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What is data management?

What is data management?

Data management is a systematic and efficient approach to collecting, storing, organizing and maintaining data throughout its lifecycle for high accuracy, accessibility, security and availability. Organizations that practice data management are able to stay ahead of the continuous tide of inbound information and derive greater value from it.

Why is data management important?

Data is the most essential resource in the digital world. Unlike a natural resource, data tends to generate more of itself over time. According to a Statista report, the world's annual data generation increased from 2 zettabytes in 2010 to 120 zettabytes in 2023. It is expected that the world’s data generation will grow to more than 181 zettabytes per year by 2025.

Given the growing requirement for and importance of data in the world, data management is like the glue of the information age.

Examples of data management systems include customer relationship management (CRM), enterprise resource planning (ERP) and document management systems (DMS).

What are the processes in data management?

Data management is a broad term, spanning several terms and processes:

  • Collection: Data collection is essential to data management. Relevant and accurate data is necessary for any data analysis decision-making. You can collect data from structured, semi-structured and unstructured data from various sources and forms. Examples of data sources include social media (Facebook, X, Instagram), websites, company reports (annual reports, financial reports, market research reports), research papers, sensor data (IoT devices), mobile apps, financial institution filings, telecommunications and law enforcement.
  • Organization and integration: Storing, accessing and analyzing data in multiple formats can be challenging. Therefore, data must be structurally organized, labeled and categorized for quick accessibility and analysis. Well-organized data enables a comprehensive view of the information management landscape.
  • Data cleaning: Data should be cleaned of duplicate records, outliers, errors, invalid data and blank values. The data cleansing process standardizes data by eliminating misleading data.
  • Data analysis: Data can be analyzed with a variety of techniques, processes, machine learning algorithms and artificial intelligence methods. Data analysis helps to get insights and patterns for data-driven decision making and optimization of business efficiency.
  • Data protection: Data is valuable for any organization. Therefore, data management involves data security for defense against hacking, data breaches and unauthorized data access. It upholds compliance with such standards as:
    • ISO/IEC 27001
    • PCI DSS (Payment Card Industry Data Security Standard) for payment-related compliance
    • CCPA (California Consumer Privacy Act)
    • HIPAA (Health Insurance Portability and Accountability Act)
    • GDPR (General Data Protection Regulation)
The benefits of data management

What is the role of data management in the organization?

Data management can provide the following benefits:

  • Improved productivity and efficiency: Imagine trying to find a book in an unorganized library; the result is a waste of time and human effort. Data management helps streamline and process data for faster retrieval and analysis.
  • Efficient decision-making: Data management helps you analyze data through insights from different algorithms and processes. The insights enable organizations to make data-driven decisions that drive success.
  • Security: Data management includes compliance measures to defend sensitive information and user privacy against hacking. It helps protect customer data and build trust in your brand.
  • Competitive advantage: Detailed, oriented, data-driven decisions give organizations a clear edge in adapting to market dynamics and customer requirements, resulting in innovation, faster turnaround and better products.

What are typical data management challenges?

In the course of efficiently storing, organizing and protecting their data, organizations face obstacles to data management, including the following:

  • Data volume: Your organization can generate vast amounts of data every day, month and year. For example, Facebook processes more than 500 TB of data daily. It is a formidable task to handle the storage scalability, performance and complexity of so much data.
  • Data security: Securing data from unauthorized access, theft and manipulation has emerged as another daunting task. According to the HIPAA Journal, in 2023, over 133 million records were exposed, up 189 percent from 2021. To implement data security, your organization should implement encryption, security certificates, multi-factor authentication, access controls, security policies and endpoint security. That means compliance with regulations such as HIPAA, CCPA and PCI DSS.
  • Data transfer: Data transfer between different systems, processes and platforms is time-consuming and prone to security breaches. It can also cause data errors, corruption, inconsistencies, network bottlenecks and transfer costs.
  • Data integration: Data integration involves handling data from various sources, formats and databases. Integrating all that variety into a unified view requires data cleansing, validation checks and conversion into standardized formats.
  • Data lifecycle management: Data lifecycle management entails data classification (knowing how long to retain data) and secure deletion without danger of breach. For example, HIPAA requires that Medicare service providers keep six years of data from the date of creation.

What are tools and techniques for data management?

Data management tools and techniques include database management, data warehouses, data integration and master data management (MDM). For example:

  • Database management systems: Database management systems include managing structured, unstructured or semi-structured data. They include relational database management systems (RDBMS) and NoSQL systems like document databases, key-value databases, wide-column stores and graph databases.
  • Data warehouses: Data warehouse systems provide a central data repository for reporting and data analytics. You can warehouse data in Snowflake, Cloudera and cloud services such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform.
  • Data lakes: Data lakes provide flexible repositories for storing raw-format data, regardless of structure. They can keep and manage native-format data and they are scalable. Example includes Azure Data Lake, Databricks and Google Cloud Platform.
  • Data integration tools: Products like Talend, Apache Kafka, Informatica and Azure Data Factory provide seamless data integration tools across various systems, applications, databases and storage systems. They use ETL/ELT data transformations and enrichments.
  • Master data management (MDM): This is a process where business and IT collaborate to establish a consistent set of data on an organization’s customers, products, suppliers and other business entities across different IT systems. Master data management helps improve the quality of an organization's data by ensuring that the enterprise’s official shared data assets are uniform, accurate, semantically consistent, stewarded and accountable.

What are examples of best practices in data management?

Consider these practices for effective and flawless data management in your organization:

  • Implement a robust data governance framework, including standards, policies, procedures, roles and responsibilities for effective data management.
  • Define processes and set metrics for data quality assessments to ensure that your data is consistent, accurate and valid for data analysis.
  • Establish and enforce data encryption, privacy and robust security protocols that comply with regulations such as HIPAA, GDPR and CCPA.
  • Carefully evaluate the tools and technologies you select for storage, processing, data integration solutions and data analysis.
  • Automate any repetitive data ingestion, cleansing and transformation tasks to save time and resources in managing data.
  • Always favor data quality over quantity. Implement processes to ensure the accuracy and consistency of data for optimized results.

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