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All the hubbub around big data and analytics has many senior finance executives wondering what the big deal is and what they should do about it. It can be especially confusing because much of what’s covered and discussed on this topic is geared toward technologists and others working outside of Finance, in areas such as sales, marketing and risk management. But finance executives need to position their organization to harness this technology to support the strategic goals of their company. To do so, they must have clarity as to what big data can do, what they want it to do, and what skills and tools they need to meet their objectives.

Big data has always been with us, just on smaller scales: The term refers to data sets so large and complex that organizations have difficulty processing them using on-hand database management systems and applications. It has become a popular buzzword because technology for handling big data has crossed a threshold, making it at the same time more capable and cost-effective. Companies now can tap into huge amounts of structured and unstructured data using advanced data processing technologies, analytics and visualization tools to achieve insights not previously available using more conventional techniques. In a recent research analysis, I covered some of the potential benefits (and potential pitfalls) of big data as it relates to company management. Increasingly, the ability to analyze large quantities of business-related data rapidly holds the promise of fundamental changes in how executives and managers run their businesses. Properly deployed, big data analytics enables a more forward-looking and agile management style, even in very large enterprises. Because it allows more flexible forms of business organization, it can give finance organizations greater scope to play a more strategic role in corporate management.

Big data and analytics are a natural combination. By itself, a mass of data is not especially useful, and there are significant challenges to teasing out insight from such large data sets. However, information technology has evolved to make assembling and working with extremely large amounts of data far more practical. As well, routines involving advanced analytics that were once the domain of people with Ph.D.s in statistics are increasingly usable by business analysts, as new analytical software packages are designed to hide the complexity of the underlying statistical work. My colleague Tony Cosentino recently summarized the progress to date in adoption of big data analytics, covering some of the existing uses (already numerous) and emerging trends.

Keep in mind that it’s not just a matter of learning how to master new software and munge data. Finance departments must sharpen their skills in determining how to best utilize big data analytics. And it’s even more important that finance executives understand vr_oi_goals_of_using_operational_intelligencehow to make practical use of big data analytics: In some cases users may want to consume only the output of the analytics created by other parts of the organization (such as demand forecasts by product families), while in others the organization may want to purchase applications that use or embed big data analytics (such as continuous monitoring for ERP systems governance) or enable price and profit optimization.

Our benchmark research on operational intelligence, a technology-driven discipline that has been using big data operating across networks and systems has been using analytics for years, shows that the most common reasons for using such applications (cited by almost three in five companies) are to manage performance, detect fraud, comply with regulations and manage risk. These areas are broadly applicable for finance organizations, but I assert that as well as governance and control, initially the three main applications of big data analytics are planning, reviews and alerts. Here’s how.

Companies do a lot of planning, so it’s useful to segment the activity. One way is by time. There are three main planning time frames in which big data analytics plays a role.

  1. Short-term tactical planning is used, for example, to project demand for specific products or create offers that might spur incremental demand. Especially in consumer products and business-to-consumer marketing, these models are statistically and computationally challenging, as they must be continually updated and adjusted. However, this is not an area of business where Finance has taken a role.
  2. Long-term and strategic planning can help determine the impact of a confluence of factors on markets and costs. Decades ago, the largest companies maintained strategic planning staffs to generate long-term forecasts to inform senior executives of important market trends. Except at companies that have very long cycles with specific demand and supply requirements, those staffs have disappeared or have been substantially reduced as corporations switched to third-party sources.
  3. In the time horizon between short- and long-term planning there are techniques for improving the accuracy of forecasts of revenue and costs using large sets of historical data, which enable organizations to better understand the various factors that influence demand. This sort of advanced modeling using predictive analytics can be useful in improving the accuracy of corporate business planning and budgeting, which is at the core of financial planning and analysis. Predictive analytics uses techniques from statistics, modeling and data mining that weigh multiple current and historical facts and their interactions to predict outcomes. Good predictive models can identify the most important factors driving outcomes, and because of this, they often can be more accurate than simple extrapolation. For example, by examining large sets of historical data, a fast-food chain can predict with reasonable accuracy demand for certain menu items at specific locations at a given hour of a given day by taking into account factors such as the day of the week, time of the year, sales patterns over the past three weeks, advertising spend and special offers.

