4 Stages to Master Data Management Success

Master Data Management is a vital capability of data- driven organizations – today more than ever. The growing amounts of event data that companies harvest come from new digital transformations, business process instrumentation, and customer engagements. Its usefulness depends on the quality of its reference data (people, places, things) to provide valuable context to run and optimize the business. There’s a higher reluctance to tackle these projects due to a long history of failed projects and the risk of major investments with limited results

While IT has taken the lead in the past due to the technical aspects of MDM projects, the ideal solution is having a business- enabled approach with IT’s integral support and guidance. This is important since business users are more likely to have firsthand domain knowledge for everyday definitions, rules/relationships, data quality, and governance necessary to drive business value with mastered data.

A balanced business-and-IT approach is critical for success in developing a business group’s capability and confidence to embrace mastering and curating their data needs. Therefore, it is crucial to change the mindset about MDM from IT project-oriented to a business capability-oriented approach that enables organizations to take the lead in mastering their data confidently.

This paper explains the four key aspects of a business-enabled approach to data management and define its maturity model’s stages and activities. This approach aligns with broader strategies that transform business culture into a data culture with the essential capability of mastering its data.

A Business-Enabled Approach

Mastering data has not fundamentally changed. The basics are:

  1. Define and enforce the uniqueness of a person, place, or thing (known as a “golden record” of an entity)
  2. Establish its key relationships (known as hierarchies)
  3. Associate the key attributes that describe the entity consistently

What’s new about MDM is the realization that the involvement of business teams is required to accurately assess and limit the scope of the data’s definition and applicability, and choose which attributes are valuable for their needs. This also improves data governance with more specific rules for using data correctly in a more accurate context.

Traditional IT-led MDM projects aimed to improve business process efficiency through synchronizing key data across operational applications. The new goal is to enable business teams to take the lead in mastering many domains of data for additional needs, including reporting, analytics, and data products. This requires a capability for the business to master its data needs with IT’s support and guidance. Neither group can realize value from MDM without partnership from the other.

Shifting the mindset requires consistent communication between both business and IT to reinforce that mastering data is a series of projects classified by their balance of scope and activities. Delivering MDM will still require IT project management and agile delivery, yet the scope and actions will be defined by the business.

Developing a business-enabled capability also differs from the traditional IT approach in that it requires an ongoing and balanced assessment of people, processes, and technology. It requires more time and effort initially for business users to gain hands-on experience mastering data and defining or improving processes for data management and governance. This is time-consuming work and should be balanced in initial data mastering projects that have a more straightforward definition and less complexity – those can be tackled later.

For hands-on learning, the MDM technology platform must deliver a business user experience. This approach does not mean asking businesspeople to become programmers. It is enabling them to work with their data intuitively and consistently. This is where IT should support and guide business users in how to work with their data, from data modeling literacy to production configurations and operations.

Initial and foundational data management and governance processes should focus on assigning data owners for accountability, data stewards for support and guidance, and data custodians in IT for operational support. Next, there needs to be focused communications and discussions around the data definition process (submission, review, and approval), followed by the issue resolution processes for teams and applications interacting with the mastered data. This will instantly benefit future mastering of other data domains.

Reference Data Management (RDM) is a great place to start, as these are defined mastered lists for internal definitions that are consistent across applications, reporting, and analytics. For example, three categories of reference data are particularly good starting places for the business: internal company lists that are unique to your business, industry-specific lists, and externally managed or international lists (e.g., NAICS codes, geographic naming, or ISO standards).

A Maturity Model for Mastering Data Efficiently

Following a maturity model for developing a business capability for “mastering data” will help set expectations and track progress within organizations and business groups. A milestone-based maturity model will show where to focus efforts and resources for quicker results. Each company can move through the stages at its own pace, and some companies will have parallel efforts in separate business groups.

This maturity model for business-enabled MDM follows four stages for balanced approach to develop mastering data as a capability.

Stage One: Recognize “Initial” efforts. Whether it’s a first- time master data project or revisiting past projects, begin with establishing tool proficiency for business users, managing data scope, and establishing data management processes. In the initial stage of your projects, minimize the complexity of data and spend more time and effort on business users gaining tool experience and developing accepted processes. Gaining proficiency with a master data tool is straightforward with training and group support, it takes time and concerted effort.

