Look Before You Leap: Making The Most Of Big Data Opportunities

Dave Frankel was recently boarding an American Airlines flight in London when he heard a song come over the intercom. Although it was a Muzak version of the tune, he recognized it as one by a group he liked. When he reached his seat, he tweeted his appreciation of the airline’s interesting choice of music. One second later, Frankel, the president of EDGAR online, received a tweet back from American Airlines, “I’m glad you liked it.” (He has since forgotten the name of the song.) It’s no surprise that he got a less positive answer when he tweeted back a request for a seat upgrade. Though the tweet was playful, the airline might well have considered accommodating him, since it had received valuable customer feedback and engagement.

Frankel tells the story to demonstrate the secondby-second, almost exponential growth of potentially actionable business information under the rubric of big data — not only via Twitter, but also Facebook, Amazon.com, Yahoo and similar sites, as well as email. He marvels that “just by sitting on a tarmac and tweeting something,” he had provided American Airlines with data it could potentially use to improve its marketing and customer services. Other fliers, after all, might like such songs, and the company might subtly help boost its revenues by playing them more frequently

Such opportunities are generated by the growing ability of companies to aggregate and draw correlations from huge amounts of unstructured data. Accompanying them, however, is the threat to corporations and accounting professionals of falling behind. “If companies don’t wake up and understand that this isn’t going away, that it’s only going to get bigger, then they’re going to put themselves at risk,” Frankel warns, adding that “accountants and [financial] reporting managers are still not there yet.”

Indeed, outside of accountants and finance executives who actually work for companies in businesses that provide or deliver data products and services, few such professionals have reportedly been able to catch the wave. A big reason may be that those involved with internal and external financial reporting are limited, by training and inclination, to working with structured data— the kind that can fit readily into tables, Excel spreadsheets and, ultimately, financial statements.

The problem for accountants is that the future belongs to unstructured data — the kind that can be dug out of tweets like Frankel’s, as well as videos, photographs and the vast amounts of text floating free on the Internet, such as word-processing documents, emails, blogs, webpages, and other social media. “Unstructured data represents the largest proportion of existing data and the greatest opportunity for exploiting big data,” according to a recent editorial by Kevin C. Moffitt and Miklos A. Vasarhelyi in the Journal of Information Systems, AIS in an Age of big data (published by the American Accounting Association). Such unstructured data includes documents like the plain text found in the Management’s Discussion and Analysis (MD&A) sections of companies’ 10-Qs and 10-Ks, corporate press releases, and interviews with corporate executives.

Valuable as such data may be, it’s outside the grasp of most accountants and finance executives. Thus, while traditional corporate accounting and auditing “are essential for economic production activities and will continue to be performed,” Moffitt and Vasarhelyi write, “current accounting and auditing methods are in danger of becoming anachronistic” in the face of an economy increasingly being driven by big data.

In short, reality is swiftly outpacing the ability of accountants to gauge it. What needs to happen for them to start to catch up? To some, the future evoked by the Moffitt and Vasarhelyi editorial might appear dark. Referring to employees and management as “nodes,” they explain that accountants “must view the possibilities associated with big data, of knowing much about a corporation, including knowing a substantive amount about who works in a corporation.”

Granting that many questions still need to be answered about the propriety of employee surveillance, Moffitt and Vasarhelyi wonder if such employee metrics as time on the Internet, sites visited, telephone calls made and geographical location should be created and reported.

Yet although “it seems objectionable and invasive that a stranger could know virtually everything about another person, knowing as much as possible about a corporation is much more palatable,” they write, noting that employees have less privacy protection than exists for individuals outside a corporate setting. Speech, documents and email generated during work and via corporate resources may be fair game for the big data gathering efforts of corporate accountants.

Swimming in Big Data

Before diving into such difficult waters, finance and accounting professionals might do better to learn from peers who actually work in the world of data analytics. While Nina Tan, CFO of Trax Technology Solutions, for example, is involved in the valuation of her company’s data assets, she also uses unstructured data in more traditional accounting and finance functions

At Trax, a Singapore-based firm that provides an image-recognition mobile application that gathers data from photos taken of shelves at retail stores, Tan is “moving finance from a reporting-centric to analytic-centric financial function,” she wrote in a recent email in response to questions from CFO.

