What are the differences between AI and Data Strategy?

A data strategy and an AI strategy are compatible. AI strategy is in my opinion a bit more offensive, so you look for applications that can generate value, for example more sales or cost reduction, but it generates money.
Data governance is a bit more defensive. So you try to prevent a data breach or a data leak in some other way, or that people get access to data that they should not have.
Defensive is difficult to quantify and build a business case for. That is why I often say start with an offensive use case, i.e. the AI ​​strategy, and follow your data strategy from that to build up your defense properly.
If you have already run a use case and you improve the data quality, you can immediately measure the impact of the data quality in the use case. This makes defense directly measurable and quantifiable.
Several studies actually show that if you ask data professionals or business people in the field, 90% say that data is important and has potential value, or is a business opportunity for the company.
At the same time, only 15-30% say they are really getting the value out of data that they think is in it. So there's a huge mismatch between what's in it and what's actually being achieved. That is exactly why you should deploy a data strategy.
In recent years that we have been working with data, we have all seen that you’re not there yet if you only put data into a data lake and hire a data scientist. The added value of an AI use case only starts to come in when the insights from the data are processed in the workplace. If it is adopted in the business.
Precisely that bridge between what happens technically and is possible and how it works in practice and the implementation in the business, that must come together and a data strategy can help a lot.
I think an AI strategy starts with your business objectives. Where do you want to go. Certain use cases emerge from this, certain initiatives that support the business strategy. Those use cases are made explicit in your AI strategy. That results in kind of a product roadmap.
If you have those products clear, you can translate that into what you need in the sense of your capability. So you have people, data, tools and techniques. You can also determine strategy about that.
For example, are you going to hire people yourself or do you work together with a consultancy? Do you buy certain AI ready-made applications or do you build it yourself? Do you organize your data science centrally or decentrally? Which cloud vendor are you going for, or are you still doing it on-premises? What kind of architecture do you need?
These are all choices that are ultimately subordinate to which products you want to do and which business objectives you want to achieve.
At GoDataDriven we have our definition of gone. So when we leave, we always know for sure that we can leave.
That's because we've neatly handed it over to someone else or delivered a piece of work that's just finished. My experience is that the customer is always very happy that they can continue independently and then thank you very much at the end of the process. Of course, I do it all in the end so that a satisfied customer can proceed independently from third parties.