Though organizations are striving to manage master data as an enterprise asset, unfortunately the success of master data management programs is quite low. Successful MDM requires strategic planning and vision plus executive sponsorships and buy-in of key stakeholders. The organization should also ensure that MDM is a business-led initiative and should jointly be owned by business units and the IT department.
The objective of this MDM maturity model is to assist organizations in assessing their current MDM capabilities and to help define the future state of MDM. It shapes the roadmap for MDM with distinct phases of maturity and the way to acquire missing capabilities. This maturity model offers a schematic approach to define and prioritize MDM activities toward stepwise improvement of the organization’s MDM maturity.
Levels of Maturity
In general, organizations need to follow a sequential step-by-step approach toward MDM maturity. However, new organizations with evolving business and IT applications can plan to build MDM while creating their operational systems. These organizations can try to achieve the higher levels of maturity right off the bat.
There are six distinct levels of MDM maturity.
Level 0: Ignorant (No MDM, Unaware of MDM)
Symptoms: This level may be viewed as the pre-MDM stage. At this stage, the organization is unaware of MDM terms, values and needs. Typically, individual business groups suffer from data quality and consistency issues. Day-to-day operations run with inaccurate and incomplete master data. Analytic processes run on outdated master data sets. Manual, expensive and time-consuming processes are being followed toward data reconciliation and integration.
Actions to be taken: The organization should initiate an MDM awareness program. Educate business and IT groups on the need for and benefits of master data management, data quality and data governance. MDM success stories should be discussed, and external faculty/thought leaders may be invited to discuss the MDM journey, benefits and best practices.
Level 1: Initial (Awareness of MDM)
Symptoms: At the initial stage of an organization’s MDM maturity, the organization is aware of its master data issues and how they affect business processes; however, what is lacking is the capabilities aspect. While the need for MDM, data quality and data governance is recognized, there is a lack of clear vision and strategic direction, and no buy-in from CXOs. Funding and executive sponsorship are the major challenges. Data ownership and governance are missing. Data quality initiatives are run in a fragmented and distributed manner. A centralized repository for data and processes does not exist.
Actions to be taken: The organization should continue with MDM awareness initiatives and try to propagate MDM concepts to all groups and levels. Organize one-to-one session with CXOs, executive sponsors, business and IT leads. Closely link MDM with business issues and enterprise data problems. MDM should be defined as the core component of the organization’s enterprise information management landscape. Establish correlation between the MDM program and organization goals.
Level 2: Isolated (Isolated MDM Initiatives)
Symptoms: At this level, there is recognition of problems arising due to duplicate master data and its effect on the business. As a result, reactive/firefighting attempts to resolve the issues are undertaken. Various business and IT groups in the organization are engaged in these isolated reactive data management initiatives. Enterprise-level initiatives are lacking; duplication of effort/initiatives exists across the groups.
Some attempts are made to fix data quality issues at an individual application level, tools are evaluated and purchased but still are managed as technical resources. The IT department often takes the lead and struggles to get the business engaged in this journey of MDM.
Actions to be taken: Create a business case for department/group-level MDM; look for some business issues that can be solved using an MDM or data quality management program. Try to connect the various business and IT groups engaged in isolated MDM and data quality initiatives. Inject the need for a common data governance framework so that different groups can interface and decide a common strategy to deal with master data and data quality issues.
Level 3: Organized (Planned Siloed MDM Initiatives)
Symptoms: At this level, there is serious focus on particular master data domains or use cases and proactive initiatives are launched to build siloed MDM for broader audiences. (For example, MDM to support the analytics, product information management to support supply chain, etc.)
CXOs advocate the need for MDM and recognize it as a key enabler; needs are clearly stated for a single view of the customer or product to improve the accuracy/effectiveness of business processes (for example, customer data integration for better customer relationship management and analytics, PIM to improve the order-to-deliver cycle, etc.).
Also at this level, a strategy has been outlined for use case/domain-based multiphase MDM implementations, and a business value-driven MDM roadmap is defined. There is a clear business-driven MDM initiative in place, and the business understands that it must take ownership and the lead in CDI and PIM initiatives. An MDM charter has been created and a program director is appointed. Data profiling and analysis has been done to understand the commonalities and divergence of the master data and its associations with the business processes. Overall, master data quality has been analyzed and key metrics have been defined to measure the quality of master data on an ongoing basis. MDM governance and stewardship structure has been established to support the MDM-related policies and decision-making. The governance and stewardship structure is evolving and in an initial stage of maturity.
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