【Data Governance Model】Which model is best for your organization?

Internal Data Governance: Part 2│The Data Governance Model

In the first part of this series, we defined data governance and examined the missteps that led to large-scale cleanup projects. In this post, we'll examine common data governance models and which ones are best for different types of organizations.

No single data governance model fits all organizations. A variety of models are commonly used in business today, some of which are better suited to smaller or larger organizations, while others are better suited to various structures or business needs. Let's look at the four most common data governance models:

  1. Decentralized execution - single business unit

The characteristic of this data governance model is that each business user maintains its own master data. This model ensures that data is created by local users, who are typically consumers of this master data.

Users, benefits and considerations:

• Best for small organizations such as a single plant or a single company

• Provides easier data maintenance

• Requires a lot of agility to set up master data

• Do not share master data with other business units

• Shorten the life cycle of master data

While this model is simpler and allows for faster setup of master data, users can also see huge inconsistencies in the data unless managed properly. The following strategies and strategies help ensure that this model works effectively:

• Clearly define data ownership and limit it to a few experts within the organization

• Make sure to clearly document how each field is populated and what each value of each field means

• Automated tools can control data consistency if budget allows

• Set up controls and audits to quickly fix any inconsistencies

• Limit the data governance organization's role to building processes and procedures and performing regular data audits

2. Decentralized execution - multiple business units

The characteristic of this data governance model is that each business user maintains its own master data. In this case, we have multiple business units working with shared customers, materials and suppliers.

Users, benefits and considerations:

• Best for small to mid-sized organizations involving multiple plants and/or multiple companies

• Provides easier data maintenance

• Requires a lot of agility to set up master data

• Allows sharing of master data with other business units

• Shorten the life cycle of master data

As mentioned earlier, while this data governance model is simpler and allows for faster setup of master data, it can also lead to data inconsistencies with far-reaching consequences when multiple parties are involved. This model really needs to be controlled, as very common side effects like duplicate master data and inconsistent data leading to inconsistent or nonsensical reporting can become troublesome. For this model to work effectively, it is critical that:

• Leverage automated tools that ensure data consistency—regardless of who created the master data

• Limit the number of fields maintained and let the rest be derived based on various custom configuration files

• Make sure to clearly document how each field is populated and what each value of each field means

• Set up controls and audits to quickly fix any inconsistencies

• Identify controlled fields that affect departments and business units, then enforce strict controls on who maintains those fields, and clearly define the meaning of each field

• The role of a Data Governance organization should not be limited to building processes and procedures and performing regular data audits, but should also include having automation tools and adapting them to business needs

3. Centralized governance - single or multiple business units

The third data governance model is characterized by centralized maintenance of master data by single or multiple business units. In this model, a central organization is responsible for setting up master data based on requests from master data consumers.

Users, benefits and considerations:

• Best for medium to large organizations with multiple plants and/or multiple companies

• bring complex data requirements

• Supports longer master data lifecycles, longer product lifecycles, and long-term relationships with customers and suppliers

• There are many legal issues involved and must be kept up to date with external factors such as government regulations

• Allows sharing of master data with other business units

• A larger system environment is required and master data needs to be distributed to various systems

This data governance model can ensure a high degree of control over master data, but it is often characterized by delays in establishing master data and requires a formal and larger data governance organization. Also, in this model, the master data created is likely to be consistent, and since the number of users setting up the master data is limited, changes and process improvements are introduced faster. To improve the model, organizations should:

• Build automated processes to provide transparency and visibility into master data maintenance processes

• Establish KPIs for different master data requests, ensuring that the data governance organization scales according to demand

• Confirm effective communication between business and master data teams to ensure master data rules adapt to business and product changes

• The role of a Data Governance organization should not be limited to processes and procedures, but should also include maintaining master data, including aligning processes to meet business needs

4. Centralized Data Governance and Decentralized Execution

The last data governance model is characterized by a centralized governing body defining the control framework, with individual enterprises creating their own pieces of master data.

Users, benefits and considerations:

• Best for medium to large organizations with multiple plants and/or multiple companies

• Brings complex data requirements but requires flexibility in creating master data

• Supports longer master data lifecycles, longer product lifecycles, and long-term relationships with customers and suppliers

• There are many legal issues involved and must be kept up to date with external factors such as government regulations

• Allows sharing of master data with other business units

• A larger system environment is required and master data needs to be distributed to various systems

This data governance model ensures agility, but at the same time organizations must ensure that appropriate controls are in place when needed. In this model, there is a shared responsibility between the data governance organization and the business.

To leverage this model effectively, organizations must:

• Identify controlled fields that affect across departments and business units, then assign ownership for centralized maintenance

• Build automated tools to avoid deduplication at source

• Ensure a central organization mediates between departments and business units when conflicts arise

• Automate the request process and leverage automation tools to help local businesses manage data on an ongoing basis

• Set up controls and audits to quickly fix any inconsistencies

• The role of a Data Governance organization should not be limited to processes and procedures, but should also include maintaining parts of master data, including making process adjustments to meet business needs. Here, the master data team also acts as a guide to the business to ensure consistency

All four data governance models can work as long as there is a control framework in place, whether manual or automated. The level of automation required depends on a variety of factors, including:

• Company Size

• Structure of company

• The Complexity of Corporate Master Data

• Number of master data records created and updated

• Master Data Lifetime Length

• Implications of master data from a reporting and legal perspective

Learn more about data governance

Want to learn more about how to manage your master data? For more information on it.mds, visit the NTT DATA Business Solutions Addstore. You'll gain insight into how it.mds can make your master data business-oriented, provide better governance across the business, and provide greater compliance through business-driven workflows.

In part three of this series, we'll cover the seven key steps of data governance.

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