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The theme of transforming the finance organization is hot again. The term “finance transformation” refers to the longstanding objective of shifting the focus of finance departments from transaction processing to more strategic activities such as providing the rest of the organization with forward-looking analysis. I focus on the technology and data aspects of this type of business issue in these analyst perspectives because they are usually essential to achieving some business objective. However, technology rarely fixes a problem by itself. If it were a simple matter of just buying software or having better data stewardship, it would be relatively easy to achieve finance transformation. But it’s not simple at all. When it comes to changing how the finance and accounting organization operates, there’s no substitute for leadership. Doing that requires changes in the habits of the department, which include the CFO changing how the department works with the rest of the company.

Our benchmark research on the Office of Finance confirms that most company executives want their finance department to take a more strategic role in running the company. It also shows little progress in achieving finance transformation. To be sure, there are enough examples of the finance organization taking the lead to provide trade publications and vendors with case histories, but for the majority progress has been slight. When we compared the attitudes of executives and managers about the finance department’s performance generally, we found a big disconnect between how well people in Finance think they’re doing and what the rest of the company believes: Half of research participants who have finance titles said that the department plays an important role in their company’s success, but just one-fourth (24%) of the rest of the company said that. In fact, most people outside of the department said that Finance is doing only an adequate job.

As with most situations in business, there are multiple factorsvr_Office_of_Finance_08_it_takes_too_long_to_close_the_books that prevent finance departments from becoming more strategic. The accounting close illustrates the range of challenges that finance executives may have to overcome in improving the department’s performance. Closing the books within one business week is generally acknowledged to be a best practice in finance. Yet our research finds that only 40 percent of companies complete the quarterly close in six or fewer business days and 53 percent close monthly in the same period. To accelerate the close, finance executives often must address multiple issues to improve performance.

Technology plays an important role in accelerating the close. Our research correlates using more automation and fewer manual spreadsheets in the close process with closing the books sooner. But that might not be the only thing that’s holding up the close. Another factor – one that’s hard to measure – is the impact of other parts of the business on preventing the department from finishing the process in a timely fashion. For example, nonfinance processes (such as doing inventory) that aren’t completed until the second or third week of the following month may hold up completion. The accounting organization has no direct control over when work performed in other departments is done. And those outside the finance organization may resist making these changes, which may seem to them arbitrary or unwarranted burden-shifting.

Some issues that hold up the close or create avoidable work in finance and accounting departments are not always easy to spot. For example, I recently wrote that companies that have even slightly complicated revenue recognition requirements under the new accounting rules ought to write and manage their contracts with customers with the explicit aim of minimizing the workload of the accounting department. Contracts that are poorly or inconsistently drafted or that do not enforce common language will make finance departments hire additional staff or temporary accountants and potentially delay the quarterly close. In addition, those working outside of the accounting department often don’t realize that they are doing things that complicate the accounting process. This is especially the case when, for instance, data is collected in spreadsheets rather than in a dedicated enterprise system or when the data entered is incomplete, inconsistent or not collected at all. Often, it’s less burdensome to address the source of the accounting hassle at the source. Here is another situation in which leadership matters. Unless the senior leadership team understands the ultimate impact of, say, people not following procedures or neglecting to fill in a couple of fields on a form, it’s unlikely they will be motivated to enforce the changes that must be made. It’s even harder if the CFO does not have a good working relationship with the rest of the organization or cannot effectively communicate the need for change.

A truly strategic finance organization is one that embraces continuous improvement, uncovering the root causes of time wasting activities, addressing them methodically and investing the time saved into finance transformation projects. Addressing the sources of time-wasting finance and accounting processes requires a CFO who is unwilling to accept the status quo and has sufficient interpersonal skills to drive change. The senior leadership team also has to support a more strategic finance department. For example, the CEO needs to make it clear that closing sooner is everyone’s business and with good reason. How soon after the end of a period the finance organization closes its books affects the timeliness of the information it provides to the rest of the organization: In our research, half of companies that complete their monthly close within four business days said the information the finance department provides is timely, compared to just 29 percent of those that take five to eight business days and 19 percent of those that take nine or more business days.

Implementing change in business is never easy. Finance transformation almost always requires fixing information and technology issues, especially those that automate and enhance control of finance and accounting processes. Without leadership by the CFO, though, very little will happen.


Robert Kugel – SVP Research

Optimization is the application of algorithms to sets of data to guide executives and managers in making the best decisions. It’s a trending topic because using optimization technologies and techniques to better manage a variety of day-to-day business issues is becoming easier. I expect optimization, once the preserve of data scientists and operations research specialists will become mainstream in general purpose business analytics over the next five years.

