As organizations examine the need for enterprise data management (EDM) most struggle with many challenges: what is EDM, are all the components necessary for this organization to have a successful enterprise approach to managing data, and how can enterprise data management solutions help in achieving these goals, what is the current state of each component and what is the next desired state, what is the optimal plan for proceeding from one state to the next, etc….  All of these challenges are important and are the focus of a plethora of articles, presentations, books, blogs, and discussions.  However, there is one issue that does not get enough visibility: how to pay for enterprise data management.

It would not be fair to say that EDM costs do not receive any attention from practitioners or management, since so much time is consumed by financial planning and budget processes.  Some companies spend months figuring and re-figuring costs for each department, each project, and each resource.  However, the current approach to financing an EDM initiative is significantly flawed since it directs all the attention to possible projects for the various EDM components, and it pays no attention to the fact that EDM is a foundational function and should be funded like other foundational components (HR, accounting/finance, marketing, etc.)

Definition and Scope of Enterprise Data Management (EDM)

Enterprise Data Management (EDM) is a comprehensive approach to managing an organization’s data assets, ensuring that data is accurate, accessible, and secure across the enterprise. EDM encompasses a broad range of activities, including data governance, data quality, data integration, data security, and data analytics. The scope of EDM includes all types of data, from structured and unstructured data to big data and IoT data. Effective EDM enables organizations to make informed decisions, improve operational efficiency, and maintain a competitive edge in the market.

Core Concepts and Implementation of EDM

The core concepts of EDM include data governance, data quality, data integration, data security, and data analytics. Data governance refers to the policies and procedures that ensure data is managed in a consistent and controlled manner. Data quality refers to the accuracy, completeness, and consistency of data. Data integration involves combining data from different sources into a single, unified view. Data security refers to the measures taken to protect data from unauthorized access and breaches. Data analytics involves extracting insights and knowledge from data to inform business decisions. Implementing EDM requires a comprehensive approach that includes people, processes, and technology.

Enterprise Data Management: Core Concepts and Implementation

EDM involves a systematic approach to inventorying, managing, and securing an organization’s data assets through a comprehensive enterprise data management strategy to support its business objectives. Effective EDM isn’t just about technology; it includes a robust strategy focusing on people and processes. Here’s how to establish a strong EDM framework:

  • Core Components of EDM: At the heart of EDM are key elements like data governance, data quality management, data integration, and data security. Each component plays a unique role in organizing and protecting the organization’s data sources.
  • Data Governance and Stewardship: Effective EDM requires clear data governance policies that standardize how data is handled across departments. Appointing data stewards to oversee specific data assets helps ensure transparency and consistent data quality.
  • Enhancing Data Flow and Reducing Silos: Data integration processes help create seamless data flow across disparate systems, reducing data silos that can inhibit collaboration and operational efficiency.
  • Data Accuracy and Integrity: Documented procedures and regular monitoring are essential for maintaining data accuracy, supporting data-driven decision-making and compliance with data protection laws.
  • Strategic Data Management: By investing in an enterprise data management system, organizations can improve data analysis, enhance customer relationship management, and optimize overall business processes, making EDM a valuable tool for leveraging high-quality data in decision-making.

Benefits of an Enterprise Data Management Strategy

Implementing an EDM strategy offers numerous advantages, enabling organizations to optimize their data assets for improved business outcomes. Key benefits of EDM include:

  • Enhanced Data Accessibility and Decision-Making: EDM improves data visibility and accessibility, empowering employees to make informed, data-driven decisions. By leveraging a centralized data warehouse, organizations can ensure that all departments have access to consistent and accurate data.
  • Reduction of Data Silos and Redundancies: Data democratization within an EDM framework prevents data silos by providing secure, managed access to a range of data sources. This approach also minimizes redundancies, reducing unnecessary data duplication and associated costs.
  • Operational Efficiency: A centralized enterprise data management solution reduces the time and effort spent validating and reconciling data, leading to streamlined data processes and faster decision-making.
  • Adaptability and Agility: With centralized access to accurate business data, companies can respond swiftly to market changes, enhancing their competitiveness.
  • Promotion of a Data-Driven Culture: A strong EDM strategy fosters a culture that values data as a critical business asset, prioritizing data integrity, proper data management practices, and security protocols to protect sensitive information.

An effective EDM framework not only supports operational efficiency and cost reduction but also reinforces a data-driven culture essential for long-term success.

Data Quality and Accuracy in Enterprise Data Management

Enterprise Data Management (EDM) significantly enhances data quality and accuracy, ensuring data consistency for sound decision-making by leveraging data effectively. By leveraging advanced analytics and standardized data practices, EDM fosters effective data management, supporting accurate, usable data across departments. Key data quality elements in EDM include:

  1. Eliminating Duplicates and Redundancy: EDM strategies identify and remove duplicate records, enhancing data quality and clarity.
  2. Standardizing and Structuring Data Formats: Standardized, structured data formats provide a single, accurate view of enterprise data, improving data integrity and simplifying data usage for employees.
  3. Data Validation and Profiling: Ongoing data validation checks, along with data profiling, help detect and correct poor data quality issues, including errors or missing values across disparate data sources.
  4. Leveraging Advanced Analytics: Advanced analytics tools allow data professionals to analyze data comprehensively, improving data quality and delivering valuable insights to business processes.
  5. Centralized Data Directories and Management Efforts: With a central data warehouse and effective access controls, organizations improve data flow and maintain data integrity across business functions.
  6. Collaborative Data Management Team: A strong EDM strategy includes a dedicated data management team, including data managers and enterprise data managers, who coordinate data management efforts and ensure sensitive data is secure.

