Predictive analysis for Big Data – not just another IT buzzword!

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The role of IT in businesses and Governments has never been more critical. In the digital world  IT has assumed tremendous significance in bridging the gap between business knowledge and creating business value. In order to accomplish this objective, more and more enterprises are leveraging Predictive Analytics to examine past performance and forecast revenue generating patterns, understand customer behaviour and use the information to offer better products and services, fine tune ability to identify risks by catching suspicious trends, optimize processes and more.

As analytics becomes mainstream and more and more businesses harness its power, the maturity of businesses is also rising, as are the expectations. Analytics has been used to examine historical data to analyse key events and occurrences. Now the focus is on getting gleaning actionable intelligence for future events.  And businesses are turning to Predictive analytics to gain this insight.  Investopedia.com defines predictive analytics as ‘The use of statistics and modelling to determine future performance based on current and historical data. Predictive analytics look at patterns in data to determine if those patterns are likely to emerge again, which allows businesses and investors to adjust where they use their resources in order to take advantage of possible future events.”

In India businesses have access to a treasure trove of information,  the power of which is yet to be realized by MSMEs,  while larger organisations have made a start in harnessing all the  information. IDC forecasts a 44-fold increase in data volume between2009 and 2020. This explosion in data volumes is mainly due to the increase in use of mobile devices and the Internet of Things (IoT).

Comprehending the role of Predictive Analytics in Big Data
Predictive analytics techniques appliedto huge quantitiesof “historical” data creates an opportunity to develop those more accurate models and forecasts in near-real-time. There isn’t one common data source that gives companies insights about the customers. One has to piece together from multiple data sources. Managing the data sources is quite complex so the analytical capability typically must have many facets to it
Predictive analytics can be used for determining events or outcomes before they happen as well as in “what-if” scenarios to determine the “best” course of action.With actionable insights from all the data in their possession, data analysts are now able to get granular visibility into systems and processes.  Companies now have the ability to make business decisions based on these real-time forecasts.

Predictive Analytics in Enterprise
Companies use predictive analytics in numerous fields. From science to financial services to insurance to healthcare companies to identify patterns, recognize potential, prevent risk and improve financial reward.Predictive analytics practices can help companies in three key areas –
o minimizing risk
o identifying fraud
o pursuing new revenue opportunities

Retailers, for example, are using data from loyalty programs to analyse past buying behaviour and predict the promotions a customer is most likely to participate in, or make purchases in the future.

Marketing functions can explore analytics for retaining or reactivating customers with the right incentives. Manufacturing organizations are also exploiting its power in various ways.On the other hand, a Governmentinitiative as rarefied as veld and forest firefighting is using advanced analytical measures to predict possible wildfires in South African grasslands.
Financial institutions are using analytics to identify high-risk probable customers and minimize default risks, as well as cross-selling and upselling their products, customer segmentation, fraud detection, cash planning etc.

Healthcare organizations are parsing patient history to enable more accurate diagnoses, studying responses to medication, reducing hospital readmissions, integrating bedside medical device data into algorithms which help detect deteriorating vital signs in critical patients in real-time and more.

Predictive analytics, thus, delivers strategic value as well as tactical guidance.  In some instances, analytics can also help automate decision making, thereby dramatically reducing the cost of operations. With actionable insights from all the data in their possession, data analysts are now able to get granular visibility into systems and processes.

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 analysed and acted upon. Armed with these kinds of insights P\predictive analytics certainly holds great promise for organisations across verticals. Big data is the way forward, and predictive analytics will continue to be the science behind all the data. Thus, Big data and predictive analytics, together is ready to add yet another dimension to business decision making.