LONDON--(BUSINESS WIRE)--Quantzig, a global analytics solutions provider, has announced the completion of their latest article on the four best practices to jumpstart your data governance program. Implementing a successful data governance strategy requires careful planning, the right people, and suitable tools and technologies.
Data governance allows organizations to apply control over the management of data assets. Data governance comprises of the process, people, and technology that are required to make the data suitable for its intended purpose. There will be a rise in the demand for a valid and reliable data governance systems with the recent chaos surrounding ‘Facebook data breach.’ Building and implementing an active, holistic data, and analytics strategy is an integral part of a strong data governance program. In this blog, Quantzig has listed four best practices to jumpstart your data governance program.
According to the data management experts at Quantzig, “Data governance is all about the overall management of the availability, usability, veracity, and security of data used in an enterprise.”
View Quantzig’s comprehensive list of the four best practices to jumpstart your data governance program
Quantzig is a global analytics advisory firm concentrated on leveraging analytics for prudent decision making and offering solutions to clients across several industrial sectors. Listed below are the four best practices to jumpstart your data governance program.
Data governance best practices to jumpstart your data governance program
- Focus on the operating model: The basis for any data governance program is the operating model. It includes activities like defining enterprise roles and responsibilities across the different areas of business. The goal is to create an enterprise-wide governance structure. The structure could be centralized, decentralized, or federated, depending on the type of your organization.
- Determine data domains: Once the data governance structure is official, the next step is to ascertain the data domains for every line of business. The kinds of domains differ from industry to industry. The most common examples are vendor, customer, and product data domains. The identification of a data domain starts with a business problem or a need.
- Spot critical data elements within the data domains: Data domains reach 10s, 100s, and 1000s of systems and applications comprising of key reports, essential data elements, business methods, and many more. Among them, organizations must find what’s important to the business. For instance, a firm’s data governance initiative might be to achieve commonality across the enterprise by making a centralized platform to adjust and control changes. But another firm’s objective might be to authenticate customer reports and related source systems. Every firm needs to recognize what is crucial to them.
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Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. For more than 15 years, we have assisted our clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Today, our firm consists of 120+ clients, including 45 Fortune 500 companies. For more information on all of Quantzig’s services and the solutions they have provided to Fortune 500 clients across all industries, please contact us.