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IBV study on analytics, part four: Enable value creation with a big data platform

December 13, 2013

This is part four of our series on the findings from IBM Institute for Business Value’s latest study and paper, “Analytics: A blueprint for value - Converting big data and analytics insights into results,” from my colleagues Fred Balboni, Glenn Finch, Cathy Rodenbeck Reese and Rebecca Shockley.

In part three we looked at evaluating the impact on business outcomes from big data and analytics with a closer view on one of three Enable levers: Measurement. In this post we will add the final Enable lever for value creation: Platform.

In examining the analytic capabilities of organizations, we find a majority of all respondents can support query and reporting (73 percent), data visualization (58 percent) and data mining (57 percent). But beyond those skills, the capabilities of Leaders begin to diverge dramatically. 

Leaders have made more substantial investments in developing the integrated capabilities delivered through hardware and software components in support of analytic activities. They have evolved beyond the traditional infrastructures and analytics techniques of a foundational business intelligence platform to a modern, flexible infrastructure that can intake, process and manage the volume, velocity and variety of today’s data. More than one-third of Leaders, for example, have implemented cloud technology and mobile solutions, while roughly another third are currently developing strategies to implement those technologies (see Figure 5).

Figure 5 for RP post 4.jpg

60 percent of Leaders have predictive analytic capabilities, as well as simulation (55 percent) and optimization (67 percent) capabilities. These skills enable Leaders to look beyond what happened yesterday and what is happening today, and begin to understand how changes in customer preferences, market forces, natural phenomenon or regulations might impact their operations and revenues tomorrow. By comparison, just over half (52 percent) of all other respondents have predictive capabilities, while fewer than half have simulation (45 percent) or optimization (49 percent) skills.

This will be increasingly important; in fact, Gartner predicts that through 2015, predictive and prescriptive analytics will be incorporated into less than 25 percent of business analytics projects, but will deliver at least 50 percent of the business value. Leaders are also investing in technologies to understand customers better. Leaders are 2.5 times more likely than others to have current analytic capabilities to support voice analytics (42 percent), an essential skill for extracting data from global call centers, citizen hotlines and judicial proceedings (see Figure 5). Leaders also have current analytic capabilities to support video analytics (36 percent), especially important in industries like retail where companies are starting to use video to analyze buyer shopping patterns, store layouts and product interactions. In addition, more than half of Leaders have capabilities to support text analysis. They apply these technologies to analyze internal and external documents, as well as customer correspondences.

But data management is still an onerous task within most organizations. The inability to create, integrate and manage data was cited as the top technology challenge by 41 percent of all respondents, while another 27 percent cited the inefficiencies within the analytics process, 19 percent cited the lack of end-user analytic capabilities and 12 percent cited the constraints of a legacy infrastructure. 

One organization that has invested in technology to augment its analytics capabilities is Celcom Axiata Berhad, a key operating company of the Axiata Group, which pioneered the mobile phone market in Malaysia in 1988. Today, the company provides mobile telecommunications to over 13 million customers. The business has evolved and adapted to changing technologies and standards over the past two decades, with a central focus on customer experience. 

Given the complexity and frequency of new device and product launches, one of the most challenging areas for Celcom’s customer service agents is to respond to queries on smartphones and provide recommendations on relevant data plans. Celcom needed to improve its approach to engaging customers in response to the demographic, social and technology shifts that were driving these changes.

Cognitive computing systems overcome the challenge through a massively parallel processing system, which runs multiple queries at the same time (that is, in parallel), as opposed to more traditional sequential processing. This, in turn, enables the system to evolve response guidelines and policies as new material is added to the data set.

“Celcom will harness the power of IBM Watson in analyzing raw data to provide deeper customer insights and preferences in near real-time,” explains Dato’ Sri Shazalli Ramly, Chief Executive Officer of Celcom Axiata Berhad. The company has seen strong results from its pilot implementation, which reduced new campaign launch time by more than 80 percent and improved campaign performance by more than 70 percent. This, in turn, increased campaign return on investment. The pilot also found the technology improves customer loyalty and reduces churn through personalized campaigns and messaging.

By deploying cognitive computing more broadly, Celcom hopes to provide consistent, high-quality support to customers across channels and agents. This facilitates Celcom’s aim to deliver targeted customer offerings, simplify end-user interactions and deliver richer customer experiences across products, services and touch points.

In part five, we will look at the second level of impact for creating value with big data: Drive. We will examine Drive’s first lever, Culture, and its impact on the availability and use of analytics.

Catch up on the entire series so far with parts one through three:

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