Iowa DOT’s enterprise, strategic data governance, and management plan in the works

Wheel cogsData in and of itself has no value. But using complete, accurate, and timely data to drive decision-making is one element that can make or break an organization. From average daily traffic counts to vacuum-saturated specific gravity testing and thousands of data points in between, we gather, store, analyze and rely on a wide range of data and information to support business functions across the agency.

Recently, creating a framework for the data that is produced and leveraged by all of our divisions has been the task of an agency-wide Data Management Committee (see the member list at the end of this post). The committee has developed a “Strategic Data Business Plan” to lay the groundwork for how we manage and govern data we produce and manage here at the Iowa DOT.

Data management refers to “the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets.”

Data management focuses on managing data through execution, while data governance is focused on the oversight and the creation of a framework to make data management possible.

The nuts and bolts of data management

There are several elements of data management.

  1. Data Strategy
  2. Data Life-cycle Management
  3. Data Architecture and Integration
  4. Data Collaboration
  5. Data Quality Management

To communicate the potential benefits, challenges, and direction of our data goals, the committee developed three documents that include the strategic, tactical, and operational approaches for managing one of the agency’s most critical assets. (See links to documents at the conclusion of this blog).

Data governance framework

Peggi Knight, director of the Research and Analytics Bureau, is heading up the team. She said, “With all divisions represented on our committee, we have drafted a three-pronged approach to managing the data that guides strategies, expenditures, and resource allocations for the department every day.”

­Garrett Pedersen from the Systems Planning Bureau sits on the committee and said, “What we are working to achieve is helping people be aware of the data collected across the agency, improving transparency, and allowing us all to improve our work. This should lead to reduced duplication and more cohesive decision-making between business units.”

Matt Haubrich from the Organizational Improvement Bureau said, “Data needs to work together to provide a fuller representation of what is happening. One piece of information may not give the whole picture. We need to look at all elements comprehensively to make the best decisions.”

In addition to better decision-making, a solid data governance and management strategy has financial benefits, as well. As part of their work, the committee estimates potential savings in the hundreds of millions of dollars over a 10-year period by reducing issues related to data security, quality, relevance and efficiency, and accessibility. 


While data remains a crucial asset of the agency, without a robust data management framework in place, data collection, access, analysis, and sharing can prove to be a difficult process. Challenges without an effective data management plan include:

  • Data underutilization: Data collected is not fully used because of limitations related to data definitions, data quality, awareness or access, or lack of proper use cases.
  • Data duplication: Data exists in multiple locations.
  • Inefficient integration strategies: Data is not properly linked to other data due to a lack of collaboration.
  • Unaddressed data needs: Critical or useful data is not being collected or made available.
  • Underappreciated data value: The value of data in terms of money, time, and people is underestimated.
  • Inefficient use of resources: Data sets are not easily accessible or able to be analyzed, or unnecessary acquisition or duplication occurs.
  • Inconsistent results: When data sets or data analysis tools exist for the same data types, but the datasets or tools are not the same, results differ.
  • Gaps in skill sets: The DOT has limited staff and gaps in technical capabilities for advanced data science and analytics.

Here are links to the current documentation for the committee. Note: You must be on the Iowa DOT network to view these.

Executive Summary

Data Management Business Plan

Data Management Strategic Plan

Data Management Action Plan

After reviewing the documents, if you have questions or comments, please contact Peggi Knight at [email protected].



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