Data Management

What is Data Management?

Data management is the implementation of technologies, tools, and processes used to carry out data governance policies. It involves the day-to-day operations of acquiring, storing, cleansing, integrating, analyzing, securing, and sharing data to support organizational goals and meet regulatory compliance requirements.

Key Takeaways

Data management is the process of putting data governance policies into practice and ensuring data is handled effectively throughout its lifecycle.
It is an ongoing process with its own structured stages, from creation to disposal.
Effective data management strategies and tools enable stakeholders to easily access and use the data they need for informed decision-making.
Different types of data management focus on various aspects of handling data, such as storage, integration, quality, and security.
By combining data management tools and techniques, organizations can transform raw data into actionable insights that drive business goals.

Table of Contents Table of Contents

What is Data Management?
Key Takeaways
Why Data Management is Important
Data Management History and Evolution
How the Data Management Lifecycle Works
Big Data Management
4 Types of Data Management
Key Elements of Data Management Processes
Data Management Tools and Techniques
5 Best Practices for Data Management
Data Management Benefits and Challenges
The Bottom Line
FAQs
References

Table of Contents

What is Data Management?
Key Takeaways
Why Data Management is Important

Show Full Guide

Data Management History and Evolution
How the Data Management Lifecycle Works
Big Data Management
4 Types of Data Management
Key Elements of Data Management Processes
Data Management Tools and Techniques
5 Best Practices for Data Management
Data Management Benefits and Challenges
The Bottom Line
FAQs
References

Why Data Management is Important

Data management is important because it transforms raw data into a valuable asset that can be used to support strategic goals. It enhances operational efficiency by organizing and maintaining data for easy retrieval and analysis and allows governance policies to be implemented as actionable steps.

Data Management History and Evolution

Before the advent of computers, data was managed manually, and people relied on ledgers to track finances, filing systems to organize documents, and libraries to catalog information. This changed with the introduction of relational databases (RDBs) in the 1970s and the widespread use of personal computers (PCs) in the 1980s.

Today, cloud computing platforms, big data tools, artificial intelligence (AI), and machine learning (ML) can automate data acquisition and preprocessing, enable better scalability, and help stakeholders gain deeper insights for decision-making and innovation.

Important Events in Data Management

How the Data Management Lifecycle Works

The data management lifecycle provides an operational framework for implementing and enforcing data governance policies and handling different types of data from creation to disposal. Project management software can be used to ensure that each aspect of a data management initiative is completed on time and track progress toward business goals.

Key phases of the data lifecycle management:

Data collection

Data can be collected manually or automatically and can come from either internal or external sources.
Data storage

Data can be stored and backed up locally or in the cloud.
Data processing

Data cleansing and transformation tasks can be automated to ensure data reliably loads into target systems.
Data use

Organizations can use their data assets to make business decisions and gain a competitive advantage.
End of life

When data is no longer useful, it can be securely deleted or archived for compliance purposes.

Big Data Management

Big data management allows businesses to aggregate different types of structured, unstructured, and semi-structured data from a wide variety of sources.

Special data management software (DMS) tools and technologies like Hadoop and NoSQL databases can handle the scale and complexity of big data and allow it to be used for predictive modeling, making decisions in real time, and other operational purposes.

4 Types of Data Management

Different types of data management address various aspects of data handling to ensure data remains accurate, accessible, secure, and usable throughout its lifecycle.

Examples of different types of data management include:

Master data management (MDM)

Centralizes and standardizes critical business data to create a single source of truth (SSoT).

Metadata management

Manages metadata to improve data discoverability and usability.

Data security management

Protects data from unauthorized access, data breaches, and data loss by using encryption and real-time data monitoring tools and implementing strict access controls.

Cloud data management

Optimizes data storage, data access, and real-time data processing in cloud and hybrid cloud environments.

Key Elements of Data Management Processes

The key elements of data management processes work together to ensure that data is managed effectively throughout its lifecycle.

Core components include:

Data collection

Aggregate structured, unstructured, and semi-structured data from various sources.

