Training
EeS National Information Exchange Model (NIEM) 101 Training – 1 Day
Problem: A lack of context and common vocabulary can make data exchange difficult between organizations.
Outcome: An understanding of the NIEM common vocabulary and why it makes data easy to understand and share.
Target Audience: Business users, information exchange developers, and data architects.
- What is NIEM and what are its benefits?
- How is NIEM structured?
- Organization data assets management
- Mapping data assets to NIEM
- Use cases and IEPD/IEP
- How to build IEPD/IEP
- Reference schema
- Core, domains
- Namespaces, types, and properties
- Extension schema
- Scenario planning and requirements analysis
- Element mapping and model search and selection
- Want-list, schema subset building, and conformance targets
- IEPD assembly
- Step-by-step IEPD development life-cycle
- NIEM tools to support IEPD development
Enterprise Information Management 101 Training – 1 Day
Problem: Poor governance of data can result in data redundancies and confusion which translate to decision-making related inefficiencies.
Outcome: Authoritative data sources and an optimized data environment may provide better visibility and control of data.
Target Audience: Business and technical personnel
- Disadvantages of stovepipe data
- Single source of truth, optimization of data environment
- Data discovery, stewardship, and privacy
- Metadata, master and reference data management
- Data governance and its benefits and relationship to metadata management
- Development of data taxonomies, collection of data assets, mapping of data assets into data taxonomies, and self-service
- Concepts of data-centricity and the cross-linking of topics across subject areas
- Collection of data assets
- Mapping of data assets into an enterprise data taxonomy
- Data assets catalogs
- Unlock data for decision-making through self-service
- Metamodeling best practices and EA models
- Data paradigm and its touchpoints to business and technology layers
- Creating classifications
- Ingesting data assets
- Mapping data assets and generating assets catalogs
- Selecting data elements and creating datasets
Big Data and Data Lakes 101 Training – 1 Day
Problem: The lack of a central place to host data can cause data redundancies and data consistency issues.
Outcome: Build data lakes to integrate your enterprise solution with a holistic view of your data.
Target Audience: Data architects, modelers, analysts, and developers
- What is big data? The storage and processing challenge
- HDFS and map reduce
- Data science and new insights
- AI: the road ahead
- Tools and technologies
- What are data lakes? Why data lakes?
- ETL vs ELT, data governance and metadata management
- Tools for ingestion, access and analysis of data
- Using tools to ingest data into Hadoop data lakes
- Setting up access to Hadoop data
- Using tools to analyze data in Hadoop
- Creating classifications
- Ingesting data assets
- Mapping data assets and generating assets catalogs
- Selecting data elements and creating datasets