There has been a lot of hype around predictive analytics over the past few years, with few real-world applications worth discussing.
Industries including manufacturing and logistics, retail and hospitality, financial services and telecommunications, are all beginning to fully embrace the technology. The goal is to radically improve efficiencies, reduce costs, create new revenue opportunities and take customer satisfaction and loyalty to new levels.
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In the era before predictive maintenance, manufacturers could only run equipment until it failed, or estimate its useful life and then retire it before it broke. Retailers only knew their inventories were low when they ran out, or when someone went to the warehouse to conduct a stock-check. Financial institutions lost millions or billions of dollars due to fraud, which was only discovered long after the fact. All these problems can now be sidelined through the power of predictive analytics.
The concept of prediction is not new in technology. What has changed is the availability of masses of data from the thousands of sensors that constitute the Internet of Things and the ability to use it for continuous prediction without manual intervention.
Today, by continuously monitoring the actual conditions and actions of equipment, staff, inventories, trades, and anything else that impacts a business, gathered data can be analyzed and acted upon. The aggregated amount of data is mind-boggling, described in terabytes, petabytes and exabytes.
The key advance in building predictive analytics has been the use of statistical models with historical data. It can now be deployed so that shipping delays can be prevented by foreseeing where bottlenecks will occur, fraud stopped before it happens and equipment fixed before it breaks. And in retail, store operators can order more inventory before it runs out.
Predictive analytics can be used to determine when a retailer’s competitors are likely to be lowering prices, prompting automatic pre-emptive action via digital shelf-edge labels. Indeed, within a store, sensors can also automatically signal when shelves are likely to be low on goods, alerting staff via smart badges.
In the logistics industry, predictive analytics allow supply chain managers to receive a definitive time of arrival for shipping, based upon a dynamic statistical prediction model.
In manufacturing, data streaming from single components or entire pieces of equipment can used to predict the possibility of future failures, allowing the arrival of new components to be synchronized with that of the repair technician.
The key requirement, of course, for successful deployment of predictive analytics, is for an enterprise to be able to analyze fast flows of Big Data. These will stream through from its own operations and from relevant sources in its customer-base, market or news channels. The volumes are so big, they cannot be fathomed without the use of data scientists, computing power and algorithms.
Once mistakenly considered lonely geeks, data scientists now have some of the most desirable and in-demand jobs on the planet. Using computer and mathematics skills along with their native curiosity and creativity, they mine mountains of data to find competitive opportunities – and to predict likely future outcomes.
Possessing a rare skillset, they are however, in short supply, which is why firms need to become more creative, integrating data science more tightly with IT departments and building teams that include computer experts, mathematicians, statisticians and business specialists. All these talents are needed if an enterprise is to crack the Big Data code and really drive value from it.
As Big Data becomes more accessible, in part through increased adoption of open data standards, and as analytical tools become more readily available, more enterprises will enjoy the benefits of predictive analytics.
We will soon see some extraordinary game-changing use cases where goods are automatically ordered and delivered to the warehouse before a sales campaign causes a shortage.
By: Oliver Guy, retail industry director, Software AG
Originally published at www.itproportal.com