The enterprise benefits of making machine learning tools accessible to all

In this guest post, Mike Weston, CEO of data science consultancy Profusion, discusses how Amazon’s cloud-based push to democratise machine learning sets to benefit the enterprise.
Machine learning is the creation of algorithms that can interrogate and make predictions based on the contents of big data sets without needing to be rewritten for each new set of information. In a sense, it’s a form of artificial intelligence.
The recent European launch of Amazon’s machine learning platform has garnered a lot of attention and is designed so non-techies can use these tools to create predictions based on data.
Amazon’s move follows Facebook’s launch of a ‘deep learning’ lab in France to undertake research into artificial intelligence, particularly facial recognition. Both tech giants will compete with Microsoft’s Azure computer service. 
Clearly, most major tech companies are pitching their tent in the data science camp. The reason is quite simple: demand. 
Data science is quickly moving from a niche service used by a few enterprises to a must have. Many business leaders are waking up to the fact that new technology like self-driving cars, the Internet of Things, smart cities and wearable devices are all powered or complimented by data science. 
The business case for using data science techniques in areas such as retail, logistics and marketing is also increasingly easy to prove. Consequently, data scientists are in demand like never before. Unfortunately, as many data scientists will tell you, their skills are still fairly rare – part computer scientist, part statistician. We’re all aware that there is an acute skills gap in the technology sector and in many ways data scientists are the poster child. 
With demand increasing for data science and the pool of data science talent struggling to keep up with it, tech giants like Amazon are naturally seeking to provide non-techies with the skills needed to do it themselves. 
It may sound counterintuitive for the CEO of a data science consultancy to welcome this move, but I’m a firm believer that data science has immense power to improve businesses, cities and peoples’ lives in general. 
If more people understand how to interrogate and use data to make informed decisions, the faster it will become an intrinsic part of how all businesses operate. Not only that, but the more repeatable tasks that can be undertaken by technology, the more time is freed up for data scientists to explore the information at their disposal more deeply and to innovate.
Addressing the big data skills gap
With the normalisation of data science as a business process or service, it should become more obvious and attractive for people to train in these techniques. This should eventually help plug the skills gap. 
Of course, the growth of data science platforms in Europe and the US won’t, in the short-term, create an army of do-it-yourself data scientists capable of everything. Self-service software can only bring you so far. A great data scientist adds value to the data through analysis and interpretation – through asking ‘why’ and ‘so what’. 
Highly-skilled data scientists are fundamental to the more complicated data science – uncovering profound insights from seemingly disparate data that radically change and improve how organisations relate to people. 
Nevertheless, the more data literate we all become the better we will be at both using data and asking the right questions. Businesses generally don’t suffer from a lack of data. The problem tends to be that those in decision-making positions do not understand what the data could reveal and therefore what problems could be solved. This means that a business can underestimate the knowledge it holds, fail to exploit all its source of data, or fail to share information with people who could make better use of it. 
Business that understand data science and can use self-service platforms and tools to undertake basic actions will become savvier at collecting, managing and analysing it. With experience, should come an understanding of the full potential of data science and a willingness to experiment. 
Amazon’s self-service platform is not in and of itself going to create a revolution in data science. However, it represents the growth in businesses seeking to empower themselves to make better use of the information they hold. 
Like any science, data science is at its most exciting when it is testing the limits of what is possible. By experimenting, repeating and refining techniques, data science becomes much more effective. 
Whether a business employs its own data scientists or gets outside help, the more these specialists work with a company, the more they understand, the better they become at creating insights and solutions, and the more value a business can extract from its data.

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