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Table of Contents
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:
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:
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.
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.
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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:
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:
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.
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.
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.
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.
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
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.
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.
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.
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.
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:
These steps must be repeated for each new program, and if changes are made in the previous program, the mentioned steps must be repeated.
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.
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:
The following points will help in implementing the Data Governance program.
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