This course will expose you to the data practices that allow you to get the most value from your data. Good data management is necessary to find the delicate balance between data enablement and data security and privacy.
Prerequisite: Basic SQL and data knowledge preferred.
Introduction to the Art & Science of Data Management touches on the entire data management lifecycle, focusing in more detail on concepts like data governance, metadata, quality, and preparation for uses like Big Data and Data Science. It covers concepts from DAMA.org¿s DMBOK (Data Management Book of knowledge) and incorporates real-life experiences from the global data management community. Interactive activities are planned to reinforce the learning and how it can be incorporated in your organization.
During this course, you will have the opportunity to:
- Identify the guiding principles for data management
- Clarify the scope and boundaries of data management
- Understand standard terms and their meanings for data management areas, including deliverables and roles
- Learn to describe commonly accepted good practices, methods and techniques, without reference to specific vendors or products
- Identify common data management organizational and cultural issues
- Help data stewards and data management professionals understand their roles & responsibilities
- Understand how to assess data management effectiveness and maturity
- Course Introduction
- Identify common organizational and cultural issues
- Reference to DMBOK (Data Management Book of Knowledge) and DAMA.org
- Data management lifecycle and key roles in the data management world
- Data management organization and role expectations
- Introduction to core data management area and concepts
- Understand Data Engineering and Data Science concepts
- Understand how data management relates to Big Data and Data Science
- Metadata management overview
- Reference and master data overview
- Data security and data handling overview
- Data governance
- Data analysis and exploration
- Data quality
- Data architecture overview
- Data integration and interoperability overview
- Data management maturity assessment overview
- Exercise 1: Role play for a typical Data Management team
- Exercise 2: Exploration of tools for data cleansing and exploration of data
- Exercise 3: Data management and lineage
- References and next steps
University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.