Thus, Tan pores over data to unearth the causes behind the company’s sales, costs and profits and to create budgets and provide input to rolling forecasts. Via analytics, Tan, a CPA, gets an overview of the data that drives revenue and operating and sales expenses, including when, where and by which salespeople different products are sold. When she needs to, she can also drill down into more granular information underlying financial statement line items

Beyond such fairly traditional “dashboard” type information, Tan is working with other senior managers to set key performance indicators (KPIs) for use in aligning employee motivations with company strategy. To confirm that the KPIs are leading the company where it wants to go, the executives will analyze the correlations between the success of certain salespeople and the performance indicators.

In one recent example, to find out whether the firm was using its people wisely, Tan used data analytics to measure why Trax’s website experienced certain peak and trough periods. She first found correlations among traffic, time of day and location. With Australia about three hours later than Asia, the firm’s site traffic builds to a peak in Asia’s morning and Australia’s afternoon. The trough occurs during Asia’s afternoon, as Australia knocks off from work

The question she was attempting to answer was: “Does it makes sense to hire additional headcount to handle the peak, but leave them idle during the trough?” Employing what she calls her “cause and effect analysis” of time zones and traffic data, respectively, she “suggested a practical solution that requires only use of our existing headcount. Basically, it only requires overlapping of two different shifts during the peak hours.” The analysis enabled Trax to trim its headcount costs and capital spending, she said

From her perch in a firm that uses data analytics on a day-to-day basis, Tan waxes bullish on the ability of the accounting and finance functions to expand their roles to encompass huge amounts of unstructured data. “In three to five years’ time, I see the finance function as a strong, formidable strategic partner to operations and sales. Finance is the natural gatekeeper of data, as information normally flows through the function,” she says.

Accountants still need to become more familiar with statistics and decision science in order to grasp how big data can enhance their skills and vice versa, she thinks. Ultimately, however, they can play a part in enabling companies “to separate the wheat from the chaff and focus on the information that counts — not on the information overload,” Tan adds.

CFO Summary

• With so much unstructured data accumulating, it can be difficult for finance functions to make best use of the data building up within their firms.
• With big data, your organization has the ability to aggregate and draw correlations from unstructured data
• Beyond traditional dashboards, senior managers can set KPIs to align employee motivations with company strategy.
• To make the most of big data’s potential, CFOs should not only consider investing in new technologies, but also develop strategies to bring their finance functions up to speed, either through training or hiring new talent.

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A Smart Investment: Is Data Mining Right For Your Organization?

While big data certainly offers opportunities for organizations to grow and thrive, finance chiefs must consider how it will fit into their organizations. This means evaluating how your company currently does business, how you’d like to improve, and how you might need to adapt in the future. Technology firm Ion Geophysical has been using big data to solve customer problems for 20 years. The company converts seismic wave data into graphs that help its clients know where to drill for oil. Although Ion has been at the forefront of big data for more than two decades, it only recently began using those tools in its business to shape its budgeting and capital-spending plans.

CFO and Senior Vice President Greg Heinlein says ultimately the oil and gas firm is using data mining “to look more organized” and to understand its potential customers more wholly. Big data will change the way Ion does business, he adds.

While Ion is embracing the benefits of new technology, businesses that don’t may be left behind. According to a recent Deloitte study, after reputational risk, 53 percent of C-level executives listed technology enablers and disrupters as the biggest threat to their business models. Of those, data mining and analytics (44 percent) was chosen as one of the top five technology “threats.”

Of course, data mining is only a threat to companies that fear the unknown, Heinlein says. “Organizations could do a better job of leveraging” data to interact with clients, says Henry Ristuccia, Deloitte’s global leader of governance, risk and compliance.

Indeed, data mining requires a significant upfront investment, Heinlein says. Ion is only in the “third of nine innings” with its data-mining practices, and the company still has a long way to go. Because Ion is still in the early stages of using big data, the finance chief can’t say for sure if data mining has helped the company avoid mistakes. However, Heinlin does have more information on hand when making investment decisions.

