Data Management-3
Tags: Data Driven Decisions, Oracle Cloud, Data Management, Data, Data Science

The remaining of the knowledge areas of Data Management.

Knowledge Area —

6. Data Integration and Interoperability — Managing the movement and consolidation of data within applications and organisations.

Goals -

Data distribution in the format and time frame needed by consumers Data consolidation. Identification of events and automatic trigger alerts and actions Support business intelligence, analytics, master data management, and operational efficiency efforts.

Activities -

Data Interoperability — Acquire -> Move -> Transform -> Integrate Data Integration

2.1.Planning — Plan and analyse, design data integration solutions

2.2.Development — Develop data integration solutions

2.3.Operational — Integrate and inter-operate data

2.4.Control — Monitor data movement operation Operational Intelligence Support

3.1. Perform Predictive Analytics and complex Event Processing

7. Documents and Content — Planning, implementation, and control activities for life cycle management of data and information.

Goals -

To comply with legal obligations and customer expectations for management of record. To ensure effective and efficient storage, retrieval, and use of documents and content. To ensure integration capabilities between structured and unstructured documents and content.

Activities -

Planning — Develop records and content management strategies Planning — Understand records and content requirements. Planning — Determine information architecture. Development — Define and develop content organisation, and E-Discovery Operational — Capture, Manage, dispose, archive, publish and deliver records and Content.

8. Reference and Master Data -

Management of shared data to reduce redundancy and ensuring data quality through use of standard data definition.

Goals -

Enable sharing of information assets across business applications within an enterprise. Reduce complexity through use of standards, common data models, and integration patterns.

Activities -

Planning — Identify Reference and Master Data Needs and requirements Control — Validate Data Definitions, evaluate data sources Development — Establish and acquire data sources for data sharing — integration architecture Operational — Publish Reference and Master Data.

9. Data Warehousing and Business Intelligence -

Planning, implementation, and provide decision support data for reporting, query and analysis.

Goals -

To support in effective data analysis and decision-making process. To build and maintain the environment and infrastructure to support business intelligence activity.

Activities -

Planning — Understand Requirements, define and maintain the DW / BI Architecture. Development — Implement and populate data warehouse, data mart, data lake. Development — Implement Business Intelligence Portfolio. Operational — Maintain data products Eco-system.

10. Metadata — Planning, Implementation, and control activities to enable access to high quality, integrated metadata.

Goals -

Provide organisational understanding of business terms and usage Collect and integrate metadata and ensure metadata quality and security. Provide standard way to access the metadata

Activities -

Planning — Define the Metadata strategy, metadata architecture and understand requirements. Development — Create Metadata model. Control — Apply Metadata Standards, manage metadata stores. Operational — Maintain, integrate, distribute and deliver metadata. Operational — Query, report and analyse metadata

11. Data Quality — The planning, implementation, and control activities for data quality management to ensure it is fit for consumption and business purpose.

Goals -

Develop approaches to measure and improve the quality of data as per defined business rules. Define requirements and specifications for integrating data quality control. Define and implement processes for measuring, monitoring, and reporting acceptable levels of data quality.

Activities -

Planning — Create a data quality culture, define requirements. Development — Develop and deploy data quality operations. Control — Perform initial data quality assessment Operational — Assess Data Quality, Measure and Monitor Data Quality.

Environmental elements aspect of data management will be covered in the next blog titled Data Management-4. 😊 😊 😊