There seems to be a bit of a sobering up going on in the B2B technology space, where software vendors are increasingly changing their narrative to say that data quality is what really matters. After several years of telling us what AI could do, we’re now being reminded that AI will only work as well as the data behind it. And while it is indeed true that AI will work much better with clean, quality data, I think that too is an oversimplification and, more importantly, not what anyone is actually looking for.

The problem is that we skipped ahead one step. We became so focused on what AI could deliver that we forgot about the data required to make it work. But even if we now take a step back, clean up our data, make it accessible, and then go full force on AI again, we still haven’t necessarily solved anything worth solving. We’ve simply improved one of the things that can help us solve the actual problem.

Clean data isn’t the end goal

You can see the same thing in almost any industry. A marketer doesn’t clean up customer data because they want a clean database; they do it because they want to better understand their customers and run more effective campaigns. A software developer doesn’t want better organized code because that’s the goal; they want to be able to build and deploy better software faster. And a healthcare organization isn’t connecting patient data because someone decided that better data quality was the objective; they’re doing it because it can help doctors make better decisions and ultimately improve patient outcomes.

We must understand what the end goal actually is. No one needs to clean data for the sake of having clean data. Organizations are looking to create value, whether that’s through increased revenue, growth, improved margins, reduced risk, better customer service, or any number of other business outcomes. To do that, they need to make decisions based on reliable, actionable information and, increasingly, prescriptive information that can help them understand what they should actually do next.

This is also where I think both buyers and software vendors sometimes get it wrong. If a supplier doesn’t really understand what the customer is trying to achieve, it’s going to be pretty hard to sell them the right solution. But it works the other way around too. If a company doesn’t really know what it’s trying to achieve, it’s going to be pretty hard for them to figure out what solution they actually need.

Getting from data to decisions

The more reliable and timely data an organization has at its disposal, the better and faster those decisions can potentially become. But that creates another problem. To get reliable data quickly, there needs to be a relatively low-effort way of cleaning and maintaining it. And before you can clean the data, you first need to have access to it. In other words, there is a whole chain of dependencies between the data sitting somewhere in an organization and the business value that organization is actually trying to create.

Data quality is an enabler, not the goal

I think that’s where some of the current conversation around data quality misses the bigger picture. Data quality is important, but it’s an enabler rather than the end goal. The same is true for AI, analytics, automation, or pretty much any other technology or process we use to help organizations make better decisions. The technology can make it easier, faster, and more scalable to get from information to action, but the reason we’re doing any of it in the first place is to create value.

So what software vendors actually provide is a way to help organizations get from where they are today to better business outcomes. That might involve getting unstructured data into a system, cleaning and enriching it automatically to a certain point, making it available to the people and systems that need it, and ultimately letting AI use that information to make better strategic decisions. The data matters enormously, but only because of what better data quality allows the organization to do with it.

And I’m not saying that companies shouldn’t take on big data projects or go all in on AI orchestration if that’s what they need to do to get where they want to go. If that’s what gets you to the outcome you’re looking for, then great. But there are probably a lot of problems that can be solved in much simpler, faster, and less cumbersome ways, and that’s the part I think we sometimes forget when we get too focused on new technology.

The goal is better business decisions

Over the last decade or so, we’ve gone from cloud, to blockchain, to AI, and now we’re talking about data quality. Along the way, we’ve also spent a lot of time on things like digital transformation and getting our data in order, because those are the things that make the technology useful in the first place. Every time, there’s a tendency to believe that the latest thing is going to be the answer to all our problems. All of these things can be incredibly valuable, but none of them are actually the destination. And that’s really the point. Whether we’re talking about marketing, software development, healthcare, or pretty much anything else, nobody actually cares about the technology or the data itself. They care about what it allows them to do.

The destination is still the same: helping organizations make better decisions and create more value.