Master Data Management - 13 Best Practices for Effective Master Data Governance
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By: Sagar Sharma

Master Data Management: 13 Best Practices for Effective Master Data Governance

Every company has to manage the organizational data from different departments such as operations, marketing, sales, eCommerce, account, human resources as well as individual employees and customers in more or less quantity.

How do you manage all of these organizational data?

You might be managing all the data in disparate systems.

If it is so, then let me ask you one more question.

Do you find that approach time and cost-efficient?

If your answer is YES, then I am sure you are not aware of the capabilities and benefits of centralized management of your organizational data. It saves a lot of time, money, and effort for your team in managing the data across multiple systems. Thus, it is highly recommended to mid-scale to large scale organizations to implement a Master Data Management system.

While developing and implementing an MDM solution, don’t miss to consider advanced security and compliance standards to protect your valuable data.

Most of the platforms available in the market come with the best security standards. But, if you want, you can add extra layers of compliance to avoid the risk of a data breach.

Before moving towards master data governance, let’s get an overview of master data management. Udertsadingfo MDM enables you with better clarity further in this post.

What is Master Data Management (MDM)?

Master Data Management creates a single master record of all the essential business data from external and internal data applications and sources. It involves the organization with a consistent and uniform set of extended identified and attributes that present all the core business entities.

In MDM, companies can manage the data of their products, customers, employees, accounting, operations, partners, websites, and more.

To get better insights about MDM, read What is Master Data Management & How Can It Benefit Your Business?

What is Master Data Governance?

Master Data Governance is an application for data governance and compliance that helps brands improve the management of a subset of master data. Master data allows managing all types of data that every entrepreneur needs to run an organization or business.

MDM helps companies to manage various operations too through centralized data management.

For example; you purchase the material from the suppliers to create products that you want to sell to your customers and deliver the products to partners.

The consistent and accurate material, product, supplier, partner, and customer data help you to boost the accuracy and efficiency of your various business processes such as record to report, procure to pay, and order to cash.

Now, it’s time to learn about the core topic. Yes, let’s explore the best practices of master data governance.

13 Best Practices of Master Data Governance

1. Definitions

Master data governance presents the core set of attributes that are part of the main master data definition and these attributes are consistent across the company.

For example; you want to create a master record of customer data in MDM. For that, MDM allows you to manage all the data that are relevant to your customer base such as

  • Name (full name of the business and customers you are selling the product)
  • Address (billing and shipping address to deliver the products)
  • Email
  • Mobile number
  • Payment terms, and many other attributes

In short, you can cover all the attributes that are essential for your business processes.

Here, the critical job is to define which attribute is important for your business. Otherwise, you will end up focusing on the least important attributes that negatively impact the success and agility of your master data management operations.

2. Data Quality Management

Data quality requirements differ from company to company. Thus, organizations need to consider tools and techniques to support data monitoring and validation processes. The data quality management processes include

  • Enabling effective reporting and quality monitoring
  • Creating control for validation
  • Data incident tracking
  • Enabling recommendation and root cause analysis
  • Supporting the triage process for assessing the level of incident severity

The right process for data quality management enables you with trustworthy data for analysis.

3. Data Access Management

For data access security, two aspects are considered under master data governance.

1. Provision of access to available assets

It is essential to provide data services that allow organizations to access their respective data. The companies need surety that apart from the company, no other individual or company can access their data. Most of the cloud platform providers offer varied methods for developing data services.

2. Prevention of unauthorized or improper access

You need to develop a Master Data Management solution that allows you to define roles, groups, and identities in order to assign access rights to establish a level of managed access rights.

Data access management is the best practice to manage the master data access services and interoperating with cloud provider’s access and identity management services by allocating and managing access keys, defining roles, and specifying access rights for ensuring that authenticated and authorized systems and individuals are able to access data assets according to determined rules.

4. Policies

Master Data Governance makes sure that external regulations and internal policies are taken care of as a part of master data management. These policies should be relevant to many aspects of the master data governance such as privacy and protection, risk management, data quality, and retention and deletion.

To address the regulation and policies, it is important for you to separate the duty in terms of

  • Who can create the master data for the cost center in a general ledger system
  • Who is allowed to approve the creation of cost centers (it is a risk control policies in order to prevent accounting fraud)

5. Rules

You might be thinking, we have already discussed the policies then why we need to talk about rules. It’s indirectly a part of the policy.

Well, it’s not so.

Policies are supposed to define what you want to do. On the other hand, rules define how to enforce and execute policies.

Want to understand this difference in detail? And, how policy and rules work hand in hand? Let’s look into it.

Policy: Before you use the personal information of a customer, you must obtain approval for processing.

Rule 1: Define the consent attributes that need to be a part of customers’ master data definition such as marketing, third-party sharing, and billing.

Rule 2: Before the customer record is created and approved, enforce the collection of those consent attributes.

