative AI, keeping in mind that it will also change many ROI equations. “The pervasive influence of generative AI transcends multiple dimensions,” says Dhoni. “It powers marketing strategies and customer experiences, aligns with existing tools, improves productivity at different stages, prompts cost/benefit analysis and redefines business requirements. »
For data analysis, this is leading to a change in the way analysts approach their tasks. For example, SQL query
generating from databases, the primary method of extracting information, may be easier to configure. “Creating accurate and efficient SQL queries often requires meticulous syntax and logical structuring,” illustrates Dhoni. “Generative AI tools automatically generate SQL queries based on specified criteria. This not only speeds up the query formulation process but also minimizes the risk of human errors.
Of course, AI-powered business intelligence will do more than simplify query generation. “These tools allow data analysts to dig deeper into data analysis by producing comprehensive reports and visualizations,” says Dhoni. “Automated creation of sample reports, accompanied by insightful data analyzes and meticulously organized tables, streamlines the analytical process. By integrating these AI-driven capabilities, data analysts can spend more time on interpretation tasks and strategic insights, rather than getting bogged down in the intricacies of reporting.
As a result, business intelligence itself will never be the same, “fundamentally transforming the way business partners engage in data-driven decision-making processes,” says Dhoni. AI-powered business intelligence also means faster mock-up approvals by stakeholders. “Analysts can skillfully translate these models into sophisticated reports, predicting the trajectory of information before it is formalized. »