In my first article, I outlined some of the challenges that organizations face in running their supply chain functions. This time, I’ll consider the difficulties specifically with master data, and how to address them.
The pain points in master data
There are many pain points across the supply chain, some of which are specific to the function, while others span broader process or technology issues. Some of these pain points relate to incorrect or incomplete master data used by the business processes. Supply chain processes are tightly integrated with reference and transactional master data, so it’s very important that the information used in business processes and day-to-day operations is of the highest quality. The success of an efficient and agile supply chain begins with complete and accurate master data.
In my experience, most MDM issues in an organization stems from the basic fact that there is no single owner of the entire end-to-end lifecycle of master data. Instead, data creation and maintenance are fragmented within the organization. For example:
- Data governance is not defined – the underlying issue with master data management in organizations is the lack of data ownership and governance. In most of organizations, the MDM is decentralized and spread across multiple functions
- Multiple ways of working – because MDM is not centralized, the definition of data varies across different regions or product groups. This leads to inconsistency and duplicity in master records
- Missing the visibility – in absence of a dedicated MDM process, no one in the organization has a clear view of the availability and correctness of master data records. In some organizations, the rigor is there when a new master record is created, but subsequently the attributes of a master record is not reviewed throughout the lifecycle, resulting in inconsistent and inaccurate data.
- Data roles and responsibilities are not clearly defined – there are no clear data roles and responsibilities defined within an organization. Typically, in MDM a clear segregation of roles is required across data owners, data stewards, data creators and data users, to ensure appropriate MDM accountability. There is no set standard which fits all organizations for master data governance. It varies, in line with the size and desired level of maturity for master data management.
…and the answer lies in…
The best way to avoid issues is to set up an integrated master data management system, with dedicated governance over the correctness, completeness and on-time availability of master data. Conducting a mature assessment of current master data with regards to people, processes, technology, and governance will help organizations to set a realistic goal on what needs to be done with a time horizon in mind.
- Define a MDM operating framework:
- Create a global target operating model for the new MDM organization
- Achieve clarity in roles and responsibilities
- Create workflow-based master data, and keep it updated
- Establish a standard input template.
- Rule-based master data processing:
- Implement a global data definition
- Maximize rule-based derivation of data attributes for bringing efficiency.
- Data quality and process control:
- Validation of MDM request against data definition and business rules
- Duplication checks
- Accurate classification of records
- Periodic data checks for any discrepancies
- SLAs and KPIs to measure MDM performance and its business impact.
As with so many things in life, an external perspective can sometimes identify issues and potential solutions more readily than can be achieved from within. What’s more, that same external view can also bring with it a broad range of relevant experience that simply isn’t available inside the organization. A knowledgeable and seasoned service provider can help to unleash the full potential of master data, and perhaps even provide an end-to end service, from advice, to implementation, to managing the services.
It’s worth considering. In an increasingly competitive business world, the efficiency of the supply chain is crucial not just to margins but to customer goodwill – and a great supply chain is built on great data.
Read Capgemini Research Institute’s “The Digital Supply Chain’s Missing Link: Focus ” report to learn more about how organizations across consumer products, manufacturing, and retail understand the digital initiatives they are adopting, the benefits they are deriving, and the way they are transforming their supply chain.
Abhishek Bikram Singh has over 12 years of industry experience (CPG, Manufacturing, Chemical, Retailers) in managing different supply chain functions. He has worked with clients across industries to define their current MDM maturity with respect to people, process, technology, and governance, and to develop their target operating model.