The Foundational Aspects of Organizational Data Management

Transportation organizations in the infrastructure construction industry are developing data analytics strategies and are striving to become more data driven in their decision-making practices. This is creating greater dependence on treating data as an organizational asset and the recognition they must put in place the foundational aspects for better data management processes. In addition to a goal of data analytics and data driven decision-making, organizations are expected to have high quality and accurate data for myriad reporting purposes, whether to legislative bodies, constituents, customers, or for more formal audit and compliance requirements. Finally, as transportation agencies pursue digital delivery as part of their core business, having good data management practices is becoming essential. This blog will explore the foundational aspects of a modern and mature data focused organization.

Data Management is the process of managing your data environment to ensure it is available, reliable, and secure. The foundational elements of data management include:

Data Governance

Data governance refers to how policy and processes around how data is collected, stored, accessed, and maintained in a repeatable and structured manner. This includes assigning responsibility to staff to act as data owners, data stewards, data custodians, and includes documented policies and processes related to how data is managed in their respective organization.

Data Objectives

Data objectives refer to a simple, essential question - are you collecting the right data for the right purpose? Having a good plan for how you intend to use your data is essential for data driven decision-making and predictive analytics. This should include an analysis of how data will be used in your organization and to answer the questions of what strategic initiatives is your data supporting, and who your stakeholders are.

Data Storage

A well-designed, scalable, responsive, and adaptable data environment is important as well. Many organizations are developing a data warehouse to manage their data environment that is flexible and adaptable to the changing business needs.

Data Catalog/Inventory

Having a clear picture of your data environment, documented metadata, and processes in place to ensure it is kept up to date is a critical element of good data management. You need to understand what data you have and the characteristics of the data environments, and you need to know what you have to effectively manage it.

Data Quality

Ensuring accurate, complete, consistent, and up-to-date data is a must for making decisions, and having data-quality standards in place is essential.

Data Integration

Gathering data from various sources to create a unified platform (one stop shop) is necessary to help break data and business silos.

Data Accessibility

The data must be accessible; this includes only appropriate access to necessary data for the objective desired. For security purposes, having good access management controls is important to ensure only necessary data is available and appropriate approvals are in place. Easily accessible to authorized users across the organization.

These elements form the foundation for mature data management practices and will drive agencies toward their strategic goals which include:

• Better support for a digital delivery/digital model, seamless data exchanges across business disciplines and support for a collaborative environment.

• Support for data driven decision-making. These practices must be in place to ensure decisions are made with the right data and accurate data.

• To allow for more collaboration across divisions and business units. This will help break down organizational silos and create a modern open data environment that allows agencies to work together toward common goals.

• Good data management practices will help with the efficient use of resources, particularly people’s time. Data is collected only once and used by many, eliminating data silos and redundant collection of data and conflicting data.

• Finally, these practices will support compliance and audit requirements (FHWA, FTA, etc.)

All of these necessitate a high quality, highly available data environment.

Transportation organizations are becoming more reliant on high quality, accurate data for achieving their strategic goals. And most are embarking on a digital delivery journey and moving their organization into a fully digital operating model, having good management practices form the essential foundation. Open data environments are becoming a necessity for a variety of reasons; efficient use and management of the data, workflow from one system to another in the data lifecycle, and to enable sharing data across business units in a common environment. Implementing structured policies and procedures will help establish a solid foundation for data management.

Modern construction management software such as Appia and AASHTOWare Project are important tools in use in most state DOT’s and they are based on an open technology with the intent to allow for seamless data exchanges across multiple platforms. The data lifecycle has become the new norm and having products and processes that support this open environment is essential to supporting this model to lead to modern predictive analytical capabilities.

A solid data foundation provides the structure that effective data management, analysis, and decision-making can be built upon. A foundational framework allows data to be integrated from various sources while also making sure the quality, consistency, and security of the data remains at a high level. Agencies that understand the importance of creating and maintaining a solid foundation are better equipped to embrace emerging technologies and adapt to the quickly changing industry we work in. If you’re interested in having a conversation about how to best build your data foundation, feel free to contact us.