Data governance promotes the availability, quality, and security of an organization’s data through different policies and standards. These processes determine data owners, data security measures, and intended uses for the data. Overall, the goal of data governance is to maintain high-quality data that’s both secure and easily accessible for deeper business insights.
Big data and digital transformation efforts are the primary drivers of data governance programs. As the volume of data increases from new data sources, such as the Internet of Things (IoT) technologies, organizations need to reconsider their data management practices in order to scale their business intelligence. Effective data governance programs seek to improve data quality, reduce data silos, ensure compliance and security, and distribute data access appropriately.
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The scope of data management is broader than data governance. It can be defined as the practice of ingesting, processing, securing and storing an organization’s data, where it is then utilized for strategic decision-making to improve business outcomes. While this is inclusive of data governance, it also includes other areas of the data management lifecycle, such as data processing, data storage and data security. Since these other areas of data management can also impact data governance, these teams need to work together to execute against a data governance strategy. For example, a data governance team may identify commonalities across disparate datasets, but if they want to integrate them, they’ll need to partner with a data management team to define the data model and data architecture to facilitate those linkages. Another example can include data access, where a data governance team may set the policies around data access to specific types of data (e.g. personally identifiable information (PII)), but a data management team will either provide that access directly or set the mechanism in place to provide that access (e.g. leverage internally defined user roles to approve access).
Implementing a data governance framework can increase the value of data within your organization. Since data governance helps improve overall data accuracy, it also impacts outcomes based on that data, which can range from more simple day-to-day business decisions to more complex automation initiatives. Other key benefits include:
Although the benefits of data governance are clear, data governance initiatives have a number of hurdles to overcome to achieve success. Some of these challenges include:
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IBM Cloud Pak for Data leverages microservices and its leading data and AI capabilities to automate the intelligent integration of data across distributed systems, providing companies with a holistic view of business performance. This facilitates faster collection, organization, and insight into enterprise data, allowing businesses to make decisions at scale. Data management teams can also trust that their data is secure with IBM’s competitive security frameworks, ensuring adherence to regulatory policies and reducing any compliance risks. Find out how IBM Cloud Pak® for Data and IBM Streams can help you understand and manage your business’s data governance across multiple environments.