Introduction
Often, data governance and data stewardship programs are cited for a lack of tangible metrics that indicate the success of the initiative. Without identifying criteria for measuring the results of the data governance program and the activities of the data stewards and data management professionals, an organization cannot feel confident that the program is achieving its business goals or contributing quantifiable business value. Enterprise data governance plays a crucial role in reducing risks and improving data quality, which are key value propositions to stakeholders. Many data governance programs are not fully funded or are canceled after a pilot program when the effort does not demonstrate detectable positive results in pre-defined criteria, which aren’t always fully thought through.
Guidelines for Identifying Data Governance Business Value
To avoid the stigma of cancellation when the program is successful but has not demonstrated that success, it is essential that every data governance and data stewardship program follow these guidelines:
1.) Create a set of business value goals for the data governance program that are approved by senior management. This should be a short set (3-5 total), based on the business’ goals and related to how the data governance program will address them. Example drawn from an EWSolutions client:
Business Goal Data Governance Capabilities / Contributions Increase Revenue Improve profitability with better analytics for improved decision making Manage Costs and Complexity Lower cost of data management and integration through enterprise data source mapping and enterprise access to business data definitions Manage Risk Provide better insights into fraud with improved analytics ; Improve quality of reporting to regulators and authorities through improved data processes and data management Improve Performance Quality Improve decision making through use of trusted data; Enable process optimization with accurate data
2.) Identify the specific measurement types to be used to calculate results for the data governance program:
Business Value Measures – ways to attribute business value to a.) implementation of the EIM and information governance programs; b.) data standardization; c.) improved data management discipline across the enterprise and in projects. Examples of business value measures could include:
Increase in revenue due to ability to manage customers / members properly as a result of the management of master data according to industry standards with a defined MDM architecture and integration with all relevant applications
Decrease in production costs due to the reduction in the need for continued questions on the definition of data, the continued searches for analytical data and the sources of operational data of high quality, the reduction in the time to market for new applications as a result of consistent data architecture, consistent meta data management, consistent data governance.
Improved productivity due to the use of consistently applied data and information governance for all mission-critical data, the ability to rely on analytical data for its high quality, the ability to respond quickly to time to market decision making due to higher quality data and information from data quality improvement
Increased profit due to faster and more accurate decisions made with correct and more available data and information, more ability to use a wider variety of data that has been organized according to established standards
Acceptability and Compliance Measures – ways to directly evaluate and measure the level of adoption of: a.) enterprise data standards; b.) enterprise data management and data governance programs; c.) performance of the business data stewards and the stewardship teams. Examples of acceptability and compliance measures could include:
% of applications that are actively governed through the Data Governance program, master data management, meta data management, data quality management
% of business departments actively involved in data governance, master data management, metadata management, data quality management
% of applications aligned to the Enterprise Data Model (EDM)
Number of data attributes defined, in business and technical meta data, by entity, by subject area, and approved by the Data Governance Committee
Number of business rules established by functional area or subject area or other criterion, and approved by the Data Governance Committee
Number of subject areas modeled for the Enterprise Data Model (fully attributed) and approved by the Data Governance Committee and EDM Council
Number of policies written by the IG Program team and approved by company leadership
Number of EDM-related standards written / revised / accepted and approved by Data Governance Committee
% of logged data stewardship problems resolved by month, quarter, annually
Number of people trained as business data stewards by month, quarter, annually
Number of people that participate actively as business data stewards
It is essential that these metrics resonate with the business leadership, so the final measurements should be approved by the executive sponsors for the data governance program. Many organizations focus on data quality improvements, as indicated in a Gartner study
3.) Measure the final set of metrics regularly, and report results and their meaning to all stakeholders. Analyze trends identified by the metrics and adjust accordingly, for the program’s continuous improvement. Communicate performance-inspired changes to demonstrate the effect the metrics have on the program.
4.) Add additional metrics as requested or as necessary, to maintain visible, demonstrated business value of the data governance program. However, do not add measurements for their own sake. Only measure what is considered to be important to the organization and that can be measured appropriately.
In the final analysis, just as “the unexamined life is not worth living,” the data governance program without the ability to demonstrate its business value will not prove itself worthy of being sustained.
