For decades, business intelligence has been a game of looking in the rearview mirror. It excels at reporting the “what”: the current state of familiar metrics based on historical data. But this focus often misses the far more critical ‘whys’ and ‘what-ifs’ that drive future success.
The difference is like watching sports. A spectator who only hears the play-by-play misses the “colour”—the deep context and insights that come from considering all the stats, athlete interviews, on-field conditions, and scouting reports.
Similarly, AI-powered data exploration brings understanding beyond just describing what’s happening. This approach, which we call “Intelligent Exploration”—data exploration augmented by artificial intelligence (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 overwhelming many organisations. They need their data analysts to do more than create another dashboard for a data point that may not even be meaningful to a business decision. AI can elevate analysts, moving them to a more strategic role where they can add more business value.
Then there’s competitiveness. Tight profit margins and a competitive labour market mean that efficiency gains and better-informed decisions are crucial for profitability. Organisations that fully understand what’s going on in their data gain a complete picture—and the nuanced insights that they are turning into business advantages.
Here’s a look at ten typical frustrations with data analysis and how Intelligent Exploration changes the game.
10 Ways Intelligent Exploration Overcomes Common Data Analysis Frustrations
| Common Frustrations with Data Analysis | How AI-Augmented Exploration Helps |
| 1. Excel and BI tools provide a limited snapshot of information based on obvious metrics. | AI explores complex datasets without preconceptions, using interactive visualisations and easy-to-apply filters. |
| 2. Decision-makers direct analysts to assess data points they believe matter most. Profitable insights may remain hidden. | AI explores all the data, examining business problems from every angle and showing analysts what matters. |
| 3. Dashboards visualise relationships between only a few variables at a time. Connections are difficult to spot across all analyses, leading to oversimplified conclusions. | AI simultaneously finds connections between dozens of attributes, discovering hidden patterns, correlations, and potential causal links. |
| 4. Traditional visualisations are poor at showing the interactions between multiple data points. | Immersive 3D visualisations showcase the interactions of complex data in an intuitive way. |
| 5. Much work is manual, time-consuming, and prone to human bias. Insight often depends on individual 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 provide the right answers. Their potential value is untapped. | Analysts use AI to provide a deeper picture of what’s happening, guiding decision-makers through insights targeted to solve high-value business problems. |
| 7. Significant amounts of data remain untapped. Key information may be missing from analyses and recommendations. | AI can analyse hundreds of columns of data at once. Every potential data point can be included and reflected in insights, recommendations, areas of opportunity, and risks. |
| 8. Analysts rely on their own interpretations to determine significant insights and detect patterns across tables. | AI guides analysts to insights based on what’s statistically 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 both 3D visualisations for complex findings and plain-language summaries 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 helps ensure that the right solutions are identified and resources are invested in the right place. |
From Report Takers to Strategic Partners
Data capabilities at too many businesses are falling behind. According to a recent survey from the MIT Technology Review, only 13% of organisations excel at delivering business value from their data.
Equipping data analysts with the tools they need to explore data—not just analyse it—shifts this dynamic. Analysts move from being report creators to strategic partners. 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 to move beyond legacy data analysis to better understand past, present, and a future of 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.
