State departments of transportation (DOTs) spend large sums of money creating, managing, analyzing and reporting on disparate data in their effort to maintain their transportation networks. At the same time, they struggle to use assimilated data to improve asset performance and public safety.

A well-defined data governance framework with data standards tailored to an organization and its business drivers will bring efficiencies in data management, data analysis and regular reporting, which, in turn, allows for timely and accurate decision making impacting public safety. Data governance improves data management—utilizing the data as an asset in a continuous effort to save lives and money.

The Ohio Department of Transportation (ODOT) recognized the need for an enterprise-wide approach to data governance in its effort to streamline the process of consolidating disparate sets of data from different business units within the organization. Even after standardizing certain data elements, complications in geospatially aligning the roadway inventory, right-of-way asset data, key performance data and project resource and cost data persisted. ODOT needed a structured, tailored data governance program to address existing issues and deficiencies.

Like all state DOTs, ODOT is required to provide extensive data sets to the United States Department of Transportation for key certification reports such as the Transportation Asset Management Plan (TAMP), Model Inventory Roadway Elements (MIRE) and Highway Performance Monitoring System (HPMS). ODOT invested much time and money developing data warehouses and reporting systems for trend analysis, annual work planning and project reporting that drew solutions from many of the same sources. However, ODOT’s IT staff still spends time scrubbing the data to meet the federal reporting requirements. Recognizing that data, and all the information technologies and labor supporting it, is arguably the third most expensive asset a DOT maintains after roads and bridges, ODOT decided to address many of the issues and inefficiencies, and resulting skepticism in some data, by developing a data governance program.

ODOT contracted Data Transfer Solutions (DTS), a member of the SNC-Lavalin Group, to tailor and implement an enterprise data governance program that consists of:

• A DOT-wide data governance committee co-chaired by two deputy directors and a chief data officer
• A top-down, bottom-up data governance framework that defines the organizational business drivers such as critical success factors and regulatory reporting requirements; enterprise data governance policy, standards and procedures; and best management practices and standard operating procedures for each stage of the DOT’s defined data life cycle

ODOT Tailored Data Governance Framework

• Creation of an Office of Data Governance, including the creation of a chief data officer and a data analyst position
• Development of a Data Governance Strategic Plan that provides a roadmap for objectives and guidelines to support the program
• A data normalization proof of concept that implements data governance standards at the source systems as much as technologies will permit; develop standardized application programming interfaces (API), representational state transfer (REST) services, and data extract, transform, load (ETL) routines into a new data warehouse using the newly defined enterprise data element standards; and developing standardized report formats with business intelligence (BI) queries for data analysis, data visualization and reporting

To support and guide data governance, DTS developed and continues to implement a tailored data governance framework and program with ODOT. The framework establishes guidelines and rules of engagement for business and management activities related to enterprise data, and formalizes data life cycle interactions between people, processes and technologies to support positive outcomes.

Applying data standards throughout the data life cycle creates consistencies and improves data quality, as well as reduces the amount of time and effort required to assimilate data for a report or simple analysis—providing higher confidence in the results the data suggests. Data business owners normalize their data sets and develop standard procedures for ensuring data quality throughout the data life cycle. These efforts will allow efficient and consistent data creation and assimilation, enabling decision-makers to make timely, data-driven decisions that impact public safety and the public treasury, helping save lives and money in Ohio.

Listen to find out what steps you need to take to ensure efficient and consistent data creation and assimilation, and then discover the benefits and impact of making timely, data-driven decisions.

For more information, contact:
John Pregler, Management Solutions Consultant, Orlando, Florida. Email T: +1.407.587.4054
Soraya Saflicki, Sr Business Analyst, Orlando, Florida. Email T: +1.407.587.4060