What is data governance, how can it be implemented, and what are the benefits of strong master data governance? Our data management experts provide deep insights and answers to frequently asked questions about data governance roles, mandates and responsibilities.
Data governance defines who does what in data management. It specifies those activities that must be undertaken to manage data as a real asset. In addition, it determines who is responsible for which data management activity in the company. A solid data governance also determines the accountabilities, responsibilities and decision rights for the people who take care of data.
In the area of business partner data, the term master data governance is used as well.
Usually the following situations indicate that an organisation suffers from unclear or non-existent responsibilities related to their data, meaning a lack of an effective data governance approach:
The problem with the term "governance" is that asking three different people what the word means, there will be at least four answers.The following definition reflects the real-world experiences from many companies and data initiatives, and is grounded in CC CDQ data management research:
Best Practices: After defining responsibilities on paper, select and train the people who will take care of the data management activities on a day-to-day basis. It is recommended to select these individuals with utmost care. All too often, the people already overloaded with work also end up taking care of data. If that is the case, it will be impossible to achieve the agreed objectives, for instance, related to data quality.
The benefits of an effective data governance framework are threefold:
Here is the good news: Defining a data governance framework isn’t rocket science. There are established guidelines for setting up data governance, for instance the CDQ Data Excellence Model or the DAMA DMBOK. A proven approach to follow: Firstly, defining which data management activities are to be undertaken by whom in the organisation. Secondly, deciding how many people are needed to run the governance plan on a day-to-day basis. Thirdly, setting the mandate and decision-making rights for the people defining and monitoring the governance policies and standards. Fourthly, defining an implementation plan, including training, communication and change management activities. Finally, select the people executing data governance.
For example, the data governance framework "CDQ Data Excellence Model" developed by the Competence Center Corporate Data Quality builds on the principles of performance management and the logic of management cycles. As a reference model, it specifies design areas of data management (and data governance) in three categories: goals, enablers, and results, which are interlinked in a continuous improvement cycle.
The required activities are defined in CDQ's data management process map. The map includes operational activities (e.g. creating and maintaining data), tactical activities (e.g. building up and running data quality management) and strategic activities (e.g. developing a data management strategy). To be effective, the activities from the process map must be assigned to employees in the organisation. However, we do not recommend assigning the activities directly to individual employees. Instead, we advise to allocate certain sets of activities to specific data management roles (e.g. a Data Steward) and to assign these roles to individual employees. A proven tool that helps to assign the responsibilities to Data Governance Roles is the RACI matrix (i.e. Responsible, Accountable, Consulted, Informed).
To obtain management approval and budget, it is necessary to estimate how many employees are needed to execute effective data governance. The best way to determine that figure is to estimate the overall workload. In a typical data governance setup, there are some full-time positions, typically in a central data management team. In addition, there will also be some employees in the business units and support functions that cooperate with the central team. However, these are typically not full-time data management positions.
To be effective, the people executing the data management activities need a clear mandate. The mandate defines the authority of the data management roles. If it is left unclear what they can or cannot decide, the entire governance setup will not work. In addition, it is necessary to define decision-making boards where stakeholders of the organisation come together and align on data management activities and issues. This is needed because data is a shared resource, that is used by various organisational units.
Implementing data governance requires an organisation to change. Therefore, change management activities are required to support the implementation. These include an effective communication strategy tailored at different stakeholders. In addition, a plan to train the employees involved in data governance is also needed.
The best data governance concept is likely to fail if the wrong employees for the data management roles are selected. Business understanding is key and often more important than technical / IT knowledge. At the end of the day, data managers often bring people from diverse backgrounds together, so strong communication skills are a must.
Data governance introduces new roles (or makes them explicit). The following role model is grounded in CC CDQ research and based on practical experiences in many companies and data initiatives:
|(Business) Data Steward
|(Technical) Data Steward / Data Architect
|Head of Data Management
Source: based on CDQ Academy, Otto (2011) and Weber (2009)
Assigning these data governance roles in an organisation depends on many factors – most importantly, the maturity of the company and the mandate for data management. In practice, many variants can be found. But independently on how roles are assigned, three groups must very closely work together: data management teams, business functions/lines as well as the IT organisation.
A central data management team typically consists of several Business Data Stewards. It is not uncommon to find also a Technical Data Steward (Data Architect) as part of a central data management team, although this role may also be assigned to enterprise or data architects in the IT organisation. It is also possible to have Data Operators as part of a central data management team to bundle resources.
To be effective, any central data management team needs to be linked to the business units and support functions. After all, this is where most of the data is created and used. Depending on the size of the company, it might be necessary to create one or more roles to ensure the link. Some organisations have established a Business Unit or Divisional Data Steward who ensure that the activities of the central team are aligned with the needs and activities of the business units and support functions. Other companies have defined Data Experts who are contact persons for the central team because they have very specific knowledge about certain data attributes (e.g. health and safety data, tariff numbers for customs). In most organisations, data is created and maintained in the business units and support functions by Data Operators.
