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What Is Master Data Governance – And Why Do You Need It?

Matthew Cawsey | May 12, 2024 | 11 minute read

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What Is Master Data Governance – And Why Do You Need It?

Master Data Management Blog by Stibo Systems logo
| 11 minute read
May 12 2024
What Is Data Governance – And Why Do You Need It? ➤

High-quality master data and a transparent master data management strategy will help protect and grow your business. Master data governance helps you achieve it.

High-quality master data is essential to all things related to your business: revenue growth, operational efficiency, risk management and compliance, analytics, and digital transformation, and your ability to control costs and stay agile. When it comes to data protection and information management, transparent master data governance is paramount to your business’s success.

Below, we’ll discuss what master data governance is, why you need it, and how to implement it.

The exponential growth in data makes it paramount to get your data right from the beginning as catch-ups will be increasingly difficult and expensive. According to the 1-10-100 rule, it costs $1 to verify data as it is entered; $10 to cleanse and de-dupe each error; and $100 per error to operate a system with bad data.

- The Impact of Bad Data on Demand Creation, Sirius Decisions


What is Master Data?

Master data describes the core building blocks (data domains) upon which your business is built—the people, places, and things that interact to create the process of doing business. Master data is, therefore, the curated source of information that’s important to your business.

Examples of master data entities include customer master data, enterprise data, reference data, product data, supplier data, data on physical stores and locations, employee data, and data related to your major assets.

Our What is Master Data Management infographic can help you understand the full picture.


What is data governance?

Data governance is difficult to define hen you consider the many types of data: transactional data, behavioural data, performance data, temporal data, operational data—the list goes on.

Your business must have reliable, trustworthy information about these many types of data, which is where master data governance comes in. Proper governance of master data helps you identify key data assets and enhance them in ways that are meaningful and useful for your business.

Having accurate and reliable information allows organizations to make decisions based on clear data rather than assumptions. For instance, many companies struggle with duplicate customer records.

These duplicates can result in negative customer experiences, the loss of upsell opportunities, and potential risks of fraud and data privacy violations.

Furthermore, these records cannot simply be deleted, because they serve specific business purposes, and they are linked to have  different owners who require access to the information. It is often unclear which record has the most trusted, complete, or up-to-date information.

To address this, organizations must maintain a single customer view across multiple lines of business.

However, this raises questions about how "customer" is actually defined, as different business needs might interpret this term differently.

Master data governance allows your business to create a glossary of agreed terminology when it comes to customer information and beyond.

This ensures that everyone in the organization uses consistent and standard definitions, which help to eliminate misunderstandings, misuse, confusion, and errors in the future.

How does master data governance accomplish this? The answer: A data model that prioritizes a single source of truth.


Master data governance


The single customer view – A master data governance achievement

The highest degree of data transparency and trust amounts to a single, curated view of your assets and data sources. For instance, having a single view of your customers enables you to uniquely identify each customer and understand their attributes and relationships with your company, leading you to make better business decisions.

This consolidation in your view of data is the indispensable foundation for becoming data-driven and maximizing the full business value of your assets. And yet, customer data can reside in different applications (ERP, CRM, etc.), making it difficult to know which version to trust.

De-duplicating and merging different line-of-business views into a single customer view, also called a golden record, can provide you with a trusted source to be shared across channels, as in the following B2C single customer view:

Single customer view

In order to reach that pinnacle of data quality and transparency, you need to consolidate and cleanse your data and govern data processes: Where is data stored? Where does it flow? Who uses it (and needs it)? And who can change it?

After data cleansing, clear policies and rules for the acquisition, storage, management, and sharing of master data must be put into place. These policies must then be taught, understood, and enforced.

A strong master data governance solution must encompass people and processes, not just technology. That said, the implementation and enforcement of your data policies become easier if they are supported by appropriate technological capabilities, such as a master data management system.


Master data governance and master data management are not the same

Master Data Management (MDM) is a comprehensive process that focuses on the creation, maintenance, and use of authoritative master data across an organization, whereas Master Data Governance (MDG) specifically refers to the policies, procedures, and standards used to ensure the quality, consistency, and security of this data.

While MDM encompasses the technology and tools for managing master data, MDG is concerned with the oversight and regulatory aspects that ensure your data is managed correctly.

Your master data management can be an important tool to support your data governance strategy as it provides useful methods for data collection, classification, modeling, quality control, clerical review by data stewards, as well as linking and merging capabilities for automated de-duplication.

As the diagram above shows, by connecting applications and unifying data, the master data management that harbors the customer data hub, effectively breaks down siloed data management, which is the single most important impediment to achieving data transparency and accountability.


Problems if you have ungoverned master data

Master data governance (in fact, all data governance) is often seen as a CDO's responsibility and as an administrative and compliance overhead. However, master data governance is not just needed for tick-box exercises; it directly impacts the business’s ability to scale and achieve new goals.

It is even necessary to prevent your business from leaking money here and now. Having ungoverned data means you will inevitably spend valuable time on manual processing and putting out fires.

