If you don't have a strategy when it comes to your reporting, what happens is you end up building reporting as it comes, based on whatever requests come through from the business. Because there's no strategy, your team of developers and analysts ends up reacting to requests instead of working toward a clear direction.
If you do this long enough, you'll end up with a lot of KPIs being requested by different departments, but your reports won't really speak to each other. Multiple reports might be saying the same thing or trying to provide the same information, while definitions become inconsistent. One report could define profit differently from another, or even something like cost of goods could vary depending on who built the report. At that point, you don't have insight, you have noise.
Many companies invest heavily in their data. They collect it across different systems, clean it, and sometimes even build data stewardship teams to reconcile and maintain data quality. But without a data and analytics strategy to define how that data should be used, you won't get enough return on that investment.
At that point, you don't have insight, you have noise.
A strategy is meant to define what needs to be shown. It involves working collectively with the business, going to each department and understanding their needs and wants. It also defines what decisions need to be made using the data.
A strong strategy should answer questions like:
- What is the point of having all this data?
- What are the crucial decisions we need to make?
- What KPIs need to be refreshed daily?
- Who needs to see this information?
- Which teams are responsible for making decisions that impact revenue or cost?
Without clarity on these, you may have all the data in the world, but no one is actually using it effectively.
In some cases, you might not even get access to the data you need in time to make a decision. It could take months to gather information, and by the time you see it, it's no longer relevant, because the business has already moved on. That's something a data analytics strategy should define upfront.
It should also define what type of data makes sense and what kind of insights you actually want from that data. This requires working with analysts to shape those insights and collaborating with executives who understand what information truly drives the business forward.
You have to collaborate with many people before building. Without that collaboration, you're just building based on whoever is asking for something at the moment, without knowing whether it has real business value.
Another issue is that without a strategy, you don't have a clear, tangible understanding of the value of what you're building. If you haven't taken the time to understand what people are doing manually, how long they spend creating reports, or how much effort goes into preparing for meetings, you won't know what kind of time savings your BI work is actually delivering.
And then there's AI.
This problem doesn't go away with AI, it actually gets worse.
Without a strategy, you won't know what needs to be in place to make AI on top of your data actually work. You've probably heard "garbage in, garbage out." If your data analytics setup isn't designed to produce reliable outputs, then your AI layer won't either. You'll end up over-engineering prompts just to get somewhat correct answers, and even then, you won't know how consistent those answers are.
You need a strong data foundation so that when the business queries the data, whether through reports or AI, they can trust what comes back. Your leverage is in that foundation, how you decide to structure your data matters.
Without planning for that in your strategy, you risk missing it entirely. And when that happens, you frustrate business users. You tell them AI is enabled, but they keep getting incorrect or inconsistent answers, so they start validating everything themselves.
They won't trust it.
And with automated data analytics, once people lose trust in your data, reports and even AI, you've lost them. They'll go around your system, look for data elsewhere, and validate things on their own.
Which means you won't get the ROI you expected. You won't reduce manual effort. And people will continue building their own reports, because they need to be confident that the numbers they're sharing are correct.