Big Data Analytics – Driving value using unstructured data

Big Data has 3 important dimensions – Volume, Variety and Velocity to add Value to the organizations. In the article data Variety dimension will be explored and discussed to understand how Big Data adds value the organizations.

Data Variety can be segmented into two groups –Structured Data such as customer age, income and name, and Un-structured Data such as customer calls, emails, social media comments and tweets, and videos.

The unstructured data can be used to augment the information availability to generate more accurate insights, or it can be used on its own to generate insights. The big data analytics plays an important role in both the aspects.

Organization can supplement structured data analytics with unstructured data & analytics. Customer calls are recorded and stored only to increase the expenditure. The structured part of the customer calls – number of times customer and what time/days a customer called- are used mainly. The central or most important information – why a customer called? Was the customer satisfied with the response? – is not analyzed and actioned upon.

The big data analytics on unstructured data can help in answering the questions which otherwise would have been significantly difficult or impossible to answer. The unstructured data analytics will help in detecting common concerns of the customers, identifying opportunities in terms of providing right product and services, and servicing the customers effectively.

Steps used in Unstructured Data Analytics

Steps used in Unstructured Data Analytics

Steps used in Unstructured Data Analytics

Unstructured Data is analyzed using text analytics and big data tools to identify categories and key words based on the data.  The categories and key words information can be converted into structured data for additional analysis, or can be analyzed to identify the patterns and trends. The identified patterns and trends can be explored to develop actionable insights.

An example of generating actionable insights using calls data

For a bank, the customer calls data were analyzed to identify reasons of customer closing a product. All the customer calls which were related to the product were segregated for the analysis. Based on verbatim spoken during the calls, the calls data were analyzed to identify the key words. The key words which were related to customer closing the product are grouped together. The key word categories and could were analyzed. The analysis has helped in identifying top 5 reasons of customers closing the product and helped the management in taking appropriate actions – better communication on product features and their value to the customers.

Leveraging Big Data Analytics across Customer Life Cycle for Retail Bank

Big Data Analytics helps in developing insights which are relevant and required across customer cycle –acquisition, development and retention.  Using Big Data analytics, the social media data can be analyzed to help marketing manager focus on right social media channel for customer acquisition. Customer interaction and transaction data offer significant opportunities for the banks to understand customer needs. The channel interaction and transaction can be analyzed effectively using Big Data platform and tools. Also, analysis of customer complaint logs and emails to identify and understand common customer concerns for building strategy to address customer concerns proactively.

Retail Bank and Big Data – Examples across life cycle

What is Analytics?

Analytics is used to validate and formulate strategies for critical aspects of a business or entity using the power of available data & information along with business understanding. Analytics is employed for understanding the current state of the business, segregating drivers of the current business performance, identifying the factors or trends which could impact the future income & profitability of the business and helping managers in formulating strategies for the business growth.

For example, if someone interested to set up a new vegetable shop, the main aim is to make it flourish and increase revenue. What are the questions which the person will want to have the answers for making informed decisions?

    • What is the size of the market?
    • Where should the shop be located?
    • Who will be target customer group who will shop in this shop?
    • What will be expected demand for each vegetable?
    • How will I optimally procure vegetables?
    • What will be price points?
    • How to measure my performance against the targets which I have set?
    • How to reassess important assumptions on an ongoing basis?

 In other example, if someone is already managing a business or shop and aspiring for next stride, the person will look for right ideas and insights to build right strategies, and ahead of competitors.  What are the questions which the person will look to answer for formulating right strategies?

    • What is the performance of the business and various business segments?
    • What is cost efficiency ratio for various business segments?
    • What is Return on Investment (ROI) for various activities?
    • How to improve performance of the lagging business segments?
    • Who are the competitors and what actions/strategies are they taking?
    • How economic condition and competitors actions are impacting the business performance?
    • What are the drivers of current business performance?
    • How are these business performance drivers going to change in future?
    • What are the new and emerging trends from customer, technology, economic & political and regulatory perspective?
    • What are the potential opportunities for the business to be ahead of other competitors?
    • How to assess feasibilities of potential opportunities with minimal investment?

The businessmen have been answering these questions for setting up shops and running the business directly and indirectly.

Due changes in business scenarios the application of analytics for business process has gone up. The businesses are using analytics as a differentiator to grow the business and profitably.

Drivers of Analytics and Insights adoption

  • Competition and choices e.g. number of companies and brands selling Mobile phones
  • Blurring competition boundaries e.g. retailers selling financial products
  • Availability of data &information e.g.  every interaction and transactions with a bank is captured
  • Technology to process information e.g. millions of transactions can be processed and analyzed quickly,
  • Advancement of analytical techniques   e.g. Neural Network and Supervised Vector Machine (SVM)
  • Skilled resources with knowledge of technology, analytical techniques

Some of the prominent examples of successful application of analytics are

Capital One ( ) for Credit Card: Capital One has mass – customized credit card and more importantly has established an analytics closed loop(Identifying, Testing, Learning and Deploying the key insights).  They run thousands of test cases every year and learn from those experiments.

Tesco Clubcard ( Tesco Clubcard is one of the most interesting and successful story of analytics application.  With the help of DunnHumby, Tesco has converted millions of shopping transactions to insight, helping Tesco to become one of prominent supermarket in UK and in the work. TescoClubcardis one of the most successfulCustomer Loyalty Program in the world.

Netflix ( Netflix is a dominant company in the DVD movie rental industry.   A collaborative filtering based recommendation engine is being used to identify movies which customers will be interested in. This is giving Netflix a huge competitive advantage.

Ecosystem for delivering Analytics and Insights

For providing relevant and actionable business insights, first the Data Driven Insights has to be a Strategic Priority for the business decision making and senior leadership shall provide required support, second delivery infrastructure – data, technology, and analytical systems- must be in place to provide Data Analytics and Insights for the business quickly and effectively, and last the availability of analytically skilled resources.

  • Strategic Priorities& Focus: Mandate and focus from Senior Management to leverage Data Analytics and Insights for Decision Making
  • Delivery Infrastructure: Availability of Data, Technology and Analytical Systems for carrying out data analytics and deriving and developing Insights
  • Analytics Resources: One of the most important cogs in analytics delivery wheel is an analyst. Analyst should be proficient in various analytical techniques and have understanding of business processes and priorities


Larry E. Rosenberger, John Nash, and Ann Graham, The Deciding Factor: The Power of Analytics to Make Every Decision a Winner

Clive Humby, Terry Hunt, and Tim Phillips, Scoring Points: How Tesco is Winning Customer Loyalty

Thomas H. Davenport, and Jeanne G. Harris, Competing on Analytics: The New Science of Winning

Analytics as a Close Loop Value Delivery Cycle

Analytics as a Close Loop Value Delivery Cycle

In these tough economic times, many organizations are facing financial hardship and must take tough and right decisions. Every investment and decision is being scrutinized. Many time business managers face challenges in terms of selecting right choices and making right decisions just based on gut feeling. Also, it is significantly important for the business managers to know what has worked and what has not. It is important to set up effective learning mechanism before start processing testing the idea(s).

An Analytics Closed Loop Value Delivery Cycle can help the business managers in leveraging data analytics and insights for Identifying potential ideas or choices, testing the list of ideas, learning from the test on what has worked and finally deploying the most successful idea for business value addition.

Analytics Closed Loop Value Delivery Cycle

Analytics as a Value Delivery Cycle

Analytics as a Value Delivery Cycle

  • Identify
  • Test
  • Learn
  • Deploy






Please find detailed views with an example attached..