Technology Doesn't Replace Talent: Part 3
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Former New York Yankees player Yogi Berra once said, “It’s tough to make predictions, especially about the future.”
He has a point; if you predicted that Spain would crash out of the World Cup after only two games, please raise your hands now.
However, a recent announcement from Microsoft could suggest that the challenge of making difficult predictions is going to become a whole lot easier. At least within businesses (I suspect that the unpredictability of the World Cup is eternal).
Azure Machine Learning (Azure ML) is a new product that utilises the power of the Cloud, and will enable companies to “build big data applications to predict, forecast, and change future outcomes”, in words attributed to Microsoft corporate vice president Joseph Sirosh (see the official Microsoft blog here).
Machine learning has been around for a while. It’s a form of artificial intelligence and a way of being able to analyse large amounts of data. At the moment it’s used to recommend new products, detect fraud, and internet search.
What makes Azure ML different is that it’s based in the Cloud. Traditionally, machine learning operates on premise for every company who wants to use it. It required data scientists to be able to identify the data set, build an application to support that, and then a significant amount of time (often months) after that to build the process up to scale.
When you’re dealing with Big Data, you need processing power. We might not all collect 500 TB of data every day like Facebook does, but you do need a solid infrastructure to be able to cope with everything you want to analyse.
By being hosted on Azure (Microsoft’s cloud offering), this will take away many of the pain points and time issues, making machine learning much more accessible for many more businesses.
What will that mean? Here’s a video from Microsoft giving some examples of how machine learning will help companies keep ‘one step ahead’:
Although the barriers to entry to analyse data on a large scale are being reduced, the key with all of this will always be the data itself.
Machine learning is all about using historical data to predict the future. But that means that the data you’re relying on has to be absolutely accurate.
Even more important than that, you need to know if it’s the right data that you’re looking at in order to uncover those treasured hidden patterns…which is of course the point of all this. And are you asking the right questions of the data?
It all comes down to people.
In the first part of Technology Doesn’t Replace Talent, I attempted to explain how data dashboards only tell you one side of the story. You need talented people who have the experience to be able to read the data correctly, in order to get the answers you need.
In the second part I talked about the new tools available which are driving a ‘data culture’ within businesses. Yet analysing what the data is telling you isn’t a skill that can be replicated just because there are tools which have made it easier and simpler for more people to do so.
This news takes it further than that. If you’re using data to not only make decisions, but also (as in the case of Azure ML) going to use it on an ongoing basis to predict the future, then people become even more important.
In this article, technology speaker Theo Priestly says,
“…I’m seeing a worrying trend of organizations still asking the same questions…and receiving the same answers as before, just with a little bit more data support behind it…. We should be asking questions we've always been told were impossible to answer before but the starting point is not with what data you hold, start with the important questions and work backward.”
Harnessing the power of any data, not just Big Data, can fundamentally change the way you work – I would go as far as to say that if you’re not using data to make business decisions, you’re flying blind.
However, the navigational dashboard will only take you so far.
In fact, it’s just the middle part of a story that has a beginning and an end too.
The bookends are the talented people who have both the foresight to ask the right questions, and the insight to be able to interpret the results so that your business can make the decisions that will drive you forwards.
Ignore either aspect, and the data will leave you stagnant. And standing still in today’s business climate isn’t the best approach.
I’d be interested to hear your thoughts on the points raised in this article (or the Technology Doesn’t Replace Talent series as a whole) – is working with increasing amounts of data a balancing act for your business, or have you found it to change the way you work? What role have your people played?