In an earlier article, we covered the similarities between ERP and MDM. In this piece, we want to touch on some differences.
Timing of the Data Analysis Stage: Unlike ERP, Data Analysis needs to come in the very first stage of an MDM project. Doing a data analysis (including data profiling) at the onset of an MDM initiative will help define the project strategy and scope. The discoveries from data profiling and analysis will lead you to identification of faulty business processes and outline the framework for how MDM will help re-engineer those processes.
Team Composition: The team makeup will be different for an MDM project than for ERP. You not only need the “business representation” from the individual source systems with which you are integrating, but you also need to create a Data Governance Council that can strategize and approve the “ownership” of data moving forward.
Multiple “Buy-ins”:As part of the MDM initiative, the “owners” of the source systems have to agree to let the Hub readfrom their systems, then match, enrich and deduplicate the data and then write back to the source systems. You have to ensure this expectation is clearly conveyed to the source system owners, and then get each individual owner’s buy-in to achieve the benefits of an MDM initiative. Do this right up front, no matter how painful, or your MDM initiative will face a huge risk.
Security and Access: Unlike an ERP system, you need to devise a data-level security policy and link it to role-based CRUD (Create, Read, Update and Replace) privileges. This may also lead to changes in some security policies of the source applications, leading them to expose or hide certain data. Given the high profile security breaches we’ve seen lately, this is likely to be a big focus area for your project. You can’t bring together sensitive information from all over the enterprise without making business owners very nervous, unless you completely nail down the security & access aspects of your project.
Define Post “go live” Key Performance Indicators (KPIs) for enterprise data: Life for a data steward begins after going live with the MDM project. Assuming the Data Governance Council has done a good job in creating the data steward organization with clearly laid out policies & procedures, it has to lay down a framework for measuring data quality and other KPIs and then deliver steady and consistent results. These measurements and results should be published and made visible across the enterprise to keep the data revolution going …
Today’s guest author is a colleague from Hub Solution Designs, Gaurav Arora. The above article is a great take on the subtle (and not-so-subtle) diferences between Master Data Management (MDM) and Enterprise Resource Planning (ERP). Best regards — Dan