Companies across all sectors are getting excited about using artificial intelligence and machine learning and, let’s face it, who could blame them? They’re definitely exciting technologies and the rewards promised include things like big revenue boosts and competitive advantage and massive cost savings – and who wouldn’t want that?
It’s no surprise that there’s a rush of companies trying to adopt it. And that means that for some, the question of what should come first, AI or Data Governance, can be a little like the chicken and egg debate.
Let’s face it when you’re faced with that kind of prize, why would you want to stop everything and do something as time-consuming as data governance before you do your exciting AI and ML? But for me, there is a clear answer…
Artificial intelligence works by mimicking human processes by ingesting large amounts of ‘training’ data and analysing it for correlations and patterns and using these patterns to make predictions about future states.
For example, a chatbot – the kind you may encounter on an online retailer’s website or in place of technical support – is fed examples of text chats and can learn to produce lifelike exchanges with people and provide help and assistance, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
All AI starts out as a program or algorithm written and taught by a highly skilled programmer. The learning aspect of AI programming focuses on acquiring data and creating rules for how the AI will turn the data into actionable information. These rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
The reasoning aspect of AI programming focuses on choosing the right algorithm to reach your desired outcome and the self-correction process is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
That all means one thing: AI needs the right data in order to learn.
So, as a consequence, if you’ve got missing or inaccurate data, your wrong and potentially inaccurate data can and will guide these exciting technologies that your organisation has spent a fortune on in the wrong direction and so they will make the wrong decisions and the consequences could be costly and maybe even disastrous.
If you’re going to spend time and money integrating AI into your organisation, then I really feel quite strongly that if you want to reap the proper rewards of these brilliant technologies you must implement data governance first so that you do get the results you wanted. It’s quite simple: make sure you’ve got your house in order before you start embarking on an AI and ML (machine learning) journey.
If you are doing that and you haven’t started doing data governance yet, there’s a free checklist you can download from my website to help you get started.
Don’t forget if you have any questions you’d like covered in future videos or blogs please email me – firstname.lastname@example.org.
Originally published on https://www.nicolaaskham.com/