Predictive Analytics for Big Data

Data Mining as Backbone of Predictive Analytics:

The rise of unmanned or autonomous devices on land or in the air depends first on a reliable, bi-directional data feed and then Data Mining to parse. Drones need to gather loads of data and feed them into analytics platforms that help humans — whether it’s about environmental sensing, surveying natural disasters and relief efforts, or making sense of traffic patterns and other urban phenomena. Connecting to a smart node to offload data for Data Mining and receive instructions on what to do next will be a crucial factor for the success or failure of usability of these systems.  No wonder, Data Mining is touted as New Black- in case your Big Data company is in Red. In short, Big Data will help define and build out the world of tomorrow’s robots, however, don’t leave home without Predictive Analytics backed by solid Data Mining.


Healthcare is “data rich” but “analysis poor,” and the consumer space demonstrate how to get out of this dilemma. Inexpensive trackers and apps for individuals lead the way for how to deal with health at large, from medical-grade apps to public health networks. We hope for the emergence of services and tools that accompany patients and doctors together, creating a decision-driven connection much earlier than when traditional preventive systems usually kick in. Electronic health records are already being collected, but we need to see progress in three specific areas: more compatibility between these different data stores, better integration with new analytics platforms specializing in health outcomes, and finally better integration between consumer-grade fitness apps and serious medical applications.

Insurers have always relied on some form of forecasting for setting policy costs. Originally, they simply made educated guesses as to the cost of premiums. As the industry matured, they began to measure single factors such as age, condition, or past history (Univariate analysis). As insurance forecasting became more sophisticated, they began to use multiple factors (multivariate analysis). Currently, Predictive Analytics is considered an industry best practice – using data mining techniques, advanced statistical models, and sophisticated algorithms – and combining additional information like credit scores and relevant economic conditions to yield more accurate predictions for potential insurance outcomes.


Data Deficiencies

Statistical models are only as good as the data used to develop them. Insurers with insufficient data may need to enlist third-party data providers (for example, rating bureaus, data vendors, and modeling organizations) to supplement their own data to ensure that the output from the predictive models is relevant and accurate.

Industry Examples:

Advances in hardware, software, and Predictive Modeling algorithms has brought operating efficiency, more accurate risk analysis and pricing. For marketing and claims these technology tools are life line to all industries.

For all businesses, Predictive Analytics is all about  how it would affect the bottom line. Our Industry Examples are on Facebook page ( ) where use cases as below can be found:

  • Claims Management: Identifying claims that will benefit the most from active case management – attorney retention, liability claims, subrogation analysis, severity analysis, etc.
  • Fraud Detection: Identifying claims that are more likely to be fraudulent so that resources can be directed on the highest-risk claims.
  • Supply Chain Analytics: Location Analytics, inventory levels, distribution center placement, routing.
  • Finance: identification of financial performance drivers.
  • H.R: prospective employees, workforce compensation, benefits selection.
  • R&D: which product features are most desired by customers?
  • Marketing Programs: Identifying the most profitable future prospects for new policies, churn prediction and retention modeling, loyalty management and customer lifetime values (LTV) analysis, cross-sell and up-sell analytics, and renewal of lapse policies, website promotion analytics.

The Devil is in Details!

  1. Define and Use Analytics:

There is no shortage of evidence to show that, in nearly every decision point in life, analytical decision-making is more accurate and produces better decision outcomes. Conversely hunches, intuition, guesstimates, and conjecture based decision making provide for below average performance, and remove the possibility of improved decision making.

   Performing analytics on a large data set starts with understanding and formulating a hypothesis. We then move on to gathering and analyzing relevant data. This then leads us to interpreting and communicating these analytical results. Developing quantitative thinking adds to a data scientist’s ability to deliver precise results. In full spectrum summarizing data, finding the meaning in it and extracting the value is a complete solution to an analytic data project deliverable.

2. Understand the Science of Prediction

If the predictive analytics problem is well defined like predicting churn on a customer or default on a credit account, data mining tools allow for savvy data professionals (data architects, data analysts, BI architects) to easily build and test models and keep trying with adding more data and variables until they like the model’s economic performance. On the other hand if the problem is not clearly defined and the selection of variables is all over the place, a more formal training in data sciences would certainly be required. On the other hand savvy business users who are used to carrying out rocket science in tool, armed with the knowledge and understanding of what predictive analytics can do, should be able to conceive problems in innovative ways that can be solved using predictive analytics. The role of a data scientist as typically defined has to be broken up in 3-4 different job roles and they will easily get the job done.

Prediction is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends. A predictive model is made up of a number of predictors, which are variable factors that are likely to influence future behavior or results. In marketing, for example, a customer’s gender, age, and purchase history might predict the likelihood of a future sale.

In predictive modeling, data is collected for the relevant predictors, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. The model may employ a simple linear equation or a complex neural network, mapped out by sophisticated software. Predictive modeling is used widely in information technology (IT). In spam filtering systems, for example, predictive modeling is sometimes used to identify the probability that a given message is spam.

Finally: We invite you to discuss with us our experiences in the field of predictive analytics, and how we empower clients with better outcomes.

As such, Insurance Industry has been trying to answer following questions for a while and Advance Analytics from different industries helps us understand the use case and possible solutions and bottom line impacts.

– How to improve customer engagement in underwriting and claims?
– How to leverage the power of analytics to get the right offer to the right customer at the right time?
– How we reduce costs and speed up settlement times by deploying analytics in Claims Fraud?