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One of the charitable causes to which I devote time puts on an annual vintage car show. The Concours d’Élegance dates back to 17th century France, when wealthy aristocrats gathered with judges on a field to determine who had the best carriages and the most beautiful horsepower. Our event serves as the centerpiece of a broader mission to raise money for several charitable organizations. One of my roles is to keep track of the cars entered in the show, and in that capacity I designed an online registration system. I’ve been struck by how my experiences with a simple IT system have been a microcosm of the issues that people encounter in designing, administering and using far more sophisticated ones. My most important take-away from this year’s event is the importance of self-service reporting. I suspect that most senior corporate executives – especially those in Finance – fail to appreciate the value of self-service reporting. It frees up the considerable resources organizations collectively waste on unproductive work, and it increases responsiveness and agility of the company as a whole.
Electronic reporting began as a solution to paper print-outs, reducing the resources required to transmit information needed by individuals and making it easier for them to find information. Over the past couple of decades, these enterprise reports also have become much easier for IT professionals to create and maintain, but they are still time-consuming and aren’t particularly flexible. Rather than have their IT department create another version of a report, people often copy an electronic report, paste it into a spreadsheet, reconfigure the information to suit their needs and distribute the modified spreadsheet to a group of people. For this and other reasons IT departments have found it difficult to get business people to stop using spreadsheets. Our benchmark research on spreadsheets finds this is the number-one impediment to change. Spreadsheet users value control and flexibility. This is precisely what self-service reporting delivers without the time-consuming hassle of manually creating and distributing spreadsheet reports.
It’s useful to think of self-service reporting as an attitude and approach to using information technology than as a specific software product or category. It starts with the basic assumption that individuals in organizations must be able to retrieve information they need from the systems they use. This does not replace periodic enterprise reporting, dashboards, scorecards and other such “push” communication methods. This is not the once-voguish concept of “democratizing business intelligence” either; that was still too complicated for the vast majority of users. It’s more like replacing telephone operators with a direct dial system. (Note to readers under 40 years old: Once upon a time it required human intervention to connect your phone to someone else’s.) The goal of self-service reporting is to make broad sets of data readily available and give people the ability to access it (subject to permissions) as well as easily organize and display it in the form and format that works best for them.
In the early days of business computing, simply collecting and having access to company data was a breakthrough. Over the past decades, corporations automated and instrumented a broad range of functions, and the challenge lay in collecting and managing the data. Although companies still face many issues in data management, devolving reporting to the individual is now a critical issue companies must address. Well-designed self-service reporting improves the productivity of individuals in both IT and the rest of the organization. The controller of a midsize company recently told me people had been spending one-and-a-half days per month creating reports for senior executives and operating managers after the monthly and quarterly accounting close. Talk about unproductive use of resources! This is an extreme example but emblematic of time routinely wasted on something individuals ought be able to do on their own. From the IT side, far too much time is devoted to creating and maintaining reports – it’s akin to still having switchboard operators on staff to route calls.
Self-service reporting exists both as a feature of enterprise applications and in stand-alone products designed to work with applications that lack this capability. In deciding whether to replace existing software and in any vendor selection process, it’s important to assess benefits of self-service reporting capabilities. This is especially true as mobility increasingly is built into enterprise business applications. Anytime, anywhere access to information is one of the most important reasons why companies invest in mobility and demand this capability in the software they buy. Being able to drill down and around in the data contained in such reports provides a powerful incentive to replace spreadsheets. But there are also stand-alone products that can provide self-service reporting capabilities within legacy systems.
For our service organization this past year I still created a limited number of spreadsheets for individuals and groups that are not on our system. The only data issues we had were created when someone copied and pasted information from our reports into another spreadsheet. Errors are inevitable, and even in our local event there are unfortunate consequences when they occur. For example, telling someone who has just spent hundreds of hours preparing his or her car that the vehicle is not eligible for an award because it was not on the list of judged cars (even though our system showed that it was supposed to be judged) provokes the same level of irate response one might expect when a CFO is informed that there’s a material error in the published financial statements.
Self-service reporting is fast becoming a standard capability within businesses. It’s part of a generational change that is redefining corporate computing. People beyond a certain age still expect information to be given to them. Younger people want to get the information they need themselves and expect to have the ability to do so. IT departments must identify opportunities to offer self-service reporting and implement it wherever possible. Business users – especially those in finance roles – should familiarize themselves with self-service reporting – especially stand-alone tools that they can use and administer – and implement it wherever it is feasible.