Data Governance in Enterprise Data Management

Data governance is the cornerstone of an effective enterprise data management strategy, designed to uphold data integrity, ensure security, and maintain compliance with data regulations. The primary ways data governance contributes to enterprise data management include:

  1. Reducing Data Breaches and Risks: By implementing a structured governance framework, organizations can control data access and storage practices, minimizing the risk of data breaches while ensuring sensitive information is available only to authorized personnel.
  2. Enhancing Data Literacy: A robust data governance strategy improves data literacy across departments by educating employees on proper data usage and security, while also fostering a culture of responsible data handling and overall respect for data.
  3. Tracking Consent and Retention: Data governance policies help track data consent, retention, and compliance requirements, which is crucial for organizations handling customer data and navigating complex regulations.
  4. Ensuring Data Collaboration and Accessibility: Governance frameworks promote collaboration, ensuring all key business entities can leverage data while preventing data silos from forming.
  5. Maintaining Data Quality and Accuracy: Data governance includes measures to avoid data redundancy, manage data flow effectively, and store data consistently across the enterprise, ensuring reliable data quality and accuracy.

Through these practices, data governance empowers organizations to protect their data assets, build trust with stakeholders, and create a sustainable, data-driven culture aligned with business objectives.

Data Management Project Approach

Most organizations address data management as a series of projects, seeing them as relatively independent efforts that must compete for funds and resources during each budget planning process. This approach shows a lack of strategic attention to data and its management, either in the business strategy or the information technology strategy. In this approach, a data quality profiling project would compete with a “project” to develop the EDM program, when strategically it would make sense to implement the EDM program as part of the organizational structure – not competing with any tactical projects. In this example, the data quality project would become an effort under the EDM program and would be funded accordingly.

Having a business strategy and an information technology strategy that recognize the value of data and information to the organization – and demonstrate that value through the development of enterprise data management solutions – would be an enabling factor for success. Establishing and sustaining these strategies can create the foundation for recognizing the need for programs that incorporate long-term goals into the organizational culture and its operation, such as acknowledging the value of data and information as business assets that can provide continuing value to the organization.

Enterprise Data Management as Part of Cost of Doing Business

If organizations were to understand the value of data and information to the organization’s functionality, some experts in business economics and in data management believe the results would initiate a move to enterprise data management (EDM) as part of their cost of doing business, similar to functions such as accounting/finance, human resources, etc.  All the activities, costs, and benefits associated with each function are shared across the organization and the charges for each function are managed as part of the “cost of doing business”.  Companies do not dispute the need for accounting or HR or marketing, and they should not argue with the need for EDM, since EDM manages the assets of data and information for the entire organization.

Establishing the EDM Program Office

Including EDM as part of the “cost of doing business” requires a change in the usual funding model at the strategic level, which would be accomplished by developing a comprehensive enterprise data management strategy. This would be accomplished by developing a new business unit called an Enterprise Data Management Program Office.  This unit would include the EDM program administration, the development, and implementation of a data governance program, the management of business metadata, and the development of a data quality program.  Additionally, the program office would coordinate the data management-specific processes of operations such as master data management, data warehousing, and business intelligence, and enterprise data architecture since the procedures and technical management of these disciplines may reside outside of the EDM area.

Centralizing EDM Operations

In the program office, the EDM activities would be combined into one unit as a managed operation similar to accounting/finance, human resources, marketing, and sales, etc.  The departmental cost of the EDM program office would be borne by the organization, and the unit would be allocated a budget managed by the department’s leadership.  This department could be the funding source for EDM-based projects, such as launching new data stewardship teams, implementing data standards through metadata management, profiling data sets for data quality, rationalizing master data, etc.  Each EDM foundational discipline would have its dedicated team within the program office.

Alternative EDM Funding Models

As an alternative, the costs of the EDM foundational program unit could be spread throughout the company, similar to the allocation of project management in many organizations, where every project is charged for the use of the project management office.  Each existing business unit could be charged an additional percentage for the use of enterprise data management foundations, exempting only the technology infrastructure areas that do receive any benefits from EDM.  During the project planning process, each business unit would be asked to estimate its use of EDM (data governance and stewardship, data quality management, metadata management, etc.) for the coming year and that percentage would be applied to the department’s projected annual spending figure.  The total value of funds allocated for EDM from all the business units’ estimates would support the enterprise data management program (and its direct functions such as data governance, data quality, and metadata management) for the coming year. 

Using this approach, however, would mean there would be no direct source of funds for EDM-based projects, and each project would have to go through the project planning process independently.  Organizations that have adopted this approach have discovered their EDM programs can suffer from continuing sustained funding challenges, like those organizations that do not have any standard approach to funding data management-related projects.

Conclusion

One of the biggest challenges in securing acceptance for EDM in an organization is gaining recognition of its value, and the value of its foundational components (program administration, data governance, metadata management, etc.).  Including EDM in the company’s “cost of doing business” either as a business unit or as a percentage charge across the organization may provide needed support to achieve acceptance and sustained visibility for enterprise data management and its major activities.