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How Predictive Modeling Affects the World Around Us

What do law enforcement, sports, healthcare, retail and agriculture all have in common? Thanks to big data and advanced analytics, these are just a few industries where predictive modeling is poised to change the playing field.

Big data keeps getting bigger. And continual advances in computing, warehousing and associated technologies make it ever more useful. We know more about the behaviors of people and the outcome of events than ever before. Cloud computing, SaaS and smart devices produce more and more data with the potential to create meaningful information all industries can use to improve their condition and predict future events.

Learning from the Past and Present to Predict the Future
Enter predictive modeling, a collection of mathematical techniques used to predict the probability of an outcome. Predictive modeling analyzes historical and current data to create a statistical model to predict future outcomes. The model is then validated or revised when additional data and experiences are introduced.

It's this validation and revision aspect that marries predictive modeling to big data. The greater the volume of relevant data that can be analyzed in a predictive model, the more accurate and meaningful the model becomes at creating useful information. Predictive modeling feeds on big data.

Predictive Modeling at Work and Play
Here are a number of industries leveraging predictive models to dive deep into big data and reap amazing insights into the way we work and live:

  • Law Enforcement -- Using information such as type, time, location and frequency of past criminal activity, police departments can forecast the likelihood of future criminal activity within given communities. Combining that data with the frequency of police presence and analyzing its effect on criminal activity, patrols within particular areas can be scheduled at specific times to reduce the volume and frequency of crime.
  • Sports -- Sport franchises are using predictive modeling to help maximize profitability of each home game. Looking at historical attendance data and demand, prices can be set for each game to maximize attendance, revenue and profitability. Beyond the business side of sports, coaches and managers are exploring the use of predictive modeling to predict competition tendencies and player match-ups that offer the greatest competitive advantage in a given game or match.
  • Healthcare -- Exercising hundreds of data points including patient and family history, past illness, medication, lab results and treatment compliance, health care providers are using predictive modeling to help diagnose, treat and assess risks and opportunities in patient care. Patients at greater risk of disease or re-admittance can receive education and preemptive care to lower their risk of health complications later in life such as heart disease, diabetes and some forms of cancer. Predictive modeling may lead to lower healthcare costs and improve overall patient care.
  • Retail -- Predictive modeling can help retailers understand consumer demand to manage supply chains and their associated processes such as forecasting, sourcing, fulfillment, delivery and returns. Retailers who can predict future demand in a given period of time can avoid having out-of-stock items and are afforded the opportunity to lower costs through just-in-time inventory management and optimized use of storage and warehouse facilities. These practices lead to better cash flow and reduced operating costs that can lead to consumer price decreases without sacrificing corporate profitability.
  • Agriculture -- Considering weather, plant moisture, existing fungus and temperature, predictive models are used to forecast the use of pesticides. This can lead to the discontinued practice of preemptively using pesticides to protect crops and instead use predictive models to trigger the use of pesticides to protect crops when they are most vulnerable, thus lowering the cost of farming and protecting the environment.

Conclusions
Thanks to the ever growing volume of big data, predictive modeling is increasingly valuable across industries to help improve customer relationships, shareholder value, business operations and patient care. The ability to predict future outcomes by analyzing the past and present have endless applications to improve human conditions.

Predictive modeling's innate ability to validate and revise itself as more experiences, outcomes and data points become available makes it an ongoing approach to business management that increases in value over time. Advanced analytic solutions such as predictive modeling enable organizations across industries to make faster and better informed decisions leading to improved market competitiveness and first-in-market opportunities.

As Director of Data Science at SoftServe, Inc., Sergii Shelpuk is a leading expert in deep learning neural networks, machine learning, artificial intelligence, and predictive analytics. A graduate of the Kyiv Polytechnic Institute and the Yaroslav Mudryi National Law Academy of the Ukraine, Sergii leads the development of innovative data science models for a wide spectrum of industries.

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Comment by Sagar Diwakar Uparkar on July 20, 2014 at 5:52am

Hi Sergii,

Good evening. I had gone through your article it is vey interesting. I would like to know more about Healthcare and Agriculture. I am keen to know which predictive model are most widely use in those sector. It will give me more insight about the use of predictive modelling in those sector.

I will be awaiting for some positive revert. Have a nice evening.

Thanks and Regards,

Sagar.

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