Overview
Embark on a comprehensive journey through the data life cycle, from inception to expiration, and gain invaluable insights into the critical management priorities at each stage. This course empowers you to develop a deep understanding of how to strategically handle data as a vital asset, with a special focus on data management practices that directly support AI and machine learning applications. By mastering the cradle-to-grave approach to data management, you'll be equipped with the knowledge and skills necessary to optimize your organization's data processes, unlocking the full potential of your data-driven initiatives and propelling your AI and machine-learning projects to new heights.
Reserve Your Seat
$25 (USD)
Group (3+): $25 USD
GSA: $18.25 USD
GSA: $18.25 USD
Education Credits:
3 PDUs
3 PDUs
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Full Course Details
Part 1: Understanding what data management is and why it is an important asset
- Why we need data management
- Who and what is impacted by data
- Data as an organizational asset
Part 2: Understanding strategic data priorities
- Prioritize data assets
- What’s at stake if we fail to properly manage data?
- How data relates to AI
Part 3: Data Strategy
- Seven goals
- Guiding Principles
Part 4: Overview of the Data Lifecycle
- Phases of the lifecycle
- Data Framework
Part 5: Where Data Management Fits
- Manage for planning
- Planning key questions
- Collect key data and questions
- Developing a quality insurance plan
Part 6: Manage Across the Lifecycle
- What is data architecture?
- What is data governance?
- Core data stewardship concepts
- Data security and privacy
Part 7: Final Assessment
This Basic Data Management self-paced course will be invaluable for:
- Data strategy owners
- Data managers
- Data engineers
- Artificial intelligence stakeholders
- Database administrators
- Digital transformation leaders
- Data integrators
- Understand why data is such an important asset
- Review who and what is impacted by data
- Why data management facilitates a culture of better data-driven decisions
- Examine the data lifecycle and each of its phases
- Learn how to manage data based on the phases of its lifecycle
- Understand data management priorities that transcend individual phases of its lifecycle
- Collect. Prepare, store, use, and manage data so it’s well-suited to AI ad machine learning applications
- Learn why a data management strategy depends on mission objectives