Data Management Essentials

Position data governance at the center of the data management framework, acting as the control tower that defines policies, procedures, and metrics, and monitors compliance across centralized and federated models.

Explore a data management framework via a case study on Lospalos Airman's data governance, assessing maturity and outlining a governance strategy for data assets across functions like sales and logistics.

Explore how data architecture links business and technology through enterprise data models and dataflow design, creating master documentation that clarifies data movement and organizational knowledge.

The case study presents an enterprise data model and data flow design across five business units, illustrating a conceptual data model and data movement in the data architecture domain.

Explore data models and data architecture, defining entities, relations, and attributes to represent data. Learn conceptual, logical, and physical models, and distinguish relational from non-relational approaches for vocabulary and integration.

Examine a four-table store data model (store, client, product, purchase) and distinguish conceptual from logical models, keys, and table relations.

Explore data architecture, data modeling and design, and storage operation as a practical domain; learn how database administration ensures data integrity, availability, and performance across centralized, distributed, and cloud architectures.

Examine a data storage and operations case study on migrating from on-premise to cloud, including scouting activities to compare cloud platform characteristics from vendors like AWS, Google Cloud, and Oracle.

Define data security policies and standards, assess risks, and implement authentication, authorization, access controls, and auditing procedures to protect data confidentiality and information assets.

Explore how organizations redefine data confidentiality and access policies, replace generic passwords with user-specific credentials, and begin an in-depth vulnerability study to design structured systems and complex products over time.

Plan and design data integration and interoperability to enable reliable data movement, consolidation, and sharing across applications and stores, using ETL, orchestration, and cloud-based integrations.

Explore a hypothetical case of data integration and interoperability between point-of-sale systems and headquarters, emphasizing seamless data exchange, dashboards, social network intelligence feeds, and external market data for analytics.

Explore documents and content management within unstructured data domains, contrasting document and content management, and outline information architecture, vocabularies, taxonomies, and lifecycle practices for retrieval and preservation.

Explore a case study of document and content management to improve a company's web presence and social channels, defining taxonomy and data flows while addressing archiving and digitization.

Define and manage master data as a subset of crucial, core business data shared across applications, fortified by reference data to become the organization's source of truth.

Explores a case study on reference data and master data, highlighting sales and customer loyalty data and the potential to store these in a dedicated master data repository.

Explore data management essentials by building a data warehouse that separates transactional data from analysis. Use data marts, extraction, transformation, and loading to enable dashboards and reports for business intelligence.

Analyze near real-time sales with a data warehouse by ingesting point-of-sale data via ETL, validating in the cloud, and using dashboards in Power BI for executives.