"With any substantial investment in new technology comes the question of value. Is there enough value in the mountain of unstructured social media data to justify the expense and effort of examining it?"

Opening Statement

Anything can be measured in an attempt to show its value. Of course, there are many ways to measure a return on investment, but the question we pose in this issue of Cutter IT Journal is: "How do you measure the value of social media analysis?"

Actress Salma Hayek once remarked:

People often say that "beauty is in the eye of the beholder," and I say that the most liberating thing about beauty is realizing that you are the beholder. This empowers us to find beauty in places where others have not dared to look.

We believe this is true with social media analysis -- that there is value in places we haven't yet explored. In this issue of CITJ, we address the value of Big Data analysis of social media content and provide guidance, insight, and actual case studies on whether or not the knowledge and insight derived from this data is worth the resources spent.

With the widespread adoption of social media sites such as Facebook, LinkedIn, and Twitter, social media has become one of the larger sources of Big Data. This increased volume of data has created a slew of new IT issues that need to be considered, the most significant one being "What do we do with all this data?" As a result, we're seeing an increased demand for more storage capacity, enhanced needs for computing power, and the introduction of new technologies (such as Hadoop), all of which make undertaking a social media monitoring campaign no small task.

As IT requirements grow, so do budgets and the level of investment required. With any substantial investment in new technology comes the question of value. Is there enough value in the mountain of unstructured social media data to justify the expense and effort of examining it? At some point, senior management must wonder if it's really worth it. Are we learning anything new from this analysis? Are the insights and knowledge gleaned sufficiently important to justify the investments in this new technology?

When we have vast amounts of data, we as humans tend to get overwhelmed. As we collect and organize that data, we start to call it "information." By applying logic and reasoning (and other sources of information), we turn that information into knowledge, something that (hopefully) gives us a competitive advantage over others. While Francis Bacon may have observed that "Knowledge is power," in the case of social media analytics, we may want to listen to Irish essayist Robert S. Lynd's words instead: "Knowledge is power only if man knows what facts not to bother with."

In the case of social media analytics, we might be able to determine our customer's view of our latest product (the positive or negative sentiment). Surely there is value in knowing this. But how do we translate that into its monetary form? If our organization has spent thousands of dollars to implement a social media listening program and determined that some percentage of potential customers don't like the color of our latest product, does that justify the investment we made in establishing that listening program? Maybe. But if the investment is in the millions, and the insights aren't as clear-cut, would our answer be the same?

Let's say we are a major movie studio, and we're about to release a new multimillion-dollar film into the theaters. With a social media listening program in place, we might determine that there is very little chatter about our upcoming release, while there appears to be an overwhelming amount of discussion about a competitor's movie due to be released on the same weekend. If we can determine that the "buzz" around our movie needs to increase, and we immediately take action to raise the public's awareness (thus saving our "box office draw"), can we claim that the social media program has proven its value to us?

What is "value"? Is it an investment, a hedge against the future, or knowledge?

IN THIS ISSUE

We begin the issue with an article by Cutter Senior Consultant Jim Love, who observes that even though the potential value of a social network grows with its size, the actual value realized depends on how effective one is at eliminating the vast amount of extraneous and redundant information. This is exemplified by the "law" formulated by science fiction author Theodore Sturgeon: "Ninety percent of everything is crap." Love suggests that in the highly competitive business climate we live in, discovering and delivering value from social media requires a strategy and an approach grounded in measurement. He argues that organizations need to discover their social media path to value or else "risk the disaster of measuring the wrong things." Love offers practical tips with real-life examples for using measurement to "drive the cultural and behavioral change that sustains results."

Our next piece, coauthored by P Gopa Kumar, Girish Khanzode, and Mallika Bahety, begins by providing a good introductory overview of the broad landscape of social media data analytics. While the "incredible volume of data on the social Web is a gold mine of market research and consumer understanding," the authors write, it creates "the need for a high-quality social intelligence platform enterprises can use to derive actionable and meaningful insights for real-time decision making and proactively beating the competition." They argue that an optimal listening platform would be based on netnography, which is the application of ethnography to social media in order to gauge the sentiments and desires of online consumers. Such a platform would "help organizations become highly mature in their social listening capabilities and culminate in building influential supportive communities that would become a source of sustainable value creation." The authors then discuss a maturity model by which an organization can assess its level of knowledge building and determine where it needs to go next.

Thad Scheer offers a different perspective on the strategy for extracting business value from social media. "Most of what passes for social media analytics today is questionably accurate buzzword data," he observes, and when every company in a particular sector listens to the same feeds, analyzes them using the same tools, and tracks the same variables, the resulting business lift is minimal anyhow. So why are enterprises so in thrall to social media analytics? Because, Scheer says, they are "real, raw, and now," not the products of surveys and focus groups. As enticing as this is, he contends that the missing link in social media data analytics is usually a data analytics strategy, as distinct from social media strategy. Rather than listen to "buzz," organizations can "apply advanced analytics to discover new value from data, value that is of strategic importance because it informs executives about unique sources of new revenue, new markets to open, or other opportunities." This is where social media's triple-digit returns can be found, Scheer argues, not in buzzword counts and sentiment trends.

Next, Nick Kadochnikov looks at the insights and value that a number of different organizations are gleaning from social media. Describing some very revealing use cases, Kadochnikov shows how organizations can sometimes get answers they didn't expect. In one such instance, he relates how a company was looking to understand consumer opinion on price reductions versus packaging changes, only to find that they wanted neither! When seeking clarification on one topic, they found a completely different insight -- one that saved them from making a costly investment or shrinking their margins.

Finally, recognizing that no organization can derive value from a social media program if the data that drives it is flawed or incomplete, Cutter Fellow Steve Andriole advises us on how to select the right social listening partner. He offers seven criteria for choosing a capable vendor, ranging from the vendor's ability to collect, filter, structure, and analyze social media data to whether it can measure its own effectiveness. Andriole does a wonderful job of looking at what an organization requires to successfully implement a social analytics program and provides a useful matrix for determining which vendor's offerings best meet the particular needs of your organization.

IS SOCIAL MEDIA WORTH IT?

Understanding consumer sentiment and opinions isn't new. But in this brave new world of social media -- where everyone has the opportunity to express an idea, a grievance, or (if we're lucky) a compliment -- the sheer scope of the task can seem daunting. In this issue, we take you from the justification for this type of analytics, to a discussion of frameworks to support it, to an examination of how to sustain this work with the help of industry partners. We hope this issue of CITJ makes the path into this new world a bit clearer and, with luck, helps you find value in a place you never thought to look.

ABOUT THE AUTHORS

Matthew Ganis is an IBM Senior Technical Staff Member (and Certified Scrum Professional) within the IBM CIO organization and part of the ibm.com site architecture team. He is the Chief Architect for IBM's Internal Social Media analysis initiative. Dr. Ganis is a Senior Member of the ACM and cochair of the Agile New York City user group. He also serves on the editorial board of the International Journal of Agile and Extreme Software Development and has authored a number of papers/books on his experiences with Agile methods, including Practical Guide to Distributed Scrum. He can be reached at ganis@us.ibm.com.

Avinash Kohirkar is an Executive Project Manager within the Social Insights Group as part of the IBM CIO organization. He has a strong technical and business background across various disciplines based on numerous projects executed within IBM as well as with IBM customers. Mr. Kohirkar currently manages social analytics projects for the benefit of IBM business units utilizing internal and external social media sources. He can be reached at kohirkar@us.ibm.com.