Big data analytics is one of the most potent tools in any organization’s customer engagement arsenal: Prasad Mamidi

In today’s customer-centric environment, personalization has become the most important brand attribute when it comes to fostering customer loyalty. Given the urgent need for personalization, big data analytics is emerging as one of the most cost-effective and powerful tools in any organization’s customer engagement strategy, asserts Ram Prasad Mamidi, Head-IT & CIO, Tata Teleservices. In an exclusive interview with Dataquest, he talks about the factors driving the demand for big data solutions and the key advantages of adopting analytics driven decision making. Excerpts:

In your perspective, what is driving the demand for big data solutions? What are the major challenges in the deployment?

Businesses, whether small or large, are churning huge amounts of data at an unprecedented rate. This data can originate from a multitude of sources—posts to social media sites, application downloads, digital pictures and video uploads, mobile GPS signals, etc. The ability to integrate all the different sources of data and shape it in a way that allows business leaders to make informed decisions is at the heart of big data. The more data available, the more accurate the analyses, the better the business.

Superior customer experience, cost effectiveness, and growth are the key priorities for businesses across the world. Big data solutions allow companies to meet these needs by optimizing usage of existing resources to create an intelligent and responsive enterprise that understands its customers and provides real-time, insight-driven services and support.

Awareness about big data and its impact on businesses has just started seeping into the fundamental workings of companies. Several businesses enter into the big data space without a thorough understanding of business requirements, goals, and data policies. This often leads to wastage of resources and the insights procured fail to add much value to the company. There is a need for more big data specialists to help effectively run and manage implementation across functions.

Other barriers are the same as one would expect with the adoption of any new transformational technology. These range from determining ownership within the organization to building a business case for recommended implementations. There is also the worry that the current database is too unstructured for effective automated analysis. The fact is, this is not a limitation for big data analytics at all.

What are the merits of adopting big data analytics solutions and what difference can it make?

A recent academic research found that companies that have incorporated data and analytics into their operations show productivity rates 5 to 6% higher than those of their peers. The reason is simple: The more information a brand has regarding its consumers, the better it is able to predict their buying behavior and customize its offerings, fostering customer trust and leading to an increased chance of sale. Another recent survey of 50 CMOs at companies large and small found that offering personalized customer experiences was their highest business priority. Mass personalization has become the most important brand attribute when it comes to fostering customer loyalty. Additionally, personalization helps companies differentiate their products/services from those of their competitors at a time when the Internet is rapidly making it easier for customers to compare offerings and make purchases at the click of a button.

Given the urgent need for customization, big data analytics is one of the most cost-effective and potent tools in any organization’s customer engagement arsenal. Never has data on user behavior been as readily available as it is today. The sheer volume of it is staggering. To use that information for determining segments and micro-segments of customers and predicate product and service innovation on those insights is the way to business success in the near future.

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 types, structures and I/O speeds, BDA has to work with an intelligent and responsive enterprise that understands its customers and provides real-time, insight-driven services and support.

Awareness about big data and its impact on businesses has just started seeping into the fundamental workings of companies. Several businesses enter into the big data space without a thorough understanding of business requirements, goals, and data policies. This often leads to wastage of resources and the insights procured fail to add much value to the company. There is a need for more big data specialists to help effectively run and manage implementation across functions.

Other barriers are the same as one would expect with the adoption of any new transformational technology. These range from determining ownership within the organization to building a business case for recommended implementations. There is also the worry that the current database is too unstructured for effective automated analysis. The fact is, this is not a limitation for big data analytics at all.

What are the merits of adopting big data analytics solutions and what difference can it make?

A recent academic research found that companies that have incorporated data and analytics into their operations show productivity rates 5 to 6% higher than those of their peers. The reason is simple: The more information a brand has regarding its consumers, the better it is able to predict their buying behavior and customize its offerings, fostering customer trust and leading to an increased chance of sale. Another recent survey of 50 CMOs at companies large and small found that offering personalized customer experiences was their highest business priority. Mass personalization has become the most important brand attribute when it comes to fostering customer loyalty. Additionally, personalization helps companies differentiate their products/services from those of their competitors at a time when the Internet is rapidly making it easier for customers to compare offerings and make purchases at the click of a button.

Given the urgent need for customization, big data analytics is one of the most cost-effective and potent tools in any organization’s customer engagement arsenal. Never has data on user behavior been as readily available as it is today. The sheer volume of it is staggering. To use that information for determining segments and micro-segments of customers and predicate product and service innovation on those insights is the way to business success in the near future.

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.

With the emerging role of chief data officers, how is the role of the CIO changing and is there a need for CIOs to adapt to more business functions?

For successful adoption of big data, cooperation between leadership teams across disciplines is crucial. The focus of CIOs today is to identify opportunities to streamline business processes through investments in data management. The CIO must recognize the importance of managing customer expectations at each stage of the lifecycle which in turn must spur the deployment of IT tools and processes to garner deeper customer insights. Owing to sheer growth in volume, velocity, and variety of data, it becomes impossible for traditional desktop analytics tools to analyze it. Thus, enterprises need to invest in advanced analytics that can deduce, compress, and store this data to give fresh business insights. Finally, ensuring requisite skill set in the organization to derive gains from big data is another key battle that CIOs need to win.

How are you making it economical to store the huge amount of data being generated? Where will large enterprises like you be investing for enterprise storage, going ahead?

Tata Teleservices has prioritized investment in an adaptive technology infrastructure to successfully meet the demands of IT consumerization and the rapid increase in data. Our datacenter facility in Hyderabad is at the heart of the company’s business. The predominantly captive facility became operational in 2012 and covers a 27,000 sq ft area. The facility handles all critical applications and is one of the few tier-3 datacenters in India. We have also successfully deployed big data solutions to manage key operational requirements. Big data plays a crucial role in addressing legal and regulatory requirements which are mandated for longer retention periods. Given the growing volume of data and the time sensitivity attached to such requirements, organizations usually look to procure additional storage and servers incurring high expenditure. As an alternative we deployed big data solutions which were very cost effective and also provided timely information.

There are other areas of business which benefit from these tools and the concept is now being extended to marketing, customer, and network management. We expect to see a spiralling trend in the deployment and adoption of big data analytics for business operations, primarily in customer experience management.