data governance program

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By: IBM     Published Date: Jul 26, 2017
Business leaders are eager to harness the power of big data. However, as the opportunity increases, ensuring that source information is trustworthy and protected becomes exponentially more difficult. If not addressed directly, end users may lose confidence in the insights generated from their data—which can result in a failure to act on opportunities or against threats. Information integration and governance must be implemented within big data applications, providing appropriate governance and rapid integration from the start. By automating information integration and governance and employing it at the point of data creation, organizations can boost confidence in big data. A solid information integration and governance program must become a natural part of big data projects, supporting automated discovery, profiling and understanding of diverse data sets to provide context and enable employees to make informed decisions. It must be agile to accommodate a wide variety of data and seamle
Tags : mdm, big data, automation, organization
     IBM
By: MarkLogic     Published Date: Jun 21, 2017
Global financial organizations are facing increasing demands from the business for more granularity, transparency, reporting and security. If you’re on the IT side, you know this adds a different set of ‘mores’ to the equation: More duplication, delays, and people. What’s the net-net? More cost and more risk. You can balance the scales to satisfy those demands. And it starts with thinking differently about data management. Our financial services technology experts will explore the implications of governance, risk and compliance (GRC) imperatives. You’ll learn: • Why data is at the heart of an effective and dynamic GRC strategy • Why technological capabilities used to enable standard GRC programs can reduce transparency and prevent you from gaining a holistic view of your data • A new approach to data can provide the business with complete transparency • Review a sample regulatory reporting architecture Stop burning time on tooling — and start building a dynamic GRC strategy that can
Tags : banking, financial services, data management, silos, data strategy, transparency, data governance, regulation, data architecture, compliance, reporting
     MarkLogic
By: SAS     Published Date: Jun 06, 2018
Written by Jill Dyche and Kimbery Nevala, 10 Mistakes to Avoid When Launching your Data Governance Program lays out a comprehensive list of issues that can trip up an organization as it gets started with DG. Learn how to use good judgment and sound decision making to sidestep these common issues.
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     SAS
By: SAS     Published Date: Mar 14, 2014
Judgment is a process and not a single event. Good judgment and sound decision making are at the heart of data governance.
Tags : sas, data governance, data management, data measurement, governance policy, payback, tactical, structured, data center
     SAS
By: SAS     Published Date: Mar 14, 2014
This Q&A with Tom Davenport, Director of Research for the International Institute for Analytics (IIA), will help you understand how analytics is evolving, where you need to go, and how to get there.
Tags : sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, analytics, analytical study, visualization deployment, deployment, institute for analytics
     SAS
By: SAS     Published Date: Mar 14, 2014
This paper explores the challenges organizations have today in implementing a data governance program via an actual business case. It highlights SAS technology that can help you solve many of those challenges.
Tags : sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, visualization deployment, deployment, institute for analytics, data center
     SAS
By: SAS     Published Date: Mar 14, 2014
This report examines how data visualization can help organizations unleash the full value of information, and outlines key considerations to guide the solution evaluation process.
Tags : sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, visualization deployment, deployment, institute for analytics, data center
     SAS
By: SAS     Published Date: Mar 14, 2014
Managing expectations before, during and after the adoption of visualization software is crucial. Users should know what the rollout process will look like and how it will take place, and have clear goals for using the tool. Make sure that the desired outcome isn’t just look-and-feel. Creating beautiful charts and graphs is not a substitute for practical business decisions.
Tags : sas, data categorization, retrieval and quality, data visualization, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, visualization deployment, deployment, institute for analytics, data center
     SAS
By: SAS     Published Date: Mar 14, 2014
This paper explores ways to qualify data control and measures to support the governance program. It will examine how data management practitioners can define metrics that are relevant.
Tags : sas, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, measured value, emergent patterns, quality metrics, potential classifications, data analyst, scorecard, reporting the scorecard, improve scorecard, business process
     SAS
By: SAS     Published Date: Mar 14, 2014
Jill Dyche and SpectraDynamo explains the importance of understanding how to manage data and issues regarding data categorization, retrieval and quality.
Tags : sas, data categorization, retrieval and quality, spectradynamo, telemetry data, data governance program, data management, data quality, business objectives, quality data, accuracy, lineage, structural consistency, relevant metrics, business relevance, measured value, emergent patterns, quality metrics, data center
     SAS
By: SAS     Published Date: Aug 28, 2018
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.
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     SAS
By: IBM     Published Date: Jul 05, 2018
IBM® Information Governance Catalog helps you understand your information and foster collaboration between business and IT by establishing a common business vocabulary on the front end, and managing data lineage on the back end. By leveraging the comprehensive capabilities in Information Governance Catalog, you are better able to align IT with your business goals. Information Governance Catalog helps organizations build and maintain a strong data governance and stewardship program that can turn data into trusted information. This trusted information can be leveraged in various information integration and governance projects, including big data integration, master data management (MDM), lifecycle management, and security and privacy initiatives. In addition, Information Governance Catalog allows business users to play an active role in information-centric projects and to collaborate with their IT teams without the need for technical training. This level of governance and collaboration c
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     IBM
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