Rule 3: Check out all the marketing consent attributes before the custom data can be used in a marketing automation system.

This example makes it clear to you that it is quite normal if you define multiple rules to address the requirements of a single policy.

6. People

By creating the documentation of master data governance, you can provide visibility to your different teams across the organization who are continuously working towards the success of MDM activities. These people from your team could be:

IT team

Your network team is responsible for the architecture and management of various business processes, applications, and databases.

Subject matter experts

Subject matter experts are mainly responsible to define both standardized master data definition along with the levels for the business and types of the quality threshold needed for varied business processes.

Data steward staff

They are responsible for remediating data quality problems for specific master data domains.

Legal and security team

This team is responsible for data protection and privacy.

Cross-functional leaders

Cross-functional leaders, who comprise the council or the governing board, are responsible for solving disputes amongst varied functions within the business.

7. Workflow

Once you determine the core team who are going to utilize the master data management. You also need to define the workflow in the document that allows your teams to collaborate effectively. With the help of workflow, you can

  • Define a mechanism for creating the request for master data creation requests.
  • Allow multiple people of the different organizations who need to be involved with parallel approval, workflows, go-live distribution, and activation of applications.
  • Determine which master data steward the request is routed to, based on domain responsibility.

8. Catalog

By implementing a robust Master Data Management system with the right Master Data Governance, you can get access to several cataloging capabilities.

  • Assuring the quality (completeness and accuracy) of master data across every single source
  • Verifying the consistency of master data definition across different sources
  • Exploring and documenting which master data domains are available across different systems, applications, and other sources such as lakes, data warehouses, and more.

The Master Data Management system understands the master records you have, knows where the required data is located and clarifies how it conforms to your policies and definitions.

Mergers and acquisitions are the very common strategies that businesses adopt to expand and grow in a new market. You need to understand the master data available in the source systems of the company you acquired. Also, you need to map that company’s data with your master data definition. By performing these activities, you can reduce financial reporting risks, reduce integration costs, and accelerate business value.

9. Process Mapping

Process mapping provides visibility on how the master data flows between different sources as a part of business activities. It is more or less similar to the catalog documents where the master data resides. Knowing how the master data flow through processes or understanding the source helps you to better visualize the varied things like

  • Where rules require to be introduced into the process to enforce the policies
  • Compliance risk exposure
  • How the master data is being used

In the process of mapping, you need to understand from where the master data is collected, which systems the data flows to, and what third party systems the data is shared. Thus, you can enforce policies and standards for clinical data submissions and acquisitions.

10. Auditing

To ensure that the systems are working as they are designed to act, companies need to access their systems. Data auditing, monitoring, and tracking (who has made what changes and when and with what information) helps your data security teams to collect data, identify risks, and act on them before any data damage or data loss occurs.

To avoid any data security threats, it is important to perform regular audits for your master data where you check the effectiveness of the security controls by analyzing overall security health and mitigating threats quickly.

11. Data Protection

Most of the companies have Perimeter security that is not enough to protect your sensitive business data. There is a risk of data leak as the users with limited access to your data cannot access all of your data but some of your data can be exposed by that user anyhow.

You need to adopt advanced data protection methodologies to make sure that exposed data cannot be read. Here you can consider various methods like

  • Encryption in transit
  • Permanent deletion
  • Encryption at rest
  • Data masking

12. Data Literacy

To ensure the success of data governance relies on training, education, and a true understanding of what can’t and can be done with your data.

But the adoption of technology alone is not enough. It takes policies, people, and processes to drive the organization level change and enables users to protect and see the value of their data as a business asset.

13. Metrics

When it comes to managing and measuring the master data, master data governance allows businesses to define matrices. It contains varied technical metrics such as the completion and accuracy of master data, how many personal data attributes are masked or encrypted, and the number of duplicate records in an application.

These types of metrics help you in the technical management of master data, leading companies will frequently also try to future determine how consistency and quality of supplier and material master data help you mitigate supply disruption risk, reduce inventory carrying risks, and negotiate better procurement terms.

Make Salability Your Core Focus for Master Data Governance Technologies

Master data governance technology is all about determining the types of capabilities that need to enhance master data governance. It’s not about implementing the MDM. These technologies involve metadata scanners and connectivity to help the catalog master data across different sources. Moreover, process management and linage capabilities assist with workflow and process mapping.

Pimcore’s MDM Enables You with Better Master Data Governance

Pimcore is an advanced platform that allows you to build a Master Data Management system that contains advanced master data governance technologies to ensure the security and protection of your organizational data.

Whether you need policy and rule management, data quality, data integration, data protection and privacy, data cataloging, Pimcore MDM enables you with incredible capabilities for master data management and governance.

Credencys enables you with end-to-end master data management capabilities integrated into a modular or comprehensive Pimcore platform that is powered by advanced technologies as well as powerful in-built frameworks. To understand the Pimcore master data governance, feel free to get in touch with our experts.

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