As useful as predictive analytics are for forecasting, they may be even more valuable when applied to reviews and alerts. Predictive analytics can provide a baseline against which to compare actuals. This, in turn, enables an organization to get an earlier warning when results diverge meaningfully from what was expected, so executives and managers can react immediately rather than in days or weeks. For example, in business-to-business relationships that involve many routine purchases (any sort of supplies, for example) a divergence from established trends could generate an alert to the sales organization. Embedded analytics in an order-entry system could highlight late or smaller-than-usual orders. These might indicate a competitive threat or some other issue that would benefit from a timely interaction with the customer. This is just one of the ways that data captured by the financial systems can be used to improve the effectiveness of other business units, enabling the department to play a more strategic role in supporting the company.

Another use is in accounts receivable, where predictive analytics can promote customer satisfaction. To illustrate, a company that does a routine analysis of payment patterns can have a good idea of when specific customers will pay. If one that routinely pays its invoice between the 16th and 19th day of the month has not paid by the 23rd day, the analytics system generates an alert. A call to the customer or an automated email notes the delayed payment, asking if there was an error in the billing or some other point in dispute. There are a couple of advantages to this approach. If nothing else, if there is an issue, it is likely to be resolved more quickly. Moreover, from a customer satisfaction perspective, it’s a far superior form of customer interaction than waiting several weeks and then sending out a dunning notice demanding payment. Resolving any issue sooner improves cash flow, and if the company did make a mistake, asking for payment will only annoy the customer. Another use of big data in receivables is to automatically identify customers that are routinely tardy in paying. This can kick off an internal company discussion about what ought to be done about the situation, such as limiting credit or finding ways to accelerate payments.

Governance is another area where big data analytics are already at work, with companies using it for fraud detection and alerting. For instance, software packages can monitor a company’s financial systems for evidence of suspicious activities such as payments to bogus vendors or top-level alterations to financial statements. Such systems are designed to be high-level controls that reduce the need for manual internal and external audit work. And even more is possible. As I noted earlier, in the not-too-distant future it may be possible to have an “auditor in a box” – a forensic system that continuously identifies and lists all suspicious activities, transactions and conditions and weighs their materiality. Such a system would permit more timely responses to the risk of material errors or fraud and facilitate examinations by external auditors. In addition to being far more efficient than periodic manual effort, the auditor-in-a-box concept is potentially more reliable because it examines everything rather than relying on sampling.

However, there are challenges. Staffing and training are significantvr_bigdata_obstacles_to_big_data_analytics issues for Finance in dealing with big data analytics. Our research into the challenges of utilizing big data shows that nearly four in five companies find staffing and training to be an obstacle in utilizing big data. Despite the fact that analytics is an inherent element of the finance function, it almost always involves the broad application of basic approaches employing simple math (ratio and margin analysis, for example). Few departments have applied advanced analytics: Our finance analytics research finds that only 13 percent of finance departments employ predictive analytics.

To be able to handle these staffing and training needs, finance executives must understand their department’s big data analytics competence requirements. A useful place to start is to become familiar with the five personas Tony Cosentino developed to describe the people working with business intelligence and analytics. These personas illustrate the various objectives, skills and interests that individuals bring to the discipline. Adapting his approach to big data analytics to this discussion, at the top of the list are highly skilled statisticians who do exploratory work and create purpose-built analytics and analytical models to address specific tasks. These people usually have advanced degrees in statistics and understand how to use sophisticated analytical software and data sets to their fullest. Few finance organizations need this level of capability. A second type of user includes business analysts who have in-depth knowledge of the business and finance issues, know how to access and apply available data relevant to the issue, and have the ability and commitment to master software that requires training but not an advanced degree in statistics. Depending on a company’s size, finance organizations will need a person or a group of people with this level of competence. A third type is the knowledge worker. This description includes executives, managers and directors who need to interact with – not just consume – advanced analytics. These types of users should not be expected to learn how to create or structure analytics, but they need to know how to employ analytics embedded in dashboards or applications as well as visual discovery tools, which are increasingly user-friendly. This level is where the need is broadest, so finance executives must focus most of their efforts in terms of developing these skills.

Big data analytics is an important development that will challenge finance organizations to use new capabilities to improve their effectiveness and enhance their company’s competitiveness. There are many ways organizations can begin to address the challenge. At least, CFOs and senior finance executives should create a steering committee to identify opportunities to apply big data analytics; identify gaps in skills, processes, data availability and software; and establish timelines and goals. Moreover, if CFOs are serious about exploiting the potential of big data analytics, they must communicate its importance to their department and demonstrate a commitment to a plan of action.

