Master Data Management Challenges: A Single Source of Truth

Data-driven organizations across industries increasingly rely on accurate, consistent data management, but some more than others. Some sectors—like banking, financial services, and insurance (BFSI), healthcare, and retail—revere data accuracy and consistency above all else.

Organizations across these specific industries are operationally complex, face stringent regulatory requirements, and operate based on customer-focused strategies. As a result, success or failure in these industries often depends on the specialized discipline of master data management (MDM).

Regardless of industry, MDM practitioners will inevitably encounter common master data management challenges. Thankfully, with each challenge comes practical strategies to counteract them—but general data practitioners still stand to learn something here. Viewing these data quality issues through the lens of MDM offers a different perspective on issues across industries, roles, and departments.

Establishing a baseline for addressing master data management challenges

As with other data-related disciplines, the term “master data management” refers both to the specific data management practice and the tools and technologies that teams use to facilitate it. In particular, MDM centers around creating and curating a single, consistent, and accurate source of truth for an organization’s critical data entities and data consumers. Because it plays a crucial role in BFSI and other specific industries, these entities often involve data about the customers, products, key suppliers, and locations that are central to a given organization.

MDM practitioners choose which high-quality data to include in this single source of truth (or “the golden record”), depending on whether that data is foundational to business operations in their organization. In other words, included data must be necessary to (and shared across) multiple systems—such as customer relationship management, enterprise resource planning, and supply chain platforms—and mission-critical for decision-making, operational efficiency, and customer experience.

Key characteristics of MDM include the below factors:

• A core focus on master data: MDM practices revolve around an organization’s golden record—all data that is collectively foundational to business operations and analytics.
• An emphasis on data quality: Like data management frameworks, MDM practices ensure that an organization’s master data remains accurate, consistent, and reliable at all times.
• Support for both operational and analytical data use: In organizations, MDM supports dual functionality for operational systems and processes (such as order processing or customer relationship management) and analytical and business intelligence functions.
• A centralized or federated approach: Despite its primary single source of truth edict, MDM can function through either a centralized or federated data governance framework. The latter distributes ownership of specific subsets of master data to different domains or systems while maintaining overall coordination.

Critical master data management challenges for organizations

Unsurprisingly, most MDM issues affect the voracity of an organization’s golden record, either directly or indirectly. Despite their impact on business-critical data quality, these challenges can stem from a mix of business operations, business processes, and an organization’s MDM strategy itself. 

While not exhaustive, the following list provides a clear sense of what challenges practitioners are up against:

Choosing the right MDM approach

While the differences between centralized and federated approaches are clear, determining exactly how to implement MDM quickly becomes complicated. Because of this, MDM practitioners need to work with data leaders and stakeholders to assess and weigh various factors, like the organization’s nature, needs regarding flexibility, and tradeoffs between scalability and control.

The organization’s size and complexity play a major role in MDM implementation. For instance, smaller or less complex organizations can benefit from how centralized MDM approaches tend to simplify governance and foster uniformity. Large organizations, on the other hand, especially those with multiple business units or geographic locations, may need the nuance and flexibility that federated approaches provide.

Additionally, some organizations are large enough to have clusters of business units that require the benefits of both centralized and federated approaches. In such cases, MDM practitioners may ultimately need to adopt a hybrid MDM governance model leading up to the implementation process.

Ensuring data consistency across units 

Whether you’re using a centralized, federated, or hybrid approach, MDM involves embracing the reality that different business units within an organization will not all use the same tools, systems, and workflows. For this reason, data definitions, standards, and management frameworks between business units can vary widely. However, this reality also requires accepting the variance in data consistency across an organization, in addition to actively reconciling it.

In order for a single source of truth to exist in an organization, MDM frameworks need to account for these initial inconsistencies through data profiling and data cleansing. Doing this makes it possible to consolidate information across departments—which lowers, if not eliminates, instances of duplicate records, imprecise reporting, and poor organizational decision-making in the process.

Navigating data governance and compliance complexity

Unsurprisingly, laws like GDPR and CCPA impose intricate requirements on organizations. The realities of issues like data mapping, privacy rights management, and keeping personally identifiable information are complex and require time, effort, and expertise to interpret and operationalize due to their layers of technical and legal nuances.

Because of this, MDM practitioners must account for the complexities that exist within their organization. This process includes securing executive buy-in, advocating for sufficient resources to implement governance frameworks, and establishing all necessary processes and accountability to remain in compliance over time.

Additionally, just as MDM’s needs and requirements vary across industries, so do the specifics of these challenges.

3 effective strategies for overcoming master data management challenges

The realities of an organization—its size and complexity, whether teams need to prioritize scalability and flexibility, and the number of departments and their operational needs—form the basis for selecting the right MDM approach. Building on this basis strategically ensures that the organization’s golden record will thrive over time. 

