Contents
Overview
Danette McGilvray is a prominent figure in the field of data governance and information management. Her work has been shaped by the principles of data quality, as outlined by experts like Larry English and Tom Redman. McGilvray has also been influenced by the concepts of data governance, as discussed by authors like John Ladley and Michael Gonzales. Companies like IBM, Oracle, and SAP have also played a significant role in shaping her understanding of data management. Furthermore, McGilvray's work has been impacted by the rise of big data and analytics, with tools like Hadoop and Spark becoming increasingly important in the industry.
💡 Career and Influences
McGilvray's career in data governance and information management spans several decades. She has worked with numerous organizations, including Fortune 500 companies, to improve their data management practices. Her expertise has been sought after by industry leaders like Microsoft, Google, and Amazon, who have all recognized the importance of effective data governance. McGilvray's work has also been influenced by the concepts of data warehousing, as discussed by experts like Ralph Kimball and Bill Inmon. Additionally, she has been involved in various initiatives related to data quality, including the development of data quality metrics and the implementation of data quality frameworks, as outlined by organizations like the Data Quality Institute.
🌐 Impact on the Industry
McGilvray's impact on the industry has been significant. She has written several books on data governance and information management, including 'Executing Data Quality Projects' and 'Data Governance: How to Design, Deploy, and Sustain a Effective Data Governance Program'. Her work has been recognized by industry leaders like Gartner and Forrester, who have cited her as a leading expert in the field. McGilvray has also been involved in various industry initiatives, including the development of data governance standards and the creation of data quality frameworks, as discussed by organizations like the International Organization for Standardization (ISO) and the National Institute of Standards and Technology (NIST). Moreover, her work has been influenced by the concepts of artificial intelligence and machine learning, as discussed by experts like Andrew Ng and Yann LeCun.
📚 Publications and Awards
McGilvray has received numerous awards and recognition for her contributions to the field of data governance and information management. She has been named one of the top data governance experts by industry publications like InformationWeek and Database Trends and Applications. McGilvray has also been recognized by organizations like the Data Governance Institute and the International Association for Information and Data Quality, who have awarded her for her outstanding contributions to the field. Her work has also been influenced by the concepts of data science, as discussed by experts like DJ Patil and Hilary Mason, and the importance of data-driven decision making, as outlined by companies like Tableau and Qlik.
Key Facts
- Year
- 2000
- Origin
- United States
- Category
- technology
- Type
- person
Frequently Asked Questions
What is data governance?
Data governance refers to the overall management of the availability, usability, integrity, and security of an organization's data.
What is information management?
Information management refers to the process of managing the flow of information within an organization, including the creation, storage, retrieval, and dissemination of information.
What is data quality?
Data quality refers to the accuracy, completeness, and consistency of an organization's data, as well as its adherence to established standards and formats.
What is the importance of data governance in modern organizations?
Data governance is essential in modern organizations because it enables them to make informed decisions, improve operational efficiency, and reduce the risk of data-related errors and breaches.
How can organizations implement effective data quality frameworks?
Organizations can implement effective data quality frameworks by establishing clear data quality standards, implementing data validation and verification processes, and providing ongoing training and support to employees on data quality best practices.