Part 2: We Need a Business Strategy and a Data Strategy

Business, Data, and AI, Strategy Over Hype

In the rush to embrace new tech, it’s easy to lose sight of fundamentals. No matter how cutting edge our tools, a business still needs a clear business strategy and a solid data strategy. These two strategies form the bedrock on which any effective use of AI must stand. In my role as a Product Manager, I constantly return to these basics. Let’s break down why each is critical.

A business strategy is essentially the company’s game plan. It defines where we’re going and how we’ll get there, put simply our goals, our understanding of the market, our competitive approach, and the key initiatives we must execute. At its core, strategy is about deriving insights from facts and data, developing options, making hard choices among them, and then taking action to create value. In other words, it’s the story of how we intend to win. Without a cohesive business strategy, technology decisions are unmoored, you end up implementing AI for AI’s sake, rather than to solve a real business problem or advance a goal.

Despite the emergence of AI, the timeless core principles of good business strategy still hold. I often remind colleagues that even as AI changes how we operate, “those core principles still exist” and remain crucial. Harvard’s Jen Stave hit the nail on the head when saying that adopting AI requires understanding business strategy and all the core components of business that have long been in play. You still need a vision of what you’re trying to achieve as a company, a value proposition for customers, an understanding of your competitive landscape, and alignment in your organisation. AI can enhance or accelerate parts of this, but it cannot provide that fundamental direction, that’s up to the business leaders.

Together with business strategy is the often neglected cousin (well until it became cool to talk about data): data strategy. If business strategy is the roadmap, then data strategy is the care and feeding of the vehicle that gets you to your destination. It plans how you collect, manage, and use data in support of business objectives. I like to think of data as the fuel for modern decision making - to be fair data driven decision making isn’t a new topic, but often seemed to lack the willingness to get there.

Without a clear data strategy, data remains a byproduct of operations rather than a driver of insight. Companies that fail to tie their data efforts to business goals often miss trends and opportunities entirely, leading to lost revenue. I’ve seen this first hand, an organisation drowning in reports and data sets, but lacking any strategy to turn that raw information into strategic insight. The result is usually analysis paralysis or missed signals, not to mention a lot of money sunk into unused data systems.

A data strategy ensures that we focus on relevant, high quality data aligned to our business needs, rather than just hoarding information. But don’t make perfect the enemy of good, if you don’t have a strategy that’s fine, start with the basics of knowing the data you have, then worry about how to categorise and manage it. Then once you’ve matured a bit you can consider some of the bigger strategy chunks like data lifecycle management.

One Forbes analysis put it bluntly, even the most advanced AI will fail if fed poor quality or misaligned data, nothing has changed from the timeless saying “garbage in, garbage out” if we neglect data governance and quality, our fanciest AI models will produce garbage results. It’s telling that over half of organisations recently surveyed (55%) are avoiding certain AI use cases because of data concerns. Without confidence in their data foundation, they rightly hesitate to let AI drive critical decisions.

So, what does a solid data strategy involve?