While specifics can and should vary, the following three key strategies can help practitioners solve these challenges, regardless of the nature of their business:

1. Vet MDM approaches strategically

With the help of key stakeholders, MDM practitioners should first define all core requirements and determine which data elements are critical throughout the organization. These are the making of the single source of truth.

Here are a couple important steps to take in this process:

Perform data profiling

Examining your organization’s current data quality issues, data silos, and inconsistencies between business units will reveal gaps in data governance and integration that you’ll need to address.

The data profiling process is also an excellent time to solicit stakeholder input—so take time to listen to data stewards, IT leaders, and departmental and business unit leaders to understand any unmet needs, pain points, and preferences for data management in general. This engagement not only informs better data practices but also gives MDM practitioners a sense of the organization’s change management readiness, like how well the organization is likely to adapt to the governance structures introduced by the chosen MDM approach.

Assess integration capabilities and data security requirements

This is a critical step to take when evaluating your organization’s technical infrastructure. Existing systems should heavily inform which MDM approach will be most practical and effective.

Organizations with strong integration tools may be better suited for a centralized MDM model, as these tools support the consolidation of data from multiple sources. By comparison, orgs with mature interoperability frameworks may naturally lean toward a federated model.

Regardless of the approach, pilot any MDM solution you select first. This involves testing it in a controlled environment, then iterating and refining it over time to ensure long-term success.

2. Leverage automated data management solutions

MDM practitioners who drive large-scale MDM systems need to embrace automated data management tools. This is because the ability to maintain efficiency and reduce manual errors will, sooner or later, scale beyond what data teams can handle on their own. 

In most industries, the tools you’ll need will include those that can perform the following tasks:

• Automating data profiling and cleansing: Those who manage MDM in their organization will leverage automation to perform advanced profiling and identify patterns, inconsistencies, or potential duplicates in master data, regardless of how large the golden record becomes.
• Enabling real-time integration across different systems: Automated data management tools play a key role in preserving organization-wide data integration during times of growth. As a result, as new teams or departments form, MDM practitioners can reduce the risk that key data records will become fragmented or outdated.
• Matching and merging using machine learning: Additionally, practitioners can employ machine learning algorithms to enhance entity resolution, mitigate inconsistencies across systems, and further unify master records in the organization.
• Monitoring with centralized dashboards: Data management tool dashboards are also invaluable, especially as organizations scale. They provide MDM practitioners with visibility into key metrics like ongoing data security in addition to data quality and performance. This makes it possible to track and manage expansive MDM initiatives effectively, regardless of how large or quickly the business needs to grow.

3. Align MDM with organizational and industry needs

Since maintaining the golden record’s fidelity is so important, it can be easy to forget that it needs to stay aligned with industry, organizational, and employee needs just as much as those needs need to stay in lockstep with the single source of truth. The more strict regulation an industry sees, the more MDM practitioners know this to be true. This is why the MDM management process needs to stay ahead of any and all industry-specific needs in play.

Here’s how this would play out in a few key industries:

• For BFSI, this would include ensuring that teams can easily audit the golden record so the organization can meet financial reporting standards.
• In healthcare, MDM alignment would instead focus on maintaining governance and interoperability standards, like the Fast Healthcare Interoperability Resources standard for managing patient data across systems.
• Concerns in retail would revolve around business agility, operational efficiency, and factors that impact customer experience. 

Often, balancing regulatory needs against departments’ needs requires MDM practitioners to create data governance councils that involve key liaisons from both business units and IT to oversee adherence to all relevant frameworks. When they manage these needs efficiently, these councils can grow into a secret weapon of sorts, as they can define and reinforce clear roles and responsibilities within the organization. 

MDM solutions may, in fact, be responsible for the golden record itself. But in data-driven organizations, contributing to optimal data quality is everyone’s responsibility. Therefore, MDM practitioners who go above and beyond for their organization’s single source of truth should ensure that their peers play as much of a role in related MDM’s data governance frameworks as the tools and systems that support it.

Future-proof your organization against master data management challenges with a shift-left approach

Ultimately, MDM practitioners—especially those that operate in BFSI, healthcare, or retail—are always looking for new, more effective ways to organize their organization’s key data. This is why data contracts should be at the top of your list of MDM enhancements to implement next.

Once you draft and strategically implement them in a data environment, data contracts preemptively address and manage key complexities related to data quality, consistency, and uniformity, especially in industries that involve stringent regulations and data standards.

To learn more about how embracing data contracts and a shift-left data approach can help your golden record shine, sign up for the product waitlist at Gable.ai today.

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