The policy of IBM’s ‘Watson’ have always been focused on allowing the public access to the power of data, and now they have taken a big step towards that goal, releasing their Watson Data Platform.

IBM’s policy is that the potential for driving efficiency and change with technologies such as big data and advanced analytics, machine learning, IoT and predictive modelling, is so great that it should be accessible to “the common man”, Forbes writes.

This new technology will allow the anyone to access the benefits that comes with artificial intelligence, no longer limiting it to the academics and professionals. Potentially, the ‘Watson Data Platform’ will enable staff in various industries to consider applying data first, as creative ideas and solutions often thrive in the work environment.

IBM’s Watson-platform is indeed the first enterprise data platform that is built from scratch to enable machine learning, Rob Thomas, Vice President of Product and Development for Analytics tells Forbes.

“For the first time you can bring all your data to one place and it’s immediately catalogued and organised and ready to apply artificial intelligence and machine learning”, Thomas explains.

“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”

This initiative is based on the belief of IBM that an organisation that is moved to an AI or big-data-platform, is not a simple CIO purchase, rather about ensuring that data can be used efficiently throughout an organisation.

“When you think about a traditional deployment today, data is actually far from simple in most organisations. 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”, Thomas explains.

Using the example of an insurance company, Thomas explains how enterprises can become “smarter” due to the assistance of machine learning.

“Most insurance companies score risk based on historical data. 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”.

Explaining how the platform is designed to make artificial intelligence and machine learning accessible to everyone, 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”.

This development could potentially be a step towards the direction of not only allowing information, but also hands-on experience of working with AI, not only reading about it in the media.

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