Data governance includes the people, business processes, and technologies required to manage and protect an organization’s data assets. It aims to ensure that the organization‘s data is understandable, correct, complete, reliable, secure and accessible, and usable.
In general, the topics in Data Governance are:
- Data Architecture
- Data Quality
- Metadata (metadata)
- Data Warehousing & Business Intelligence
- Reference & Master Data
- Documents & Content
- Data Integration & Interoperability
- Data security
- Data Storage & Operation
- Data Modeling & Design
Objectives of Data Governance
The main purpose of Data Governance is about creating methods and a framework with clear responsibilities and processes for standardizing, integrating, protecting, and storing business data. In general, the main goals of Data Governance are:
- Reducing the risk and risks caused by data
- Clarifying and determining internal rules for data use
- Implementation of data compliance requirements
- Improving internal and external communications
- Creating value from the organization’s data
- Reducing organization costs
- Helping to ensure the continued presence of the organization in the field of competition through risk management and optimization
- Facilitating the implementation of the aforementioned
- Data Governance will always have an impact on the strategic, tactical, and operational levels of organizations.
The Data Governance program should be implemented as a continuous and repetitive process in order to organize and efficiently use data in the context of the company and in line with the implementation of other data-oriented projects.
Components of a Data Governance Framework (DAMA)
A data governance framework includes policies, rules, processes, organizational structures, and technologies that are implemented as part of a governance program. It also states things like the mission statement for the program, its goals, and how its success will be measured.
Furthermore, it calculates decision-making responsibilities and accountability for the various functions that are part of the program. An organization’s governance framework should be documented and shared internally to demonstrate how the program works. So that how the framework works are initially clear to everyone.
On the technology side, data governance software can be used to automate the management aspects of a governance program. While data governance tools are not a mandatory framework, they support program and workflow management, collaboration, development of governance policies, process documentation, creation of data catalogs, and other functions.
They can also be used in conjunction with data quality, metadata management, and master data management (MDM) tools.
Why is Data Governance Important?
Although Data Governance has not yet been institutionalized and widespread in organizations, most organizations have implemented Data Governance programs for some departments or some of their applications.
Therefore, the creation of Data Governance methodically in organizations means a big evolution in the field of data from informal rules to formal controls in the organization.
Usually, formal Data Governance is implemented in a company or organization when that company has reached a point where functional tasks can no longer be effectively implemented.
Data Governance is a requirement for countless tasks and projects and will have valuable benefits. Below are some of the benefits of Data Governance:
- Consistent and uniform data and processes throughout the organization are a necessary condition for better and more comprehensive decision support.
- Growth and enhancement of IT perspectives at operational, tactical, and strategic levels by establishing new rules for changing processes and data.
- Optimizing data management costs through centralized control mechanisms.
- Increase productivity through the created synergies.
- Building greater confidence in data through guaranteed data quality, data with a seal of approval, as well as complete documentation of data processes.
- Ability to meet globally or nationally defined rules and standards
- Internal and external data security of the organization by monitoring and reviewing privacy policies
- Increasing process efficiency by reducing long coordination processes
- Creating clear and correct communication through standardization.
Reasons to Use Data Governance
Today, more than ever, Data Governance will help organizations to be accountable. Also, Data Governance creates new and innovative fields in business, such as big data analysis, which will be very difficult with the traditional thinking of new approaches.
Currently, the most important driving factors that will force organizations to revise their current approaches are:
- Creating a data-driven view to support digital business models
- Improving the quality of organizational data and master data management
- Creating data management capabilities in big data environments
- Creating standards to increase the ability to react to influences outside the organization
- For self-service business intelligence, users want to perform analysis independent of IT.
- Data Compliance: Clear and understandable data processes to meet legal requirements
In addition to the mentioned cases, there are other developments and requirements that have made Data Governance more important. For example, advanced analytics, social networks, 360-degree view of the customer, cloud-based business intelligence, information strategies, and compliance with data protection guidelines for internal or external uses such as CRM or SCM.
Challenges of Data Governance
The necessity of data governance is not hidden from anyone, but despite its great benefits, many organizations do not implement the Data Governance program due to the complexity or uncertainty of its success.
Implementing data governance programs is by no means a trivial task. The following are some of the biggest barriers to implementation:
Data governance requires an open corporate culture where, for example, organizational changes can be implemented, even if this means only naming roles and assigning responsibilities.
As a result, data governance becomes a political issue, as it ultimately means the distribution, awarding, as well as withdrawal of responsibilities and competencies. So a sensitive approach is needed here.
Reception and Communication
Data governance requires the adoption using a working relationship between parties by the right employees in the right places. In particular, project managers must have an understanding of both technical and business aspects, different terminology, and preferably a conceptual overview of the company.
