Data Management

What it is, why you need it, and best practices. This guide provides definitions and practical advice to help you understand and establish enterprise data management.

Diagram illustrating data management and usage techniques.

What is Data Management?

Data management refers to the process of collecting, storing, organizing, and maintaining data to support analysis and decision-making. Given the exponential growth of data today, good data management practices are essential to integrate different types of data, ensure the quality and integrity of data, reduce errors and duplication, and comply with legal and ethical standards.

Two key trends in data management are the shift from on-premises systems to cloud platforms and the shift from batch to streaming data processing.

Types of Data Management

There are many disciplines you should consider when developing your comprehensive data management strategy. The diagram below shows a typical customer data management system, including the five main approaches to data integration. Modern data integration architectures such as data fabric and data mesh are described later in this guide.

Let’s dig a bit deeper into the types of enterprise data management:

Architecture & Strategy

  • Data architecture: the formal design and structure and flow of data systems, including databases, data warehouses, and other data storage systems.

  • Data modeling: the process of creating a diagram that represents your data system and defines the structure, attributes, and relationships of your data entities.

  • DataOps (data operations): a methodology that streamlines data-related processes by combining aspects of DevOps and Agile principles.

Data Processing

  • Data wrangling: the process of cleaning, structuring, and transforming raw data into a usable format for analysis. Wrangling extends beyond identifying and rectifying data errors, inconsistencies, and inaccuracies to tasks such as reshaping, merging, and enriching data to make it suitable for analysis.

  • Data integration: the process of bringing together data from multiple sources across an organization to provide a complete, accurate, and up-to-date dataset for analysis and other applications and business processes. It includes data replication, ingestion and transformation to combine different types of data into standardized formats to be stored in a target repository.

Data Storage

  • Data warehouse: a system which aggregates large volumes of data from multiple sources into a single repository of highly structured and unified historical data.

  • Data lake: a centralized repository that holds all of your organization's structured and unstructured data. It employs a flat architecture which allows you to store raw data at any scale without the need to structure it first.

Data Governance

  • Data governance framework: the set of roles, processes, policies, and tools which ensure proper data integrity, quality, privacy, and security throughout the data lifecycle and proper data usage across an organization. Data governance allows users to more easily find, prepare, use and share trusted datasets on their own, without relying on IT.

  • Data integrity refers to the accuracy, consistency, and completeness of data throughout its lifecycle. It’s a critically important aspect of systems which process or store data because it protects against data loss and data leaks.

  • Data quality refers to the accuracy, completeness, and consistency of data, and involves ongoing monitoring and management to ensure that data meets established standards.

  • Data privacy involves protecting personal or sensitive data from unauthorized use or disclosure, and includes compliance with relevant regulations and standards.

  • Data security involves protecting data from unauthorized access, use, or disclosure, and includes measures such as encryption, access controls, and monitoring.

  • Data catalog: an inventory of data assets, organized by metadata and search tools, which provides on-demand access to business-ready data.

  • Master data management involves creating a single, authoritative source for key data elements, such as customer or product information, which can be used across multiple systems and applications.

  • Metadata management involves managing information about the data, including data definitions, relationships, and lineage, to ensure that data is properly understood and used.

Manage Quality and Security in the Modern Data Analytics Pipeline

Key Benefits

Overall, effective data management can help your organization derive more value from your data, leading to improved performance, greater efficiency, and increased competitiveness. Here are the key benefits:

Improved Decision-Making: It enables you to find and access accurate data more easily, allowing for analysis and better-informed decision-making.

Increased Efficiency: By ensuring that data is accurate, complete, and readily accessible, it can help streamline processes and reduce the time and effort required to complete tasks.

Better Customer Service: By providing access to accurate and timely data, it can help your organization better understand your customers' needs and preferences, leading to improved customer service.

Better Compliance: It can help your organization ensure compliance with data privacy laws by implementing appropriate security measures, such as data encryption, access controls, and monitoring, to protect personal data from unauthorized access, use, or disclosure.

Improved Risk Management: By ensuring that data is properly secured and protected, it can help your organization mitigate risks related to data breaches or other security incidents.

Enhanced Collaboration: By providing a shared view of data across your organization, it can help promote collaboration and knowledge-sharing among teams and departments.

Data Management System

A data management system is typically built on a data management platform and includes various components and processes such as a database management system, a data integration tool, data warehouses and lakes, and analytics.

Here we’ll cover the 3 main components in detail and then discuss additional features.

  1. Data management platform (DMP) refers to software designed to collect, organize, and analyze large amounts of data from multiple sources. The platform acts as a central hub for managing and activating data, enabling your organization to gain insights into customer behavior, optimize marketing campaigns, and improve overall business performance.

    DMPs typically integrate with a variety of data sources, such as CRM systems, website analytics tools, and third-party data providers. The platform collects and aggregates this data, allowing your marketing team to segment and target audiences based on specific attributes and behaviors. DMPs can also help you comply with data privacy regulations by providing tools for managing user consent and data access.

    In addition to data collection and segmentation, DMPs offer various tools for data activation, such as audience targeting, personalized content delivery, and programmatic advertising. This allows you to reach your target audiences with more relevant and engaging messages, leading to better engagement, higher conversion rates, and improved ROI.

  2. Database management system (DBMS) refers to a software system that allows you to create, access, and manage databases. A database is a collection of related data that is stored in an organized way to allow for efficient retrieval and manipulation.

    A DBMS provides a way to define the structure of a database, create and modify tables, and specify relationships between tables. It also provides mechanisms for adding, modifying, and deleting data from a database, as well as for searching and retrieving data.

