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Leveraging Predictive Analytics

Predictive Analytics with Big Data makes for harnessing actionable intelligence leading to better business outcomes.

The World Economic Forum mentioned in one of its Global Technology Reports 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 or 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 analyze 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 looks 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 behavior and use the information to offer better products and services, fine tune ability to identify risks by catching suspicious trends, optimize processes and more. 


Analytics for big data is an area driven 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 analyze past buying behavior 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.  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 predictions 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 to 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.

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. Businesses could make good use of structured data and unstructured information using Predictive Analytics models to enhance their customer service levels and operational performance.

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