Data Governance Best Practices, Need for Data Governance| Quantzig

4 Best Practices to Boost Your Data Governance Programs

In the wake of the recent chaos surrounding ‘Facebook data breach,’ several top companies are on their toes to ensure maximum data security in their organizations. With only two months left until the General Data Protection Regulation (GDPR) goes into effect, we can expect to see more headlines about improper data governance (DG) – leading […]

In the wake of the recent chaos surrounding ‘Facebook data breach,’ several top companies are on their toes to ensure maximum data security in their organizations. With only two months left until the General Data Protection Regulation (GDPR) goes into effect, we can expect to see more headlines about improper data governance (DG) – leading to significant fines and tarnished brand images. In a nutshell, this means that there will be an increasing demand for an efficient and reliable data governance systems. Data governance boils down to the overall management of the availability, usability, integrity, and security of data used in an enterprise. However, implementing a good data governance framework isn’t as easy as it sounds. Careful planning, the right people, and appropriate tools and technologies form the essence of developing a successful data governance strategy. Building and rolling out an effective, holistic data, and analytics strategy is also an integral part of a strong data governance program. Here are some data governance best practices you should start with while kickstarting a data governance program:

Focus on the operating model

The operating model forms the basis for any data governance program. It comprises of activities such as defining enterprise roles and responsibilities across the different lines of business. The idea is to establish an enterprise-wide governance structure. The structure could be centralized (if a central authority manages everything), decentralized (if operated by a decentralized or group of bodies), or federated (if controlled by independent or multiple groups with little or no shared ownership), depending on the type of your organization.

Determine data domains

Once the data governance structure is established, the next step is to determine the data domains for each line of business. The most popular examples include customer, vendor, and product data domains. The kinds of domains differ depending on the industry. Typically, the identification of a data domain begins with a business need or a problem.

Spot critical data elements within the data domains

Data domains touch 10s, 100s, and 1000s of systems and applications containing key reports, essential data elements, business processes, and much more. Out of these, companies must identify what’s critical to the business. For example, a company’s data governance initiative might be to attain commonality across the enterprise by creating a centralized platform to regulate and control changes. Whereas, another company’s objective might be to validate customer reports and related source systems. Each business needs to identify what is crucial for them.

Define control measures

It is highly important for businesses to set and maintain control to sustain the data governance program. It should be taken into account that data governance is not a one-time project. It is an ongoing program to fuel data-driven decision making and creating opportunities for business. Data governance control measures include factors such as defining automated workflow processes, capturing feedback through automated workflow processes, and applying workflow processes to the governance structure.