Transforming Tax Departments into Strategic Entities

The steady march of technology’s ability to handle ever more complicated tasks has been a constant since the beginning of the information age in the 1950s. Initially, computers in business were used to automate simple clerical functions, but as systems have become more capable, information technology has been able to substitute for increasingly higher levels of human skill and experience. A turning point of sorts was reached in the 1990s when ERP, business intelligence and business process automation software reduced the need for middle managers. Increasingly, organizations used software to coordinate activities as well as communicate results and requirements up and down the organizational chart. Both were once the exclusive role of the middle manager. Consequently, almost every for-profit organization eliminated management layers so that today corporate structures are flatter than they once were. Technology automation also eliminated the need for administrative staff to perform routine reporting and analysis. Meanwhile, over the course of the 1990s, the cost of running the finance department measured as a percentage of sales was cut almost in half as a result of eliminating staff and because automation enabled companies to scale without adding headcount. During the last recession, companies in North America and Europe once again made deep reductions to their administrative staffs, relying on information technology to pick up the slack.

Given this history, the best career choice that an individual can make today is to stay ahead of the trend. Information technologies, especially cognitive computing, will continue to eliminate relatively high-paying white-collar jobs in corporate life, especially in the finance and accounting function. Executives and others working in tax departments in particular should recognize that a major shift is under way in their field. Automation will transform their work over the next five years, driving a fundamental change in what they do. To succeed (or even survive), they will have to embrace automation.

Spreadsheets are a major impediment to making the tax function more strategic for a company and more remunerative for those working in the department, as I have noted. Our Office of Finance benchmark researchvr_Office_of_Finance_15_tax_depts_and_spreadsheets finds that half (52%) of tax departments use spreadsheets only for tax provisioning and another 38 percent mainly use spreadsheets; just one in 10 utilize a third-party tax application. One well-known issue with spreadsheets is that they are error-prone – not a risk that tax professionals can be comfortable with. To be certain that the tax provision and other tax-related calculations are correct, individuals must double- and even triple-check the numbers. This overlaps with a second major issue with spreadsheets: They are time-consuming. Our spreadsheet research finds that those working heavily with spreadsheets on average spend 18 hours a month (equivalent to more than two full workdays) just maintaining their most important spreadsheet. Spreadsheets as so time-consuming that they prevent individuals from doing more valuable work, in this case tax analysis and planning.

Another related issue is that using spreadsheets for the tax function diminishes visibility into a company’s tax provision in at least two respects. First, using them takes so long that executives get to the numbers late in the financial close process. This matters because of the impact that tax expense has on a company’s profits. Second, spreadsheets are black boxes: That is, they are difficult to control, and it’s difficult for anyone other than the spreadsheet’s owner to understand their construction. Often, assumptions are buried in formulas and therefore hard to uncover. If these formulas are inconsistent or wrong, it’s not easy to spot them. (This was an important factor behind J.P. Morgan’s multibillion dollar trading loss, which I discussed.) When a spreadsheet is constructed with a given formula repeated in multiple cells, each of these must be updated when circumstances change, and it’s difficult to be certain that all of the changes have been made. Even with advanced techniques designed to make updates consistent, it’s hard to be sure that some cell wasn’t overwritten with another number.

Some people who work intensively with spreadsheets still view them as a form of job security because of their opacity. They think they’re indispensable because they are the only one who understands how their spreadsheet works. This is one of several reasons why their use persists in functions where they constitute more of a problem than a solution. However, these spreadsheet jockeys should recognize that their tools’ inherent inefficiency, lack of visibility and proneness to error make them vulnerable to being replaced by better technology. The real value of tax professionals is not their ability to overcome spreadsheet limitations. It’s in their training in understanding income taxes. Once freed from the drudgery of performing computations, massaging data and checking (two or three times) for errors, tax professionals can turn their attention to performing analytical work aimed at optimizing a company’s tax spend – and thus ensuring their value as employees.

Midsize and larger organizations, especially those that operate in multiple direct (income) tax jurisdictions and that have an even moderately complex legal entity structure, must use dedicated software to automate their income tax provision and analysis functions. They must manage their tax-sensitized data using what I call a tax data warehouse of record. Tax departments must be able to tightly control the end-to-end process of taking numbers from source systems, constructing tax financial statements, calculating taxes owed and keeping track of cumulative amounts and other balance sheet items related to taxes. Transparency is the natural result of a having controlled process that uses a unified set of all relevant tax data. An authoritative data set makes tax department operations more efficient. As noted, reducing the time and effort to execute the tax department’s core functions frees up the time of tax professionals for more useful analysis. Having tax data and tax calculations that are immediately traceable, reproducible and permanently accessible provides company executives with greater certainty and reduces the risk of noncompliance and the attendant costs and reputation issues. Having an accurate and consistent tax data warehouse of record enables corporations and their tax departments to better execute tax planning, provisioning and compliance. Using dedicated software today rather than relying on spreadsheets helps the tax department, and those working in it, increase their strategic value today so they won’t be obsolete tomorrow.


