The sheer volume of data that organizations have stored in various big data environments, such as data lakes or Teradata warehouses, has underscored the critical importance of data governance programs to organizations who understand that without trust in their data, there is little hope of gaining widespread adoption by business users.
The emergence of big data, along with new data sets, formats, and structures, have moved data governance programs from novelty to necessity. The rapid proliferation of data has resulted in a multitude of business issues caused by mismanaged or misunderstood data, and this challenge is only compounded with shared data. Data spans not one or two systems, but a series of systems (internal and third party) that comprise an end-to-end business process, often called the data supply chain. As data traverses multiple systems, it’s often touched by multiple people that might change or alter the data. Without knowing each stop along the supply chain, nor the various people that accessed the data, it’s hard to determine who owns the data. Even more difficult to determine is the quality of the data.
Organizations need a way to help build trust in the accuracy of their data if they want to sustain continuous usage of their data infrastructure. Understanding where data originated, who owns the data, what systems and changes it encountered, etc. is critical to understanding the data’s quality in data governance, and thus its usability. To answer these questions and ensure organizations are making business decisions based on accurate data, a comprehensive data governance strategy is needed.
Data governance is about setting processes and driving accountability to maintain trust over time. First comes measurement and monitoring. A data governance strategy outlines Key Performance Indicators (KPI’s) used for management to monitor progress, and data stewards or data owners ensure that data can be trusted and that people can be held accountable for any adverse events that happen because of low data quality. It is about putting these data stewards in charge of fixing and preventing data issues so that the organization can trust the data and as a result become more efficient.
Data is exchanged and employed across applications, integrations, and interfaces, making it imperative to guide its journey with an effective data governance strategy across all relationships, details, and dependencies. A data governance strategy can help facilitate a broad and comprehensive understanding of an organizations’ data, enabling data owners, data stewards and data consumers to effectively manage and apply data to extract maximum business value. With the deluge of data, it does an organization no good to simply store data for the future. What puts an organization ahead of its competition is being able to manage data quality in data governance and trust that its quality will help them make better, informed decisions.
However, implementing a data governance strategy is no easy task. To do so effectively, organizations need a data governance solution that delivers an all-inclusive view into an organization’s data landscape. This has to include data quality in data governance.
Data governance can be a complicated prospect for any organization to tackle. A data governance solution, then, should bring clarity to the process, not add complications. The solution should be easily navigable and not overly technical and keep all users in mind. The solution should empower an organization’s data governance team, while simultaneously putting important, trustworthy information right at the fingertips of all users.
A proper data governance solution should allow organizations to easily define, track, and manage all aspects of their data assets, enabling collaboration, knowledge-sharing, and user empowerment through transparency across an organization. The solution builds collaboration among data owners and data consumers by clearly defining roles and responsibilities, to ensure full understanding and frequent updates throughout an organization.
Implementing a technology solution that also provides the proper tools to perform data profiling in order to rate the reliability and quality of an organization’s data is key. If there are any exceptions in the quality, it will be sent back to the owner/steward for clean-up. When used in conjunction with automated data controls, organizations are provided with an end-to-end solution for performing critical data integrity checks and balances, through to the higher-level definitions, stewardship and governance of all phases of the data lifecycle.
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