To begin, it is important to define what data governance is. One of the most extended definitions says it is a work plan structure where is specified what person in the organization has the authority to control data and they can be used. The mentioned structure includes people, processes, and technologies necessary for data management and protection within the data life cycle.
The Data Governance Institute defines it as: “A system of decision rights and responsibilities for processes related to information, executed following agreed models, which describe who can take action, what information with, when, under what circumstances and using what methods”.
It is a very important part of data management strategy since it defines roles in the organization and its framework of action. This way, data management will not be a matter of individual definitions, but a coordinated action where everyone has a clear view of actions intended to collect, manage, secure, and to store data.
What to do for successful data governance and data strategy?
There are some standard steps useful to achieve such a mission. They include role assignation and their hierarchy. In the next paragraphs, you will find advice to be successful in following the mentioned steps.
- Involve your employees
Promote participation of your employees in the creation, implementation, and following of data governance policy. This way you will nurture an environment of interest and trust among them. Otherwise, it is very plausible lack of interest and trust leads to bad practices or not following the rules.
- Be aware of continued improvement
Hand in hand with the previous point, data governance requires organizations and their employees are aware these processes are suitable for updates in order to be more efficient or meet new regulations requirements such as GDPR. (General Data Protection Regulation) In this matter, a team with an open mind is a highly valuable asset. - Create a glossary
It is crucial everyone within the organization is on the same page. This means there is an agreement in meanings and concepts of every element which is part of the data ecosystem, for example, do sales include the ones that have not been billed yet? This way, it will not exist different versions of the same information and corporational alignment will be accurate.
in the same way, it is very important to socialize the glossary with all the teams that work with data in the organization. - Define roles and rules
This helps to make faster decisions, but it needs mechanisms that grant successful resolution of conflicts. A practical example is defining what people will be in charge of what data and what is the organization’s criteria for data management. - Choose tools and solutions aligned with your vision of data governance.
Data governance is rather a company program of continuous improvement than a particular technology. Keep this in mind before investing in software so your purchase decisions will be aligned with your data governance vision and strategy. It is important to mention that business intelligence solutions are not an exception so choosing options that look after data integrity and processes optimization, will bring a lot of benefits themselves.
A good example of how business intelligence can be aligned with best practices of data governance are those options that include centralization of data definition, it means a system of roles and permissions enabled to eliminate or mitigate risks of having different versions of data, such as BI4Web.
Taking into account this advice, you can check on your organization’s status and create a plan to solve those areas suitable for improvement in order to enjoy all benefits of successful data governance. This helps organizations to be ready to monetize their data.
