Master Data Management (MDM) and Data Governance are two closely related disciplines that work in tandem to ensure the integrity, quality, and consistency of an organisation's data assets.
While these two practices serve distinct purposes, they are interconnected and mutually reinforce each other in the context of data management. And with the huge growth in data usage and its inherent complexities, in parallel with an unprecedented amount of data being generated - now is the time to ensure Data Governance and MDM are working hand in glove.
In this article I want to talk about the relationship between these two disciplines and describe how for an organisation to successfully use its data it needs to make sure that they are working together rather than at odds.
Data disciplines that work hand in hand
At its core, MDM focuses on managing the critical data entities or "master data" that are essential for an enterprise to operate, such as customer information, product data, or supplier records.
The primary goal of MDM is to create a single, authoritative “source of truth” for this master data, ensuring that it is accurate, complete, and consistent across the enterprise. By centralising and standardising master data, MDM enables an enterprise to eliminate data silos, reduce redundancy, and improve data quality.
Data governance, on the other hand, is a broader framework that encompasses policies, processes, and controls for managing data assets effectively. It involves defining rules and standards for data management, establishing roles and responsibilities for data stewardship, and ensuring compliance with regulatory requirements and internal policies.
Data governance supports the overarching framework and governance structure within which MDM operates. For data management to be truly effective it needs oversight to enforce data quality standards, protect sensitive information, and promote data integrity.
The Data Governance rules and policies lay down how master data is to be managed and maintained. For example, Data Governance may establish data quality metrics and requirements that MDM processes must adhere to, such as data accuracy thresholds or validation rules.
Conversely, MDM plays a crucial role in supporting Data Governance initiatives by providing the technical infrastructure and tools needed to enforce Data Governance policies and standards.
MDM solutions serve as the operational backbone for implementing Data Governance processes, enabling organisations to enforce data quality controls, manage data access and permissions, and track data usage.
Read more: 3 Signs Your Business Needs Stibo STEP Master Data Management Solution
How does MDM and Data Governance work together?
While Data Governance delivers the governance framework and oversight, MDM provides the technical capabilities and infrastructure needed to operationalise Data Governance policies and standards effectively.
Together, these two data practices form a cohesive approach to data management that enables an enterprise to derive maximum value from their data while mitigating risks and ensuring regulatory compliance. Data privacy concerns are on the radar of most enterprises today, with legislation like the EU’s GDPR (General Data Protection Regulation) that requires organisations to implement specific data governance practices to protect personal data.
MDM supports compliance efforts by providing a centralised repository for data management and audit trails for tracking data.
Data governance is the process of managing the availability, usability, integrity and security of the data in an enterprise organisation as laid out in internal standards and data usage policies.
Effective Data Governance ensures that data is consistent and trustworthy and doesn't get misused. It's increasingly critical as companies today face expanding data privacy regulations and rely more and more on data analytics to help optimise operations and drive business decision-making.
But as any business knows, there is a need to strike a happy medium of also being able to focus on the expected business outcomes of a governance programme, rather than only showing that they have delivered on the governance part.
Without effective Data Governance, data inconsistencies in different systems across an organisation might not get flagged and addressed, hampering not just regulatory compliance but also the effectiveness of business decision making.
Data governance goals and benefits
A key goal of Data Governance is to break down and prevent data silos forming. For an enterprise to see the benefit of their investment in data it needs to take down these metaphorical walls.
Data Governance aims to harmonise the data across the enterprise, fueling the collaborative process, with stakeholders working together and sharing a common understanding of data.
Additionally, Data Governance ensures that data is used properly following regulatory compliance, both to avoid introducing data errors into systems and help manage the risks around using personal data and other sensitive information. All businesses want to avoid the cost and reputational damage that comes with a data breach.
Data Governance can only work by creating uniform policies on the use of data, along with procedures to monitor usage and enforce the policies on an ongoing basis, helping to strike a balance between data collection practices and privacy mandates.
Besides more accurate analytics and stronger regulatory compliance, by streamlining the process it also reduces data management costs and improves efficiency, enabling key resources to dedicate time to other revenue generating activities.
And by increasing the accessibility of accurate data, that means more-informed business decisions based on better data that creates competitive advantages that are hopefully turned into business gains.
Read more: How can a Master Data Management solution facilitate a circular economy?
The following is a breakdown of the various components that need to come together with MDM to create a successful data management strategy:
Read more: How to Get Started on Your Master Data Management (MDM) Journey
Next Steps
So, while MDM focuses on ensuring data consistency, accuracy, and reliability, we will always need Data Governance to be able to implement the controls needed to maintain data quality standards.
Get in touch to have a conversation about managing your Data Governance and MDM data disciplines.
Meanwhile, a question you need to think about is “Are you making your data work for you and are you able to leverage data as a strategic asset?” Often we find clients have fallen into bad data practices where a lack of good data quality may be hindering your growth and causing expensive operational mistakes.
You can use the Unit of Measure’s free self-assessment test to help you quickly get a sense of what you are doing well and where there is room for improvement.
Use this test to take a view on your organisation's level of maturity when it comes to how well you follow best practices for managing data and harness the full benefits of a data-driven business.
Self assessment test: What is the maturity level of your data management?
Book a free consultation call with us today to learn more about how we can help with regards data maturity, MDM or Data Governance.
