Don’t Build Your House on the Sand – you need a firm foundation!

Every house, building, or structure needs a firm foundation. An improperly laid foundation will lead to major structural problems in both the short and long term – and potentially a collapse of the entire structure! Just as we take the time to lay a proper foundation during building construction, the information collected from environmental investigations lays the foundation for proper investigation, analysis, and remedial decisions enabling property owners to mitigate risk to human health and the environment most effectively. The data collected from environmental investigations are the foundation of all environmental investigation and remediation work. Wilcox works for its clients to ensure a solid foundation is built for each and every environmental project utilizing WiDMS – Wilcox Data Management System – which incorporates a variety of software, hardware, workflows and a company culture of sound data management. Let’s take a look at how to build a solid foundation of environmental data by implementing a sound environmental information management system with WiDMS.

Photo by Shustov / CC BY

Key Principles

There are seven key principles that form the foundation of an environmental data management system.

  • A clearly defined data management plan
  • Implementation of data lifecycle control
  • Identification of data ownership and stewardship
  • Ensuring data security
  • Maximizing data usefulness to avoid re-collecting or re-processing data
  • Establishing data quality standards
  • Ensuring proper documentation and tracking of data

Principle #1 – A data management plan

A data management plan defines the types of data that exist and how they are stored and secured. It also outlines the best practice workflows and quality assurance procedures, including data verification and data validation. Finally, a data management plan describes the generalized outputs or data uses and how the documentation associated with the entire system is managed. It is a blueprint that specs out how the foundation gets laid as well as how each floor is added on top of the foundation.

Principle #2 – Data lifecycle control

From the time data is collected or acquired until the end of a project and even beyond, we need to have a clear understanding of how the data is being managed in order to maintain the data quality and usefulness. Thinking about the lifecycle of our data allows us to store, validate, and manage the appropriate data and also gives us guidance on when to archive or delete data.

Principle #3 – Data Ownership and Stewardship

Identifying data owners and data stewards allows us to ensure that the right people are assigned to the right roles within our data management system. We integrate our data management team with our subject matter experts to ensure that data integrity and quality is maintained while also making smart decisions about what information needs to be captured. A data management system is not a black box and ensuring that all team members recognize their role in the data management system enables a collaborative data management approach that avoids silos of knowledge.

Principle #4 – Ensuring data security

In this digital age, data security is vital for every business, regardless of industry. We must ensure appropriate, industry standard security protocols are in place for all systems, including our data management system. This includes both infrastructure security measures as well as adhering to best management practices regarding user access and authority to access various types of data within the system.

Principle #5 – Maximizing data usefulness

We also know it is essential to take the time to collect our data properly the first time to avoid re-collecting data. It is always more costly to do a job a second time. In addition, reviewing our workflows to ensure that we avoid re-processing data as much as possible increases our efficiency. Electronic data deliverables (EDDs) from labs enable smooth upload of data into our database and automated reporting drastically reduces data manipulation – increasing efficiency and decreasing the possibility of errors being introduced into the data set. These are just some of the ways we maximize our data usefulness.

Principle #6 – Establishing data quality standards

Whether working within a formal Data Quality Management framework or establishing internal company-specific data quality standards, it is vital to establish the level of data quality required for various decision-making scenarios. By establishing data quality standards both on a project level and on a corporate level, we can ensure the data used to make decisions maintains the appropriate level of data quality.

Principle #7 – Ensuring proper documentation and tracking

Without the ability to review and examine how data has been collected, verified, reported, and analyzed we cannot effectively troubleshoot areas of concern. Sound environmental data management includes documenting workflows and tracking anomalies. The documentation and tracking serves as “as-builts” describing how the whole system actually works and providing key insights to improvements moving forward.

A data management system is not simply a database, as oftentimes is assumed, rather it is the integration of software, hardware, workflows, and the culture that is created around data management in an organization. These seven key principles provide a firm foundation for building a strong, cohesive data management system when combined with a robust, scalable database software package. Wilcox provides this high quality data management service to all of its clients. To discuss our data management services further, contact Wilcox at (317) 472-0999 or