Big Data's Gold Rush

That said, Saleh remains extremely optimistic about the range and depth of Big Data-related consulting opportunities. His quick rundown of Big Data applications includes supply chain efficiency, oil pipeline safety, retail, product pricing, customer segmentation and much, much more. His competitors wholeheartedly echo Saleh's optimism. "Everyone is starting to recognize that by mining data you can you create a lot of economic brand in pretty much every industry," says Shanker Ramamurthy, Global Managing Partner, Strategy and Analytics for IBM Global Business Services. "Enterprises are recognizing that when you have a lot of data, whether it is structured or unstructured, you can mine it to create new business roles and fundamentally new business models. That's the universal trend."

While it seems unwise to bet against the sustainability of this trend, savvy consulting firms intent on sustaining the revenue gushing from analytics-related offerings are taking time to get a firm read on the nature of the opportunity and the state of their clients' readiness to pursue these types of projects.

OPPORTUNITY ANALYSIS
When PwC posted the results of its "Global Data & Analytics Survey 2014: Big Decisions" research last year, the landing page's contact list bulged with names of different practice leaders and principals. To hold a deeper conversation on Big Data, readers could contact a PwC consulting leader in Consumer and Industrial products. Or the firm's Technology, Information, Communications and Entertainment practice. Or a Principal in Financial Services, Risk Assurance, Healthcare, Cybersecurity, Strategy and Operations or Public Sector practices.

As Ramamurthy emphasizes, the Big Data opportunity is truly universal. Big Data "is a really powerful trend and it's being applied by every CXO," he says. "It's not just the Chief Marketing Officer or the Chief Sales Officer. We see a desire for this in the supply chain, from the CFO, and from the Chief Risk Officer. We're also seeing a demand in spaces like human resources where talent analytics are being used to address retention and promotion. Big Data and analytics are being used very, very broadly in multiple industries."

Marketing functions have been at the forefront of applying analytics, as evidenced by the sparing influence of CMOs within client companies as well as the explosion of marketing automation vendors—many of which are being snapped up by Oracle, SAP and IBM among a few other, very large technology companies. Marketing functions were drawn to analytics for several reasons, including the allure of quick results through A/B testing.

A marketing team could send one campaign to 10,000 targets a different message (for the same offering) to a second set of 10,000 prospects and immediately find out which message was more receptive, based on click-throughs, site visits and other behavioral measures. These insights helped improve the quality of leads marketing sent to sales, which, in turn improved—in some cases, quickly and dramatically—the volume of sales.

Analytics derived from large data sets also helped marketing functions get a more accurate read on customer value (primarily through customer centricity and segmentation initiatives), helping them slash spending on low-value customers while boosting investments in high-value customers. More recent advancements in predictive analytics help marketers spot which targets are likely to become customers who deliver the highest lifetime value.

The same technology can help companies spot candidates, supply chain partners, shipping routes, strategic risk decisions, new geographies and new offerings likely to deliver the highest value. Recent advancements in marketing analytics suggest that these insights can be acquired through technology that can be implemented quickly and at minimal cost. "With Big Data, people think they have to go invest billions of dollars or millions of dollars to get started," says a highly successful data scientist in Russell Glass and Sean Callahan's new book, The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits .

Glass and Callahan, successful marketers who now hold senior positions at LinkedIn, are careful to qualify that claim by adding, "Well, as long as you can find the people with the combination of marketing, and computer science to help you build it."

Data analytics talent is a major challenge client companies—and consultancies—confront, but it is not the only hurdle. Marketing functions, which have grown accustomed to disruptions in recent years, are a somewhat deceiving Big Data model for other corporate functions to emulate. A/B testing is easy to apply to measuring whether or not 8 percent of targets clicked an e-mail; it is much more difficult to quickly apply to hiring a human being, promoting a future leader, selecting a vendor in Saigon, or deciding whether or not to reroute ships through Baffin Bay. Many clients already are besieged by data-analytics offerings from software and services firms that promise easy implementation and immediate returns.

"Their claim is: Come to us, we'll help you solve a problem, and don't worry about understanding why," says Haig Nalbantian, a Senior Partner at Mercer and co-founder and leader of the firm's longstanding Workforce Sciences Institute. While that may suffice in spots, many clients very much need to understand why. "They want a coherent story that puts decisions in a business and economic context," Nalbantian adds.

Those deeper insights require a more sophisticated analytics capability, which in turn requires enablers—governance, process, talent, knowledge—that extend beyond nifty software applications.

MARKET ANALYSIS
Many client companies want the best of both worlds: the ability to obtain the deeper understanding, as well as a more comprehensive analytic capability that blends skill, process knowledge and implementation ability.

