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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

Finance departments don’t immediately come to mind in conversations about social collaboration technology. Most of the software used for social collaboration that I’ve seen demonstrated focuses on the vr_bti_br_technology_innovation_prioritiessales process or for broader employee engagement. The Facebook-style interface may cause finance department managers and executives to roll their eyes, especially if they’re over 40 years old. Yet business and social collaboration is an important set of capabilities that has been taking hold in business. Our benchmark research shows it ranking second behind analytics as a technology innovation priority. It will gain adoption over the next several years as software transitions from the rigid constructs established in the client/server days, which force users to adapt to the limitations of the software, to fluid and dynamic designs that mold themselves around the needs of the user. Perhaps because most of the attention so far on the benefits of collaboration has focused on front-office roles, there’s less awareness of the potential in back-office and administrative functions. Indeed, the same research reveals that those in front-office roles five times more often than those in accounting and finance roles (21% vs. a mere 4%) said that business and social collaboration are very important to their organization. However, I assert it’s just a matter of time before the finance group understands that social collaboration has substantial potential to improve its performance.

In examining why this change will occur, let’s start with some background. “Doing business” is all about collaboration, on which my colleague Mark Smith commented in an earlier perspective. Before communication technologies began to eliminate the constraints of time and space, people relied mainly face-to-face collaboration. (Postal letters were another option but they were very slow and limited interaction.) Voice mail was the first breakthrough in enabling people to collaborate quickly across time and space. Busy individuals could conduct conversations through a series of voice messages, discussing an issue in some depth and agreeing on an approach without speaking in real time. Much of business investment in information technology over the past two decades has been aimed at enabling good communications among different elements located in separate buildings, cities and even countries. The same is true for finance.

We all know that the eruption of social media – in both group settings like Facebook and one-to-many channels such as Twitter – has changed the dynamics of how people – especially those under the age of 40 – communicate. A couple of years ago, a group of teenage girls became trapped in a sewer under Adelaide, Australia. It took several hours to rescue them because the one with a phone used it to post their plight on her Facebook page rather than call someone. This example may be extreme, but it illustrates intergenerational differences in expectations of how one communicates. As with IM, software companies that build business applications are beginning to include Facebook- and Twitter-like capabilities to support collaboration. Examples include application platforms such as Salesforce.com’s ChatterIBM’s Connections and stand-alone software that can be integrated with another vendor’s offering such as Socialtext that is now owned by Peoplefluent. Software that fosters collaboration can improve efficiency, for example, by resolving issues faster or finding easier or less expensive alternatives to addressing a need. It can improve effectiveness by improving customer satisfaction or enabling more informed decisions sooner. It can foster better alignment across business units as well across and within departments by enabling closer communications among their people.

Social collaboration is off to an encouraging start, but it’s easy to see where improvements are needed, especially to be useful to the finance function. Ideally, collaboration software will be able to understand the context of the work at hand, the role of the individual participant and the relationships the individual has with others in that context. A technology like Google Glass has the potential to enable a manager, while reviewing a report, to see that there have been comments posted related to specific numbers, text or charts and then select and read these just by moving his or her eyes.

As well, software imbued with social collaboration capabilities should understand and automatically manage the various types of relationships among individuals. For example, people in a company typically have a general role (“I’m in Finance”) and one or more task-specific ones (“I’m the director of financial planning and analysis”). Some relationships are persistent while others begin and end with a project. Issues that arise may be open to all or confined to specific groups, subsets of groups or a private dialogue. Queries or comments may be general, specific or somewhere in between. Some conversations, especially in finance and tax departments, must be tightly controlled. Software that understands the context of the work performed and automates the process of managing the who, what and when of the communications will support more effective collaboration, faster completion of tasks, greater situational awareness with the organization and as a result better decision-making.

Which brings me back to the relevance of social collaboration for finance professionals. There are many use cases for comprehensive collaboration capabilities in ERP or accounting and financial performance management software. A good deal (maybe too much) of what goes on operationally in finance departments involves checking details and correcting errors – activities that require direct communications. Resolving billing issues could be streamlined if receivables and sales or payables and purchasing were connected to the appropriate collaborative network in the context of executing business processes. For example, end-of-period reconciliations could proceed faster if communications among the right people in the departments involved less effort. The financial close has multiple steps where time saved by resolving snags or clearing up ambiguities consistently can have a meaningful impact on shortening the process. Likewise, planning and review involve a great deal of collaboration, especially in understanding assumptions and expectations or providing perspectives on causal factors behind better or worse than expected results.

Unlike those in sales and marketing, the stereotypical accountant and finance specialist is not thought of as “social.” And at the moment, few people working in finance departments say that social collaboration capabilities are very important to their jobs. An important aspect of my research agenda for this year points to the need to address the demographic shift from executives and managers from the baby-boom generation to those who grew up with computer technology. These shifts will drive demand for a new generation of software, one that emphasizes IT-enabled collaboration, mobility and agility. Social collaboration used in business applications should be more than a Facebook metaphor. It addresses a key drawback of instant messaging systems: the fact that in business, individuals have multiple roles and multiple networks of people with whom they interact. When tightly integrated into business software of all kinds, social collaboration will become an essential capability by enabling people to resolve issues faster and with less effort than other means of communication. Vendors that focus on the finance function should ignore today’s lack of enthusiasm for social but more practical collaborative capabilities and ensure that their software is designed for the next generation of financial software users.

Regards,

Robert Kugel – SVP Research

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