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Signal Extraction Methodology In Big Data (Deep Dive)

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Mahesh Kumar

Let’s start with the principals, so what exactly is the signal , in plain English Signal is a pattern , so there are lots of patterns , there are some patterns which are highly impact full from cost point of view & there are some patterns which are not so important , so data side is all about the hunt for revenue impacting patterns . So signals are an early warning to the business to intervene an influence in an outcome. For example
  • In Telecom, billing resolution errors is a signal for customer churn
  • In Retail, search frequency is a signal of purchase intent
  • In Healthcare, decrease in inter hospital visit is a signal of a medical condition

 

  • Early warning sign – Detection of the signal gives time for the business to intervene and influence an outcome

Let’s have deep dive of Signal Extraction methodology step by step. Simplistic nine step process. 1 The first step is to identify the very important business problem to solve. Crystallizing the right business problem to solve is very important and getting as narrow as possible, like do you want to solve the churn of high value customers or churn of low value customers, drilling down to the specificity of the problem is very important. 2 3 4 5 6 7 8 9 10 then you get the data model once you have the data model you create an analytic model. Then you do what is called as Univariate analysis next step you will do some correlation analysis. Sixth step you will do what is called cross tab analysis and seventh step you will do what is model building, based on the significance in the model you will tell the business what is happening in the process finally the whole process of building a model is incomplete without driving an action and Return on Investment.

Author Bio:

Mahesh Kumar CV is A Big Data Entrepreneur, ChiefExecutive Officer & Founder at Big Data Force Pvt Ltd. I have about 14 years of experience in architecting and developing distributed and real-time data-driven systems. Currently my focus is on ensuring that my customers are happy, by addressing their business problems through robust data platforms that are fuelled by the advances in Big Data technologies and algorithms. Provided thought leading, practical, cutting edge solutions in the areas of BI,Big Data Analytics , In Memory Computing, Analytics to transform Fortune 500 Clients to deliver higher performance. Achieved great results for many clients through consulting and solving complex decision making environments. Specialties: translating big data into action, Big Data Trainings, Product Engineering Services, and Building Big Data CoE & Big Data Incubators. Specialties: Big Data Transformations (Strategy, Value Articulation, Architecture, Assessments, Portfolio analysis and rationalization), Information Management, Innovation.

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