"Our clients seek services that provide an end-to-end way forward to making analytic solutions come alive inside their enterprise," explains John Lucker, a Deloitte Consulting Principal and the firm's Global Advanced Analytics market leader. "They often don't want a point solution that they need to spend time and money integrating themselves. Rather, they recognize that by learning how to develop and integrate analytic solutions in a rapid and efficient end-to-end fashion, they can develop the necessary internal capabilities that will allow them to become more analytic and 'Big Data mature.'

A mature analytic solution-creation capability has six competency components, according to Lucker:

Analytic strategy;
Analytic solution development;
Business and operational implementation and integration;
Technology integration;
Organizational adoption and change management; and
Performance measurement/management.

Mercer's Nalbantian also says that many companies are interested in building their own analytic functions. Companies "have been coming to us as practitioners with a long history in this area for advice and guidance on how to build this function," he reports. "We actually have a practice now that spends a good deal of time and effort helping organizations do just that." That work begins with identifying the new function's mission, evaluating various possible structures for the organization, where it will be located and how it will execute its mission. Saleh relays a similar interest among client companies. "When you do a search to find out how many big companies are currently looking for chief data officers," he says, "you'll find that there is a significant jump in the past 18 months."

Saleh and other analytics-focused consulting leaders assert that the demand for these offerings comes from all domains. "Our clients are seeking advice, counsel and consulting services in an array of analytics areas ranging from information management to business intelligence to performance management to advanced analytics, predictive modeling, cognitive analytics and machine learning," Lucker reports. "Client companies across virtually every industry and market sector, including public sector, are looking for [big data and advanced analytic] solutions that can transform and advance their strategies and missions."

OBSTACLE ANALYSIS
Many companies are struggling to get the returns they seek from Big Data investments, however, and consultants should of course address these challenges. "Inherently, clients understand that having the required data, information and knowledge at the right time, at the right place with the right reliability and usability provides a demonstrable competitive performance advantage," notes Rich Iler, COO for Northpoint Group. "However the issue they are struggling with is: What process do we use to identify what data, information and knowledge is missing, how to acquire it while providing the competitive intelligence and enhancing the wisdom required for competitive performance leadership?"

There are other, related forms of struggle. Lucker ticks off a list that includes scarcity of analytic and big data talent, tension between analytic solution development and analytic solution return on investment (ROI), preparedness for the mindset and behavioral changes required to move to a more data-driven way of operation, behavioral and cognitive bias factors (e.g., making decisions more objectively vs. subjectively), and understanding when data quality is sufficient (rather than perfect—which is often unnecessary and frequently impossible).

These challenges stem from several interrelated dynamics, including the rapid evolution of analytic technology, a surge in demand for these capabilities and the fact that client companies are all over the map (see related story "Breaking Down Big Data for Clients" on opposite page) when it comes to the degree to which they have the enabling components necessary to harness value from Big Data. The demand is being driven by some pretty amazing experiences in some pretty surprising places. Glass points to the A/B testing of President Obama's 2008 campaign website, an exercise that reportedly helped generate an extra $60 million in fund-raising.

For more companies to achieve those kinds of results, consultants will have to treat Big Data breakthroughs as more than just a quick fix. The following steps can help address some of the top big data challenges client companies currently face:

‹ DEFINE AND DISPEL: In one 30-minute discussion on the profession's Big Data opportunities, Saleh expresses his concern about hype four times. PwC's U.S. Retail and Consumer Sector Advisory Leader Ron Kinghorn voices a related concern—that consultants are stoking the hype. "We need to be sensitive to information overload," he says, "because consultants often contribute to it."

One way to defuse big data hyperbole is by helping clients understand what it is and what it can deliver in straightforward terms. "Many companies, big and small, are put off by the term 'Big Data,' " Glass notes. "It sounds overwhelming and expensive.

It also sounds like it will require lots of new software and people to run it. Many companies are just looking for an experienced expert who can tell them how to take advantage of what Big Data offers, without breaking the bank and without breaking down their organization. Big Data is not as intimidating as it sounds." A narrow definition, Glass continues, describes Big Data as the application of powerful software to analyze massive amounts of unstructured data with the goal of realizing insights that were previously impossible to unearth. More broadly defined (Glass' preference), Big Data helps companies of virtually any size mine new insights about customers and prospects—and themselves.

‹ THE IMPORTANCE OF PLUMBING: Saleh's hype concerns are well-founded given how analytics technology has greatly increased in sophistication while greatly decreasing in price. "If you're among the Fortune 100 … and you wanted these capabilities five years ago, it would have cost you tens of millions, sometimes hundreds of millions," he says. "Now, it's a few hundred thousand. But we should not underestimate the fact that technology itself is not the answer… You still need to have people who can analyze the data as well as data governance and the space to possess meaningful data in the first place."