Having ungoverned data means you will inevitably spend valuable time on manual processing and putting out fires.

Ungoverned data causes goods to be dispatched to the wrong addresses or customers to receive goods that don’t match the publicized descriptions. Both result in bad customer experiences, loss of reputation, and loss of loyalty.

Among the more subtle effects of poor data quality are:

  • Missed opportunities to upsell to a customer because you can’t accurately identify the product categories that they purchase
  • Time wasted on correcting and re-processing
  • Not being able to negotiate purchasing discounts because the supplier is duplicated so many times that you can’t say what your total spend is
  • Lost web sales because your inaccurate sizing data makes you look bad on comparison sites
  • Lack of insight into your supply chain, including sourcing and manufacturing methods, use of sub-contractors, etc., can cause expensive recalls and brand damage
  • The inability of manufacturers to share accurate information with distributors and retailers 
  • An impedance on product master data compliance with data standards and requirements, such as government and trade regulations, or GS1 standards

Finally, inconsistent data across systems and processes leads to a lack of confidence in your analytics. Your intelligence becomes subjective and decision-making becomes based on opinions rather than facts.



Formalize your data governance

People in your organization are already governing data as part of their regular job.

For example, your accountants are probably ensuring that postings are made to the correct ledger codes, and your accounts payable department is ensuring that invoices are sent and matching payments are received. Most of your operational data is already managed actively, but the focus is primarily on tracking quantities and financial values.

This master data, which underpins many of your business processes, often receives insufficient quality checks. Master data governance seeks to establish formal management responsibilities to ensure the overall quality and reliability of this data, shifting your business away from a reactive approach and toward a proactive one.

Poor data quality is often only found when a business process fails—when a delivery can’t be made or when your IT system stops working—which is hardly the best way to find problems. Plus, when disasters occur through poor data quality there is no one to turn to for solutions.

Data governance ensures that somebody is clearly responsible – not just for fixing the disasters but also for reducing the likelihood of one occurring.

Key elements of master data governance

Every organization is different. There can be no universal one-size-fits-all framework for master data governance, although there are key elements that everyone must pay attention to.

These include transparency, maintenance, data ownership, change management, compliance, accountability, authority, auditability, data stewardship, standardization, and education.

Many proponents of data governance have fixed models which have been proven to work in previous engagements. The issue is that many of these fixed solutions disregard your organizational capabilities.

Use the steps in this infographic as a starting point for your master data governance journey:



Many vendors claim to offer data governance tools, and there are tools that can help you govern, tools that can enable you to store and communicate the defined business rules, tools to measure data quality, and tools to identify compliance issues.

Governance is, however, about the organization, processes, and responsibilities within which such tools can be deployed. Without the correct organization, the benefits of these governance tools will not be realized.

Without the correct organization the benefits of these governance tools will not be realized.


Data maintenance needs to be governed, too

Is data governance the same as data maintenance? Although the two are very closely linked through data quality, but they are independent functions.

Maintenance organizations tend to be aligned with specific IT systems or with specific lines of business (LoB) within your organization, whereas data governance is about a common set of rules that everyone should adhere to.

The key to understanding this dichotomy is to understand the two parties’ relationship to standards. As part of master data governance, you need to define a set of best practices or principles that will ensure that you create and maintain good quality in your data.

Define these as your standards. The data maintenance teams must comply with these standards, but data governance must define the standards and ensure that they are being met.

What is data ownership?

Data ownership is a very confusing term. For example, it is common in businesses to split data responsibilities geographically—the UK sales force manages all customers and their data in the UK region, whereas the US team takes responsibility for those in the States.

But then again, we are proposing a single data governance organization that is responsible for the data, and to add to the confusion, in many such governance organizations, we see the role of the data owner.

However, the data governance organization’s role, named “data owner,” is a misnomer because in practice, what they own is not the data but the standards (the principles and best practices) that guide the users in achieving good quality.

So, while many departments may claim the data's contents, the data governance organization owns the structures and quality rules.



What activities does a data governance organization perform?

When viewed at a high level, the data governance organization only performs two activities, but in practice, these two activities can be very complex and require a network of resources to achieve them. The data governance team is responsible for change management and compliance.

Change management: – Once you have defined a set of standards and aligned your data to these, it is important to control changes to these standards.

For example, if you define that all dates are stored in the UK format of Day/Month/Year, then it’s a big issue if somebody wants to change to the European format of Month/Day/Year. 

The data governance team assesses the impact of any such change, liaises with relevant stakeholders, measures the cost and benefit of such a proposal, and, if the change is deemed appropriate, manages those changes across all affected areas of the business.

Compliance: Wherever there are rules, there is a requirement for policing. The data governance team must be that police force—to measure the organization’s compliance with any standards that it governs and to act to improve the level of that compliance.


Outline of a data governance organization:

Data governance organization


How to get started with master data governance

As stated above, there is no one-size-fits-all, yet as a minimum, you need to consider the following steps toward a data governance program:

  • Make someone accountable for the program, e.g., a CDO
  • Make it central to all data management disciplines
  • Assess where you are – you can use a maturity model – then plot your journey
  • Define roles and responsibilities
  • Measure progress by setting KPIs

When digging a little deeper into the program, the next level could consist of these six blocks.

