Have you ever sat in a meeting where everyone has a different number for the same performance measure? This typically results in spending the next hour trying to reconcile the differences rather than making the important business decisions required. Upon further analysis, it is likely everyone will have the right number according to the system from which it was derived.
The differences can likely be attributed to inconsistent hierarchical master data across these systems. It has existed ever since organizations start implementing more than one business system. But today, the problem is magnified across the many systems most organizations have and by the large numbers of changes today’s business environment generates. It is therefore essential for organizations to effectively manage hierarchical master data across multiple information systems. Organizations need to move beyond the mix of email, spreadsheets and adhoc systems that many currently rely on to execute this extremely important function. Numerous organizations are looking for enterprise software solutions to help them effectively manage these problems without relying on manual processes.
Why is Enterprise Data Planning Important?
Data is usually shared across many enterprise systems. For example: John (Sales Representative) who works in California (Territory) sells 10,000 (Quantity) of a new widget (Product) to a customer (Customer) based in New York (Geography) for $50,000 (Total Sale) on December 15, 2017 (Date). Taken together, this information is about one transaction, but included in the transaction are individual elements of master data—Sales Representative, Territory, Quantity, Product, Customer, Geography, Total Sale and Date.
Today’s Enterprise Data Planning Challenges
How do most enterprises manage enterprise data today? Remarkably for something so important, they do it through conversations, telephone calls, spreadsheets and e-mail. For example, if a departmental manager wants to add another cost center, or if management wants to move facilities from human resources to finance, the business decision must first be approved by all the relevant decision makers. This takes time. Once the change is approved, IT receives the request to make the change and ensure that it ripples through all of the enterprise’s transactional systems, data warehouses, business intelligence and enterprise performance management solutions. Because changes are made manually, often the end result is a lot of people making a lot of mistakes with a lot of mission critical data—mistakes that go undiscovered due to a lack of visibility or traceability in the process. This is compounded by the sheer number of changes that take place in enterprises today. We constantly cite the increasing rate of change in business which inevitably leads to increasing change in enterprise data.
Modern Enterprise Data Management
World-class performers experience significant benefits from taking a modern, agile approach to enterprise data management across their entire business systems landscape. Key characteristics of this approach include:
• Eliminating the need for a formal, upfront data governance program and initiative that requires burdensome commitments including executive sponsorship, agreement on terms and definitions, enterprise policies, and a host of other coordination costs between Business and IT to orchestrate people, time and resources across lines of businesses, divisions or geographies.
• Taking an elastic approach to managing enterprise data that is evolutionary, iterative, incremental and flexible. One that does not force mastering to achieve desired outcomes, but is fit for purpose based upon desired scope: peer-to-peer within a small workgroup, application-to-application to support local alignment, or enterprise-wide to enable global mastering initiatives as desired based upon the aspirations, capabilities, and maturity of an organization at a point in time.
• Facilitating easy-to-use, web-based, self-service experiences for streamlined application maintenance, collaborative change management, faster data sharing, and accelerated new application development.
• Utilizing a request-driven approach to all change management and data hygiene activities in an easy-to-use, self-service experience that promotes timely, accurate changes across a spectrum of business users.
• Employing a business-driven approach to snapshot historical versions, branch off production data sets to explore what-if scenarios, and merge approved plans into production in a timely manner to drive value among connected business applications.
• Comparing alternate business perspectives within and across applications to understand differences, and rationalize on a fit-for-purpose basis.
• Streamlining last mile integration with connected business applications, across public, private, and hybrid cloud environments.
• Have fully transparent activity trails that enable regulatory compliance and risk mitigation.
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