Saleh estimates that many organizations' ability to harness the promise of Big Data lags three to five years behind the promise of current data analytics technology. Lucker sounds a similar note of caution.
"Some client companies do not yet recognize the difficulty of making an analytic solution come alive inside their business environment," he explains. "Often, too much focus is placed on the data acquisition and analytic wizardry for developing the analytic solution vs. the plumbing and machinery to make the analytics useful in an end-to-end solution. They sometimes learn that merely purchasing data infrastructure and hardware and software doesn't necessarily lead to a successful outcome."

‹ BEAT THE BUSHES FOR TALENT: "We have a major problem globally," Saleh asserts. "There are not enough data scientists … If you told me there are 200 qualified people available today, I'll have them." Client companies are feeling the analytics talent crunch as well. "I've lost track of the number of clients who have posted [hiring] directives for workforce analysts and human capital analysts," says Ravin Jesuthasan, the global head of talent management for Towers Watson. "As you might imagine, there's not a lot of this talent in on the corporate side of things because this is relatively new."

The insufficient supply of analytic talent is a double-edge challenge in that it hinders clients as well as consulting firms. The ideal Big Data skill set, Jesuthasan explains, combines domain knowledge (e.g., functional knowledge of sales and marketing, human resources, finance, supply chain, etc.; or industry expertise) with data science savvy.

Data-science talent "really understands the mechanics of the data, possesses ability to conduct the analysis—linking quantitative and qualitative data, mining social media data and other unstructured data and then turning it into predictive analytics," Jesuthasan continues. "The data scientist expertise is important, but it's often very distinct from the domain knowledge. And it's rare—not impossible, but rare—that you find someone who can do both." There's also a third dimension of the data science skill set: broader business acumen.

The final expertise area is abundant in many consultancies, especially strategy firms and practices. That bodes well for consulting firms seeking to meet client demand for Big Data and analytics-related work, especially for those firms that help clients understand the difference between hype and reality.

SIDEBAR: BREAKING DOWN BIG DATA FOR CLIENTS

WHAT DO CLIENTS WANT FROM BIG DATA? The question is on the minds of every consultant whose work involves data analytics, which is to say nearly every consultant. It seems like a difficult question to address because the answer has to vary wildly—right? Yes and no, according to two consultants who have invested significant time and effort examining exactly what big data means for clients.

>> Ravin Jesuthasan, the global head of talent management for Towers Watson (TW), has been leading a Big Data effort at his firm for more than a year, during which he's held dozens of highly analytical conversations with decision-makers at TW's largest clients. He says he found that "everyone is at very different stages of evolution both in terms of awareness, understanding, and process." Some companies "are just trying to get data," Jesuthasan explains. "Some companies, because they've gone through mergers and acquisitions, face a major challenge in accessing clean data. Other companies have started to move beyond collecting the data to organizing it."

For HR executives and CEOs who invest in Towers Watson's offerings, the purpose of most of these data-organization endeavors is to ultimately understand what skills are needed to execute a given strategy and then predict which leaders are likely to succeed in the roles responsible for executing the plan.

>> Rich Iler, COO for Northpoint Group, also wanted to get a better sense for what clients want from big data-related investments. His research, also conducted through interviews (and surveying) of business executives, distilled big data into straightforward terms tied directly to business value.

ILER EXAMINED TWO QUESTIONS: What is the measurable impact of having the right information, at the right place with the right reliability and usability as a competitive asset that raises the enterprise performance, lowers the risk and raises new value opportunities? Is there a relationship between available intelligence and performance?

"The answer was 'yes' to both questions," Iler notes. "Data management and knowledge availability with proper use is the most important asset to improve and sustain improved market performance and industry leadership. Organizations that achieved a match between "highly requested" and "highly acquired" data, information and knowledge consistently delivered the highest sustained, winning performance."

Those insights helped reshape how Northpoint Group consults with clients on analytics-related engagements. Now, the firm starts with customer strategy; then identifies the decisions the client company needs to make to implement the strategy; and then identifies the data, information and knowledge necessary to make those decisions. There are five more steps in this process, which includes homegrown software designed to sniff out what data is missing from decision-making.

The process works very well, but there are always external challenges to contend with. For example, clients often don't realize that their decision-making processes—particularly those that they are very comfortable with—are actually neglecting valuable data and information.

However, there is one big challenge—identifying what clients expect from big data—that Iler and Jesuthasan have largely removed thanks to their commitment to looking at challenges and opportunities through their
clients' eyes. —E.K.

SIDEBAR: BIG DATA'S BIG PROBLEM

The Qualitative Pursuit of a Quantitative Opportunity . Three Ways Big Data Consultants Differentiate

NO M