Why Data Strategies Fail
The failure modes are consistent: strategies that are too ambitious, too vague, or disconnected from the business problems people actually need to solve. The antidote is not better technology — it is better scoping.
A Framework That Works
Start With the Business Problem, Not the Data
The most successful data programmes begin with a specific business question: "Why are we losing customers at checkout?" or "How do we forecast demand more accurately?" Starting here keeps the work grounded and stakeholders engaged.
Inventory What You Have
Before building anything, understand what data you actually have, where it lives, and how trustworthy it is. Data quality issues discovered mid-programme are programme-killers.
Build the Foundation Before the Intelligence
The temptation is to jump to machine learning and AI. The reality is that most organisations need to fix their data pipelines, governance, and quality first. A well-designed data warehouse serves the business for years; a hastily built ML model becomes shelfware within months.
Prioritise Use Cases by Value and Feasibility
Map potential data use cases on a 2x2 of business value vs. implementation complexity. Start with high-value, lower-complexity use cases to build credibility and fund the harder work.
The Three-Year View
Year 1: Foundation (governance, pipelines, quality, one or two high-value use cases). Year 2: Scale (self-serve analytics, broader adoption, first ML models). Year 3: Intelligence (advanced analytics, predictive capability, AI integration). Organisations that try to skip Year 1 spend Years 2 and 3 going back to fix it.