Principles of AI-ready data: Put yourself to the test
You can’t just throw data at AI programs and expect magic to happen. This approach may work for the first few AI projects, but it means that data scientists increasingly spend more time correcting and preparing data as projects mature. Instead, we have defined six principles that need to be followed to ensure that the data that goes in is accurate and reliable.
Because it may seem pretty obvious that AI can’t exist without good data, but you’d be shocked at how many organizations don’t put enough rigor into ensuring they have robust data foundations. That’s why in this episode of Visionary Voices, Ronald van Loon sits down with Accenture’s Managing Director of Data & AI, Tim Zhou, to discuss the importance of strong data foundations, the six key principles for making your data AI-ready and the challenges behind it.
Ronald and Tim dive into real-life use cases of AI in action. From financial services and automotive to the manufacturing industries, Tim talks about the ROI and impact these organizations have seen. And what is the common element throughout all successful AI implementations discussed? A solid and trusted data foundation.
So, if the pressure to scale AI projects quickly is tempting you to skip the critical first step of the AI journey, think again. Because, as Tim says, it’s important that “the data foundation is trusted, reliable and up-to-date, so the insights coming out of the data can really help the business achieve value.”
Now, it’s time to put your knowledge to the test and see how familiar you are with the six principles that make AI-ready data.
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