Day #1 - Data Management Course Notes

Course Link

Key Notes
Module #1 - Introduction Data Management 

• Development and execution of Architecture, policies, procedures to manage data

Key Capabilities (People, Process, Technology Aspects for each Capability)

• Metadata management
• Data Quality
• MDM
• Data Governance
• Data Integration
• Analytics
• Data Privacy
• Data Architecture

Data Element - Representation of data. Attributes, permissible values, Identification defined.
Critical Data Element - Key elements capturing business process. Examples - Business Facts, Support Business Process, Data appears in Key Reports, Unique Identifiers - CustomerId, SupplierId
Metadata management

• Data structures from different models
• Information about Attributes, models, columns, glossary

Data - Definition, Business Rules, Ownership, Logical Data Model, Physical data - Schema
Data Sources - OLTP, OLAP, Integration - Data Movement
Business Metadata - From Business Perspective, ownership. Customer Name - Client Name, Legal Name, Trade Name. Rules to validate those names
Roles - Business Owner, Data owner, technical owner
Technical Metadata - Entities, Attributes, Mutual Relationships, Associations
Data Lineage - Traceable path from data sources, data marts, data warehouses
Identify Data Elements, Collect Business, Technical Metadata, Enforce Data standard
Tools - ETL tools, Modelling tools, BI tools, Domains, Definitions, values, Hierarchies. With all structured, unstructured data this would be done at data lake.

Module #2 - Data Governance

• Availability, Usability, Integrity, and Security of Data
• Establish a process for standards
• Same policies across the organization
• Leadership, Data Standards, Ownership, Monitoring, Change Control, Executive Support
• Hierarchy - Business Sponsor - Council - Data owners

Module #3 - Data Quality Management

• Approach, policy, procedure for accuracy, timeliness, completeness, and consistency of data in system and data flows
• Data Questions like accuracy, validity, on-time arrival, completeness, uniqueness, consistency

Technical tasks

• Data Profiling, Set Rules, RCA for identified issues, Resolutions, Set a threshold and identify accuracy percentage detected

Happy Learning!!!