A lot of data, not much information. It’s a quandary facing many of the organizations I work with. In today’s fast-moving business landscape, organizations are eager to gain greater insights from the data they have, and many hurriedly implement the latest data technologies and analytics tools. The problem is many of these initiatives fail to deliver real value because they begin with the wrong focus. The key to building a successful data strategy isn’t starting with technology—it’s starting with the right questions.
The Common Pitfall: Technology-First Thinking
Too often, organizations approach their data strategy by asking, “What tools should we buy?” or “Should we implement a data lake?” This technology-first mindset typically leads to expensive investments in solutions that don’t address core business needs. It’s like buying a Swiss Army knife before knowing whether you need to cut, screw, or file—you end up with capabilities you don’t need and might miss the ones you do.
The Better Approach: Question-First Strategy
Instead of beginning with technology choices, successful data strategies start with fundamental business questions:
1. What Business Outcomes Are We Trying to Achieve?
Before diving into data solutions, clearly articulate your business objectives. Are you trying to increase customer retention? Optimize your supply chain? Reduce operational costs? Your data strategy should directly support these goals.
2. What Decisions Need to Be Made?
Identify the key decisions that drive your business outcomes. Who makes these decisions? How frequently? What information would make these decisions more effective? Understanding your decision-making framework helps focus your data strategy on delivering actionable insights rather than just collecting information.
3. What Questions Need Answering?
Break down each decision into specific questions that need answering. For example, instead of a vague goal like “understand our customers better,” ask specific questions like “Which customers are at risk of churning in the next 90 days?” or “What features do our most profitable customers have in common?”
From Questions to Requirements
Once you’ve identified your key questions, you can begin mapping out the requirements for answering them:
Data Requirements
- What data do we need to answer these questions?
- Do we already have this data? If not, how can we collect it?
- What quality level do we need for this data to be useful?
Process Requirements
- How frequently do we need to update this information?
- Who needs access to these insights?
- How will the insights be delivered and consumed?
Organizational Requirements
- What skills do we need on our team?
- What governance structures should be in place?
- How will we measure success?
Technology Comes Last
Only after understanding these requirements should you begin evaluating technology solutions. This approach ensures that your technology choices are driven by actual needs rather than hype or vendor promises. You might find that you need:
- Simple analytics tools rather than complex AI solutions
- Data quality tools more than advanced visualization platforms
- Basic reporting capabilities instead of real-time analytics
Case Study: Increasing engagement
Consider the media publisher that wanted to increase consumer engagement across their properties. By collecting and processing terabytes of realtime clickstream data, they were able to identify important trends and present them in clear, understandable dashboards. Armed with the ability to get content metrics quickly, editors were able to increase the recirculation of trending content by more than 25 percent.
Working Backwards: Building Your Question-First Strategy
To implement a question-first approach to data strategy:
- Start with a workshop involving key stakeholders to identify critical business questions
- Prioritize these questions based on potential impact and urgency
- Map out the data and capabilities needed to answer high-priority questions
- Evaluate existing resources and identify gaps
- Only then begin evaluating specific technology solutions
Answering the right questions
A successful data strategy isn’t about having the most advanced technology—it’s about having the right capabilities to answer your most important business questions. By starting with questions rather than technology, you ensure that your data investments directly support your business objectives and deliver measurable value.
Remember: The most sophisticated data platform in the world won’t help if it’s answering the wrong questions. Start with the questions that matter most to your business, and let those guide your technology choices. This approach might not be as exciting as jumping straight into the latest tech trends, but it’s far more likely to deliver real business value.