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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
Anaplan, a provider of cloud-based business planning software for sales, operations, and finance and administration departments, recently implemented its new Winter ’14 Release for customers. This release builds on my colleagues analysis on their innovation in business modeling and planning in 2013. Anaplan’s primary objective is to give companies a workable alternative to spreadsheets for business planning. It is a field in which opportunity exists. Our benchmark research on this topic finds that a majority of companies continue to use spreadsheets for their planning activities. Almost all (83%) operations departments use spreadsheets for their plans, as do 60 percent of sales and marketing units. Yet the same research shows that satisfaction with spreadsheets as a planning tool is considerably lower than satisfaction with dedicated planning applications. But despite general agreement in companies that the planning process is broken and spreadsheets are a problem, companies seem reluctant to break the bad habit of using spreadsheets. This conclusion suggests that either switching to dedicated software hasn’t been easy enough or that the results of doing it have not been compelling enough to motivate change. Anaplan intends to address both of these issues.
Anaplan designed its software to support business planning integrated across an enterprise in a practical way that’s an attractive alternative to spreadsheets. Its HyperBlock architecture is a hybrid of relational, vertical and OLAP databases with in-memory data storage and calculation. To translate that technology-speak into a plain concept, it’s easier than ever for those trained in spreadsheet modeling to transport their skills to a dedicated planning application. Anaplan simplifies the process of creating a planning environment that can be used by sales and marketing, finance, operations – any part of a company. Individual business units can create their plans without IT involvement. Customer companies don’t have to move all plans at once to Anaplan, but when they do, integrating all of the plans into a unified company view is straightforward.
The bulk of the changes in the Winter Release are aimed at refining and improving the user experience and facilitating model creation and updates. One of the most obvious changes is in the individual user interface, which opens up with “model tiles” representing each of the plans each individual has in his or her portfolio. It’s fairly typical for individuals to participate in multiple planning activities. Our benchmark research on business planning finds that, on average, employees participate in five sets of plans. Each of these may have multiple versions, and some may have subsidiary plans to a main plan. Some plans may be current while others are no longer used and are archived. The new interface makes it easier to organize this collection, making the most important plans readily accessible. This enhancement and others that will follow reflect Anaplan’s intent to incorporate ergonomics in the design of its software.
Choosing a model opens a dashboard relevant to the specific role of the user and the plan he or she has selected. Organizations can configure the layout of the dashboard, which provides high-level summarized information and different ways of navigating into and around the details in the plan. Navigation is now role-based to enable users to zero in on only those models and dashboards relevant to their function or role. Anaplan can be configured to drill down to specific items or transactions if necessary. Doing this in a multidimensional model is not always straightforward. An Excel add-in is a must for any planning application because it provides a familiar user interface that enhances productivity while eliminating the disadvantages of desktop spreadsheet, since the individual is working with a formal application and an advanced database environment. Anaplan’s Winter Release simplifies installation of the add-in. All of these enhancements go beyond a simple “consumerization” of business software – layering a snappy gloss onto software that remain tedious to use – to provide a more satisfying working environment.
Another notable addition in the Winter Release is “intelligent mapping,” a useful way for one person to create templates of components used in a model (say, all of the costs of adding a store, doing a marketing campaign or performing heavy maintenance on capital equipment) that others can use. Since organizations tend to handle most processes in much the same way, the operational and financial aspects of those processes are likely to be modeled in almost exactly the same ways. Being able to quickly copy a useful exemplar and easily customize it to an individual’s specific needs saves time. Moreover, making it simple to achieve consistency can improve the effectiveness of planning. Using intelligent mapping needn’t be the product of a conscious effort to create a template, either. An equally likely use is when someone looks at a plan created by another business unit and sees some component in that plan that’s useful to his or her model. Intelligent mapping makes it easy to copy and modify it to suit the need.
Effective collaborative planning is a structured dialog. Structured because it involves hard numbers and a dialog because it involves a back-and-forth exchange between executives and managers to mediate between the results desired and what’s feasible. Toward that end, Anaplan has added a capability in its models it calls a “hold,” which fixes one or more values in the model while the rest are adjusted. This simplifies the process of setting month-by-month, line-by-line objectives because it enables executives to impose selective constraints (minimum or maximum values such as sales by a product line or advertising expense) while adjusting assumptions quickly to assess whether the resulting changes are realistic. Fixing and releasing holds iteratively simplifies and shortens the process of assessing specific details to achieve a plan that is workable and agreeable.
For analysts that create or support planning models, the Winter Release adds a floating formula editor. This is a small but important element because it improves the productivity of modelers – typically a constrained resource in most companies.
The new release further advances Anaplan’s strategic objective to provide corporations with a tool that reduces the amount of effort needed for collaborative planning in any part of the business and enhance the value of this planning by better aligning business unit objectives with market opportunities. Our planning research finds that companies have many plans but, other than the annual budget, very little of it connected and coordinated. Anaplan focuses on collaborative business planning as a way to differentiate its offering from budgeting tools – a mature market with entrenched competitors. Its objective is supported by the underlying architecture of the software, which is designed to lower the barriers to switching from spreadsheet planning and budgeting as well as generating greater business value from a company’s planning processes.
Having said all this, I have to add that making it easier not to use spreadsheets is necessary but insufficient to alter corporate behavior. Companies need a business incentive to change. Anaplan’s use of in-memory technology provides that incentive because it adds considerable value to the planning process. Since the software can process even complex models with large data sets in seconds, in-memory computing can change the nature of planning, budgeting, forecasting and reviews. For example, the technology enables organizations to run more simulations during a planning or review session to understand trade-offs and the consequences of specific events. It can change the focus of reviews from what just happened to what to do next. Rather than relying on intuition or simplistic scenarios to make that decision, in-memory systems support structured, numbers-driven conversations to develop the details of a plan. This is the breakthrough to any planning or budgeting process that in-memory processing provides and a good reason for businesses to make the leap to more capable software.
Anaplan’s product doesn’t do everything. For example, companies that want all of the rigor that goes with a formal sales and operations planning effort should focus on applications dedicated to this process. And Anaplan doesn’t have all of the features that dedicated project planning software can provide. That noted, I recommend that companies that are looking for a dedicated application for general business planning and financial budgeting consider Anaplan. This is especially true if their objective is to have a planning environment usable by all parts of the business that can serve as the integration point for all business planning. We have found their customers have made significant progress to improving the modeling and planning which is why it received the 2013 Ventana Research Leadership Award. If you have not taken a look at Anaplan it is well worth your time.
Robert Kugel – SVP Research