You are currently browsing the tag archive for the ‘Performance Management’ tag.
A core objective of my research practice and agenda is to help the Office of Finance improve its performance by better utilizing information technology. As we kick off 2014, I see five initiatives that CFOs and controllers should adopt to improve their execution of core finance functions and free up time to concentrate on increasing their department’s strategic value. Finance organizations – especially those that need to improve performance – usually find it difficult to find the resources to invest in increasing their strategic value. However, any of the first three initiatives mentioned below will enable them to operate more efficiently as well as improve performance. These initiatives have been central to my focus for the past decade. The final two are relatively new and reflect the evolution of technology to enable finance departments to deliver better results. Every finance organization should adopt at least one of these five as a priority this year.
Close faster. Because the process of closing the books is similar for all corporations, it should be seen as a universal performance benchmark. Our research finds that only 38 percent of all companies with more than 100 employees complete their quarterly or half-yearly close within five to six days of the end of the quarter (which is the generally accepted performance standard), while the remaining majority take longer. And for all the discussion over the years about the need to close faster, our most recent benchmark research on the close discovered that companies on average are taking a half-day longer to complete the process than they did five years earlier. For the most part, much of this increase appears to have been among companies that were already taking more than a business week to close. I’ve written that the close is a good litmus test for the overall effectiveness of a finance department.
Our research into how companies close shows that its common for two companies with exactly the same characteristics (the same size, in the same industry, located in the same country) to demonstrate big differences in how quickly they complete their accounting cycle: Company A does it in two days while company B needs nine days to get the job done. The difference is likely to be due to some interplay of people, process, information and technology. Common issues are poor process design, overuse of spreadsheets in the process, consolidation software that no longer meets current business requirements and too little automation of repetitive tasks. Our research shows the correlation between increased automation, for example, and achieving a faster close. We found that, on average, companies that have automated the process completely close in 5.7 days compared with 9.1 days for those that have automated little or none of the process. Shortening the close is important because it enables finance organizations to provide management and financial accounting information to the rest of the company sooner, reduces overtime and frees up resources that can be put to better use. Addressing such issues in a concerted program with measurable objectives is the best way to achieve progress. Moreover, in the process of shortening the close, broader issues can be addressed at their source, improving the performance of the Office of Finance. Focusing on the root causes behind too long a close process can uncover hidden issues common to many finance processes, including poor data availability and quality, poor communications and training, and too much complexity.
Even if your company is closing its books within a business week, chances are there’s still room for improvement that can come from automating existing manual tasks. For instance, reconciliations are an activity where companies with as few as 250 employees are likely to find savings of time and money using technology to automate the process and enhance accuracy and auditability.
Master Excel. Our research shows that spreadsheets are a problem when used in any repetitive collaborative enterprise-wide task (for example, planning, forecasting, closing and managing sales operations). At the same time, spreadsheets are an essential tool in business and cannot always be replaced by other software and systems. For this reason, it’s important for finance executives to ensure that the people who are designing and using spreadsheets know what they are doing. One of the root causes of spreadsheet problems is lack of competence by those designing models and analyses. Spreadsheets’ lack of transparency easily masks poor design. Typically, people are self-trained. Although they can complete assignments, the resulting spreadsheet may be inefficient, difficult to audit and brittle (difficult to change without making major modifications) and have so many vulnerabilities to mistakes and tampering that they are disasters waiting to happen. It’s common, for example, for people to create dense and complex nested logic expressions because they don’t know how to use lookup tables. Our research found that almost half (45%) of companies provide no training and just 8 percent provide regular Excel training sessions, with the rest providing only initial training or leaving it to the individual to take the initiative. Just as armies march on their stomachs, finance organizations operate in a world of spreadsheets. It makes sense to invest in the productivity of those responsible for creating spreadsheets because that investment is likely to promote productivity as well as reduce errors and the resulting rework and other costs that go with them. Along with training, testing is useful to ensure that people have the necessary skills to create spreadsheets, but almost all companies (87%) do not test their users.
