Realizing Actionable Intelligence Using Predictive Analytics & Big Data

By Jay Shah, Head, ERP and BI practice Nihilent Technologies

The World Economic Forum mentioned in its 2014 Global Technology Report that data is “a new form of asset class and is now the equivalent of oil or gold.” Big Data has slowly crept its way in the daily lives of the common man, without much fanfare. With our without our knowledge, we too have somehow contributed significantly to the big data juggernaut.

When a telephone service provider suggests that you change your existing billing plan in order to help you in economizing your phone usage, it’s only technology at work in order to provide convenience to consumers and more revenues to service providers. They are relying on current and past data to determine future performance, through the use of statistics and predictive modelling, which in other words is known as predictive modelling.

The trend of Predictive Analytics is becoming mainstream and more and more businesses are incorporating it in day-to-day activities to harness its power. The maturity of businesses is also rising, as are the expectations and now the focus is on getting gleaning actionable intelligence for future events. And businesses are turning to Predictive analytics to gain this insight. Analytics has been used to examine historical data to analyse key events and occurrences.

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.”
Predictive Analytics is being leveraged 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.

Big Data and India
The world is giving itself in to big data, so is India. The UID-Aadhar project is the largest citizen database. Online shopping companies. like Myntra encourage customers to transact only from apps because consumer data is most valuable when tied to specific individuals, as it enables a closer tracking of user behaviour. It is also why Google, Facebook, and other tech companies want your mobile number. The Report also highlights that the number of app downloads have increased from 10 billion in 2010 to 77 billion in 2014, and there is a $19 trillion global opportunity to create value over the next decade. As per industry estimates, in India alone, it is set to touch $1 billion in 2015.

Predictive Analytics in Enterprise

Analytics for big data is an emerging area, stimulated by advances in computer processing power, database technology, and tools for big data. 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.

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 Government initiative 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.

The accuracy of the prediction will depend on the volume of data available for analysis. Many customers may be wary of giving their data due to privacy issues. Many applications of data can arise far from the purposes for which the data was originally intended. Big data and predictive analytics could raise a number of concerns. Minor variations in the data accuracy of predictions may often lead substantial changes in business decisions in the long term. In some instances, analytics may also help automate decision making, thereby dramatically reducing the cost of operations. However nowadays there are fewer inaccuracies in the measurement of big data. With actionable insights from all the data in their possession, data analysts are now able to get granular visibility into systems and processes. Predictive analytics, thus, delivers strategic value as well as tactical guidance. Business could make good use of structured data and unstructured information using predictive analytics models to enhance their customer service levels and operational performance.