Best Practices for Master Data Governance Success in Informatica MDM

What is Master Data Governance?

Master data governance is the application of data governance elements to a subset of data called master data. Data governance elements are documenting definitions, sources, processes, policies, rules, metrics, and people to improve data governance.

Master Data Governance Factors.

Data management is about creating trust. Have confidence that your data is handled and managed correctly and meets high quality standards. Master data governance elements help create optimal conditions for delivering high-quality, reliable data, controls, and processes. Let's take a look at eight key master data governance elements that can help build trust within your organization.

Master Data Definitions

Master data definitions describe business entities and their attributes to create a common definition for all master data domains used in your organization. For example, your customer base can include company name, email address, phone number, shipping address, billing address, and more. Product masters can include attributes for category, SKU, size, color, and material. A device master can also include type, model, serial number, manufacturer, and location attributes.

Master Data Policies

Master Data Guidelines describe the internal and external regulations that must be followed when managing and using master data. Policies can address various aspects of master data management and usage. An example of a data quality policy that you can create is that master data must contain the complete set of domain-defined attributes. Your privacy policy may require you to obtain consent to processing before using your personal data. Risk management policies may dictate that there should be separation of duties between creators and approvers of new cost centres.

Master Data Rules

Master data rules determine how policies are executed and applied. Below is an example of how this works with a policy that requires consent to be obtained for processing before using personal data. Rules can force the collection of consent attributes such as billing, marketing, and third-party approvals before a customer record is created and approved. Another rule might check whether the marketing consent attribute is set to "Yes" before the marketing automation system sends a marketing message to the customer. It is not uncommon to have multiple rules to meet the needs of a single policy.

Master Data Catalog

A master data catalog documents where master data resides in application and analytical data stores on-premises and across multiple cloud ecosystems. It also documents areas of master data, their attributes, and hierarchical and graphical relationships. Understanding master data is important to ensure consistency across all sources, as well as accuracy and completeness of each source. For example, if mergers and acquisitions are part of your company's growth strategy, you can quickly compare master data in the acquired company's systems to your own master data definitions to reduce integration costs and increase business value., can reduce financial reporting risk. This can be achieved in Informatica MDM Tools.

Master Data Lineage

Master data lineage describes how master data moves between sources and is used in analytical and operational processes. Lineage is beneficial for many business activities, including data protection compliance supporting record of processing activities (ROPA). This helps you understand what data is used, by whom, and how. Artificial intelligence (AI) in business processes, such as recommendation engines and robotic process automation (RPA), can be used to identify where algorithms should be placed within a business process and what data structures they can expect as input., benefit from master data lineage. Master data lineage is also important for activities such as customer on boarding in financial services, product tracking in pharmaceuticals, and sustainable sourcing of consumer goods.

Master Data Stakeholders

Master data stakeholders are people across all functional areas of a company who are critical to the success of master data management. This includes him two groups of people. IT staff responsible for designing and managing databases, applications, and business processes, and business subject matter experts who create standard definitions, policies, and rules for master data. Data stewards, who are responsible for resolving data quality issues for specific master data domains, have legal and security personnel who are responsible for protecting and safeguarding the data, as well as governance committees who are responsible for resolving disputes between different organizations. Includes cross-functional executives. Functions within the organization are responsible. company.

Master Data Workflow

Master data workflows define processes for managing master data. Workflows include a variety of task-based processes, such as creating, updating, and approving master data definitions, policies, and rules, and creating, updating, and deleting master data records. Well-defined workflows improve productivity and collaboration between stakeholders responsible for different aspects of master data management. For example, supplier on boarding requires collaboration between finance, procurement, and legal teams to ensure compliant reviews. This may include parallel workflows such as address and bank details, credit score and sanctions list checks, and compliance and insurance certificate checks in Informatica MDM.

Master Data Metrics

Master data metrics must be defined to help measure and manage data, processes, and people. Common data metrics include the number of duplicate records in the application, and the accuracy and completeness of the master record. You also need service level agreement (SLA) metrics for the end-to-end process, for example, to understand how long it takes to approve changes to the master data definition and implement those changes to the master data source. Metrics allow you to monitor the productivity and efficiency of people performing specific tasks within an end-to-end process, such as: For example, how long does it take for data managers in different domains or applications to process change requests?

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