Effective Strategies for Implementing a Data Governance Framework
To implement a successful data governance strategy, organizations must focus on clearly defined steps and structured communication to ensure alignment with business objectives:
Define Scope and Objectives : Establish the boundaries and focus areas of the governance framework to avoid overreach while addressing key data-related issues. This ensures resources are effectively managed and aligned with the business value of data governance. Enterprise data governance ensures alignment with business objectives and improves data quality across the organization.Identify Key Performance Indicators (KPIs) : Develop KPIs that reflect measurable outcomes, such as improving data security, optimizing processes, or ensuring compliance with relevant regulations.Establish a Baseline : Before initiating governance programs, assess the current state of your organization’s data practices. This baseline serves as a reference to evaluate improvements and track business intelligence outcomes over time.Engage Stakeholders Early : Include business leaders, data users, and data leaders in the initial phases of governance planning to align the program with organizational priorities and specific needs.Utilize Data Visualization Tools : Leverage tools to communicate progress and the value of data governance visually. This approach aids in tailoring communication for different audiences, from technical teams to executives, enhancing transparency and stakeholder trust.
Benefits of Data Governance: Driving Organizational Excellence
Effective data governance offers tangible benefits that can resonate across an organization, fostering a data-driven culture while delivering measurable business outcomes. These include:
Reduced Data Redundancy : Well-governed data eliminates duplicate and inconsistent information, streamlining data integration and reducing associated costs.Enhanced Data Security : Implementing robust data governance policies, such as access controls and monitoring, protects sensitive data and mitigates security concerns. Enterprise data governance drives organizational excellence by reducing risks and improving data quality.Facilitated Collaboration and Innovation : Data democratization ensures that the right data users, across different departments, can access reliable data, promoting innovation and informed decision-making.Leverage Data as a Strategic Asset : By maintaining well-managed data, organizations can maximize the value of data assets to drive long-term growth while achieving competitive advantages.Optimized Processes and Efficiency : With governed data, businesses can optimize processes and align operations more effectively with their strategic planning efforts.
By addressing these areas, a robust data governance framework not only ensures compliance with regulatory requirements but also strengthens customer trust and supports a data-driven decision-making process.
Enhancing Regulatory Compliance with Data Governance
In today’s increasingly regulated world, ensuring compliance with data protection laws like GDPR and CCPA is a critical — and necessary — business requirement. An effective data governance framework helps organizations establish policies and procedures that enforce regulatory compliance, protect sensitive information, and reduce the risk of costly penalties. Enterprise data governance helps organizations meet regulatory requirements as well as protect sensitive data.
By improving data visibility, businesses can identify and mitigate risks related to data privacy and security, which can help prevent potentially disastrous data breaches. Additionally, maintaining control over data access and data lineage builds trust with customers by safeguarding personally identifiable information and ensuring data is used responsibly. This trust strengthens long-term customer loyalty and the organization’s reputation while fostering operational efficiency. Organizations leveraging robust data governance initiatives also benefit from cost savings associated with avoiding non-compliance and improving processes to manage sensitive data more effectively.
Measuring the Business Value of a Data Governance Program
Measuring the business value of a data governance program is essential to demonstrate its effectiveness and justify continued investment. Here are some key performance indicators (KPIs) to measure the business value of a data governance program:
Data Quality Metrics : Metrics such as data accuracy, completeness, and consistency help ensure the organization’s data is of the highest quality data, something crucial for reliable analytics and corporate decision-making.Data Security Metrics : This includes the number of data breaches, instances of unauthorized access, and data loss incidents. These metrics help assess the effectiveness of data security measures.Regulatory Compliance Metrics : Evaluating metrics such as audit results, fines, and instances of non-compliance helps in understanding a program’s success in meeting important legal requirements.Operational Efficiency Metrics : Measuring operational efficiency metrics like data management costs, data processing time, and data storage costs ensures an efficient data management process can contribute to cost savings and faster operations.Business Value Metrics : Business value metrics, such as revenue growth, customer satisfaction, and return on investment (ROI), can demonstrate the direct impact of data governance on a business’s performance.
Calculating ROI and Assessing Qualitative Benefits
Calculating the return on investment (ROI) of a data governance program is essential to demonstrate its financial value. The steps to calculate ROI and assess qualitative benefits are as follows:
Identify Costs : Identify the costs associated with implementing and maintaining a data governance program, including personnel, technology, and training expenses.Identify Benefits : Identify the benefits of a data governance program, such as improved data quality, increased operational efficiency, and enhanced data security. Quantify these benefits wherever possible.Calculate ROI : Calculate the ROI of a data governance program using a cost-benefit analysis. Compare the total benefits to the total costs to determine the program’s financial return.Assess Qualitative Benefits : Assess the qualitative benefits of a data governance program, including improved decision-making, enhanced customer satisfaction, and increased competitiveness. These benefits, while harder to quantify, are crucial for long-term success.
