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Data strategy should be clear, inspiring, and focused, with clear goals aligned with business priorities.
Key Takeaways:
Always thought of the “Why”? Well, it can be thought of as the inspiration because the one thing is to explain why it's important. But the other thing is, what do you aspire to do about it? So, what is your vision? Where do you want to go? Here, there are three things that really matter. The first is quite trivial, but it's imperative to set a clear scope, especially if it's bigger companies. You start developing a data strategy, and then people ask about a particular use case or system. But then you say, “Oh no, that's not my scope because I'm just doing this for the market, enterprise, or business unit, "? And that's fine, especially if it's a big organization.
You don't need always to develop one data strategy for everything. Of course, it's nice if you do that, but it's time-consuming. So, it's fine also to develop something for a smaller scope. But you must clearly define it and ensure it aligns with the other strategies. I have done it many times wrongly, not clearly defining the extent of what we're functioning in and the obligation to explain or shape that. Sometimes, we think too big, and sometimes we think too small when we try to solve problems.
That's why it's so important to be clear: is that for the whole company, or is that for that division? Is that covering, you know, really to be very clear on what you want solved as part of the data strategy, and it can expand over time? It's really about inspiration. We see many of these vision and mission statements everywhere, and that's all right. But does it touch you emotionally? Does it create that momentum that you want to strive for? It? Maybe, yes. But for most of us, mostly no.
Really think about how you can create that momentum and touch people emotionally about the topic, what should it matter to them? What will it change for them? Then, the third, where it becomes more specific, is to set clear goals and commit to them. Is it about enhancing the current business? Maybe it's about cost reductions and efficiency? Are risks and compliance more important? But does it really create direct business value? So, you must pick at least one from the top and not just go into the office. You must pick your battles here and then build your portfolio around that product. After that, you can go for others and think about whether you want to enable new business growth.
We see more and more data strategies tackling process efficiency, and alongside democratization and self-service analytics, we saw many companies that had more and more analytics putting up everywhere. You have lots of data teams these days, and they start to create a lot of great things. But it's not always clear how these teams work together. And it’s not always very, very effective. It's not so much reusability of what and building on each other's people work. So, in those a little bit more mature companies, this is often a significant driver of data process efficiency across a large culprit. Each of them is very important. So maybe it's not only about business process efficiency, like automating a business process but also about how data engineers and analysts work.
Focus on value pools that match your company's strategic goals, market position, and industry landscape. Some companies focus on numerous value pools at once to balance growth, efficiency, compliance, and innovation.