How to Choose the Right Strategy for Data Governance

Challenges and Importance of Implementing Data Governance

Implementing an enterprise-wide data governance program is not a trivial exercise. New methods for handling data can be met with resistance from various parts of an organization. It can be very challenging to attain the necessary level of cooperation from everyone involved in the program. 

Without this cooperation, the data governance program will not provide the intended results and may simply fail altogether.

Data Governance as a Behavior-Modification Initiative

An important point when discussing データガバナンス is that it is an initiative designed to modify the behavior of the people who interact and use enterprise data. The data remains the same, but the way it is used is modified through data governance. 

Defining data governance as the execution and enforcement of authority over the management of data and data-related resources highlights the importance of people in the process.

Three Approaches to Data Governance

Selecting the right way to implement data governance in an organization can have a tremendous effect on the program’s success or failure. The corporate culture and structure need to be considered when determining the best way to approach data governance implementation. Psychology may play a major role in the employees’ acceptance or resistance to the program.

Command and Control

The command and control approach to data governance is a top-down methodology that is considered the most invasive type of data governance.

• Implementation: Data stewards and data owners are assigned by the leaders of the initiative and are essentially told that they will do what the organization decrees as necessary.
• Scope: Only a subset of the enterprise’s population is involved in data governance, making it difficult to cover the entire company.

Potential Challenges

• Workload Issues: Problems that can occur when a command and control approach is chosen usually revolve around the assignment of new duties to employees who are potentially already overworked.
• Lack of Buy-In: People may consider the new responsibilities to be above and beyond their job description and not give them the attention they deserve.
• Risk of Failure: Without the proper buy-in by the parties involved in the data governance program, it is doomed to failure.

Traditional Approach to Data Governance

The traditional approach that many companies use when implementing data governance is similar to the command-and-control method, with a few subtle yet important changes:

• Selection of Data Stewards and Owners:
  Instead of assigning individuals as data stewards and owners, they are identified and selected. The rationale behind this selection is to enlist their help in implementing new data handling control procedures for the organization.
• Challenges in Role Acceptance:
  This approach can encounter issues related to the acceptance of new roles and duties for which individuals have been chosen. Leaders of the initiative hope that those selected will recognize the importance of data governance and dedicate their full attention to it.
• Limited Scope of Responsibility:
  The traditional approach often restricts data governance responsibilities to a subset of the organization.

The Non-Invasive Approach to Data Governance

The non-invasive approach identifies people as data stewards based on their existing relationship with data. This method involves everyone in the organization since all employees interact with data in some capacity. By defining roles and codifying procedures, this approach seeks to improve how individuals are already using data.

Minimizing Resistance Through Familiar Roles

Since new responsibilities focus on improving existing data-handling practices, resistance to change is minimized. Everyone is involved to some extent, preventing individuals from feeling burdened with additional duties. This inclusive strategy fosters a shared sense of responsibility, increasing the likelihood of program success.

The Role of Terminology and Culture in Acceptance

A people-centric program benefits from thoughtful terminology when defining roles, ensuring acceptance among participants. Recognizing individuals for their potential contributions rather than assigning new roles helps integrate the approach with corporate culture.

Adapting to Organizational Needs

In some cases, a hybrid solution—combining elements from different methods—may best suit the organization’s culture and requirements. This flexibility ensures a tailored approach to achieving data governance goals.

Focusing on the Important Components

Six core components of a successful data governance program need to be reconciled with the strategy selected for implementation.

1. Data as the Prime Currency

Data is the prime currency and determination needs to be made regarding which data is most important to who and to differentiate between data, info, records, and knowledge.

2. Defining Roles

Roles need to be defined and how that is done can have a big impact on the effort needed to implement governance successfully.

3. Processes and Their Application

Processes define how the roles are being applied to business activities.

4. Communication for Accountability

Communication is necessary to formalize accountability and educate the organization regarding rules and policies.

5. Metrics for Measuring Impact

Metrics are used to measure the impact of the initiative. There may not be a direct ROI associated with data governance but its effects will be felt in many areas of the enterprise.

6. Tools for Formalizing Accountability

Tools are used to formalize accountability and improve the knowledge of rules and processes.

Tools to Help Implement Data Governance

アン IDERA Geek Sync Webcast presented by Bob Seiner delves more deeply into the different approaches to data governance. Bob uses his experience and unique view of data governance to educate the audience on the best way to implement a successful program in their organization. 

He provides a sensible framework that can be used to identify the best way your enterprise should move forward when putting together a data governance program. It’s essential viewing if you are considering getting data governance working in your company.

The Role of Collaborative Tools in Data Governance 

Collaborative tools are an essential component of data governance. ER/Studio Enterprise Team Edition provides teams the ability to create multiple data glossaries and definitions and easily share them across the organization.

• Building a Shared Data Language: Business concepts can be represented with full documentation and metadata can be cataloged to assist the governance program.
• Foundation for Strong Data Governance: The tool helps build the shared data language that forms the foundation of a strong data governance program.

Flexibility for Different Data Governance Approaches 

ER/Studio Enterprise Team Edition can be used with whichever approach your organization selects as they embark on their data governance journey.