What Data Leaders Can Do In Early Data Maturity Stages To Setup For Long-Term Success
This question came from someone creating and leading a new data function.
"I've got a long-term delivery roadmap - it's not set in stone but shows the business how we can add value. In your experience, what should we look out for or be aware of now that we're in the early stages to set us up for success?"
Few companies or data leaders ask this question before moving ahead with execution. The result is 2-3 years of churn and lessons learned after expensive setbacks. The AI last mile problem, getting data and model-supported products into customers' and users' hands, is actually a first-mile problem.
I teach the 3 sides of this solution in my courses. When businesses get serious about making money with data and models, they hire 3 roles:
Strategic/Executive Data Leaders
All 3 are critical. The most common pitfall is mistaking tactics for strategy. Data leaders and data product managers will showcase technical solutions, architectures, hiring plans, and infrastructure roadmaps as strategies. None are, and we will lose credibility at the C-level by making this mistake.
What's worse is when these are implemented as strategies. The business invests in technology and tries to create value with it. That's the wrong order of operations. Data and AI strategy must drive technology which is one way the data strategist adds value.
What's the difference between strategy and tactics? Tactics are what you do. Strategy is why you do it. A tactical data leader directs the team to do what's possible and could be valuable. A strategic data leader uses data strategy to make decisions differently.
How Data Strategy Supports Data Team Leaders
A data strategy is the top-level opportunity discovery artifact that connects the technology with business value. It defines the opportunities the business SHOULD go after. All strategy is an evaluation of tradeoffs. The company doesn't have the resources to take on every available opportunity that data and AI creates.
The data strategy informs decision-making about the opportunities that best align with business goals, competitive strengths, capabilities, long-term vision, immediate needs, and potential threats. The business is built to monetize and operationalize certain products. Other types of products are suboptimal or infeasible without significant changes to the business's structure.
The implications are that a data team can deliver a high-value product, but the business isn't appropriately structured to generate returns. There's a gap between potential and realization.
Technology must align with opportunities C-level leaders have decided the business SHOULD go after. That's where enterprise alignment begins. Without a data strategy, the data team's direction and initiatives are informed by what is possible instead of the path C-level leaders have chosen for the company.
Data leaders can find themselves fighting the business's overall direction unless they have a data strategy to keep them in alignment.
What Pieces Must Be In Place?
Here's my list of prerequisites to lay the foundations for successful data products. Someone must assess the business, implement opportunity discovery frameworks, manage top-down and bottom-up opportunity processes, then present the costs and returns to C-level leaders. Until that happens, getting buy-in and creating alignment is a non-starter.
The initial assessment of the business for data maturity covers 7 dimensions: