The National Academies

NCHRP 23-27 [Anticipated]

Strategies to Strengthen Data-Driven Decision Making

  Project Data
Funds: $300,000
Staff Responsibility: Ann M. Hartell
Fiscal Year: 2022

This project has been tentatively selected and a project statement (request for proposals) is expected to be available on this website. The problem statement below will be the starting point for a panel of experts to develop the project statement.

State DOTs are seeking to derive more decisions from data, improve real time performance management, and integrate advancements in data science. While data analytics, automation and machine learning are increasing, current data architectures are fragmented and costly, adding complexity and delay for information system development and management. This makes it difficult to maintain alignment with business needs. Business and enterprise architectures are used by some state departments of transportation as well as many other public and private organizations. Examples of these architectures include the Zachman Framework for Enterprise Architectures, The Open Group Architecture Framework (TOGAF), and the Federal Enterprise Architecture (FEA).


The goal of this project is to identify business architectures that support and optimize faster decisions, data relevance and usability across the organization, and current business needs and responsive to evolving needs.


The objective of this project is to explore business and enterprise architectures for their potential to improve the alignment of data with business needs and provide timely support for changes in business strategies. Tasks are anticipated to include:

1. Collecting and reviewing the business and enterprise architectures and summarizing common elements and strengthens and intended uses of the models.

2. Surveying state DOTs for uses of these architectures and/or the elements of the architectures.

3. Analyzing the results and developing recommendations for architectures that optimize nimble data strategies.

4. Prepare a guidebook of these architectures and the relative strengthens and weaknesses for common uses in state departments of transportation.


There is rapid evolution of data science and analytics in the transportation sector, and it is anticipated that this will continue at an accelerated pace. Therefore, it is timely to identify strategies that can optimize and streamline data architectures to promote more responsive integration of these new approaches.

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