Commercializing a product, opening a new facility, or starting a new business unit often involves multiple systems, creating immense complexity, tight timelines, and frequently changing requirements. With a few common frustrations in these large programs driving up costs, our Program Management Series explores how to understand the source of these frustrations and turn problems into advantages. For our second topic of the series, we are diving into master data management. Master data management can seem daunting for large programs with multiple systems, and it can be hard to know where to start when making small changes to improve master data management practices. Various issues can arise across data harmonization, maintenance, and data governance decisions. However, we will share our insights to help clarify some of the nuances of master data management.
Why Master Data Management is Important for Large Programs
Master data management can have a big impact on streamlining data for large programs. Many companies may have multiple instances of the same system across different locations or different systems running in different locations. Often, the data created and maintained in one of the systems may have different meanings or uses in the other systems. This makes it difficult to pull insights out of the data, which then hinders the ability to use the data to determine actions – which increases the difficulty of tasks within global financial consolidation and global reporting. It also makes it much harder to ensure that you have the right data for good decision making.
It can take anywhere from days to weeks to gather the data across the different systems, make any necessary translations, and finally consolidate the data into meaningful information. With all the manual processes typically required to complete these steps to gather insights, there are many areas high at risk for mistakes to be made. However, with master data management, data has the same meaning from one system to the next, allowing it to be more easily compiled, and therefore ensuring that it can be more trusted.
Key Issues in Master Data Management
Three main issues that can arise with master data management are: navigating agreement among business locations and units, deciding whether to centralize or decentralize maintenance, and maintaining data integrity along with data governance.
- Agreement across Business Units: One of the biggest issues that programs face with master data management is ensuring agreement across different business units or locations on how to harmonize the data. Developing master data management can be especially challenging in a large program, since many business units with different data needs will all have to approve the proposed standards.
- Centralized or Decentralized Maintenance: Another major issue in master data management is determining whether to centralize or decentralize maintenance across the organization. A centralized approach may be more effective in the long-term, especially when a high level of data security is required, but a decentralized approach could be easier to manage within a larger organization.
- Data Governance & Data Integrity: Data governance is intertwined with the master data management system and should be a key factor for consideration, especially because issues with maintaining data integrity, creating reliable data, and keeping data governance rules and regulations up to date can become problematic.
It can be hard to know where to start if you are planning to focus on only small changes to the process, however one good area to begin with is profiling data. This helps you better understand the depths of data cleansing that will be required and can also clarify what decisions will need to be made to harmonize the data. Overall, it is important to consider how master data management can benefit your organization, while also preparing for any potential issues that could arise.
Clarkston has been managing platform level programs since our founding. Our meaningful program management processes will mitigate program complexities, standardize processes, and efficiently execute multiple operations that deliver a robust and resilient organizational model that can scale with a client’s needs.
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Contributions from Courtney Loughran