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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