What big data mean for a telecom major like Tata Teleservices? Let’s hear it from the guy who calls the shot in the organization’s IT decision making. Ashish Pachory, CIO, Tata Teleservices, interacts with Dataquest.
What role does data play in a telecom enterprise like Tata Teleservices? Please point out some specific opportunities and challenges arising out of big data?
Telecom companies tend to deal with large volumes of data on a daily basis. At Tata Teleservices, coded insights derived from the analysis of these data sets help us personalize our products and services.
In this era of mass personalization where 65% of consumers expect companies to provide customised offers, leveraging the end-user information for deeper insights into customer preferences gives us a definite edge over our competition.
A thorough understanding of how big data ties-in with business requirements, goals, and data policies, will ensure minimum wastage of resources and help procure insights that add significant value to the company. Employing efficient big data analytics could enable cost reductions, processing time reductions, aid in new product development and delivery of optimized offerings; amounting to smarter business decision-making and operations.
What is your approach in adopting big data solutions? In what ways have you reaped benefits out of it?
There is a growing emphasis within Tata Group companies to leverage big data solutions to enhance customer experience across the value chain. TTSL is an active player in this initiative. We have elegant predictive tools and robust data warehousing in place. While these solutions enable us to leverage structured data, we are also looking at available options for effective mining of unstructured data. Through the Hadoop framework we have used big data for more effective vigilance tracking and monitoring. Big data also plays a crucial role in addressing legal and regulatory requirements which are mandated for longer retention periods.
Other business functions that benefit from these tools include marketing campaigns, customer service and network management. We expect to see a spiraling trend in the deployment and adoption of big data analytics, especially in customer experience management.
Can you bring out some key challenges that you faced in using big data? Please elaborate on internal as well as external factors.
Some of the common barriers to big data analytics are the same as one would expect with any new transformational concept. First, there are organisational barriers – Who owns it? Is it IT/CIO, Business/Marketing, or a brand new ‘department’ for big data analytics? How do we ramp up the skills to design, establish and run functional big data analytics? Do we need to engage expensive consultants? Fair questions – but no different from, say, when you launched ERP. Then there are the inevitable RoI worries – sponsorship, business case, TCO. There is also the worry that the current data base is too messy, or unstructured, to lend itself to big data analytics. The fact is, this is not a limitation for big data analytics at all.
What should be the ideal strategy for adopting big data solutions? What would be your advice to other enterprise IT heads looking to take a plunge?
Maximizing big data’s full potential requires advanced analytics to cull and leverage data from inside and outside the organization. These 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 speeding up processes, resulting in quicker, more relevant and effective business decisions. Gartner suggests that by 2015, around 20 per cent 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:
o Define business value: Identify areas which benefit most from big data implementation and define short and long term value. Big data being new, 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 variety and volume of data
o Plan for Sandbox/PoC environment: This will help in prototyping and performance measurements
o Identify stakeholders: As with any other project identify all your stakeholders with clear project scope and success criteria at each step
o Identify and acquire tools and skills
o Define data interface and data governance policies
o Expand to other areas
How mature is the vendor landscape for big data in the Indian market? What are your expectations from the vendor community?
We are seeing a gradual growth in the vendor landscape in relation to an increase in the awareness about big data analytics. Our expectation from vendors is simple – enable us to adopt big data in the most efficient and effective way possible. There is thus a need for vendors to be able to manage the large volumes of big data and the immense variety in terms of data types and sources; to deliver insights in a speedy manner.