In the first two of our three-part series of blogs discussing building strong data foundations for smarter AI outcomes, we discussed how to turn your data chaos into clarity by unifying your structured and unstructured data and then the importance of governance.
You’d think that with those topics tackled, you’d be on top of things and have endless valuable insights at your fingertips, right? Think again.
Too many reports, too few insights?
Most organisations have no shortage of reports - countless Power BI workspaces, shared spreadsheets, and ad-hoc exports. Yet when your leadership team ask, “What’s driving growth?” or “Why is churn creeping up?”, the room often falls silent.
The disconnect isn’t about collecting data; it’s about shaping that data into actionable business insights. Which brings us to the purpose of Layer 3 in a modern data foundation: insight preparation.
After unifying your information (Layer 1) and then governing it (Layer 2), Layer 3 ensures the right people focus on the right questions and receive answers they can actually use.
When is too much data a bad thing?
Sadly, abundant data rarely equals abundant insight. A ‘bring everything on board’ approach can result in:
- Competing KPIs: Marketing, sales, and finance track metrics that don’t line up with shared business objectives.
- Report sprawl: Every new request spawns another dashboard, while outdated visuals linger. Analysts spend more time maintaining yesterday’s numbers than discovering tomorrow’s opportunities.
- AI without focus: Data-science teams experiment with models, but outputs stay in notebooks because no one has agreed on the problem the model should solve.
A study by MIT Technology Review and Databricks underlines the human gap: 40% of organisations cite “training and upskilling staff” as the biggest hurdle to adopting data and AI platforms. Tools alone can’t fix unclear questions or limited data fluency.
Layer 3: Insight preparation
Insight preparation transforms your raw information into guidance that moves the needle. It rests on three practices:
1. Define your business questions and KPIs
- Facilitate workshops where leaders translate strategy into measurable targets.
- Document each KPI - its owner, definition, and calculation - in a living catalogue.
- Validate that the data required for each metric is already unified and governed.
2. Enable self-service analytics
- Expose a curated semantic model that hides technical complexity.
- Provide governed Business Intelligence dashboards and no-code exploration tools so users can answer follow-up questions instantly.
- Support adoption through training, office hours, and user communities - true analytics enablement.
3. Tell stories with data
- Design visuals that emphasise narrative: trend, context, and recommended action.
- Lead with an executive summary, then provide diagnostic drill-downs.
- Embed guidance - why the metric matters and what to do next - using tooltips, annotations, or linked playbooks.
When these elements align, your dashboards shift from informative to decisive, becoming catalysts for strategic data analysis.
3 benefits you'll notice when you take the right approach
- Clear direction - With strategy-aligned KPIs front and centre, your teams can focus on performance drivers rather than debating metric definitions.
- Empowered teams - Business users explore trusted data without waiting in IT queues, shrinking time from question to answer.
- Analytics that improve performance - Dashboards highlight recommended actions and display outcome metrics, turning analysis into a feedback loop of improvement.
What's a practical workflow for implementing Layer 3?
Step 1: Audit your existing reports
Catalogue current dashboards, their audiences, and data sources. Identify duplicates and retire unused artefacts.
Step 2: Workshop your KPI design
Collaborate with your executives and domain leads. Ask: “If this metric changes, will we act?” Keep only the KPIs that pass that test.
Step 3: Build or refine the semantic layer
Surface business-friendly dimensions - Customer, Product, Channel - mapped to governed tables. Consistent definitions eliminate reconciliation debates.
Step 4: Craft narrative-driven dashboards
- Page 1: Executive overview with goal-lined KPIs.
- Page 2: Diagnostic views - decomposition, cohorts, trends.
- Page 3: Action resources - playbooks, owner contact, next-step checklist.
Step 5: Launch your own enablement programme
Provide short tutorials, live Q&A sessions, and a community forum. Recognition badges for power users help sustain engagement.
Step 6: Monitor usage and iterate
Finally, track dashboard views, filters used, and time spent. Gather feedback; refine visuals and retire views that no longer add value.
In short, how do Layers 1 and 2 support insight preparation?
Unification assures that every KPI pulls from the same data, preventing “multiple versions of the truth.” And governance enforces quality, access control, and lineage, so your users can trust what they see, and regulators can audit as needed.
Together, those layers free Layer 3, allowing you to focus purely on clarity and action.
Some common pitfalls to avoid
Boiling the ocean
Trying to define dozens of KPIs at once dilutes focus. Instead, start with a handful of high-impact metrics.
Pretty charts without narrative
Visual polish can’t compensate for unclear messaging. Decide the story first, and only then choose the graphic.
Self-service chaos
Opening the data floodgates without guidance can lead to metric confusion. Pairing freedom with a semantic layer and effective stewardship can help mitigate this issue.
AI before alignment
Building a predictive model is premature if your leadership hasn’t agreed on the definition of the outcome being predicted.
How can you gauge success?
Adoption - Measure active dashboard users and session length. Growing engagement indicates relevance.
Decision velocity - Track time from question to answer, and from insight to implemented action. Faster cycles signal effective insight preparation.
Business impact - Link metrics to tangible results (revenue gains, cost savings, customer satisfaction improvements) to demonstrate value.
Looking ahead - Once insight preparation is routine, your organisation can layer advanced capabilities - predictive alerts, scenario modelling, or narrative generation - knowing inputs are governed and questions are clear.
Key takeaways?
- The volume of your reports doesn’t equal insight; clarity and focus do.
- Insight preparation aligns your data with strategic questions, empowers users, and employs Data Analytics storytelling to drive action.
- Success depends on prior unification and governance plus deliberate enablement.
- A continuous loop of definition, exploration, and narrative turns dashboards into engines of actionable business insights.
Ready to transform reports into insights that matter? We can help you define KPIs, build narrative dashboards, and develop the skills that unlock the full potential of your data. Let’s ensure your next decision is driven by clarity, not guesswork.
