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When applying information technology to drive better business performance, companies and the systems integrators that assist them often underestimate the importance of organizing data management around processes. For example, companies that do not execute their quote-to-cash cycle as an end-to-end process often experience a related set of issues in their sales, marketing, operations, accounting and finance functions that stem from entering the same data into multiple systems. The inability to automate passing of data from one functional group to the next forces people to spend time re-entering data and leads to fragmented and disconnected data stores. The absence of a single authoritative data source also creates conflicts about whose numbers are “right.” Even when the actual figures recorded are identical, discrepancies can crop up because of issues in synchronization and data definition. Lacking an authoritative source, organizations may need to check for and resolve errors and inconsistencies between systems to ensure, for example, that what customers purchased was what they received and were billed for. The negative impact of this lack of automation is multiplied when transactions are complex or involve contracts for recurring services.
Our benchmark research shows that data fragmentation, consistency, availability, usability and timeliness are key issues for companies. The information management issues in process design and execution are similar to those at work for analytics. However, addressing them effectively requires a different approach than just creating a separate data store to be the “single version of the truth.” Careful consideration is required to determine the best method to manage data throughout a core business process, particularly when multiple applications are required to automate and support the execution of the process. Software application platforms offered by some vendors make it far easier to integrate niche software applications into processes in a way that may eliminate the need for an operational data store.
The information dimension is usually overlooked in designing business systems because data is viewed as a given, is not explicitly considered (“we’ll work out the details later”) or is considered only an afterthought. This may occur because the information dimension of systems engineering is treated as being of secondary importance to defining the best process and determining the required applications capabilities. But we think making data an afterthought is a mistake. Ventana Research uses a framework that explicitly calls out information (all forms of data) and technology (software, hardware and networks) as separate elements in addressing business issues, rather than lumping the two together as “technology.” Explicitly taking the data perspective into account provides a broad perspective that frames process and technology requirements. We assert that treating data as a core consideration can result in better process design and clarify the issues companies must consider to select the appropriate systems to support the people and process aspects of business operations.
Quote-to-cash is a useful example of where an end-to-end process requires more than just workflow to manage the handoffs as tasks are executed. In some simple cases, an ERP system can handle all of the details. In others, automating the process and data flows may require multiple systems (such as a CRM system for customer and account information, as well as systems for product configuration, contract management, billing and collection in addition to ERP. Some of the data assembled in a quote-to-cash transaction may have to be transferred to other operational systems to fulfill the transaction. To achieve best results, data must be staged and controlled from start to finish and there must be a single system of record. Deciding on what application (or applications) to use to manage the process and where to locate the system of record physically and logically depends on a company’s specific circumstances.
Engineering quote-to-cash end to end from both process and data flow perspectives can speed its completion (thereby improving customer responsiveness), remove unnecessary manual steps (generating efficiencies) and reduce or eliminate errors at every step (resulting in better customer service and lower costs).
Another example that benefits from a data-driven end-to-end process is requisition-to-pay. It may seem counterintuitive, but accelerating the payment of invoices can improve a company’s bottom line. With interest rates in much of the developed world at historic lows, the greatest return on available cash is taking advantage of early payment discounts. Yet few companies take advantage of these. One important reason why they don’t is deficiencies in the data and technology needed to make early payment practical. Starting the automated process at the point of initial requisition gives the treasury function better visibility into the amounts and timing of future outlays, making cash forecasting more certain. Greater certainty about the corporation’s cash position lowers the amount of cash it needs to hold to meet payment obligations while maintaining an adequate operating liquidity buffer to allow for forecasting errors and unanticipated needs. Companies that have limited visibility will be cautious about making payments and must maintain a larger, more conservative buffer stock of cash. Using automated systems to speed the processing of invoices by eliminating delays in handoffs is only one element needed to make early payment discount feasible. Timely access to accurate data to support processing invoices is necessary, as is data needed by an analytical application that supports the treasury function to handle the complexities of managing cash effectively.