Budget and Shareholders
Convincing stakeholders in the organization of the need for data governance programs and getting funding is often still difficult. Furthermore, changes are often hindered by entrenched, but operational processes and information processing deficiencies are not compensated by directly observable resources in business sectors.
Standardization and Flexibility
Businesses must be flexible to rapidly change needs. However, finding the right balance between flexibility and data governance standards is critical to each company’s business needs
Organizing Data Governance
Implementing data governance requires an open organizational culture. For example, it should be checked that Data Governance rules are enforceable in the organization, even if these rules require the creation of certain roles and responsibilities.
Ultimately, data governance will take the form of new policies that will help grant, distribute, and take away responsibilities and competencies. This will require a very sensitive and precise approach.
Accepting and Establishing a Relationship with Data Governance
Data Governance requires acceptance and proper working relationships between all the people involved in this process. The right people should interact with each other in the right place to implement the policies. Especially, project managers need a correct understanding of technical aspects as well as business knowledge and preferably a comprehensive conceptual view of the organization.
Budgeting and Stakeholders
It is often difficult to convince the organization’s stakeholders of the need for a Data Governance program and to get funding to implement the program. In addition, change in the organization is not desired and there will be resistance against it.
Flexibility and Standardization
Businesses must be flexible to meet rapidly changing needs. Establishing the right balance between flexibility and Data Governance standards according to business requirements is of vital importance.
Success Factors of Data Governance
Data governance is not a big bang project to be implemented in the organization. It can be said that Data Governance is a comprehensive plan in the organization that must implement complex and long-term projects. So there is always the risk that the people involved may lose motivation and interest over time.
According to the mentioned cases, it is recommended to start the controllable or functional prototype project and continue this approach intermittently. In this way, the project is controllable and the experience gained can be used for more complex projects and the expansion of data governance in the organization.
Normally, the stages of the Data Governance project are:
- Defining goals and understanding the benefits of data governance
- Analysis of the current situation
- Preparation of road map
- Stakeholder and project budget justification
- Planning and developing data governance
- Implementation of the data governance program
- Monitoring and control of data governance
These steps must be repeated for each new program, and if changes are made in the previous program, the mentioned steps must be repeated.
How to Implement Creative Data Governance?
Before starting the data governance program in the organization, the questions related to the reasons for the implementation of data governance should be answered in order to avoid unnecessary and redundant work.
In the same way, the existing processes should be evaluated to determine whether it is possible to adapt the existing processes to the new needs, instead of developing unnecessary new processes within the framework of the Data Governance program.
The strategic, tactical, and operational levels of the company, as well as its organizational, business, and technical aspects, form the basis of the corporate matrix in data governance. With this structure, Data Governance projects can be defined with specifications, processes, roles, and tasks related to each one.
It is worth noting that the different levels of the data governance plan and the mentioned aspects and the role of data governance in the company should be very specific.
The DAMA framework has presented all topics related to Data Governance with documented criteria.
According to the materials presented for the implementation of Data Governance, it is possible to compare and check the current situation with the expected situation in a structured way. By doing these things, it is possible to determine the point of entry into the discussion, determine the priorities and design the road map according to the main actions.
Data Governance Roles
Roles are essential in the Data Governance program. Today, there are software tools that will provide Data Governance patterns for metadata management, data quality, master data management, and data integrity.
Theories about the roles in Data Governance differ slightly, but the main roles mentioned in Data Governance are as follows:
- Strategic Data Governance Committee (Strategic Level Leadership Committee)
- Data governance management committee (tactical level)
- Data manager
- Data owner
- Data controller
- Data users
Data Governance Implementation Recommendations
The following points will help in implementing the Data Governance program.
- The Data Governance program should not be implemented without the support of senior management in the organization
- Do not start with a big bang. Data Governance is a continuous and iterative process that contains sub-projects.
- Start with small pilot projects and expand the experience across the company.
- Data governance programs can run for many years. However, related projects should not last more than three months.
- Set goals clearly and carefully.
- The success of Data Governance is a priority. The involvement of stakeholders and the transparency of the work implementation process are important. It is recommended to establish free and transparent communication with all stakeholders without concealment.
- Do not reinvent the cycle, but try to use existing patterns. Useful models and experiences already available in the market can be very helpful. Software tools, frameworks, and libraries or consultants can help the organization.
- Define the roles of Data Governance in the organization by considering political issues and sensitivities. The communication skills of the Data Governance Manager are very important.
- Carefully review established processes and solutions and determine why they are not simple and clear enough.
- Examining the platform and fields of Data Governance.
- Creating clear structures and responsibilities.
- Creating a complete and good method for documenting the useful experiences of the organization.