Data governance is a process by which the data in the enterprise systems are managed by the availability, usability, integrity, and security, based on the internal data standards and policies. A good data governance model ensures consistent and accurate data, leading to better analytics and informed business decisions.
Without effective governance, data inconsistencies in different systems across the organization will not get fixed. For example, customer names may be listed differently in sales, logistics, and customer service systems. That could complicate data integrity issues and affect the accuracy of business intelligence (BI), enterprise reporting, and spend analytics.
Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals. It establishes the processes and responsibilities that ensure the quality and security of the data used across a business or organization. Data governance defines who can take what action, upon what data, in what situations, using what methods.
Data governance helps in the management of data across ERP / CMMS / EAM systems and ensures that data is consistent all across and is not misrepresented.
There are some pitfalls in master data management and listed below are some reasons as to why it may fail:
Lack of governance system: Organizations need a robust governance system to manage consistent data, on different systems and integrate to demonstrate a single version of the truth. For proper data governance, one must ensure that the rules are clearly defined and if not, then that leads to a second opinion and too many opinions then lead to a problem where the user does not know which governance to follow, thus making the system to fail.
Lack of data ownership: With the lack of data ownership, the scope of growth is vague and business decisions made become scattered, baseless, and loses responsibility on the quality of data that hinders businesses in the long run.
Lack of managerial support: Data governance can also fail when there is a lack of support from the management. This usually happens when the senior management fails to look at the benefits of data governance and only focuses on the costs that are associated with it. This probes a fear of incorrect system implementation and even if done, there won’t be any further maintenance which would cause the program to end prematurely.
Lack of communication: It is necessary to ensure all functions communicate between each to make the data work for the entire organization. Data governance, in the beginning, is bound to bring about a lot of changes in the processes and if there is a lack of communication between departments understanding the process can become an issue causing it to fail.
While implementing a Data Governance System the organizations must consider all of these pointers that might cause it to fail and always make sure to take up this process if one is completely sure about handling it well. Talk to our Master Data Governance specialist today to get insights to your industry best practices and how it can benefit your organization.