Robert Kugel – SVP Research

The Stupidity of KPIs in Business Analytics

In my last rant, on business analytics and the pathetic state of dashboards, I pointed out significant flaws in business intelligence software created by technology providers and in how it is being deployed by business and IT. Now I want to follow up with some insight on disconnects to a critical asset that is essential to the success of business analytics. I mean key performance indicators (KPIs), a term used in inaccurate ways that have diminished the value of the concept for business.

Let’s start with the definition; for practicality I will use Wikipedia, which says that a KPI is used “by an organization to evaluate its success or the success of a particular activity in which it is engaged.” The success being evaluated could be a goal, a target or something else that is important. To set a baseline, you calculate two measures and compare them to create a performance metric. For example, units sold and unit price are two separate measures that can be calculated to produce a metric called sales. Then through iterations of more precise calculations, this measure can be refined to compare against the sales quota or goal for a specific time period; this creates a key performance indicator on the outcomes of sales and even marketing efforts.

In actual use, however, the KPI, its use and its value have been dumbed down in ways that diminish the quality of intelligence we gain from using business analytics. First is the vague and contradictory ways in which the term is applied by technology providers and practitioners. Over the last decade I have seen “KPI” used to describe what are actually metrics – the building blocks of KPIs – and only sometimes performance-related. A metric like revenue or sales is not a KPI; neither are cost-specific, throughput-related metrics based on quantity or processing, nor customer-related metrics like first-call resolution. Such metrics are commonly presented in dashboards through visualization and called KPIs. Today we seldom see scorecards that use business analytics, which once were common for presenting KPIs properly to business users. Maybe it is time to start using scorecards for managing performance and not just measuring it.

The second issue has to do with the performance part of KPI, which should show how an organization or any of its business processes measure up to expected outcomes. Ideally, upon viewing performance-related metrics or indicators, within seconds an individual should be able to determine what, if any, action should be taken to improve performance, such as discovering what is contributing to the subpar performance or identifying opportunities for improvement. This root-cause level of actions requires examination of different classes of metrics related to performance and can range from people and processes to customers or risk. Understanding the cause and effect of metrics requires knowing and presenting the process and interconnects of how a business operates. Unfortunately most business analytics software just will provide you a table of data with no insight on what metric is contributing to the issue. By creating the right types of metrics underlying a KPI, we can reduce the time and resources required to support the communications (email, phone calls and meetings) that people normally use to investigate performance shortfalls. To get to this point requires creating a library of measures, metrics and indicators that can cross a variety of situations and help inform action-taking and decision-making. Let’s drop the P and just say key indicators (KIs) to set a new context that focuses on the indicators and the types of metrics that support them. This could lead organizations to make substantive improvements.

The third step is to make KPIs or KIs relevant to the particular roles and responsibilities of individuals. Company or divisional KPIs are interesting but only provide a general view of how an organization is performing. Where the rubber hits the road is the context of the indicators and metrics at the department, team and individual levels. We need to provide the ability for individuals to select their own focus within the scope of these facts and figures to determine how well their activities are contributing to the execution of business processes and outcomes. Here the role of business analytics is critical. To make the analyst buzzwords self-service BI and agile BI being pushed by IT analyst firms a reality, tools have to make analytics more intuitive to users. More tools for data discovery are not the answer, and making users select their scope every time they get an updated report or dashboard is a waste of time that decreases productivity and increases costs in running an organization. Instead let’s design a new generation of business analytics based on roles and individuals developed through a profile; this could go a long way toward streamlining the focus of analysis and preparing individuals to quickly determine what action to take.

To erase the stupidity in how KPIs are spoken about, demonstrated and actually deployed, we need to advance our dialogue and educational discussion of what key indicators and range of metrics are required to support particular deployments. I have already said that just placing more charts in a dashboard, no matter how pretty and interactive they might be, will not help support the actions and decisions that business analytics should enable. The effort to make KPIs more valuable begins with ensuring they are properly developed and represent performance in terms of the state of success toward achieving the goal or target. Showing past performance is insufficient without knowing how well it met expectations. Presenting a KPI does not necessarily require a chart; it can be done equally well by text presenting it within the context of how the people or process is performing over time and where it is in progress toward the expected target. These indications can be linked to additional facts with a directional arrow or other simple representations that make it easy to determine whether to take action. If your business intelligence software does not support a simpler way to communicate key indicators and metrics, maybe you have the wrong tool.

If we admit the flaws within our deployments and technologies and force ourselves to have more realistic conversations, we could advance the science of business analytics. Over the years we have made strides forward and then taken steps backward in trying to meet the needs of the lowest competency denominator. We need to aim higher and take steps to find out what should be done to produce full value from business analytics. Increasing the value of these investments can help an organization increase its efficiency and effectiveness. If you are not sure if you are heading in wrong direction with your metrics and indicators, just let me know, that is what myself and others at our firm do for a living.


Mark Smith – CEO & Chief Research Officer