    Some of the key features of a DBMS include data integrity and security, which ensure that the data in a database is accurate and protected from unauthorized access. A DBMS also provides tools for backup and recovery, so that data can be restored in the event of a hardware failure or other disaster.

    There are many different types of DBMS, including relational, object-oriented, and document-oriented systems. Relational databases are the most common type and use tables to store data, with each table consisting of rows and columns. Object-oriented databases store data as objects, which can contain data and behavior, while document-oriented databases store data as documents, such as JSON or XML files.

  3. Data integration tools are software solutions designed to combine data from multiple sources into a unified view. The tool enables you to extract, transform, and load (ETL) data from disparate sources, such as databases, files, and applications, and consolidate it into a single location for analysis and reporting.

    Data integration tools provide a range of features for managing the ETL process, including data mapping, data cleansing, data transformation, and data quality assurance. These features enable users to standardize data across sources, ensure data accuracy and consistency, and transform data into a format that can be easily analyzed and used for decision-making.

    Data integration tools may also provide additional functionality, such as data profiling, data enrichment, and data governance, to support management and compliance requirements. They may also integrate with other data management solutions, such as data warehouses, data lakes, and BI platforms, to provide comprehensive enterprise data management.

  4. Additional components of your system may include data warehouses, data lakes, data fabric, data mesh, IPaaS, and augmented data management.

  • Data warehouse: a system which aggregates large volumes of data from multiple sources into a single repository of highly structured and unified historical data.

  • Data lake: a centralized repository that holds all of your organization's structured and unstructured data. It employs a flat architecture which allows you to store raw data at any scale without the need to structure it first.

  • Data analytics: the use of processes and technology to combine and examine datasets, identify meaningful patterns, correlations, and trends in them, and most importantly, extract valuable insights.

  • Data fabric: a machine-enabled data integration architecture that utilizes metadata assets to unify, integrate, and govern disparate data environments.

  • Data mesh: a data architecture where data is owned and managed by the teams that use it. A data mesh decentralizes data ownership and provides a self-serve data platform and federated computational governance.

  • Integration platform as a service (iPaaS): a cloud-based software solution that allows you to integrate data and applications from different sources and environments. iPaaS provides pre-built connectors and APIs for popular enterprise applications, data transformation and mapping tools, workflow automation and orchestration capabilities, monitoring and alerting tools, and governance and compliance tools. iPaaS enables you to streamline business processes, reduce infrastructure costs, and improve efficiency.

  • Augmented data management uses machine learning and AI technologies to automate and enhance traditional management tasks such as data integration, data quality management, data governance, and data security. Through machine learning algorithms, machines can identify and classify data, monitor and manage data quality, detect and prevent security breaches, and make recommendations for data-driven decision-making. By automating routine tasks, augmented management allows you to focus on more strategic tasks that require human decision-making and expertise.

  • Streaming data refers to data which is continuously flowing from a source system to a target. It is usually generated simultaneously and at high speed by many data sources, which can include applications, IoT sensors, log files, and servers. A streaming data architecture allows you to consume, store, enrich, and analyze this flowing data in real-time as it is generated.

Challenges

There are several challenges associated with data management. Overcoming these challenges requires a well-designed data architecture, investment in the right data management software technologies, skilled personnel, and a commitment to data governance and compliance. Here are four key challenges:

Managing large data volumes. Your organization is probably flooded by large and complex datasets from many sources. This data can be both historical and real-time streaming data and it’s likely a mix of structured, semistructured and unstructured data. The traditional approach of having your IT team prep data for every use case can make it complex to manage accurate datasets across your organization.

Combining data. You know you want to avoid having siloed systems. But integrating and managing data from different systems can be challenging as the data may be stored in different formats, structures, and locations. Ensuring data consistency, accuracy, and completeness can be difficult, and data integration requires significant effort and coordination.

Finding data. Finding data from different systems in a large organization can be challenging as the data may be dispersed across multiple locations, systems, and departments. The lack of a centralized data catalog or metadata repository can make it hard for you to locate the data, and data owners may be unaware of the existence of data that may be relevant to other parts of the organization. Plus, inconsistent data naming conventions and data access permissions can further complicate the process. Implementing a data catalog and metadata management tool can help you address this challenge.

Compliance. Compliance regulations change frequently and it can be challenging to stay up to date with the latest rules and ensure that you comply. Compliance requirements also vary across different jurisdictions, adding more complexity. Be especially careful regarding personally identifiable information (PII). You should detect it, track it, and monitor your compliance in an on-going way.

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Data Integration Challenges & Solutions

Learn how to overcome the top 14 challenges you face.

Best Practices

Establishing effective data management requires a systematic approach and a clear understanding of your organization's needs and goals. Here are 10 key steps to follow:

  1. Clearly define your organization's goals, objectives and vision for your data management system.

  2. Establish a data governance framework that describes your policies, procedures, and standards.

  3. Conduct a comprehensive inventory of all data sources and identify the key data elements required to achieve your goals.

  4. Assess the quality of your data and establish data quality assurance processes to ensure accuracy, completeness, consistency, and relevancy.

  5. Implement robust data security measures to protect data from unauthorized access, theft, and cyber-attacks.

  6. Develop a data architecture that supports data integration, security, and quality assurance.

  7. Adopt a metadata management approach to enable data discovery and understanding.

  8. Identify the most appropriate storage solution for different types of data and ensure that data is stored securely and cost-effectively.

  9. Use the right tools and techniques to extract meaningful insights from data and support decision-making.

  10. Implement a data lifecycle management approach that includes data retention policies, data archiving, and data disposal procedures.

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