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DATA MANAGEMENT

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Data management refers to the systematic organisation, storage, protection & usage of data to ensure its accuracy, accessibility & reliability. Effective data management is critical for supporting organisational decision-making, enhancing operational efficiency & maintaining regulatory compliance.​

Core Components of Data Management:

Best practices for Data Management:

Develop a Data Strategy:

Align data management goals

with business objectives.

 

Identify key data sources &

their value to the organisation.

 

Centralise Data Management:

Implement a unified data management platform.

 

Avoid silos by promoting integration across departments.

 

Automate Where Possible:

Use automation tools for

data cleansing, monitoring

& backup.

 

Streamline repetitive processes

to reduce errors & save time.

Adopt Data Standards:

Use industry standards for

naming conventions, data

formats & integration protocols.

 

Ensure consistency across systems:

 

Train Employees:

Educate staff on the importance

of data management.

 

Provide training on tools,

policies & compliance requirements.

​

Monitor & Audit Regularly

Continuously track data

quality & access.

 

Conduct periodic audits to

ensure compliance & identify areas for improvement.

Data Management Frameworks & Tools:

Frameworks:

DAMA-DMBOK (Data

Management Body of

Knowledge), COBIT

& TOGAF.

Tools:

Microsoft Azure, AWS,

Google BigQuery, Snowflake, Talend, Informatica

& Apache Hadoop.

Product

Data Governance:

Establish policies, procedures,

& standards for managing data.

 

Define roles & responsibilities, such as Data Stewards &

 

Data Owners.

Ensure compliance with regulations (e.g. GDPR, HIPAA).

 

Data Quality:

Ensure data accuracy,

consistency, completeness

& timeliness.

 

Implement processes for

data cleansing & validation.

 

Monitor & resolve data

anomalies proactively.

 

Data Storage:

Select appropriate storage solutions (cloud-based,

on-premises, hybrid).

 

Optimise data architecture for performance & scalability.

 

Implement backup & disaster recovery strategies.

 

Data Integration:

Consolidate data from multiple sources into a unified system.

 

Use Extract, Transform, Load

(ETL) or real-time data pipelines.

 

Ensure compatibility across systems & applications.

 

Data Security:

Encrypt sensitive data both

in transit & at rest.

 

Implement robust access

controls & authentication mechanisms.

​

Conduct regular audits & vulnerability assessments.

Data Access and Sharing:

Facilitate secure access to authorised users.

 

Use role-based access

controls (RBAC).

 

Implement data sharing policies, including

cross-border considerations.

 

Master Data Management (MDM):

Create a single source of

truth for key business entities (e.g. customers, products).

 

Ensure consistency across systems & applications.

 

Regularly update &

synchronize master data.

 

Metadata Management:

Manage data about data, including its origin, format

& usage.

​

Use metadata to improve searchability & traceability.

 

Facilitate data cataloging

for better discoverability.

 

Data Lifecycle Management:

Define processes for data creation, usage, archiving

& deletion.

 

Ensure data retention

policies comply with legal & business requirements.

​

Safely dispose of data no

longer needed.

 

Data Analytics & Business Intelligence:

Use data to derive insights

& drive decision-making.

 

Employ tools like

dashboards, reports & predictive analytics.

​

Ensure data models &

analyses are reproducible

& transparent.

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