Discover how Intelligent Exploration, powered by AI, revolutionises data analysis. Michael Amori, CEO of Virtualitics, shares insights on overcoming common frustrations in data analysis. Uncover the shift from traditional business intelligence to strategic, data-driven decision-making.
Traditional business intelligence focuses on the “What:” Reporting the current state of the same old metrics, usually based on historical data. Too often missing are the Whys and the What-ifs. Think about sports broadcasting. A spectator who only hears the play-by-play misses the “colour.” They don’t have the context and insights when all the stats, athlete interviews, conditions, competition, injury and scouting reports, post position, or the like are considered.
Similarly, AI-driven data exploration brings understanding beyond just describing what’s happening. “Intelligent Exploration”—data exploration augmented by AI—is changing some fundamentals of data analysis. Businesses are using it to leverage all the data that’s now accessible.
Why now? For one, the quantity, scope, and connectivity of collected data are swamping many organisations. They need their data analysts to do more than create one more dashboard to look at one more data point that may not even be meaningful for a business decision. AI can upskill analysts, elevating them to a more strategic role where they can add more business value.
Then there’s competitiveness. Slight profit margins and a tight labour market mean that efficiency gains and better-informed decisions are crucial for profitability. Organisations that fully understand what’s going on in data gain a complete picture—and nuanced insights that they are turning into business advantages.
Here’s a top ten list of companies’ typical frustrations with data analysis and how Intelligent Exploration with AI changes the game.
Ten ways Intelligent Exploration overcomes common frustrations with data analysis
| Common frustrations with data analysis | With AI-augmented data exploration | |
| 1 | Excel and BI tools provide a limited snapshot of information based on obvious metrics. | Complex datasets get explored without preconceptions, with interactive visualisations and easy-to-apply filters. |
| 2 | Decision makers direct analysts to assess data points they believe matter the most. Profitable insights may be undiscovered. | AI explores all the data, looking at business problems from every angle and telling analysts what matters. |
| 3 | Dashboards visualise relationships between a few variables at a time (monthly sales, revenue by client size). Connections are impossible to spot across all the analyses, leading to over-simplified conclusions. | AI simultaneously finds connections between dozens of attributes, discovering patterns, correlations, and causality. |
| 4 | Visualisations are poor at showing the interactions between multiple data points. | True 3D visualisations showcase the interactions of complex data. |
| 5 | Much work is manual, time-consuming, and prone to human bias. The ability to spot insight comes down to experience. | AI algorithms automate much of the analysis, discovering drivers, explaining patterns, and providing recommendations for subject matter expert (SME) review. |
| 6 | Analysts are buried in requests for dashboards that don’t even provide the right answers. Analysts’ potential value is untapped. | Analysts use AI to provide a deeper picture of what’s happening. They guide decision-makers through insights targeted to solving high-value business problems. |
| 7 | Significant amounts of data are untapped. Key information may need to be included in analyses and recommendations. | AI can analyse hundreds of columns of data at once. Every potential data point can be included and reflected in insights and recommendations, areas of opportunity, and risks. |
| 8 | Analysts rely on their observations to determine significant insights and detect patterns across tables. | AI guides analysts to insights based on what’s significant in the data. |
| 9 | When data teams discover the interplay between multiple attributes, they have difficulty translating the findings for decision-makers. | AI generates 3D visualisations for complex findings, generating plain language insights to clarify potential value. |
| 10 | Surface level analysis means that resources may be directed toward fixing problems that don’t offer the greatest potential return. | Deep data exploration assures that the right solutions are identified, and resources are invested in the right place. |
AI data exploration assures nothing is overlooked or overvalued
Data capabilities at too many businesses are falling behind. Fully 60% of data and analytics leaders in a recent CIO.com survey said their organisation’s data is not being used to the fullest. And 85% say their organisations still use BI dashboards or Excel spreadsheets to explore data.
Equipping data analysts with what they need to explore data—not just analyse—shifts this dramatically. Analysts move from being report order takers to strategic partners with the keys to business advantage. Stakeholders can make fully informed decisions with the confidence that every possible angle has been included in the analysis. Companies can identify and predict trends, detect business opportunities, quickly respond to market changes, and pursue the right innovations.
Intelligent Exploration is the pathway for going beyond legacy data analysis to better understand past, present, and future unknowns. It’s the difference between decision-making that remains based largely on assumptions and decision-making that is, finally, truly data-driven. In full, living colour.
