Navigating the Labyrinth: An Executive Guide to Data Management

Technics Publications
4.5
4条评价
电子书
208
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关于此电子书

If you are leading an organization or if you need to communicate with leaders about data management, Navigating the Labyrinth is your guide.

Organizations that want to get value from their data need to manage that data well. But to most executives, data management seems obscure, complicated, and highly technical. You don’t have time to learn all the detail or cut through the hype. Navigating the Labyrinth helps you get there. Based on best practices from DAMA’s Data Management Body of Knowledge (DMBOK2), it explains the fundamentals and says why they are important. It focuses your attention on what you need to know to help your organization build trust in and get value out of its data. 

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4.5
4条评价

作者简介

About DAMA

DAMA International is a not-for-profit, vendor-independent association of technical and business professionals dedicated to advancing concepts and practices related to managing data and information in support of business strategy. With chapters throughout the world, DAMA International encourages best practices through a network of connected individuals and organizations who share ideas, trends, problems, and solutions and who look to DAMA as the trusted, collaborative central resource for all things data. Visit dama.org to learn more.

About Laura

Laura Sebastian-Coleman has worked on data quality in large health care analytic data warehouses since 2003. She has implemented data quality metrics and reporting, launched and facilitated data quality working groups, and developed data consumer training programs. She has contributed to efforts to establish data standards and to manage metadata for large analytic data warehouses and big data environments. Named DAMA Publications Officer in 2015, she was production editor for the DMBOK2, for which she received DAMA International's prestigious annual award for Outstanding Service to the Data Management Profession in 2018. She is also author of Measuring Data Quality for Ongoing Improvement.

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