Data Stewardship Performance Measurement

Published in July 2004


Enterprises that are implementing data management best practices and data management programs often struggle while developing performance metrics for the new (or renewed) discipline and function.
Data Stewardship performance metrics have become the focus of many of these enterprises as they look for ways to demonstrate the value and success of the improved discipline and accountability for
the management of enterprise data. This article is focused on briefly identifying different types of measures that an enterprise may consider when developing their data stewardship programs and
measuring the success of their data management programs.

The ability to implement metrics for a data stewardship program often depends on two primary factors – 1) The enterprise’s willingness to consider permanent (longer-term) value
alongside the immediate measurable benefits of the programs and 2) The enterprise’s ability to attribute the improvements in information-based capabilities to the action of the stewardship
program. The second factor (for the purposes of this article known as Attribution Analysis) is not the focus of the data stewardship performance measurements described in this article.

Please note that Attribution Analysis plays a vital role in the ability to relate improvements of quantifiable permanent value in information-based capability to the activities of the data
management program and specifically the data stewardship program. For a well-written paper on attribution analysis as it relates to performance measurement please visit: Addressing Attribution Through Contribution Analysis: Using Performance
Measurement Sensibly
. Note: I gratefully acknowledge Ed Levine for pointing me to this resource.

One of the critical success factors for improved information-based capabilities is the standardization and consistent use of data across the enterprise. This long-term value of the consistency in
enterprise data will only be achieved through incremental improvements in data standardization, a process that must be guided by individuals that know the business and the data – namely the
Data Stewards.

In my experience, data steward roles are strongly aligned with the deployment of enterprise data standards that will lead to the improved information-based capabilities. A quick review of data
steward roles from an earlier article I wrote called A Simplified Approach to Stewardship:

  • Data Definition Stewards (Data Definers) play a critical role in defining the data that is required to operate their business and to maintain the values and consistency of this
    data across the enterprise. Data Definers are also responsible for identifying and leveraging existing data resources prior to creating new data and documenting the business
    definitions/descriptions of the data they need and their related values.
  • Data Production Stewards (Data Producers) are accountable for making certain that all data that is created in – or passed into – information systems, complies with
    the standards set forth by the Data Definers.
  • Data Usage Stewards (Data Users) are responsible for understanding the data and the values of the data and using the data for its intended purposes.
  • The responsibility to accept, propagate and comply with enterprise data standards is the primary responsibility of all data stewards.

Enterprise data management functions often measure the performance of the Data Stewardship program using two primary methods – Through the use of 1) Business Value Measures (longer-term) and
2) Acceptability & Compliance Measures (immediately measurable).

Business Value Measures

Business Value Measures directly attribute business value to the implementation of the data stewardship program, data standardization and improved data management discipline. In the private sector,
business value is most often measured by increases of revenue and profitability, reduction of cost and improvements in productivity. In the public sector only some of these types of business value
measures are relevant, while others may require customization for not-for-profit businesses.

For many enterprises, information-based capability improvements are the long-term business value goals of the data management function. Sample statements and measures in terms of quantifiable
business value include:

  • Within 2 years the senior management of the enterprise would like to have the ability to evaluate organizational compliance of a business entity, facility, site, and/or plot of land across
    departmental program areas on a whole rather than by division as we do now.
  • Within 3 years the enterprise would like to be able to evaluate businesses for acquisition depending on cross-departmental area standard data.
  • Within 5 years the enterprise would like to have the ability to make permitting decisions based of cross-departmental area standard data.

In order to meet these measurable objectives, the enterprise must adopt enterprise data standards for “enterprise” data. As stated earlier, the individuals that know the data (the data
stewards) will guide the adoption of these standards. The Acceptability & Compliance Measures will be used to measure the adoption of the enterprise data standards.

The business value measures listed here are only samples of how standardization of data through the implementation of the data administration discipline and the data stewardship program can result
in new business capability and value. The ability to quantify these types of business measures will depend on the enterprise’s ability to associate improved business decision making,
increases in revenue/income, reduction in costs, improvements in productivity, etc. to these improved information-based capabilities.

Acceptability & Compliance Measures

The second type of data stewardship performance measurements are called Acceptability & Compliance Measures which are ways to directly evaluate and measure the level of adoption of enterprise
data standards and the data stewardship program.

Sample measures in terms of quantifiable acceptability and compliance:

  • Percentage / Number of departments where a data standard (for a specific piece of data or data element) is accepted
  • Percentage / Number of information systems data elements that share a data standard
  • Percentage / Number of business processes that utilize data standard
  • Percentage / Number of production reports (outputs) that utilize data standard
  • Percentage / Number of people that use data standard elements
  • Percentage / Number of integrated business processes

Rolling out new technology and integrated data applications alone won’t standardize data. The enterprise must adapt its culture and involve the department-areas and key stakeholders in data
definition, production, and usage. Technology alone will not solve the standardization issue.

A strong commitment to enterprise standardization of data and recognition of the impact standards have on improved information-based capabilities will allow the enterprise to successfully measure
the business value of data stewardship stated earlier in this document.

Measurement Critical Success Factors

There are two factors critical to measuring the success of the data management function and the data stewardship program:

  • Management must adopt the enterprise data standards.
  • Success and failure is not 100% up to the data management function and the adoption of data standards, but rather depends on the successful interaction among the data stewards, discipline in
    data management, integration with current and existing policy, directives and methodology, and oversight processes and technology.
  • A tenet of data management, data stewardship and data standardization is that “People, processes, and technology have to come together on this.” (Gartner report 05/2004). Short-term
    acceptance and compliance to enterprise data standards will set organizations on a correct and measurable course to recognize the permanent improvements from data management and data stewardship.

This article focused on identifying different types of metrics and measures that an enterprise may consider when developing their data stewardship programs and measuring the success of their data
management programs.

For more information on how to build & implement a data stewardship program, please visit

Copyright © 2004 Robert S. Seiner – All Rights Reserved

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Robert S. Seiner

Robert S. Seiner

Robert (Bob) S. Seiner is the President and Principal of KIK Consulting & Educational Services and the Publisher Emeritus of The Data Administration Newsletter. Seiner is a thought-leader in the fields of data governance and metadata management. KIK (which stands for “knowledge is king”) offers consulting, mentoring and educational services focused on Non-Invasive Data Governance, data stewardship, data management and metadata management solutions. Seiner is the author of the industry’s top selling book on data governance – Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success (Technics Publications 2014) and the followup book - Non-Invasive Data Governance Strikes Again: Gaining Experience and Perspective (Technics 2023), and has hosted the popular monthly webinar series on data governance called Real-World Data Governance (w Dataversity) since 2012. Seiner holds the position of Adjunct Faculty and Instructor for the Carnegie Mellon University Heinz College Chief Data Officer Executive Education program.

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