Building the brainpower behind AI

By Micha van der Ende, Partner, Data

Everyone is talking about AI. And all organisations are feeling the pressure to get involved – immediately! There’s not a second to lose – they don’t want to miss out, get left behind, or, god forbid, lose their competitive edge.

But that is easier said than done. As with anything else in technology, you really don’t want to run before you can walk with AI, or it just won’t work. There are a whole raft of things you need to sort out before you even think of getting on that AI train.

In the world of AI, data is the fuel and the quality of that fuel has a direct impact on the performance of your final product. Stands to reason. So, before you get started, what are the absolute must-haves to make sure you end up with top-notch AI data products?

The data foundation

The absolute first step is that your data has to be in tip-top shape. It’s a bit like giving your AI a healthy breakfast – you give it honey Cheerios, and it will have a blood sugar drop by 10 am. You give it wholesome muesli and fruit, and it will power until lunch. So for your data, you must ensure it is clean, accurate and free from errors and inconsistencies. No one wants an AI spewing out nonsense based on typos and error-strewn information – remember that garbage in is garbage out.

You also need to make sure your data is relevant to the problem you’re trying to solve – you can’t build a dog breed identifier if you feed your AI picture of cats And it’s also vital you keep it secure, protecting it from unauthorised access or manipulation. It’s like building a vault for your precious information – so you need a solid data platform with strong data pipelines ingesting the data.

Data management – the organised genius

Imagine AI as a brilliant scientist with a messy lab – strewn pipettes, upended test tubes, and chemicals precariously stored. Data management is all about keeping everything organised and efficient. This involves having clear ownership and access controls, like assigning roles in your lab. Version control and audit trails act as your lab notebooks, tracking changes and ensuring everything is documented. Finally, you need a robust governance framework to keep all these processes aligned through your entire organisation.

The right scrum team

Having the right team is all about talent, collaboration and learning. The best data and development processes can’t work their magic without the right team. Assemble a diverse group with expertise in AI & data science, data engineering and data management. Think of them as your rugby team, each of them bringing their different skills to the field.

Collaboration and communication are key. Imagine your team working in silos; your AI recipe would be a disaster. Encourage open communication between data teams, AI developers, and stakeholders. And remember, the AI landscape is constantly evolving, so continuous learning and adaptation are essential. You need to be like a curious rugby coach, always exploring new techniques and tactics to improve your AI creations.

By following these steps, you’ll be well on your way to building robust and successful AI data products. The key is to have a clear vision, use super high-quality data in a well-architected data environment and follow a well-defined process with a talented team with the right skill set and resources. Get this in place and the data world is your oyster.

Want to know more?

If you would like to speak to someone at Valcon about getting ship shape for AI, please get in touch with Micha van der Ende at [email protected].

If you want information about Valcon’s data offerings, take a read here, or dive into Valcon’s World of Data.