Please share a use case of how Tata Teleservices is leveraging the power of big data to serve its customers?
While the previous generation was happy to consume any content or offering by a brand, the millennial generation wants brands to hear their voices and incorporate their opinions before creating anything for them. Keeping this insight in mind, we place solid emphasis on end-to-end customer experience management. Customer analytics is at the heart of understanding who your customers are, what is their behavior/preference, and how to maximize the revenue with both existing and new customers. This requires the integration and analysis of data both inside and outside the business. A real-time algorithm does this task and with the requisite data and the correct analytics, you can get an accurate view of customer usage and how best to offer better value.
Tata Docomo’s *123# is a self-help functionality that allows the customer to access customized offers developed based on their usage behavior. The same functionality enables retailers to fetch the best offer for the customers. Since the offer is made keeping the customers’ requirements in mind, it increases the relevance of the offer helping establish preference and thereby conversion.
The customer can either check the offer on his own simply by dialing *123# from his device or go to the retailer who can check the offer for him. The customer can avail any of the available offers anytime, from any of the retail outlets.
This service has provided a simplified self-help platform for our consumers, and has now been running successfully for close to two years. The ultimate intent is to get every consumer, before every recharge, to check out his very own personalized ‘not available in market’ offer. The obvious business benefits include better customer retention rates and better uptake of higher value recharges. With the sheer volume of new data being generated today, data trustworthiness is an area of concern.
How do CIOs and CFOs tackle this situation and do you consider big data as something different especially when it comes to data quality?
The one thing that makes big data so desirable is its intersection with advanced analytics. Big data is only as useful as the analytics deployed to exploit it. The world of Big Data Analytics (BDA) is quite different from our familiar world of data processing, management, and analysis.
Apart from its innate ability to juggle different data completely new processing and programming models. It is an ecosystem that needs to be carefully planned and implemented; a combination of processing technologies all working in parallel on distributed servers. You cannot just buy an application to make big data analytics happen. It is an evolutionary process.
The fact is that there is already an increasing sense of urgency around big data and as businesses establish faster and stronger connections with their customers, the case for big data becomes stronger. Big data enables you to dive deeper into more varied and voluminous records to yield actionable insights which could not be accessed earlier. As it is emerging concurrently with a host of complementary trends—social media, enterprise mobility, etc,—we are seeing a very new kind of convergence, which brings all of these trends together to create the enterprise information architecture of the future.
How can one make the critical transition to connected intelligence and analytics-driven decision making?
Maximizing big data’s full potential requires advanced analytics to cull and leverage data from inside and outside the organization. The analytics must overcome any size, speed, and structure limitations. This also requires a shift from the concept of a single enterprise data warehouse that earlier used to contain all information needed for decisions. Multiple systems is the key, where specialists are involved at each stage to speed up processes, resulting in quicker, more relevant and effective business decisions. Gartner suggests that by 2015, around 20% of Global 1000 organizations will have established a strategic focus on ‘information infrastructure’ equal to that of application management.
The key steps to deploying big data analytics solutions are:
Identify areas which benefit most from big data implementation and define short- and long-term value. Prepare a long-term roadmap and start small (internal data first), providing measurable and faster returns. Subsequent steps will include expanding the project to include the variety and volume of data.
Plan for Sandbox/PoC environment as it will help in prototyping and performance measurements.
As with any other project, identify all your stakeholders with clear project scope and success criteria at each step.
Identify and acquire tools and skills.
Define data interface and data governance places.