The importance of timely access to reliable data is often overlooked, but it can be the key ingredient to improving the execution of core business and finance department functions. Engineering data and data management into the design of technology-driven processes must not be an afterthought; it must be integral to the decisions about what software is used and how processes are to be performed. Our research shows that data issues plague companies, and the larger the company, the bigger the problem may be. Effective data management is essential to improve corporate performance. We advise companies to review their current processes and take steps to modernize and automate any that are a drag on performance.
Robert Kugel – SVP Research
Finance and accounting departments are staffed with numbers-oriented, naturally analytical people. Strong analytic skills are essential if a finance department is to deliver deep insights into performance and visibility into emerging opportunities and challenges. The conclusions of analyses enable fast, fully informed business decisions by executives and managers. Conversely, flawed analyses undermine the performance of a company. So it was good news that in our Office of Finance benchmark research 62 percent of participants rated the analytical skills of their finance organization as above average or excellent.
The research finds that having strong analytical skills is associated with good analytic practices. It’s important to have an effective process for creating analyses that enable effective management of a corporation. Having strong analytical skills is a key ingredient of being able to manage that process. Having skills and having an effective process are linked. Almost all (89%) companies with excellent analytic skills have a process for creating finance analytics that works well or very well, compared to half of organizations in which skills are average and none in those where they are below average. Furthermore, almost all (92%) companies with excellent or above-average finance analytic skills have been able to use analytics and performance indicators to improve individual or business performance. By comparison, fewer than one-third (30%) of those with average or below average skills were able to do so.
Having these skills is good, but the research suggests that most finance departments don’t use them to full potential. When we dug into some of the underlying data, a less rosy picture of the state of analytics in finance departments emerged, including somewhat pedestrian use of analytics. Most companies are good at handling the basics, such as financial statement analyses, and in creating and assessing models used in forecasting and planning. However, very few (just 12%) of those that have above-average or excellent skills use predictive analytics; only one-fifth of them apply relevant economic and market indicators or price optimization techniques; and just one-third apply profitability analysis to products and customers on a regular basis. Staying above average or better in applying analytics in today’s environment means going beyond well-established financial analyses. Corporations today have vast amounts of business data that demand the application of advanced techniques. Predictive analytics is a valuable tool that can harness this big data to create more nuanced and more accurate forecasts as well as alert executives and managers to threats and opportunities earlier than ever. In addition greater availability and accessibility of external information enables organizations to produce better insights into how economic, market and financial markets affect their performance.
Another issue that can hinder a finance department’s efforts in delivering valuable analytics is the timeliness with which they provide it. Only one-third (31%) are able to provide information on a timely basis. Just over half (56%) said that the information they provide is somewhat timely, which in practice can mean a day late and a dollar short when key decisions have to be made immediately. Two related reasons why information may not be timely are a lack of automation and overreliance on desktop spreadsheets for reporting. Spreadsheets are indispensable for personal productivity and ad hoc analysis and reporting. However, they are almost always the wrong choice for routine business analysis and periodic reporting because using them can be very time-consuming. Because it takes so long to prepare the analysis and generate a report distributed through email, information is less timely and often less valuable.
Senior finance executives need better understanding of advanced analytics and how these techniques can be employed to improve the performance of the finance organization and serve the needs of the rest of the company. Desktop spreadsheets are an overused technology that wastes time when applied to collaborative or repetitive enterprise-wide analytics. In practice, they are incapable of delivering the forward-looking analytics listed above. They are not difficult to replace if there is a will to do so. Advanced analytic software is becoming increasingly more affordable and more accessible to business analysts. Often they use a Microsoft Excel interface because of its familiarity and therefore require less training to get users to a reasonable level of proficiency.
What constitutes excellent and above-average analytical skills is evolving daily as new tools and techniques become mainstream. Senior finance department executives must remain current on what’s possible and push their department to keep up. To make this possible, as I have written, they must set their sights higher and find ways to eliminate time-wasting manual processes so there’s time for their analysts to extend their highly valued skills.
Robert Kugel – SVP Research