Big Data and Rise of Predictive Enterprise Solutions

Given the three Vs of Big Data, namely Volume, Variety and Velocity (read this for more), challenge before large and medium sized companies is how to unlock the potential of Big Data and productively leverage its value in running the business.

In “traditional” Data Analytics or Business Intelligence, focus is more on analysis and reporting of “historic” or past data stored in the database. Take for example how most organizations use data from their CRM or ERP applications. Almost all the reports that are generated pertain to past or “historic” information. Running a business based on “historic” or past data is like driving a car looking in the rear view mirror and is not going to work.

Instead, companies must analyze all the available information in real time, apply statistical modeling techniques to available information in order to predict future outcomes and take action/run the business based on predicted outcome rather than analysis of historic data as is being done currently.

Since Big Data is characterized by not only Volume, but also Velocity and Variety, it is very important that Big Data is used for analysis in real-time to predict the future and take corrective action based on that analysis. How about using Causal Path analysis on Social Media data (like Twitter and Facebook) to predict Churn or Customer Attrition in Telecom industry and taking corrective action to prevent Churn/Attrition rather than analyzing “historic” attrition rate, call volumes or average response time as being done currently. The real value is in using predictive analytics and taking corrective action before it is too late, rather than just reporting historical information.

Techniques like Multiple-regression analysis coupled with Factor analysis, Cluster analysis and Causal Path analysis can be used very effectively with Big data – now that we have many variables and multiple observation for each variable at a customer level to generate statistically significant difference in analysis.

In future, no ERP or CRM system will be complete without Predictive Analytics functionality that will enable companies take preventive steps (rather than reactive) in real time. For example, rather than analyzing “historic” attrition rates, Predictive CRM application will make it possible for companies to identify critical incidents leading to customer attrition so that steps can be taken to retain the customer before he/she defects.

And thanks to Predictive Analytics, ERP or CRM applications will no longer be just a repository of “historical” information but will be transformed into a Predictive Knowledge base or Engine driving business decisions looking forward and not backwards/in the rear view mirror of “historic” information.

What do you think? Do you agree that Big Data will result in rise of Predictive Enterprise Solutions? Please do share your thoughts.

  • Faisal Iqbal

    I agree completely… This reminds me of the key message i took from a book i read a few years back called The Master Strategist

  • http://hkotadia.com/ Harish Kotadia, Ph.D.

    Thanks!

    Harish Kotadia, Ph.D.

  • http://www.stewarttechnologies.com/crm.html Carlene Schnitzer

    Definitely, Harish! Any business that uses CRM has an edge in the business industry. Plus, the uses of Predictive Analytics will enhance their capability of integrating their functions to gain an edge in their production. Customers have a very significant impact in business, it is only best to provide them a superior customer service with the help of software management.

  • About Dr. Harish Kotadia


    That's me with photo gear,  taking snaps of Texas wild flowers. #texas

  • Dr. Harish Kotadia

  • Dr. Harish Kotadia is an industry recognized thought leader on Big Data and Analytics with more than fifteen years' experience as a hands-on Big Data, Analytics and BI Program/Project Manager implementing Enterprise Solutions for Fortune 500 clients in the US.

    He also has five years' work experience as a Research Executive in Marketing Research and Consulting industry working for leading MR organizations such as Gallup.

    Dr. Harish Kotadia's educational qualification includes Ph.D. in Marketing Management. Subject of his doctoral thesis was Customer Satisfaction and it involved building a statistical model for predicting satisfaction of clients with services of their ad agency.

    His educational qualification also includes M.B.A. and B.B.A. with specialization in Marketing Management and Diploma in Computer Applications.

    Dr. Harish Kotadia currently works as Principal Data Scientist and Client Partner, Big Data and Analytics at a Global Consulting Company. Views and opinion expressed in this blog are his own.



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