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A company’s enterprise resource planning (ERP) system is one of the pillars of its record-keeping and process management architecture and is central to many of its critical functions. It is the heart of its accounting and financial record-keeping processes. In manufacturing and distribution, ERP manages inventory and some elements of logistics. Companies also may use it to handle core human resources record-keeping and to store product and customer master data. Often, companies bolt other functionality onto the core ERP system or extensively modify it to address limitations in the system. Because of the breadth of its functionality, those unfamiliar with the details of information technology may perceive ERP as a black box that controls just about everything. So it’s not surprising that when a company’s information technology becomes more of an issue than a solution, many assume that the ERP system needs replacing. This may or may not be true, so it’s important for a company to assess its existing ERP system in the context of its business requirements (as they are now and will be in the immediate future) and evaluate options for it.
A common scenario for a company to replace its ERP system is because the business has outgrown (or will soon outgrow) its capacity to handle transaction volumes. Replacement also becomes necessary when the system no long meets business requirements, as, for example, when it is too difficult to configure to specific requirements. This issue might have developed because the company’s business model has changed significantly since purchasing the system or because it had to adjust its go-to-market strategy, added a new product line, expanded geographically or made an acquisition. Another reason to change may be that for a company with an adequate on-premises ERP system migrating to the cloud can eliminate a substantial portion of work done by its IT staff, enabling the department to focus on more strategic efforts, reduce headcount or both. A shift to the cloud also may improve the performance of an ERP system, especially if it’s an on-premises system running on aging hardware and the organization does not have the resources to maintain the system well.
Then, too, there are less obvious reasons that necessitate replacement. ERP systems are inherently complex, as I have noted, because they cross multiple business functions in many types of business, each of which has its own requirements. Seemingly trivial elements, such as the particular sequencing of tasks in a process by an ERP system, may be irrelevant for many businesses but have a negative impact on some. For example, when customer orders are almost always infrequent, it doesn’t matter when in the sequencing of the sales order process the system records the use of credit to confirm that the order can go through. An order must be rejected if adding it to the customer’s outstanding balance will bring the account over its limit. However, if orders occur frequently, the ERP system must execute the credit check at the first step or customers routinely will exceed their credit limits. It’s easy to overlook a detail such as this in the software selection process and even in the initial implementation. If that happens, dealing with the credit limit may require software customization or a process workaround if the root cause is the application itself. However, replacing the existing ERP system often is necessary if there are multiple issues such as these and the overall impact of them is severe enough to be measured by a combination of monetary losses, wasted time, lax controls, an inability to measure performance or limited visibility of information and processes.
At the same time, replacing the ERP system may not be the most cost-effective solution to business issues. To gauge that aspect, an important first step is determining whether the process or data issues identified by users are the result of a poorly executed implementation. Midsize companies in particular don’t always get the most competent consultants to set up their software, especially if the consultant (or the individual running the project) is not familiar with the peculiarities of the company’s industry or its specific operating requirements. Checking in with user group members in a similar business is an easy way to confirm if the issue is systemic or simply a poor job of setting up the software. If, based on feedback from other users, the situation appears dire enough, it may be worthwhile to engage a new consultant to fix the mistakes of the first one.
In some instances a “bolt-on” application (that is, software designed for easy integration with another, specific application) may be the most cost-effective way of addressing existing shortcomings. This is especially true for companies using a cloud-based system. Most ERP systems have rich functionality for handling core tasks such as accounting, human resources and inventory management. Yet the package a company is using may not have sufficient functionality for a specific process needed to run the business. For example, companies (particularly growing midsize ones) may find that their human resources department needs software to automate recruiting and onboarding of employees and that these capabilities are absent or insufficient in their ERP package. In our benchmark research on workforce management almost half (45%) of companies said they need new applications to address the full range of their human resources management requirements. In other cases, functionality necessary to manage the business may be missing. Companies that have a recurring revenue or subscription business usually find that the ERP system falls short of their requirements for invoicing. Bolt-on applications usually replace spreadsheets, ensuring that data is captured and available in a single controlled system where it can be accessed in an extended process (such as order-to-cash). Replacing desktop spreadsheets can save considerable time by automating tasks and eliminating the need to re-enter data into one or more systems. Having accurate and controlled data makes reports and metrics more reliable. It saves the finance and accounting departments time by eliminating the need to perform periodic reconciliations to ensure the accuracy of the data. Of course, the challenge with any bolt-on is that it is one more piece of software that requires attention, and integration with the core ERP system can pose challenges, especially over the long run.
A company also may believe that it needs a new ERP system in order to consolidate data in a single system to facilitate analysis and reporting. In this instance, however, it may find that an operational data store, which integrates data from multiple sources for additional processing, will address all or most of its issues, especially if the company uses custom software or some niche application that supports its operations but is unavailable in an ERP system that otherwise meet its needs. A data store may prove to be a more practical choice because it’s much less costly and disruptive than replacing an otherwise well-functioning system. It also can provide flexibility in the longer term. As the company adds new applications, data from this new source can be fed into the operational data store. But be aware of challenges in setting up an operational data store or adding new system data feeds to it, using one usually requires an IT organization with the skills to maintain it over time.
Many companies are loath to replace an otherwise well-functioning ERP system because doing so is expensive and usually disruptive to operations. Also, implementing a new system almost always requires retraining and some adjustments in operating procedures. Our research on the Office of Finance finds that on average companies are keeping their ERP systems one year longer today than they did a decade ago. Deciding whether to replace an ERP system is not always straightforward. The process is made more difficult because today organizations have many more software and data options than they used to. Few companies have the expertise in-house that will enable them to decide the best course of action. There may even be vested interests within the organization that will prevent them from making the best choice. Finding a truly independent advisor that understands both information technology and the specific business requirements can be the best way to sort out the options and help make the difficult technology decisions.
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