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Data management
involves the processes, technologies, and policies used to collect, store, organize, secure, and utilize data efficiently
Data management
It increases data accuracy and accessibility to ensure user reliability.
Structured Data
Data stored in a well-defined format, typically in relational databases.
Example: A MySQL database storing employee records with
fields like Employee ID, Name, Department, and Salary.
Unstructured Data
Data without a predefined format.
Example: A collection of customer support emails or images stored in cloud storage.
Semi-Structured Data
Data information that doesn't have a fixed structure, but instead uses tags or markers to separate data elements.
Example: Emails: The written content of an email is
unstructured data, but the email itself is semi-structured data
Data Creation & Collection
Data Lifecycle Management (DLM)
Gathering data from various sources.
Example: A company collects customer information through
an online form.
Data Storage
Data Lifecycle Management (DLM)
Keeping data in databases or cloud storage.
Example: Amazon Web Services (AWS) stores customer
purchase history in Amazon RDS.
Data Processing
Data Lifecycle Management (DLM)
Cleaning and organizing data for analysis.
Example: An e-commerce platform processes transaction
logs to detect fraud.
Data Usage
Data Lifecycle Management (DLM)
Utilizing data for reports or decision-making.
Example: Netflix analyzes viewing history to recommend
shows.
Data Archiving & Retention
Data Lifecycle Management (DLM)
Storing historical data for future use.
Example: A bank keeps transaction records for 10 years for compliance.
Data Disposal
Data Lifecycle Management (DLM)
Securely deleting obsolete data.
Example: A healthcare provider deletes patient records
after the retention period.
Data Governance
Policies ensuring data consistency and compliance.
Example: A hospital follows HIPAA regulations to secure patient data.
Data Privacy
Protecting sensitive information.
Example: Facebook encrypts user passwords to prevent data
breaches.
Encryption
Data Security Measures
Securely encoding data (e.g., AES encryption for banking transactions).
Access Control
Data Security Measures
Restricting data access (e.g., HR can access employee records, but other departments cannot).
Backup and Disaster Recovery
Data Security Measures
Maintaining copies of critical data (e.g., Google Drive automatically backs up files)
Informed Decision-Making Process
Importance of Data Management
Data is the most important component for businesses and organizations because they make their important decisions based on data. A proper data management process ensures that the decision-makers have direct access to the updated information which helps to make effective choices.
Data Quality and Efficiency
Importance of Data Management
A well-managed data set leads to a streamlined process, which helps to maintain data quality and efficiency. It reduces error risks and poor decision-making.
Compliance and Customer Trust
Importance of Data Management
Many organisations have strict regulations to maintain the data management process properly. It also follows effective processes to handle client data responsibly.
Strategy Development and Innovation
Importance of Data Management
In the modern context, data is
a valuable asset that can help organisations to identify trends and potential opportunities with the challenges. An effective data management allows you to analyse the previous data to identify the patterns which lead to the development of new products and solutions.
Long-term Sustainability
Importance of Data Management
Proper data management helps
organisations to plan for the long run. It helps to master data
management efficiently by reducing redundancies, data duplication, and unnecessary storage costs.
Competitive Advantage
Importance of Data Management
Proper data management entitles organisations to explore market trends, customer behaviours, and other insights that can help them outperform competitors.