The world of analytics is changing quickly, yet many companies are stuck in a cycle of ‘dashboard paralysis’. They have plenty of visual data but no clear direction. Decision-makers increasingly want ‘decision intelligence’ systems and workflows that don’t just explain what happened, but also recommend what to do next.
Therefore, for research and insights leaders, this shift is the next big step: moving from simply reporting data to directing decisions.
What's Changing: From Explanation to Recommendation
Traditional Business Intelligence (BI) and dashboards show what’s happening. However, Decision Intelligence (DI) combines predictive models, scenario testing, and AI-assisted storytelling to suggest next steps.
A DI system brings together data science, business rules, and specialist knowledge into a continuous learning cycle. For instance, instead of just showing customer churn figures, a DI system tests how different loyalty offers might affect customer retention and profit margins, turning insights into immediate action.
Why This Matters for Insights Teams
- Faster Business Agreement: DI puts researchers, marketers, and sales teams on the same page for decision-making, reducing issues and speeding execution.
- More innovative Scenario Planning: Predictive models can estimate the Return on Investment (ROI) or the risk of each strategic plan before a company implements it.
- Continuous Learning: Every decision feeds back into the model, improving its accuracy and the team’s confidence over time.
The Technology That Supports It
- Decision intelligence uses several maturing technologies:
- Generative AI and Predictive Modelling: This converts unstructured text, such as customer feedback and transcripts, into structured signals for decision-making.
- Causal AI: This moves beyond showing simple connections to identifying the real reasons for outcomes, like “what actually improves NPS or retention.”
- Knowledge Graphs: These link survey data, customer relationship management (CRM) information, and customer behaviour signals into understandable networks.
- Human-Guided Dashboards: Analysts use these dashboards to guide model interpretation and refine the resulting narratives.
Eklavya's Way to Build Decision Intelligence
- Check and Combine: We assess separate data sets and merge attitude, transaction, and behaviour data into a single decision layer.
- Model and Test: We deploy causal and predictive models to test different actions and rank the best options.
- Recommend and Manage: We turn the analytics into practical playbooks, with clear management and human approval checks.
- Learn and Improve: We set up closed-loop measurement to track the impact of decisions and refine future strategy.
Clear Results
The process becomes 60-70% faster, reducing the time from insight to action. Furthermore, scenario testing improves the ROI of decisions. Finally, we achieve more substantial business alignment between marketing, research, and customer experience (CX) teams.
Where to Start
If your current dashboards offer plenty of information but little insight, then it’s time to build your Decision Intelligence Framework.
Eklavya helps organisations move from basic analytics reporting to intelligent recommendation, combining data, AI, and human judgment to drive business actions that truly matter.
Would you like to explore a Decision Intelligence Readiness Audit with Eklavya Analytics to identify where your insights can start driving measurable business decisions?
