By Bernard Marr for Forbes
Big data is about ideas, and empowering people to use technology to bring those ideas to life. Right now, the race is on to put that technology into the hands of the workforce where great ideas and creative solutions are likely to shine through. Much has been written about the apparent benefits of retraining staff to think “data first”, and now tools, such as the Watson Data Platform, are emerging to support that vision.
In fact, IBM say that the Watson platform is the first enterprise data platform built from the ground up to enable machine learning – as Rob Thomas, vice president of product and development for analytics, puts it, “steeped in artificial intelligence.”
“For the first time,” Thomas tells me, “You can bring all your data to one place and it’s immediately catalogued and organized and ready to apply artificial intelligence and machine learning.
“We’re focused on meeting collective needs, whether you’re a business analyst, data engineer, application developer or data scientist – it’s about letting all professionals harness the power of AI and, importantly, making it a collaborative environment.”
IBM’s move is founded on the belief that moving an organization to an AI or big data platform is not a simple CIO purchase – but about ensuring that data can be efficiently used throughout an organization.
“When you think about a traditional deployment today, data is actually far from simple in most organizations,” says Thomas. “There’s a lot of different platforms and siloing. But when you move to enterprise data, that really makes it dramatically simpler. It changes the nature by empowering a lot of individuals to take analytics into their own hands.”
At the core of the Watson Data Platform is the Watson Machine Learning Service – which carries out the actual AI and machine learning functions.
Thomas uses an insurance company as an example of a simple way in which enterprises might become “smarter” thanks to machine learning. “Most insurance companies score risk based on historical data,” he tells me. “We’re enabling real-time scoring so for every policy they write, every external factor – all that data can go into the model and the model changes in real time. You’re getting different underwriter outcomes depending on the data coming in. That’s the essence of machine learning and that’s what we’ve enabled in terms of the core technology.
WMLS is designed to make AI and machine learning accessible, Thomas says – “You don’t even have to understand machine learning. You can build a statistical model inside a data science experience in any language and that will kick off the machine learning process under the covers – we are enabling people to do machine learning without even knowing that they’re doing it. We think that’s what will bring this to the masses.”
Bernard Marr is a best-selling author & keynote speaker. His new book: ‘Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results‘
First appeared at Forbes