Data governance is a critical component for all organizations, especially as industries adopt cloud migration and digital transformation. To ensure success, it is important to understand the four essential best practices for big data governance. These include establishing effective information governance to improve quality, privacy, and security; maximizing the impact of business intelligence and master data management (MDM) programs; preparing for trends such as artificial intelligence (AI), Hadoop, the Internet of Things (IoT), and blockchain; and striving for simplicity. The volume, variety, and velocity of big data can make analyses complex.
To address this complexity, data scientists must strive for simplicity. This is a fundamental skill that will help them to offer solutions that can be maintained over time. Additionally, it is important to detect the data domains that will affect business objectives and identify the critical elements within each domain. Data governance also requires collaboration and participation from the entire organization.
Establishing and complying with service-level agreements (SLAs) in relation to data flow, both internally and externally, are important concepts when developing big data analytical strategies. Furthermore, it is essential to measure the success of the data management framework through the use of metrics in order to meet objectives. Finally, data scientists must remember that their organization does not own all the information it can collect. They are also responsible for protecting both raw data and individual consumer privacy represented in that data.
By implementing these four strategies, data scientists can improve their ability to make decisions based on big data and move their businesses forward.