Since data is processed and stored in IT systems, the link between the data management roles and the IT roles is important. Therefore, it is important to align the Data Steward roles with the IT roles, for example a Demand Officer or Solution Architect. Data Management roles also need to closely work with process management roles, for example a Process Owner and Process Manager. After all, business processes require high-quality data to operate efficiently. In addition, process changes typically have implications on the data that is used in the processes. Finally, with analytics becoming ever more important in most organisations, the data management roles must be linked to the Data Scientists and Data Analysts roles.
Data is a shared resource, that is used by several business units and support functions. Consequently, data-related priorities, requirements and issues must be aligned between them. This alignment happens in data governance boards. Most organisations require one board for strategic data management topics and one or more boards for tactical data management issues.
This board (the name varies across organisations) is the central decision-making board in data governance that aligns and approves the data management strategy, annual objectives and budget. It also tracks the progress of the data management activities and resolves issues that had to be escalated. Oftentimes, the board consists of data owners from the business units and managers of the support functions as well as the Head of Data Management / Chief Data Officer. It is possible that these roles already meet in other boards or committees. If so, it should be considered whether an additional strategic data management board is really needed or whether the topics to be discussed can be integrated into the other boards or committees.
Tactical board(s) are needed to align more operative data management issues. These can include changes to the data model that affect several business units or the prioritization of data quality issues that must be resolved. Depending on the size of the firm, it might be necessary to have several tactical boards. If that is the case, they are typically established per data domain (e.g. one board for customer data, one board for material/product data etc.) Tactical board(s) meet more frequently than the strategic board. Members are the Business Data Stewards and Technical Data Stewards / Data Architect from the central data management team as well as the Business Unit / Divisional Data Stewards and / or Data Experts.
The mandate defines the authority granted to data management roles within the organisation, and from this, the responsibility. This is particularly critical for the central data management team. Are they allowed to enforce the standards and guidelines for data creation and maintenance or are they dependent on the goodwill of the business units and support functions? The type of mandate has a significant influence on the overall impact of data management on the organisation. Like the mandate of a project management office (PMO), four types of mandates can be distinguished.
It is not uncommon that the authority of the (central) data management team is not defined. It is unclear what they can decide or how they can influence the organisation to improve data management practices. If there is no clear mandate, discussions about data management priorities and initiatives tend to be endless. The problems with having no clear mandate for data management is often the trigger for defining and implementing a data governance.
If the mandate is to support, the data management team provides templates, best practices and access to information. However, the influence on the data management activities in the business units and support functions is very low. They get to choose if they want to cooperate with the (central) data management team or not. Since data is a shared resource that is used across organisational boundaries, the mandate to support is not very effective.
In this mandate, data management has the authority to align and coordinate all data management activities across the entire organisation. This helps to reduce duplicate work. A central data management team with a mandate to coordinate can also advise on methods and best practices. However, it cannot enforce them.
In a directive mandate, data management has the authority to enforce data management standards through various means. Firstly, it defines the framework, methodologies and tools to be used for data management across the organisation. Secondly, it also monitors whether the standards for creating and maintaining data are upheld. If not, it can sanction non-compliance, for example by revoking access to IT systems.
Best Practices: The mandate should be defined early in the data governance journey. If the difficult questions about authority and decision are not discussed upfront, they will return during the implementation of the data management initiatives. Since these discussions can’t be avoided, we recommend having them early in a data governance journey.
How and where is the required data quality defined? What are acceptable values for a specific data attribute and what aren’t? How should data be documented as metadata? Data Governance related decisions refer to some fundamental principles of data management. They include standards and guidelines, for instance related to data creation and maintenance, but also more general policies and principles that make employees understand the true importance and value of data.
Guidelines, policies and standards (sometimes also referred to as data management rule books) define how quality is measured, how data is documented (metadata) and how the data life cycle managed. But most importantly, they comprise documents that define, how to create and maintain data correctly. For each data domain, they describe the key data objects with permitted values for the data attributes. For example, if we have a German postal address, is it permitted to shorten the word “street” to “Str.” or is it mandatory to write “Strasse”? Similarly, should the legal form of the company be part of the name or not? These guidelines are relevant for the entire data life cycle and for all systems in which the data is stored or used. Good guidelines, policies or standards are the foundation for high-quality data that is also harmonized across the conpany. The key to success is to train the organisation and to monitor whether users adhere to the rules. Some rules defined in the guidelines can probably also be coded as validation rules into the IT systems.
Effective and efficient data management requires organisations to change. As known, change is hard. Many change initiatives fail simply because employees don’t understand why they need to change. This is where data management principles come into play. They describe the data management aspiration in easy to understand words and give enough concrete guidance on how to deal with data on a day-to-day basis. Principles also help to communicate the abstract topic of data management in a more tangible way.
Best Practices: No one comes to work in the morning to create poor data quality on purpose, but data quality is often the result of ignorance or negligence. Guidelines, policies and standards define what good data quality looks like. Data management principles help to explain why data matters and why it is everyone’s responsibility.
The simple answer is there is no difference. More details will follow soon.
Do you have any questions? Our data management specialists will be happy to help you with any questions you may have about data governance. Just contact us!