The ROI of Intelligence: How to Measure Data Strategy Success

Overview:

Most organisations measure data strategy success with activity metrics. Examples include dashboards shipped, models deployed, users logged in, tickets closed. However, activity does not mean progress, even if these numbers look good. Good numbers still fail to prove impact if the business is not measuring the right things. In this session, we will show how to move beyond vanity metrics to a value measurement approach. We will explore ways to link AI and BI to meaningful numbers, such as bottomline outcomes, operational efficiency, risk reduction, and customer satisfaction. Join this session to learn how to define decisioncentric KPIs. It will involve a range of concerns, such as how to establish baselines, quantify benefits, and create a measurement cadence that stands up in leadership conversations and budget cycles.

Why you should Attend:

Because “we built a dashboard” and “we launched an AI pilot” aren’t business outcomes. If you can’t connect AI/BI work to money, time, risk reduction, or customer impact, your programme becomes a cost centre-easy to cut, hard to defend. This webinar gives you a practical way to define success measures that leaders trust, track value endtoend, and prove what your data strategy is actually delivering.

FUD liner:

If you can’t measure the value of your AI and BI, someone else will measure the cost-and that’s the number that will win.

Areas Covered in the Session:

• Why vanity metrics persist (and why they’re dangerous)  
• What “success” looks like for AI vs BI vs data foundations  
• The value chain: linking data work → decisions → actions → outcomes  
• Choosing the right measures: leading vs lagging indicators  
• Establishing baselines and counterfactuals (so you can claim impact credibly)  
• Quantifying benefits: revenue uplift, costtoserve, cycle time, quality, risk, churn/NPS  
• Adoption metrics that matter (behaviour change, decision velocity, reduced rework)  
• Building a value scorecard and reporting cadence leadership will trust  
• Common measurement traps (double counting, proxy metrics, “savings” nobody can bank)  
• Q&A + takeaway template for your own measurement plan

Who Will Benefit:

• CIO / CTO  
• Chief Data Officer (CDO) / Head of Data & Analytics  
• VP/Director of BI / Analytics  
• Head of Data Science / Head of AI  
• COO / Head of Operations (efficiency + throughput)  
• CFO / Finance Director / FP&A Lead (benefits realisation)  
• Transformation Director / PMO Lead (value tracking)  
• Product Leaders (data/AI products, customer analytics)  
• Customer Experience Director (CSAT/NPS, churn)  
• Risk/Compliance leaders (where value includes risk reduction)