For example, when there’s a downturn in the global economy, exploration spending slows down because oil and gas companies cut their capitalexpenditure budgets. “If we can see it coming earlier, we can reduce our investment decisions to lessen the impact of the industry downturn,” Heinlein explains.

The more a business uses data to inform decisions and provide context, the better choices it makes. Companies such as Facebook, Amazon, Target and Google are experts at using data mining to create thorough profiles of their customers. For social media giant Facebook, big data is under-hyped, explains Brian Boland, vice president of ads product marketing at Facebook. The company is using its data on billions of users who provide detailed information about their likes and dislikes to provide advanced targeting practices for advertisers.

Facebook can mine through status updates, interests, page likes, and other user activities to create efficiencies for advertisers looking for their target audience. “We know the consumer very well from what they store on Facebook,” Boland says.

Ion, in contrast, uses multiple sources to build its data store. Sources include a customer-relationshipmanagement system, earnings-call transcripts and analyst reports to glean information about its customers and prospects. During events or trade shows with clients, Ion executives already know a client intimately from the company’s research, and find a boost in their sales numbers—yielding the ROI finance is looking for with its big data investment.

However, the results from data mining may not be seen immediately. It’s a complex, long-term process for CFOs, who are tasked with prioritizing information-technology investment dollars and allocating some of that to data mining. For companies that have been around for a while, like Ion, it requires getting systems to “talk to each other.” Merging new systems with older, legacy systems is also complicated and expensive. “That takes time, money, and resources,” Heinlein says. New companies have an advantage because they’re not wedded to legacy systems, he adds.

Training employees on new software and hiring consultants to bridge platforms add time and expense to the process. For employees used to an older system, it can be difficult for them to engage with new, higher-level platforms that are better at mining for information than outdated ones.

Once data is culled from systems, it should be translated into intelligent information that steers investments. That’s the “scary part” for most companies, Heinlein says. If companies don’t know what to do with the data, then data mining becomes an exercise in futility. But if a company can figure out the secret, one that Ion is trying to learn, it can see lucrative returns on investments in big projects, he says.

“It’s a big investment,” Heinlein explains. Finance chiefs need to make sure the ROI their company is looking for is realized.

CFO Summary

• Big data has been garnering buzz for its ability to transform the way companies do business.
• Consider the ROI of an investment in analytics. While data mining can be complex and expensive to implement, it can also be a very useful decision making tool when used well.
• Make a plan first: To get the results you’re looking for, finance chiefs should develop a clear idea of what they will do with the data they receive.

Gaining Visibility: Four Ways Big Data Is Useful To Finance

O nce you’ve determined that implementing analytics technology can yield the strategic growth and insights your organization is looking for, it’s crucial to define the business objectives of the technology implementation. How do you expect finance and the greater organization to realize results from big data?

These days, CFOs are challenged with producing more accurate forecasts of their business, complying with ever-changing regulations, and reporting more frequently based on newer streams of data. Businesses today are influenced as much by events outside of their control as they are by those within it. In a hyper-connected world bridged by mobile, social and other real-time technologies, the consumer is truly the king and wields the potential to dramatically impact the “firm value” of even the most respected brands in the world.

The finance function’s most important task is to predict, optimize and grow the value of a company. But how can this objective be accomplished when a significant number of influencers are outside of his or her control? This is where the world of big data analytics intersects with the challenges of today’s finance function.

While it is important for senior finance executives to understand the historical performance and benchmarking of a company, it is imperative that they be aware of, gain insight into, and understand the impact and influence of key external variables— macroeconomic, customer behavioral, demographic, and geo-political—on their organizations. Big data analytics enables them to do so.

But many analytics initiatives are over before they even begin. A number of organizations, in the rush to understand and derive value from big data streams, are deploying expensive data infrastructure and complex technologies, while also investing in significant human capital. A siloed initiative or a “boil-the-ocean” approach can lead to analysis for the sake of analysis, or worse yet, paralysis through analysis. An organization may end up with a handful of new metrics and dashboards from such investments, but are they meaningful? And more importantly, are they adaptable and actionable in dynamic environments?

