For many, the contents of the Financial Times or Wall Street Journal are a bit like maps that guide them into battle against their competitors each day. Information on stocks and shares, mergers and acquisitions, and even speculation on how things might pan out in the future can all be drawn on to inform business decisions.
Now, imagine if you had access to the Financial Times or Wall Street Journal before anybody else. What if, instead of waiting until Wednesday for that day’s paper, you got it on Tuesday? How valuable would the information be, and what sort of advantage would it give you? You could correctly predict the stock price movements, corporate takeovers, and major government decisions on economic and monetary policy. You would be able to forecast future outcomes and steal a march on the competition. What’s more, you would be more informed about what would be in Thursday’s paper and beyond.
With predictive analytics, this type of scenario is becoming a reality. ‘Tomorrow’s newspaper today’ may be just a dream, but the capability to use data to predict the future through advanced analytics has been a possibility for some time. While stock prices may be a little too volatile to predict accurately, there are many areas where future outcomes can be forecast with confidence from analysing relevant data. Let me give you a couple of examples.
Using existing customer data on buying patterns, consumer response to promotions and social media conversations, retailers can not only gain insight into individual preferences, but can also make accurate predictions about demand for certain products. This in turn can be reflected in stock management, ensuring fulfillment even when demand spikes during periods like Black Friday and Cyber Monday. A large retailer that is able to increase sales or reduce stock on shelves by just a few percentage points can make a big difference to the bottom line.
Telematics is being used by car insurers to more accurately estimate individual driver risk. By tracking a vehicle’s movements using GPS, insurance providers can assess how safely someone drives and adjust the cost of insurance accordingly. But the usefulness of the data goes beyond tweaking premiums.
Telematics is providing automotive companies with mountains of information on how their cars are being driven in the real world. This is helping them understand patterns, and influence the way new vehicles are designed and manufactured. According to Gartner, 150 million cars will be connected to the Internet by 2020, with 60 to 75 per cent capable of consuming, creating and sharing web-based data. Using real-time predictive analytics, the data from these vehicles could be used to reduce traffic congestion, improve car performance and even save lives.
What’s really moved things on is the capacity to store all this data much more cost effectively and process it more quickly. In the past it cost £60,000 to store a gigabyte of data – now it’s just one pence! And we’re also seeing the emergence of new, easy-to-use software that puts the capability to analyse big data into the hands of business users rather than analytical geeks.
They can also access analytics as-a-service to effectively hire in the expertise to provide answers to their business problems. There is also access to the cloud, which does away with the need for an expensive data warehouse. The upshot of all these changes is the “democratisation of analytics” or “analytics for all” – it’s no longer just an option for the likes of a large retailer or insurer, but also small and medium-sized organisations where analytics would not have crossed their minds before. Gartner estimates that big data analytics will be top of CIO’s priority lists until at least 2016, by when it will have also permeated through all facets of society.
This throws up two challenges. One is the lack of skills in the marketplace – recent research by SAS and the Tech Partnership has highlighted the increase in demand for big data staff will be 160 per cent from 2013 to 2020, as against just six per cent for UK employment as a whole. This is partially offset by availability of new, easy-to-use technology and the as-a-service offerings. The other challenge is giving providers of analytics a route to this new, emerging market. That’s why companies like SAS have embarked on a new approach working with technology partners that are well-established to provide industry scale, and already have expertise in relevant areas such as business intelligence.
As computing power continues to develop, along with the amount of data available to analyse, so too will our ability to gain insight and forecast future outcomes, whether it’s demand for a particular toy at Christmas, or accident black spots on our roads. We may never be able to recreate tomorrow’s Financial Times, but analytics has the power to give many more organisations far more insight as to what it could look like.