Changing the equation of business with big data

By: Anshul Srivastav, VP-IT, Bharti AXA General Insurance

Big data is carving out new ways and is helping decision makers revolutionize traditional business models. Decision makers are now closer to customers and partners than ever before. Take the case of an online retailer—with the help of big data, it can capture behavior patterns of customers and is able to display products to customers based on their personal preference.

Competitive sectors like financial services and retail are at the forefront of big data adoption. For example, financial services companies are using social media platforms like Facebook and Twitter to gauge customer sentiments and preferences. Facebook likes and Twitter tweets are being tracked and this unstructured data is transformed and translated into meaningful customer insights. Based on these insights, new financial products are being designed. These new products predict the future buying patterns of the customers. In fact, financial services firms are using big data to target customers and get unique insights regarding whom to offer loans/credit cards/insurance, etc. Predictions are of the level that financial institutions can understand when a particular customer will look for financial products.
That said, sheer size of data is growing at a staggering
pace—estimated 2.5 zettabytes of data was generated in
2012 and by 2020 it will be around 45 zettabytes (one
zettabyte is around billion terabytes). With the influx of
data, IT systems are being strained to manage this kind
of volume of data.

DERIVING INSIGHTS FROM DATA
Complex algorithms are being written to transform unstructured data into structured and meaningful output. More unpredictable variables are now equating with predictable values. This, in turn, is translating into more accuracy for predictions. Some predictable scenarios can be: Predicting who is more influential on Twitter, salaries from job descriptions, the churn and retention of mobile users, the customer’s financial distress, flight delays, etc.
However, it is impossible to gain insights through big data without cleansing the data. Thus, organizations should not jump onto the big data bandwagon if the quality of data is poor. CIOs are articulating strategies to manage unbelievable growth of data and data sources. Computing resources are getting better equipped to handle data growth and complexity.
Yet, as data volumes with varying degrees of structure explode in every dimension, IT organizations are faced with challenges in terms of shortfalls in strategy, infrastructure (hardware and software), and organization (talent and structure) required to make effective use of data. CIOs should work closely with businesses on understanding the key drivers of big data. They should run workshops on how and where the data in the organization exists and how the data can be transformed into meaningful outputs. They should also develop matrices around decisions on customers, products, and partners and then build revenue models and business models around it. Key levers which should drive big data strategy are: Decision analytics (faster and better decisions and predictive analytics) and transformational analytics (elimination of redundancy or waste and automation of internal processes).

BUILDING BLOCKS OF BIG DATA STRATEGY
To ensure a successful big data strategy, organizations
should answer the following key questions:
#1 „„What is the current understanding of its customers,
partners, and products? What are the data points of
improvement?
#2 „„Is there any data available for new revenue streams
internally or available in the public domain? How data can
be transformed into new business opportunities or new
revenue streams?
#3 „„How can we monetize the existing data?
„„#4 What are the non-existing data sources? How can we
find and exploit the new data sources which are not there
in the current ecosystem?
#5 Considering these questions will help organizations ride
the big data wave successfully and derive unique business
benefits.