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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

Technology for the Office of Finance can have transformative power. Although progress has been slow at times, today’s finance organizations are fundamentally different from those of 50 years ago. For one thing, they require far fewer resources (chiefly people) to perform basic accounting, treasury and corporate finance tasks. In addition, public corporations report results sooner – sometimes weeks sooner – than they could in the mid-20th century. And finance departments are able to harness substantially more data and a wider array of analytics to promote insight and support more agile decision-making.

Even in this context, in many corporations the tax function remains a backwater in its use of technology. Most of their tax professionals are awash in desktop spreadsheets, tools that they initially thought would promote efficiency and accuracy. But the inherent problems with desktop spreadsheets make tax processes not only needlessly time-consuming (even using macros and other spreadsheet automation techniques) but also prone to errors and inconsistencies. This is especially true if people must enter the same information multiple times, which increases the chances of mistakes. In addition, assumptions made and rationales behind formulas used in data transformations may not be documented or readily accessible to others in the organization. This legacy can be problematic years later in an audit, especially if the individual who prepared the spreadsheet is no longer employed at the company. And with spreadsheet-driven processes, auditing taxes and the underlying data and calculations is difficult and time-consuming. On top of all this, the effects of the desktop spreadsheet’s inherent shortcomings multiply with the size of a corporation. By their nature, these spreadsheets hinder a corporation’s ability to understand tax issues and optimize  related decisions.

Organizations do not have to put up with these outdated, counterproductive practices. New tools can help streamline tax processes. One is Vertex Enterprise (which I reviewed earlier this year). VR_tech_award_winner_2013Vertex recently was awarded Ventana Research’s 2013 Innovation Award for the Office of Finance for this suite of application and integrated use of tax data and analytics. Vertex offers a single-platform approach to managing all types of taxes (direct and indirect) across the entire tax life cycle, from analysis through provisioning to audit defense, using a single data source. Direct (income) tax management is still a largely manual process involving a plethora of desktop spreadsheets. Calculating and accounting for direct taxes is complicated, largely because income tax laws can be quite convoluted, especially in certain industries. As well, the larger the number of tax jurisdictions it operates in and the more numerous its subsidiary legal entities, the more complex tax management becomes.

Vertex Enterprise includes tax provisioning software for both indirect and direct taxes. Indirect Tax is managed by the company’s O Series software, a single platform that handles sales-and-use-, VAT- and goods and services taxes on a global scale. Direct taxes is handled by Vertex Tax Accounting, global tax provisioning and reporting software that works with multiple general ledger systems in  multiple currencies and across multiple years within the context of multiple accounting regimes such as US- and other national generally accepted accounting standards (GAAP) and International Financial Reporting Standards (IFRS). The web-based software supports a distributed, collaborative, enterprise-wide tax decision cycle process that can achieve more optimal tax-related decision making. Direct taxes exist in a parallel universe, and one advantage in having a single system is that it facilitates the process of reconciling the business accounting performed in ERP systems with the tax accounting governed by law. Vertex automates tax account reconciliation and reporting and manages adjustments that span multiple time periods.

One notable advance for corporate tax departments offered by Vertex Enterprise is its tax data warehouse (TDW). Because a TDW can automate much of the painstaking work that occupies much of the tax practitioner’s time, it can free these trained individuals to focus on making the best tax decisions possible. Also, because a TDW can increase visibility into tax analyses and calculations, corporations can be more confident in their tax-related decisions. And with this greater visibility and confidence, tax departments can become a mainstream participant in the finance function. This is especially important in an era of increasing cooperation between tax authorities worldwide. More than ever, corporations that operate globally must be able to optimize their tax positions over time and across multiple jurisdictions. A TDW enhances that capability.

Conceptually, a TDW is simple: It’s a data store that makes all tax data readily available and can be used to plan and provision a company’s taxes. However, in larger companies (those with more than 1,000 employees) that operate in multiple tax jurisdictions and have even moderately complex legal entity structures, tax-related data structures and calculations become fiendishly complex, as I noted in an earlier perspective. For that reason, the first attempts to create TDWs proved unworkable because the sheer complexity of the direct (that is, income) tax domain overwhelmed the available information technology. These limitations forced companies to take shortcuts, which meant that each TDW had to be a largely custom effort and therefore expensive to build. And because these shortcuts rendered the systems brittle and difficult to change, they were expensive to maintain. Today, however, technology is available to make TDWs practical.

A TDW has several purposes: to ensure accuracy and consistency in tax analysis and calculations, improve visibility into tax provisioning, vr_Fast_Close_2012_ERP_01_multiple_erp_systems_are_commonand cut the time and effort required to execute tax processes. The technology is especially useful because data management is one of the biggest operational challenges facing tax departments today. That is, the information necessary for tax provisioning, planning, compliance and audit may not be readily available to the tax department because accounting and other information is kept in multiple systems from multiple vendors. Our benchmark research shows that 90 percent of companies with 1,000 or more employees use financial systems from multiple vendors, and 43 percent use four or more. In addition, not all of the data necessary for tax department purposes is captured by the ERP system. As well, data collected in a general finance department warehouse or pulled together in a financial consolidation system may not be sufficiently granular for tax department purposes.

Moreover, most companies’ ERP systems (the core technology for gathering transaction data) are not inherently “tax aware,” so tax departments repeatedly need to perform transformational steps to ensure that data is formatted and organized properly. Sometimes the data must undergo multiple transformations because, for example, the transaction information collected in an overseas subsidiary must be reported locally using the local currency and accounting standard but translated to the parent company’s tax books in a different currency using a different accounting standard. In some industries (such as financial services) there may be multiple local reporting standards, one for general statutory purposes and another reflecting specific rules for that industry demanded by some regulatory authority. In short, there are numerous data-driven headaches tax professionals have to address before they even get down to work. A TDW addresses this problem.

Ventana Research is dedicated to helping organizations enhance the effectiveness and strategic value of the Office of Finance. Tax departments, CFOs and controllers in larger, more complex corporations should examine how the tax organization spends its time and determine to what degree more enterprise automation and fewer desktop spreadsheets would enable the company to understand and manage its tax needs more intelligently. Vertex Enterprise can be a useful foundation for transforming the tax function, which is why it received our Technology Innovation Award for Office of Finance for 2013.

Regards,

Robert Kugel – SVP Research

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