Optimization was first adopted by businesses in the middle of the 20th century, aided by the introduction of digital computers. The first technique that gained broad for a few specific purposes was linear programming, one of the most basic optimization methods. Linear programming enables analysts to quickly determine how to achieve the best outcome (such as maximum unit volume or lowest cost) in a given situation. They do so using a mathematical model that captures the key variables that go into the decision and any constraints that may affect that decision. A food processor, for instance, may use three types of cooking oil to make a product. To maximize its profit, the company needs to determine the exact proportions of the three oils that result in the lowest production cost. However, it can’t just choose the cheapest of the three in every case because for flavor and shelf-life requirements there’s a limit to the maximum percentage of each oil that it can use. Linear programming using the simplex algorithm quickly solves the problem.

As computing capabilities became increasingly affordable, companies could use more complex algorithms to handle ever more difficult optimization problems. For instance, the airline industry used it to determine how best to route aircraft between two cities and to staff flight crews. Not only can softwarevr_Big_Data_Analytics_01_use_of_big_data_analytics find the best solution for scheduling these assets in advance, it also can rapidly re-optimize the solution when weather or mechanical issues force a change in how aircraft and crews are deployed. Airlines were also in the vanguard in the 1980s when they started using revenue management techniques. In this case, the optimization process was designed to enable established airlines to compete against low-cost startups. Revenue management enabled the large carriers to offer low fares to price-sensitive but flexible vacationers without sacrificing the higher fares that the less flexible businesspeople were willing to spend. The same approach was adopted by hotels in pricing their rooms. Starting in the 1990s markdown management software, which I have written about gained ground. It enables retailers to make more intelligent pricing decisions by monitoring the velocity of purchases of specific items and adjusting prices to maximize revenue. To be feasible, each of these optimization problems require large data sets and sufficient raw computing power.

We’re now on the cusp of “democratizing” what I call optimization analytics. Big data technologies are making it feasible and affordable for even midsize companies to work with much larger data sets than they have been able to in the past. Our benchmark research on big data analytics finds that about half of participating companies already use analytics with big data. This is partly the result of more powerful and affordable data processing resources but also because companies have invested in systems to automate many functions. The rich data sets created by these business applications provide corporations with the raw material for analysis. This data has the potential to enable businesses to make more intelligent decisions. From a practical standpoint, though, the value of these large data sets can only be realized by moving optimization analytics out of the exclusive realm of data scientists and into the hands of business analysts. These analysts are the ones who have a sufficient understanding of the business and the subtleties of the data to find useful and repeatable optimization opportunities. Three-fourths of companies in our research said that they need these business skills (“domain expertise”) to use big data analytics successfully.

vr_Big_Data_Analytics_14_big_data_analytics_skillsOptimization analytics is a breakthrough technology with the potential to improve business performance and create a competitive advantage. You can’t do optimization in your head, and it’s not feasible in desktop spreadsheets for anything but the most basic use cases, such as linear programming optimizations on relatively small data sets. This is a good reason for almost any company to consider adopting optimization software.

Another reason why companies will find it attractive to apply optimization analytics broadly is that the results of applying optimization routines may be superior to using common rules of thumb or relying on instinct and experience. One of the most important lessons for executives about optimization analytics is that optimal solutions are sometimes (but – crucially – not always) counterintuitive to established norms. For instance, in markdown management, retailers often have found that smaller, more frequent price reductions maximize profits and produce a considerable improvement in sales over the end-of-the-season price slashing that was once considered to be the best practice. In financial services, charging your best customers more for loans and other services turns out to be the optimal choice for the bottom line of financial institutions. Another important insight from our collective experience with optimization is that while the value of these analytics as realized in a single event or transaction may be small, it can have a measurable impact on profitability and competitiveness when applied broadly in a business.

At this point optimization analytics is in a dual mode. On the one hand, there are proven examples of the narrow application of optimization such as those mentioned above. On the other, bringing optimization analytics to the masses is only beginning. Some vendors have made progress in simplifying their analytics, but mainstream products are only on the horizon. It’s also important to recognize that, as with past breakthroughs in information technology, there are bound to be more duds than success stories in initial attempts at using optimization analytics. Experience suggests that a small number of companies that have strong analytical skills and a rigorous approach to managing company data will prove to be the leaders in finding profitable opportunities for applying optimization technologies and techniques. Others will do well to find these examples and consider how to apply them to their own organizations.


Robert Kugel – SVP Research

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