Robert Kugel – SVP Research
Our research consistently finds that data issues are a root cause of many problems encountered by modern corporations. One of the main causes of bad data is a lack of data stewardship – too often, nobody is responsible for taking care of data. Fixing inaccurate data is tedious, but creating IT environments that build quality into data is far from glamorous, so these sorts of projects are rarely demanded and funded. The magnitude of the problem grows with the company: Big companies have more data and bigger issues with it than midsize ones. But companies of all sizes ignore this at their peril: Data quality, which includes accuracy, timeliness, relevance and consistency, has a profound impact on the quality of work done, especially in analytics where the value of even brilliantly conceived models is degraded when the data that drives that model is inaccurate, inconsistent or not timely. That’s a key finding of our finance analytics benchmark research.
A main requirement for the data used in analytics is that it be accurate because accuracy affects how well finance analytic processes work. One piece of seemingly good news from the research is that a majority of companies have accurate data with which to work in their finance analytics processes. However, only 11 percent said theirs is very accurate, and there’s a big difference between accurate enough and very accurate. The degree of accuracy is important because it correlates with, among other things, the quality of finance analytics processes and the agility with which organizations can respond to and plan for change.
Although almost all (92%) of the companies that have very accurate data also have a process that works well or very well, that assessment drops to 43 percent of companies that said their data is just accurate. Even in small doses, bad data has an outsized impact on finance analytic processes. Inaccuracies, inconsistencies and not comparable data can seriously gum up the works as analysts search for the source of the issue and then try to resolve it. As issues grow, dissatisfaction with the process increases. Just 22 percent of those with somewhat accurate data and none of the companies with data that is not accurate said their company has a process that works well or very well.
To be truly useful for business, analytics provided to executives, managers and other decision-makers must be fresh. The faster a company can deliver the assessments and insight as to what just happened, the sooner company can respond to those changes. Almost all (85%) companies with very accurate data said they are able to respond immediately or soon enough to changes in business or market conditions, but only 35 percent of those with accurate data and just 24 percent of those with somewhat accurate data are able to do so.
Moreover, having data that is timely enables companies to react in a coordinated fashion as well as quickly. Companies that are able to operate in a coordinated fashion are usually more successful in business than those that are somewhat coordinated in the same way that a juggler who is somewhat coordinated drops a lot of balls. Almost all (86%) companies whose data is all up-to-date said they are able to react to change in a coordinated or very well coordinated fashion, compared to just 38 percent of those whose data is mostly up-to-date and 19 percent that have a significant percentage of stale data. Three-fourths (77%) of companies that have very accurate data are able to respond to changes in a coordinated or very well coordinated fashion, but just one-third (35%) of those with accurate data and 14 percent with somewhat accurate data are able to accomplish this.
Speed is essential in delivering metrics and performance indicators if they are to be useful for strategic decision-making, competitive positioning and assessing performance. Companies that can respond sooner to opportunities and threats are more able to adjust to changing business conditions. The research finds that fewer than half (43%) of companies are able to deliver important metrics and performance indicators within a week of a period’s end – that is, soon enough to respond to an emerging opportunity or threat.
One way to speed up the delivery of analytics is to have analysts focus their time on the analytics. But the research shows that not many do: A majority of analysts spend the biggest chunk of their time dealing with data-related issues rather than on the analysis itself. Two-thirds (68%) of participants reported that they spend the most time dealing with the data used in their analytics – waiting for it, reviewing it for quality and consistency or preparing it for analysis. Only one-fourth (28%) said their efforts focus most on analysis and trying to determine root causes, which are the main reasons for doing the analysis in the first place. In other words, in a majority of companies, analysts don’t spend enough time doing what they are valued and paid for.
The results also show that there are negative knock-on effects of spending time on data-related tasks rather than on analysis. More than half (56%) of the companies that spend the biggest part of their time working on analytics can deliver metrics and indicators within a business week, compared to just one-third (36%) of those that spend the biggest part of the time grappling with data issues. Having high-quality, timely and accessible data therefore is essential to reaping the benefits of finance analytics.
Data issues diminish productivity in every part of a business as people struggle to correct errors or find workarounds. Issues with data are a man-made phenomenon, yet companies seem to treat bad data as a force of nature like a tornado or an earthquake that’s beyond their control to fix. Our benchmark research on information management suggests that inertia in tackling data issues is more organizational than technical. Companies simply do not devote sufficient resources (staff and budget) to address this ongoing issue. One reason may be because the people who must confront the data issues in their day-to-day work fail to understand the connection between these and getting the results from analytics that they should.
Excellent data quality is the result of building quality controls into data management processes. Our research finds a strong correlation between the degree of data quality efforts in finance analytics and the quality of the finance department’s analytic processes and output, and ultimately its timeliness and its value to the company. Corporations generally – and finance organizations in particular – must pay closer attention to the reliability of the data they use in their analytics. The investment in having better data will pay off in better analytics.
Robert Kugel – SVP Research