Data storage

Organize and store data securely in databases, data warehouses, or data lakes for easy access.

Data integration

Combine data from multiple sources to provide stakeholders and software applications with a unified, consistent view.

Data quality management

Implement data cleansing and validation processes to ensure data is accurate, complete, and consistent throughout its lifecycle.

Data security

Protect data from unauthorized access, exfiltration, or corruption by using encryption and access controls.

Data analytics

Analyze data to gain insights and identify actionable information.

Data backup and recovery

Regularly back up data and institute recovery plans that will prevent loss in the event there is a breach or catastrophic failure.

Data Management Tools and Techniques

Data management tools and techniques are often combined to create and support an organization’s data management strategy.

Data management techniquesData management tools

Normalization: Reduces redundancy in relational databases and improves data integrity.

ETL (Extract, Transform, Load): Transfers data and prepares it for analysis.

Data encryption: Ensures data privacy and security.

Data archiving: Securely stores historical data for future reference.

Data cleansing: Identifies data errors and corrects inconsistencies.

Data deduplication: Eliminates duplicate copies of data to save storage and improve analytical efficiency.

Data cataloging: Organizes metadata to improve data discoverability and support data management.

Database management systems (DBMSes): MySQL, PostgreSQL, and Oracle can be used to manage structured data storage and retrieval.

Data warehousing tools: Snowflake and Amazon Redshift can be used to aggregate data for analytics.

Big data tools: Apache Hadoop and Spark can be used to work with large, complex data sets.

Data integration tools: Talend, Informatica, and Apache Nifi can be used to combine data from multiple sources.

Data quality platforms: Trifacta and Ataccama can be used to improve data accuracy and consistency.

Data governance tools: Collibra and Alation can be used to manage compliance and policy enforcement.

Data security tools: IBM Guardium and Varonis can be used to safeguard data and prevent data breaches.

Cloud data management tools: AWS, Azure, and Google Cloud can be used to provide scalable storage and management capabilities.

Examples of combining tools and techniques include:

Using a cloud-based data warehouse like Snowflake with a data visualization tool like Tableau to analyze large datasets and create dashboards that allow stakeholders to gain insights from data in real time.
Implementing a data governance framework that combines data quality rules with access controls to ensure data integrity.
Using a combination of relational and NoSQL databases to store and manage different types of data.
Integrating an ETL tool like Apache NiFi with a machine learning platform like AWS SageMaker to streamline and automate the preparation and deployment of machine learning models.
Combining a master data management tool like Informatica MDM with a data cataloging solution like Alation to make it easier for stakeholders to locate and use data.

5 Best Practices for Data Management

A Data management plan (DMP) describes what data management strategies an organization will use to collect, store, share, preserve, and dispose of data at the end of its lifecycle.

Best practices include:

Following data governance policies to ensure consistency, compliance, and proper data handling
Regularly cleaning, validating, and updating important data to maintain its accuracy and reliability
Implementing robust security measures to protect data and conducting regular audits to identify and address potential security vulnerabilities
Utilizing cloud or hybrid storage systems, when possible, to accommodate growing data volumes and provide scalability and flexibility
Testing data recovery protocols regularly to ensure business continuity in the event of data loss or system failures

Data Management Benefits and Challenges

While data management can present significant challenges on a day-to-day basis, the benefits far outweigh the difficulties.

Benefits

Data management plans turn data governance policies into a series of actionable items
Streamlined data processes save time and resources
Effective data management reduces exposure to compliance fines and legal liabilities
Accessible trustworthy data improves satisfaction for both internal and external users
Leveraging data effectively can drive innovation and provide a competitive advantage

Challenges

Data silos can hinder accessibility and integration
Data quality issues can interfere with efforts to make data-driven decisions
Adhering to GDPR and CCPA regulations can be complex and resource-intensive
Combining data from diverse sources requires expertise and specific types of tools

The Bottom Line

A clear data management definition can help stakeholders understand the difference between data governance, which focuses on policies and accountability, and data management, which focuses on the step-by-step operations required to implement data governance policies.