For data management processes, start by looking at what is not documented and how people deal with day-to-day data discrepancies to establish standardized data quality and governance. For minimal data scope, start with mastering reference data. They are generally well-understood drop-down lists that need better management for consistency across applications and analytics.

Stage Two: “Build” the data management experience. The Build stage is characterized by leveraging tool proficiency and testing the new data management processes for improvements or gaps. Mastering data in a business domain where the entities are more isolated and specific to a particular business team facilitates communication, makes it easier to gain consensus and exercise hand-offs with data owners, stewards, analysts, and custodians.

Mastering data capability growth will happen by working on specific data domains that can push more challenging data projects’ scopes and involve the broader organization. Data complexity and complicated business rules will come from involving more operational systems and requiring more overall organizational consensus. Stage two is where business teams strengthen their organizational process capabilities to define, approve, and resolve issues for the primary three steps of entity resolution, hierarchy definition, and attribution. There will be new processes for data reconstitution or exception handling that were not a common occurrence with RDM early on.

Again, this requires mentorship and support from IT as the business team embraces more technical concepts of MDM, but by the end of stage two a business team will be confident in tackling further multi-domain data mastering.

Stage Three: “Scale” the data mastering capability by enabling additional business groups. The early business teams now showcase their experiences and lead by example, which provides a source of support and inspiration. Early teams share their lessons within a growing competency center and serve as sounding boards on new projects.

Stage three is a critical shift from a single business team focus to an organizational focus that enables any business team to start mastering data that’s important to them. Each new business group will also go through the balanced approach in stages one and two, but they will be able to leverage the tools and processes of early teams within the multi-domain master data platform for a shorter learning curve. This can be further accelerated by having a single data platform designed for multi-domain data mastering for consistent terminology and discussions.

From a data architecture perspective, a single platform also centralizes a data catalog of mastered data entities for other business groups and developers. IT teams still support operationalizing mastered data for performance, scalability, and safeguarding.

Stage Four: Sustain organizational “Competency.” Competency is an ongoing goal rather than a finish line. This stage continually focuses on raising the overall business capability and resolving data management and governance complexities in broader company-wide terms.

After mastering data becomes ubiquitous throughout the company, emphasis on its role in the data culture becomes more day-to-day and engrained in the business users’ mindsets. While independently thriving among business groups as federated and scalable, here the approach will start to encounter challenges with enterprise-wide consistency. Business teams use their mastered data while being more aware of potential cross-organizational conflicts or opportunities. In this stage, a specific focus on communication and tracking projects across the company will keep teams aligned and accountable.

The business data mastering community that started in stage two will now become a competency center that benefits all business teams with a centralized information hub of contacts, case studies of best practices, standards, and lessons learned. Further, it announces what data domains are being mastered, and are available, by various business teams.

When building a thriving competency center, first create ongoing awareness of a program that supports the business capability to master data and welcomes everyone to learn and share their experiences. Consistency will be critical in communications, updates, and meeting virtually or in person to reinforce that the competency center is ongoing. Be sure to promote and emphasize engagement.

The competency center is not simply one person or team finding time to publish all the content. It is more about community involvement to communicate what business teams are doing, learning, and sharing in mastering their data. Engagement needs

to be informal so it’s not intimidating for people to demonstrate their work and create recorded sessions with transcriptions.

Summary and Key Takeaways

Shifting to a business-enabled MDM capability is the mindset. Get started by analyzing your current approach to mastering data and master data management projects.

  • First, are you shifting the mindset from an MDM project to one of building a business capability to master data?
  • Second, are you using a maturity model or some other framework to set the proper expectations around your journey? Priorities will also reduce risk and accelerate adoption.
  • Third, are you taking a balanced approach, which allows time and resources to identify process needs early, and permits time for learning the tools and developing proficiency through experience?

Transforming your business culture towards a data culture is a critical step in mastering data. Not only will this enable you to mature your organization’s data competency for the ever-changing future, it also aligns with the broader strategies that shape business outcomes across the enterprise.



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