Six steps to build a data governance program:

1. Build a clear vision for your desired data quality and processes

Ensure you have a clear vision and scope for your data governance initiative so that your organization can smoothly complete your data integration

2. Define data standards

Each standard should have a business rationale as to why it exists, defined benefits that can be achieved from having the standard, definitions of what level of quality should be achieved to realize the benefit (not always 100%), and metrics that will show that the benefits are being realized.

3. Design a data governance organization

This organization must be suitable for managing the standards you have defined. This includes the roles and responsibilities of those governing data, the internal governance processes that will be used to manage activities (such as the change management for standards), and changes to any external process that affect the organization’s ability to govern (such as the IT project management process).

4. Engage your data owner

– to own your standards and to build the data quality roadmap.

5. Build a data quality roadmap

The roadmap must document your current quality level. Measure this against the requirement defined in your standard and propose actions to bridge the gap and/or maintain good quality.

6. Populate the remaining data governance roles

Engage resources for the data governance roles that are needed to operate the ongoing compliance measurements and to manage the activities identified in the data quality roadmap.


How can you make sure that your data governance organization succeeds?

One of the keys to a successful master data governance organization is the authority to address when someone refuses to comply with your standards.

Where there is no authority, you usually find the growth of local standards and the proliferation of complex interfaces to manage the transition between areas of the business with differing standards.

As the number of standards increases, you eventually come to the point where there is no standard at all. The types of businesses that often have issues such as this, are those that have grown through acquisition but have kept data quality management of these subsidiaries at arm’s length.

Conversely, the most successful data governance initiatives are in the pharmaceutical industries where compliance to standards is enforced by external agencies.

Data governance is not complicated, in principle, but its application can become become both complex and very political.

It benefits from expert design guidancehaving expert guidance in design,to design but it also requires local knowledge of the enterprise and its peculiarities to build something that works in your situation and delivers real benefits.



How master data management helps you govern your master data

Ensuring data definitions from the beginning can provide high-qualityhigh quality of data throughout the data lifecycle.

That way, data stewards and data owners across the enterprise can work with accurate data. This is where master data management can help: by defining permissions and tasks for users at a granular level.

Master data management can automate the execution of processes making data flow from department to department in a seamless manner.

Rules and gates between workflow states enable audit trails that help data owners track changes that are not authorized. This ensures operational efficiency while maintaining accountability.

Below are two examples of how master data management can be configured to support and enforce your master data policies

1. Customer data governance

The screenshot below shows the completeness statuses of an organization’s customer data policies. The screen lists all configured policies with some general metric at the top (in this case number of current breaches) and each current policy score. In addition, you can see from which business application customer data is fed into the master data management.

These policies can be sorted and filtered through the toolbar, from which you can also create new policies. The policies are based on a metric and a dataset.

Customer data governance policies overview

Diving into one of the customer data policies, you get a historical chart of the policy score with some snapshot data widgets on the left and a timeline of policy activity on the right.

From this screen, you can edit the policy status, breached threshold, and accepted deviation per scoring period. The toolbar also allows you to subscribe to the policy and receive email notifications

Customer data governance policy details

This capability of viewing and editing customer data policies at high and granular levels supports your data quality goals, as well as suitability by highlighting which processes need your attention.

2. Product data governance

The screenshot below shows an example of a product data configuration to ensure that a certain level of quality is met before the product can be pushed to the next stage in the workflow. As part of the workflow, the product manager needs to fill in marketing information on three pairs of jeans.

The three product attributes, Feature Bullets 1-3, are mandatory.

This gate in the workflow ensures both data completeness and accountability.

Product data governance completeness

The screenshot below exemplifies how master data management allows you to configure tooltips for each product attribute to inform the user of what value they are expected to key in.

Furthermore, the user can open a wiki page with more information on the attribute itself (when, what, who created it, etc.).

Each attribute is defined with a type, such as text, item number, or list of values, and with each type, there are attribution validation rules, such as min-max value, max number of characters, etc.

If a value does not comply with an attribute validation, then the master data management warns the user with a color code and prevents the user from saving the item.

Product data governance accountability


The takeaway: Master data governance must have definitions

Master data management can’t work without governance. But to take one step further back, you can’t implement a data governance framework without definitions. Data collection, classification, and quality control must be applied before implementing a data governance framework.

To govern data, you need clear definitions of acquisition and accessibility, which are essentials of master data management.

In that sense, your master data management and your data governance framework are mutually dependent. Learn more about master data management and how it supports your master data governance.


Master Data Management Blog by Stibo Systems logo

Driving growth for customers with trusted, rich, complete, curated data, Matt has over 20 years of experience in enterprise software with the world’s leading data management companies and is a qualified marketer within pragmatic product marketing. He is a highly experienced professional in customer information management, enterprise data quality, multidomain master data management and data governance & compliance.

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