Regards,

Robert Kugel – SVP Research

A recent news release by Robert Half, a staffing company that specializes in accounting and finance personnel, covered what it sees as the most important attributes required for auditors in the 21st century. “7 Attributes of Highly Effective Internal Auditors” covers the people dimension of the profession and focuses on the non-technical requirements of the role, including relationship-building, teamwork, and diversity. No doubt these skills are a must for just about anybody working in a modern (Western) corporation. For me, though, the most important quality on the list is at the bottom: continuous learning. That’s because the role of internal and external auditors will be transformed radically by big data, in-memory processing and other advances in information technology that will make enterprise automated fraud discovery and mitigation a reality before the end of this decade.

A bit of history: Before computers took over, auditors used to examine paper accounting records for suspicious physical evidence, such as erasures, out of sequence entries, blank spaces and different-colored inks. When companies first adopted computer-based accounting systems, auditors lost access to these clues that might point to fraud. Worse, numerous computer-based accounting frauds in the 1960s and 1970s were hard for auditors to spot because the proprietary systems of the day were far from transparent. These frauds led to the formation of the Treadway Commission, which promulgated the COSO framework, which was the underpinning of the Sarbanes-Oxley Act’s Section 404 requirements.

Meanwhile, somewhat ironically, the computer-based accounting systems that once aided swindlers are about to make it much more difficult to successfully commit financial fraud. (I have too much respect for the criminal mind to think for a moment that fraud will be impossible.) Big data and in-memory processing techniques are about to give auditors a clearer and more comprehensive picture of what to audit, and even provide alerts that a fraud is being committed. These systems will provide a digital equivalent to the search for erasures, suspicious sequences and missing items.

Applying automated governance and control techniques to electronic financial systems is nothing new. Since the 1990s, enterprise systems such as ERP have become far more transparent, and this has enabled business to use software to make it more difficult to successfully perpetrate financial fraud. Identity and access controls are an important barrier that ensures only those with the proper credentials are able to perform specific tasks or view sensitive information. Vendors such as Oversight Systems and Infor Approva, for example, provide software that performs continuous monitoring to ensure that control-related processes and policies are being observed. I see these as precursors to more comprehensive enterprise systems that will continuously monitor and review a broader set of data that comes from all financial management systems, including accounting, consolidation, planning and analytics (to name four), as well as supply chain and warehouse management systems and, perhaps, machine data.

Being able to view a comprehensive set of corporate data is a prerequisite for effectively automating enterprise fraud discovery. A completely effective system would be one that gives no false negatives (that is, it doesn’t miss a suspicious indicator) and no false positives (which waste time sending auditors on what turn out to be wild goose chases). Taking an enterprise approach to managing fraud is potentially much more efficient. It is also likely to have a better chance at spotting sophisticated frauds sooner because it should be able to connect many more dots than is currently feasible. Of course, no system will ever be 100 percent effective, so business will still need to employ other, non-automated techniques, including relying on tips. While uncovering material financial fraud is critically important, decades of experience have made it clear that automated systems usually fail in practice because they do not reliably limit false positives. Justifying the investment in automated fraud detection, mitigation and management depends on those systems’ ability to ensure that the cost of uncovering fraud doesn’t exceed the cost of the fraud itself.

The most challenging aspects of implementing an enterprise fraud detection and prevention system involve identifying the things that need to be monitored and measured, creating algorithms or describing patterns that define suspicious events, items, values, ratios or relationships (to name five), and then defining the thresholds and conjoint conditions (to name two) that indicate a situation that is worth investigating. Many of these algorithms and techniques are likely to begin as generic constructs, freely available to all. The art of establishing an “auditor in a box” will be in determining how to apply these algorithms and techniques to an individual company’s situation, and the science will be in the way they are implemented, since every company’s specific IT environment and systems provisioning makes each one a unique set of permutations of the generic model.

Which brings me back to the initial point of this piece: Information technology will transform the role of the auditor radically over this decade. The focus of the Robert Half list on people skills is well-taken, because automation is likely to diminish the relative importance of applying an auditor’s purely technical skills. As a result of automation, the number of people employed in internal audit teams is likely to decline. One can also hope that the hours required to complete an external audit will decline as well, although I won’t argue with skeptics who expect the Big Four and other auditing companies will somehow manage to maintain the number of hours billed. Those who remain in the auditing profession are likely to be occupied in more interpersonal and analytical tasks, and they will need to have more knowledge of IT systems and analytics. Those studying accounting today would do well to ensure they have sufficient background in information technology systems to be able to compete in a future where IT and accounting are even more tightly linked. Those working in audit roles today must take the seventh and last recommendation, to engage in continuous learning, to heart. Otherwise, they’re likely to find themselves in the same position COBOL programmers found themselves in a decade ago, their skills made obsolete by the march of technology.

Regards,

Robert Kugel – SVP Research

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