Data Management

Data Management Definition

Data management comprises all the disciplines related to ingesting, organizing, and maintaining data as a valuable resource.

Image shows OmniSci's data management platform used for visualizing billions of rows of data from disparate sources.


What is Data Management?

Data management refers to the development, execution, and management of policies, strategies, and programs that govern, secure, and enhance the value of data collected by an organization. The data management framework is concerned with the entirety of the data lifecycle management process and how it can be optimized to help guide strategic operational decision-making.

A robust data management plan employs an array of data management tools and data management techniques, including but not limited to: data architecture, data governance, data integration, data modeling, data security, data warehousing, master data management, business intelligence and analytics. Business intelligence systems combine data gathering, data storage, and knowledge management with data analysis to evaluate and transform complex data into meaningful, actionable information, which can be used to support more effective strategic, tactical, and operational insights and decision-making.

The data management process starts with the design and deployment of a data architecture; the creation of data models to identify dataset relationships; the generation, processing, and storage of data in a data repository; integration of data into a data warehouse or data lake for further analysis; performance of data quality checks to identify and resolve errors via data cleansing; and the creation of data governance to ensure data consistency. 

A data management platform serves as the foundation on which IT teams, data scientists, and database administrators can execute management tasks, such as optimizing responses to database queries, identifying and resolving database infrastructure faults, altering database design, and allocating resources to store data. Data management systems are built on data management platforms and may consist of databases, data lakes and warehouses, and data analytics tools, which work together as data utility to deliver algorithms and data management services to organizations.

What is Master Data Management?

Master data management is a data management strategy used to create a single, uniform master reference source for all critical business data from different IT systems. Master data is the set of consistent, uniform identifiers in an enterprise that describes its core entities, such as citizens, customers, hierarchies, prospects, sites, and suppliers. Master data domains are typically categorized as either customers, products, locations, or other sub-domains. 

The goal of the master data management strategy is to improve data quality, ensuring accuracy and consistency by applying data cleansing, data integration, and transformation practices. Master data management solutions are particularly beneficial for organizations with highly distributed data, frequent or large-scale acquisition activity, or segmented product lines.

The systematic approach for managing master data, which encompasses customer and enterprise data management, includes: understanding the business process, identifying master domains, specifying use cases, reviewing performance metrics, and determination of the initial scope. Choosing the appropriate master data architecture depends on how organizations want to structure their master data and the relationship between source systems and the master data hub:

  • Registry architecture: creates a consolidated index of master data for analytical purposes without changing any of the individual source data, using matching tools and data cleansing to identify duplicate data entries.
  • Consolidation architecture: a centralized repository of consistent master data is created by pulling master datasets from various source systems. Operational systems still use their own master data for transactions.
  • Coexistence architecture: changes to master data in individual source systems are updated in the hub, which can then be disseminated so that all other systems can use the same data.  
  • Transaction architecture: all management and master data updating is moved to the master data management hub, which then publishes data changes to each source system.

Master data management tools and master data management software involved in the master data quality management process includes data analysis, data federation, data marts, data mining, data networks, data virtualization, data visualization, a data warehouse, file systems, and operational data stores.

The benefits of master data management are accountability, accuracy, semantic consistency, stewardship, and uniformity of the enterprise’s shared master data assets. Master data management best practices should include frequent data audits, the organization of a metadata layer, structured data storage, simplified data access, prioritized cybersecurity, and adequate employee training.

Data Governance vs Data Management

Data governance, a data management principle, is the establishment and oversight of strategies and programs related to the manner in which data is accessed and treated for the purpose of ensuring that the complete data lifecycle is of high quality. Adhering to internal data standards and policies, the data governance process manages the integrity, security, and usability of the data in enterprise systems. Governance teams and data stewards recommend improvements and ensure that data governance processes and guidelines are enforced.

The core goal of data governance is to unify data deployed throughout separate transaction processing systems, with benefits including: establishing performance baselines, optimizing team effectiveness, minimizing re-work, decreasing risk of regulatory fines, improving decision making, designating accountability for information quality, and maximizing income generation. Data management is responsible for the execution of architectures and processes to achieve the data governance objectives.

Data Management Best Practices

Organizations increasingly consider data to be their most valuable resource. Establishing data management best practices and strategies is vital to ensure that collected data is accurate and aligned with business goals. Some data management best practices include:

Access: Set up specific logins and access permissions to keep data secure but accessible. Use a common query layer to simplify access to multiple, disparate sources of data storage. This is especially useful in instances of cloud based data management.

Automation: Leverage data management technology. Develop a data science environment and autonomous data capabilities to automate data transformation and to continuously monitor database queries

Data Quality: Adhere to a data governance program. Incorporate data cleansing into the data integration flow to improve the quality of existing data processes.

Documentation: Document the entire data life cycle management process. Create and maintain a text file logging all data files associated with each project. 

Identifiers: Create a data discovery layer to easily identify data. Assign unique, persistent identifiers to data and document descriptions for how to cite the dataset.

Metadata: Sharing metadata across data management domains facilitates consistent repetition of data preparation processes.

Recovery: Develop a data recovery strategy. Regularly and frequently export and create back-up data files. 

Security: Implement data management software and data discovery tools to automate and comply with the General Data Protection Regulations (GDPR).

Why Data Management is Important

Data has enormous potential when managed properly. Data management ensures that data is in the right place, at the right time, in the correct format, and that usage is governed for all users. Managed data that is high-quality, consistent, and compatible can be used to make informed business decisions and reports, optimize business operations, reduce costs, facilitate sustained business growth, and generally improve overall operations management.

Unmanaged data may lead to faulty conclusions, create chaotic data environments, expose organizations to data breaches, and may violate regulatory compliance requirements regarding data privacy and protection laws.

Does HEAVY.AI Offer a Data Management Solution?

Data integration is a core component in big data management. Modern big data integration and processing tools must connect with a wide variety of data sources and networks. HEAVY.AI big data integration tools easily handle ingestion of millions of records per second from various data warehouses and data lakes into the HEAVY.AIDB open source SQL engine. HEAVY.AIDB harnesses the massive parallelism of modern CPU and GPU hardware to query billions of rows in milliseconds and reveal data’s untapped insight.