From the very beginning—before the data collection process even starts—executives must understand that their companies are more than the sum of their is processes, information, and products. Within its unstructured data, every company has potentially invaluable information, which, once captured and defined, can provide the deepest level of insight possible into the company’s most important goals and strategies. Management must use laser-like focus to derive actionable results based on continuously changing data. An ideal big data environment solves specific problems, answers specific questions, provides predictable guidance, and allows for insights so companies can adapt to real world changes as they occur. From an operational standpoint, this data-driven strategy needs to deliver faster, more accurate and more focused results than any previous strategy. And from the CFO’s standpoint, the company needs to avoid long implementation times, as well as the unbelievably high costs often associated with big data projects.

There are many ways CFOs can take advantage of big data analytics momentum to drive firm value and gain a seat at the strategy table. Here are some examples:

1. Customer Risk

One of the biggest risk areas CFOs need to understand, quantify, and predict is customer risk. Big data analytics solutions can uncover deeper insights into shifting customer behaviors, loyalties, and attitudes towards an organization’s products and services. Correlating these behaviors to sales, cash flows, and profits could provide significant ammunition for CFOs to make sound strategic planning decisions.

2. Growth & Innovation

Today’s CFOs are challenged to help their organizations drive growth and innovation beyond pure cost cutting. Big data analytics can precisely spot incremental growth opportunities from adjacent and newer markets that help organizations achieve scale and efficiency. Deeper and broader insights through data analytics into potential collaboration between once-siloed functions or with new partners can help organizations achieve their desired growth.

3. Customer Equity & Valuation

CFOs are frequently asked to forecast firm value based on discounted projection of free cash flows. By quantifying customer lifetime values (LTVs), big data analytics enable a CFO to deliver a better prediction into these future cash flows. With a better grip on customer behaviors, their purchasing predictions, and wallet share, CFOs can accurately estimate the LTV of an organization’s current and future customers, and then provide an estimate of customer equity, which is the sum of all customer LTVs. Customer equity gives CFOs a valuable and customer-centric view into “firm value.”

4. Compliance & Regulation

Compliance and regulatory issues represent a time-consuming resource drain and a significant risk if all the information required is not in place. Large organizations often face unforeseen compliance and regulatory issues, especially if they are engaged in new business development, mergers, or acquisitions. Thinking about what’s required today and tomorrow is an essential component of successful growth

Big data can solve problems and help guide the business, but by itself it’s an incomplete solution. Finance chiefs must help their organization to implement analytics with a defined and focused objective. Identifying problems—and how best to solve those problems using actionable analytics— enables CFOs to find the answers they need to specific problems, and to play a strategic and influential role in steering the direction of their companies.

CFO Summary

• CFOs are taking on a more-strategic role than ever before, and big data can provide finance chiefs with the insights to make tough decisions.
• When done right, big data can solve problems and provide guidance, as well as deliver faster, more accurate and more focused results than any previous strategy.
• Big data itself is not a solution. Make sure you define an objective for your analytics system before you make an investment.

Conclusion: Finance Takes On Big Data

By now most senior finance executives have witnessed the transformative powers of technology. From automation to cloud, technology has changed the way we do business. As finance’s own role changes, it needs these and other tools to enable the finance team to spend less time on the day-to-day and more time thinking strategically. Big data has the potential to give finance chiefs the visibility and insight they need to move their businesses forward.

Finance has a lot to offer their enterprise. Many senior finance executives are strategy-minded by nature and welcome the new challenges of data analytics to inform decision-making. Despite their willingness to take on more and more responsibilities, there are risks associated with change. IT investment is not one-size-fits-all. Data mining can be an expensive, time-consuming proposition, but it also has the potential for huge ROI.

Big data can help many companies to gather and interpret unstructured data—taking once unusable information and transforming into game-changing information. But before taking the plunge, finance chiefs need a clear idea of their objectives. Data mining has the potential to increase firm value in areas such as customer risk, growth and compliance, but the sheer amount of data can be overwhelming and some businesses aren’t equipped to handle the influx of information

Once you have analytics tools in place, they are meaningless unless your team knows how to use them. From developing training programs to hiring on new employees, make sure your organization has the players necessary to make the most of data and analytics.

The risks are high, but the potential reward is even higher.