Enterprise data management: Definition and how it works

Enterprise data management (EDM) is the function of collecting and making an organization’s data assets accessible to a range of users as well as to assure data quality. This requires working with IT for the systems and infrastructure components and other teams within the organization to find and gather the data (e.g., sales, marketing, finance, operations, and legal). 

The broad function of data management also includes data governance to ensure the confidentiality, integrity, and accessibility of data. A significant component of enterprise data management involves humans and ensuring that they follow data governance and have access to the information that they need. 

What information is handled with enterprise data management?

Enterprise data management encompasses information from across an organization that needs to be organized, stored, secured, and made accessible to users. The content includes structured data (e.g., data in spreadsheets and relational databases) and unstructured data, such as: 

Structured data 

• Financial information 
• Inventory details 
• Customer orders 
• Accounting data  
• Human resources records 
• Marketing and sales databases 
• Third-party data 
• Operations data 

Unstructured data 

• Photos 
• Graphics 
• Videos 
• Social media content 
• Mobile data 
• Internet of Things (IoT) data 

Enterprise data management roles and responsibilities 

Enterprise data management is executed with teams and contributors from multiple areas of an organization. Following are several of the roles commonly associated with enterprise data management. Note that the number of roles and scale of responsibilities will vary by the size and scope of the enterprise. 

Enterprise data managers

• Assemble and manage cross-functional opportunities.  
• Establish and enforce data governance frameworks, processes, workflows, and change management. 
• Oversee the coordination and governance of data collection, synchronization, cleansing, and migration. 
• Identify and resolve data integrity, risk, and compliance issues. 

Database administrators

• Ensure system availability. 
• Ensure that data is efficiently organized and stored.   
• Handle backups and restorations. 
• Manage the organization’s databases.  
• Optimize database performance. 
• Oversee monitoring and security. 

Data architects

• Develop plans for an organization’s data management framework. 
• Identify and select data platforms and systems that best fit the architecture. 

Data engineers

• Integrate data sets by converting them from the source data type to a different target. 
• Load data from source systems into a data warehouse. 
• Prepare data for use by application developers, data scientists, and other analysts.  
• Standardize data according to business rules.  
• Transfer data into analytical data stores. 
• Use data and extract, transform, and load (ETL) tools to move data from one system to another. 

Data modelers

• Create diagrams of data structures and associated business rules. 
• Ensure the quality of models on an ongoing basis. 
• Handle the process of building the conceptual, logical, and physical models for the organization’s data.  
• Keep models up to date and aligned with changing requirements. 

Data quality analysts

• Assess data sets on various data quality standards (e.g., accuracy, completeness, consistency, conformity, and lack of duplicate data).  
• Establish and maintain data quality standards.  
• Identify and correct errors and deficiencies that compromise the quality of data. 

Data scientists

• Develop analytical and statistical models to analyze data. 
• Develop predictive models using machine learning and other advanced analytics technologies to forecast future trends and answer what-if questions. 
• Help business users understand the data insights.  
• Use data visualization tools to create graphics, reports, and dashboards. 

Data analysts

Data analysts support and are overseen by data scientists to: 

• Build dashboards and reports. 
• Cleanse, organize, and analyze data sets. 
• Turn raw data into meaningful insights. 

Business intelligence analysts

• Analyze data to find information to improve decision-making. 
• Build dashboards, reports, and data visualizations.    
• Focus on data analysis and visualization.  
• Identify patterns to optimize operations and direct business strategies.  
• Run basic queries (e.g., to track key performance indicators, customer buying habits, and production metrics). 
• Work with business managers to identify data assets. 
• Work with structured data stored in data marts and data warehouses. 

Benefits of enterprise data management

Enterprise data management benefits organizations that invest in developing, implementing, and maintaining a strong program. Benefits include the following: 

• Consolidate data from multiple sources. 
• Ensure the accuracy and accessibility of data.  
• Have a consistent data architecture that scales quickly and easily. 
• Identify data-related risks with visibility across all data sources and silos.  
• Improve decision-making by making high-quality data readily available.   
• Protect sensitive data. 
• Provide real-time access to data. 
• Reduce data-related costs. 
• Store data in an accessible format. 
• Store in a standardized and accessible format. 
• Streamline and optimize operational processes by identifying areas that can be improved. 
• Support compliance with government and industry regulations. 

