Don’t learn your mistakes!
No knowledge, no AI? If you want meaningful results from an AI or ML project, then make sure your data house is in order – data, metadata, content & their curation – otherwise you are just fuelling your systems with your past mistakes. Of course, it may be an expensive way of doing it, but launching an AI project will also draw attention to data quality or the lack of it, when questions don’t produced meaningful answers.
What is the point of hiring lots of data scientists when you don’t ‘know’ your content? What is the point of hiring lots of (scarce) data scientists when they are going to spend 80% of their time sorting out the data before they can actually do any ‘science’? If you had ‘the knowledge’ these problems would be substantially mitigated:
Knowledge graphing is a necessary element of getting the most out of your AI or ML:
If you gain knowledge about your organisation, you will have better-informed strategy; decision-making; objectives & tactics.
Imagine if Amazon had been unable to tell the difference between ‘apple’ and ‘Apple’…..