September 8, 2019
Data Governance and Data Knowledge
You know, the quality of data used in business is more important now than ever before. Accordingly, in order for organizations to deliver good business results, their data must be accurate, and the use of that data must be governed through policy and monitoring. How do business leaders prevent data errors and ensure quality governance?And also, the most crucial step is establishing a data governance structure, which is the foundation for data governance success. Data governance teams are tasked with ensuring that data is handled smoothly and effectively.
With the amount of information in the digital universe doubling every two years, big data governance issues will continue to inflate. This backdrop calls for organizations to ramp up efforts to establish a broad data governance program that formulates, monitors and enforces policies related to big data. Find out how a comprehensive platform from SAS supports multiple facets of big data governance, management and analytics in this white paper by Sunil Soares of Information Asset.
From common models for the data governance structure to policy enforcement, this white paper explores how to establish a data governance structure in 10 essential steps. Included are the key roles and functions needed to most effectively improve an organization’s data quality.
By following the steps in this white paper, business and IT leaders will improve data quality, reach business goals, and ultimately, avoid the costs associated with bad data.
Making an Impact: Data Governance’s Missing Piece
Digital disruption (and transformation) is happening at a daunting scale and speed – and no business or industry is immune. With data at the heart of digital transformation, scalable and sustainable governance is more important than ever. In today’s big-data-fast reality, many organizations have hundreds of thousands of data sources, potentially millions of different data sets, and a growing number of self-service users consuming that information broadly. Traditional top-down, workflow-driven data governance just can’t keep up. What’s needed is a modern approach to governance that’s agile, scalable, and takes advantage of machine learning to meet the needs of data-driven businesses.
Watch First San Francisco Partners’ Chief Innovation Officer Malcolm Chisholm and Alation Co-founder Aaron Kalb discuss best practice tips and practical approaches for:
The scalable and sustainable data governance is at the heart of digital transformation. Watch the on-demand webinar to learn all about agile data governance methodologies. Creating a framework for bottom-up-driven governance that balances user empowerment and collaboration with data regulatory/privacy compliance. Managing data acquisition and data stewardship with agile methodologies. Integrating the data catalog into holistic governance practices to link data producers and consumers. Building a data architecture foundation to support agility and scale.
The Data Catalog Company
Data governance involves decision-making, management, and accountability related to data in an organization. Often, a data governance team is built to ensure data will be handled smoothly and effectively and to instill data quality. Data governance programs are designed to prepare rules and regulations for an organization and to handle any issues that may come up regarding data. They also ensure compliance with policies.
They tell a corporation who the owner of the data is and who can perform certain functions with it. There are many models available to aid in enabling data governance structure development in an organization. This paper outlines a ten-step plan for instituting such a structure.