Plan – don’t just budget. I have asserted that annual budgeting should evolve into a process that’s more focused on planning the business. Many people speak of planning and budgeting as if they were the same thing, but they’re not. Budgeting is essential for control, but budgets are focused on money, not things. So while they’re good for finance departments, budgets don’t deliver much value to the rest of the company. Business planning as practiced today is a relic, a process hemmed in by obsolete conceptions of what it should be. Individual business units make plans, but they are narrowly focused and not well integrated. Our business planning research found that companywide planning efforts are not as coordinated as they could be: Just 22 percent of the participants said they can accurately measure the impact of their plan on other parts of the business. While today’s budgeting and operational planning efforts are loosely connected, the next generation of business planning closely integrates unit-level operational plans with financial planning. At the corporate level, it shifts the emphasis from financial budgeting to business planning and performance reviews that integrate both operational and financial measures. This new approach uses available information technology to enable businesses to plan faster with less effort while achieving greater accuracy and agility. The approach addresses a deep-seated issue: Our research shows that in most companies the budget is not collaborative on an ongoing basis and therefore hinders coordination as companies adapt to changing circumstances. It doesn’t enable managers to anticipate how best to adapt to those changing circumstances, so when things change, as they always do, companies lack the sort of coordination they need to make changes quickly and maximize their performance. The data from our research shows that traditional budgeting does not promote strategic and operational alignment, which winds up hurting performance. And because companies take too long to review their results and in these reviews aren’t able to immediately determine the source of variances between their plan and actual results, they do not react quickly to seize opportunities and address issues.
Adopt price optimization and profitability management. For companies that close within a week, have mastered Excel and focus more on planning than budgeting, price optimization presents a new frontier on which to improve company performance. Price and revenue optimization (PRO) is a business discipline used to create demand-based pricing; it applies market segmentation techniques to achieve strategic objectives such as increasing profitability or market share. PRO first came into wide use in the airline and hospitality industries in the 1980s as a way of maximizing returns from less flexible travelers (such as people on business trips) while minimizing unsold inventory by selling incremental seats on flights or hotel room nights at discounted prices to more discretionary buyers (typically vacationers). Today, PRO is a well-developed part of any business strategy in the travel industry and is increasingly used in others. Optimization is not maximization, since the objective of the former is to achieve the best trade-off between sometimes mutually exclusive goals and their constraints. Focusing solely on profit maximization may result in wider margins but lower sales and profits, for example. Optimizing price means using analytics to gain a better understanding of customers’ price sensitivity in order to achieve the best mix of price and volume consistent with the company’s strategy. This allows businesses to achieve the highest possible margins consistent with their volume and mix objectives. Analytical software is available that enables companies to implement and manage a PRO strategy, which I covered in an earlier perspective.
Manage taxes more effectively. Corporations’ largest tax outlays fall into two main categories, indirect and direct. Indirect taxes are those collected by an intermediary such as a retailer or wholesaler and then paid to government entities. This includes sales and use tax (in the United States), goods and services tax (in Canada) and value-added tax (in Europe and other regions). A large percentage of midsize and larger corporations in North America use software to manage their indirect taxes. In the U.S., such indirect taxes are difficult to handle because of the complex and overlapping tax jurisdictions, changes in rates as well as the definitions of what’s taxable at which rates. The issue is not just calculating the amounts at the time of the transaction, but also being able to mount an audit defense as inexpensively as possible at some point in the future. If your company is not using a third party to manage its indirect tax calculations, 2014 would be a great year to start, especially if your business operates in areas where the tax authorities are most aggressive. Direct – or income – taxes are another matter. Because of their size and complexity, many midsize and almost all larger organizations need to automate more of their tax provisioning process using dedicated software rather than spreadsheets. Corporations that operate in multiple income tax jurisdictions with only moderate complexity in their corporate structure can save considerable amounts of time, have better insight into their tax positions and improve their audit defense posture by switching from spreadsheets.
Senior finance executives often spend time fighting fires rather than addressing their root causes to prevent new ones. Companies that take more than one business week to close must determine why it’s taking them so long and address those issues. The same causes behind a longer-than-necessary close are likely to be at work in all or most finance processes. Further, providing employees with Excel training and testing will improve their productivity and the quality of work they perform. And if nothing else, taking a fresh look at planning and budgeting can identify ways to streamline the process, freeing up time to invest in efforts that will improve the department’s performance. Finally, finance departments that already operate efficiently should focus on ways to play a more strategic role in their company’s business, particularly by managing pricing analytics and improving their tax provisioning acumen.
Robert Kugel – SVP Research
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 how 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.
- 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.
- 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.
- 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 significant 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.
Robert Kugel – SVP Research