Enterprise data management vs master data management

Enterprise data management	Master data management
Catalogs all of an organization’s information, including internal and external assets	Involves creating a single view of internal data in a master file, master record, or golden record
Focuses on moving and consolidating data to make it accessible	Focuses on reconciling an organization’s data from multiple sources to make it useable
Covers all data sources, including digital and paper-based	Covers only digital data sources

Enterprise data management components

The key elements of any enterprise data management practice include some version of the following. The specific implementation will vary based on the organization’s requirements and size.   

Data architecture

To provide the guidelines for data handling at each stage of its lifecycle (i.e., creation/acquisition, storage, usage, security, archival, recovery, and erasure).   

Data governance 

To ensure the integrity, quality, and security of data.   

Data integration 

To ensure that data is in uniform formats and validated, easily incorporated into applications and systems. 

Data quality management

To maintain the quality and integrity of data. 

Data security 

To protect data throughout its lifecycle, including data at rest and data in transit.  

Data stewardship

To manage the lifecycle of data from creation to end of life.   

Data warehouse

To store current and historical data from disparate sources.   

Master data management (MDM)

To create master versions of data and provide a consistent view of disparate information.  

Enterprise data management best practices

Perform an enterprise-wide data need assessment

Before developing an enterprise data management practice, take time to evaluate and prioritize users’ information needs. Considerations during this process are:  

• Does the organization have permission to collect and process the data?  
• How will the data be used? 
• What data is sensitive and requires special protection? 
• What data types need to be collected, stored, and managed? 
• Why is the data needed? 
• Will the data be stored on-site or with a cloud service? 

Define objectives for the enterprise data management practice

To avoid scope creep and unnecessary work, clear goals should be established for the enterprise data management practice. Defining the scope and focus of the practice can be facilitated by considering and answering these questions:   

• What is the ultimate objective of the practice? 
• What is considered in scope, and out of scope? 
• What are the metrics for measuring the performance of the enterprise data management practice? 
• What are the incremental deliverables, as well as associated timelines and milestones? 

Identify any compliance or other rules that must be considered

Most organizations are subject to some data-related compliance standards and rules. These must be part of the plan when preparing to build out an enterprise data management practice. This includes data protection systems to prevent data loss, breaches, and corruption.   

Invest in technology to provide an effective infrastructure

Enterprise data management objectives will dictate the right technology for the practice. Invest in the right systems, software, and services to ensure data is accessible to all users.  

Technology commonly used to support enterprise data management practices includes: 

• Data encryption 
• Data lakes 
• Data schemas 
• Data warehouses 
• Erasure tools 
• Filing cabinets 
• Master data management 
• Metadata catalogs  
• Security tools (e.g., data loss protection, firewalls, and access controls) 
• Variety of backups (i.e., hot, cold, and archival) 

Make quality a priority

Data quality is critical. Having volumes of data accessible is useless and a waste of resources if it is of poor quality. Creating a culture that appreciates and supports high data quality ensures that the information collected meets requirements for downstream usage. 

Educate users on how to use systems and their benefits

The success of enterprise data management is predicated on the participation and support of users. Ensuring they understand the policies and procedures for data handling and why it is important goes a long way to securing their buy-in and the program’s success.   

Extract maximum value with enterprise data management

With information being the lifeblood of most organizations, enterprise data management has become imperative rather than a nice-to-have resource. The enterprise relies on information to generate business intelligence that gives them a competitive advantage, helps respond to customers’ needs, and optimizes operations.  

Investing in and taking time to develop enterprise data management consistently delivers a high return on investment. The data that drives business intelligence is higher quality, more users can access information, and storage resources are minimized by eliminating redundant, stale, or obsolete data.   

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