Running multiple ERP systems simultaneously can be quite painful for any mid-size organization. Since each ERP maintains their own chart of accounts, financial consolidation and reporting can become all-consuming for the finance teams. When each ERP has its own Customer Master, sales team visibility into strategic accounts is limited, while smaller accounts receive terms that can become big problems for AR. These separate ERP systems lead to issues for other departments—marketing wants a single comprehensive product master; supply chain managers want a single comprehensive vendor master.
Obviously, there is hyperbole involved in my description. However, these are some of the many reasons executive management would like all business units working from a single ERP, with integrated financial reporting, consistent business processes for the whole company and lowered costs of operations.
So, you initiated a multi-year ERP implementation / migration / consolidation project.
At the outset, each ERP specialist is skeptical of the consolidation strategy. “Our ERP is tailored to our business unit” is a common argument for keeping each ERP running. When asked, “How’s the quality of the data?” the same ERP specialists may complain that the data quality is poor. Unfortunately, data problems don’t get better by maintaining the status quo.
Severe master data quality problems present an obstacle to an efficient ERP transition. Let’s think about the customer: if you were to bring all customer master records into a new system wholesale, you’d have many duplicated accounts. You’d have diverse naming convention issues. You’d have some accounts that refer to distribution centers, some to end users, some to drop ship locations. You’d have a wide variety of payment terms.
Get your ERP ambitions moving again, and focus on data quality in a way that enables the final goal—centralized and integrated business processes. Here’s how:
- Build Golden Records for Customer. A Golden Record is a representation of your master data, which is the fullest, cleanest and most accurate information available. They are created from consolidating master data from multiple Systems of Record (ERP’s and other systems), standardizing that data, verifying the accuracy where possible, and then building clusters of similar records. This process of matching facilitates the creation of Golden Records, which contain the best information from all the master data in the cluster.
- Do the same for Product
- Do the same for Vendor
Are you sensing a pattern? Provided your systems of record have a reasonable amount of data characterizing each row of data, similarity clusters can be built. Inaccurate, non-standard data makes the process a little harder, but feasible. Accounting Master Data (i.e., GL Accounts) further benefit from a Uniform Chart of Accounts, to which all other systems may be mapped.
Golden Records Management is a non-intrusive, low-risk tool for accelerating the ERP migration process. Building Golden Records is repeatable for many types of master data and provides a means for preparing the best possible data for import into any new system. In Part 2, I’ll talk about how Golden Records and Master Data Management deliver a perpetual framework for Data Quality, extending the lifetime of legacy systems.
Want to learn more about the impact of master data on your organization? Join us on December 6 in Hartford, CT for our half-day workshop Discovering the Value in Your Data. Hear from data governance experts from BlumShapiro Consulting and Profisee as they address key topics for business, finance and technology leaders on data and master data management.
About Brian: Brian Berry leads the Microsoft Business Intelligence and Data Analytics practice at BlumShapiro. He has over 15 years of experience with information technology (IT), software design and consulting. Brian specializes in identifying business intelligence (BI) and data management solutions for upper mid-market manufacturing, distribution and retail firms in New England. He focuses on technologies which drive value in analytics: data integration, self-service BI, cloud computing and predictive analytics.