Data Analytics Life Cycle and Role of Big Data

Data Analytics and Big Data are getting importance and attention. They are expected to create customer value and competitive advantage for the business. We have depicted the Data Analytics Life Cycle in details. Considering focus around big data, an analysis is undertaken to understand impact of big data on data analytics life cycle. Typical analytics projects have following (column chart below) effort and time distribution. Of course, various factors influence time taken across data analytics life stages such as complexity of business problem, messiness of data (quality, variety and volume), experience of data analyst or scientist, maturity of analytics in an organization or analytical tools/systems. But, data manipulation is one of the biggest effort drains of analyst time1.

Effort Distribution across Data Analytics Life Cycle

What is an impact of big data across Data Analytics Life Cycle?

  • Understanding Business Objective

Big Data or any other technology plays little role in understanding the business objective and converting a business problem into an analytics problem. But the flexibility and versatility of the tools and technology guides in what all can or can’t be done.  For example, a brick and mortar retailer may have to launch a survey to understand customer sensitivity toward prices. But an eCommerce retailer may carry out an analysis using customers’ web visits – what different ecommerce website customers visit pre and post the visiting the eCommerce retailer.

  • Data Manipulation

Data manipulation requires significant effort from an analyst and the big data is expected to impact this stage the most. The big data will help an analyst in getting the result of a query quicker (Velocity of Big Data). Also, the big data facilitates accessing and using unstructured data (Variety of Big Data) which was a challenge in traditional technology. The data volume handling (Volume of big data) is expected to help by taking away a data volume processing constraint or improving the speed.  Statistical Scientists had devised sampling techniques to get rid of constraint of processing high volume of data. Though, big data can process high volume of data and the sampling techniques may not be required from this perspective. But the sampling is still relevant and required.

Speech Analytics and Big data Example: In one my previous experience Eureka Call miner3 was used to understand customers’ needs and concerns along with monitoring agent performance.  Due to size of the call volume and space requirements, only latest 2 weeks of data were available for an analysis. This was a constraint on what hypotheses can be validated. Due to big data technology, this constraint may not be relevant and many more hypotheses could be validated to add value to the end customers and the business.

  • Data Analysis and Modeling

Most of the machine learning and statistical techniques are available in traditional technology platform, so the value add of big data could be limited. One of the arguments in favour of machine learning in big data is “more data is fed to the machine learning algorithm more it can learn and higher would be quality of insights”2.  Many practitioners do not believe in simply volume leading to quality of insights.

Certainly having different dimensions of data such as customer web clicks and calls data will lead to better insights and improved accuracy of the predictive models.

  • Action on Insights or Deployment

Big Data has created a new wave in industry and there is a lot of pressure on organizations to think of big data. The big data technology is still maturing, but organizations are making investment to tap big data for competitive advantage. A few organizations such as Facebook and Amazon have already adopted and are using the big data.  The real differentiator between successful and non-successful originations will be rights insights and action on the insights.

 Big Data technology is expected to enables deployment of insights or predictive models quicker but more importantly speed to action on analytics will be almost in real time.

 Offer Recommendation on Web and Big Data

A generic offer is prevalent on a web without much success. A personalized and relevant offer is the customer expectation and the organizations are proceeding in this direction. One of the ways to identify customer needs is combining web clicks behavior and transactional behavior in a real time, and providing a personalized offer to the customer. This may be a realty using big data & big data analytics.

  • Learning and Guiding

Due to Big Data and Big Data Analytics, data analytics cycle time and cost is expected to come down. The cost reduction and shrinkage in cycle time will have propitious impact on analytics adoptions. The organizations will be open proceed toward experimentation and learning culture. Of course, this is not going to happen automatically.


Big Data is industry buzz word with a lot of focus, attention and investment. Big Data investment is going to add value to the customers and the business only if right insights are developed and actioned upon. Big data is going to impact each stage of Data Analytics life cycle, but the main value add (till Big Data analytics tools matures) will be around data manipulation.




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