Considering where an organization is with big data can guide where it should be: Celent

International financial research and consulting firm Celent recommends that organizations consider five tiers of data capability to help determine not only where they currently are when it comes to big data, but also where they should be.

A new report released yesterday, Big Data: A Guide to Where You Should Be, Even If You Don’t Know Where You Are, notes that as consumers become more digitally driven, the opportunities for face-to-face contact will shrink.

“Leveraging this data to create compelling and timely support and advice in an increasingly cross-channel, digitally driven world will soon no longer be optional in financial services. Arguably, it is a key element for success,” states the report from Celent, a division of Oliver Wyman Inc.

The report seeks to provide guidance on how an organization can get to the level of capability it wants and needs. The first step a financial services company has to take is to define an enterprise data strategy and roadmap aligned with its business objectives and goal, the report notes.

“There are many different Big Data paths/alternatives that a company can explore and leverage, but the data roadmap will allow you to better understand which paths to traverse.”

In its simplest form, the report’s advice is as follows:

• examine the organization’s current capability level;
• evaluate where the organization should be; and
• invest appropriately to close the gap.

“Organizations need to look to where they want to invest. In addition, the organization must look externally, to what their competitors are doing,” adds the report.

Celent notes the five tiers of data capability are as follows: spectator (focused on business priorities and only watching big data projects); experimenter (investing in a few pilot big data projects); practitioner (purchased and implementing tools and solutions in production, often focused on pilot programs rather than broad big data solutions); innovator (leveraging tools, building tools and using open source code); and scientist (leading the way by developing new algorithms and pushing the boundaries of computer science and mathematics while dealing with the most complex data sets).

“Whatever level of capability an organization has now, Celent believes that there is an achievable, affordable route for organizations to start experimenting to better understand the value of the data it has access to and the benefit that might be derived,” states the report.

“As consumers become increasingly digitally driven, the manner in which financial services are delivered will change inexorably,” it notes. “Big data, when sensibly integrated with core, channel and decision support systems, and directed from an enterprise perspective, holds the promise for enabling the survival of financial service companies within this epic transformation.”

Among the reasons for this are as follows:

• online/mobile channel usage has driven explosive growth in the number and variety of customer interactions, meaning that excelling in customer experience will increasingly require delivering timely and relevant personalized information in real time;
• products and marketing campaigns are increasingly customer-centric;
• news is breaking more in social media and spreading faster than ever before, influencing everything from purchasing decisions to stock prices; and
• the economy and competitive pressures are forcing firms to focus on very lean and efficient operations, with growth potential coming from intelligent (data-driven) decisions across the enterprise.

“Big data is likely to redefine itself as the boundaries of data volume, velocity and variety are pushed inexorably forward,” Celent suggests. A table in the report defines and outlines the challenges presented by the “three Vs” of big data:

• Volume: The amount of information gathered is often in terabytes, sometimes in petabytes and will soon be in exabytes. Digital information to process is exceeding the current data mastery investment in most financial firms.
• Velocity: This is defined as the speed at which data is collected, analyzed and presented to users. The challenge is high-speed data flows and response expectations of end-users.
• Variety: Data can take many forms, as well as be gathered from many devices and from internal and external systems and sources, including social media. The challenge is the growth in the number of data types available, especially with unstructured data.

“Financial services is a segment with vast amounts of data that needs to be processed in real time,” Celent emphasizes. “For the last several decades, data has been stored in relational databases, which require the data to be structured,” the report notes. “However, there are now large amounts of data that is unstructured or semistructured (80% of all available data by some estimates), and growth is expected to remain strong.”

The report points out that social media has become a powerful tool for monitoring geopolitical and weather events, including natural disasters, in real time. With sites such as Twitter, cellphone and smartphone users can post comments and photos during these events. “This information can help firms monitor the well-being of their employees as well as update risk models they have developed for affected areas.”

There is also the growth in the use of digital sensors as a way to monitor patient health, climate, weather information and vehicle operation. “For example, some automobile insurers are using digital sensors to help provide telematics information to help in analyzing risk as well as offer new services such as emergency roadside assistance,” the report states.

It characterizes big data as complex, “a journey, not a destination. No firm will master it in one fell swoop. More broadly, handling data well requires an enterprise approach, or a holistic approach to data mastery.”

Although much of the popular discussion around big data is focused on technology – and perhaps rightly so – Celent cautions that the failure to appreciate big data in a larger context invites failure. “Big Data and the opportunities and challenges it represents are best thought of as part of a larger and growing ecosystem. Establishing how your institution fits in the larger ecosystem can go a long way towards understanding the implications of Big Data and leveraging it for competitive advantage.”