1. Maintaining quality data throughout the life of a project
National Environmental Monitoring Conference
August 2017
Christina Hiegel, P.E.

2. SOLUTIONS YOU CAN COUNT ON. PEOPLE YOU CAN TRUST.
Agenda
SOLUTIONS YOU CAN COUNT ON. PEOPLE YOU CAN TRUST.
Reporting Data
Why is Quality Important?
Auditing
Definition and Planning of Data Quality
Final Thoughts
Storage

3. Why is Data Quality Important?
Laboratory
Certification
Methodology
Standard Operating Procedures
Legal Defensibility
Consultant/
End-User
Regulatory Requirements
Project Objectives
Regulatory Agency
Public Scrutiny
Public Protection
Consultant – Proving to clients and regulators that they are collecting representative data.
Responsible Party – Ensuring that the client has legally defensible data when scrutinized by any regulatory agency.
Regulatory Agency (ex. EPA, MDEQ) – To prove that data collected are appropriate for making decisions based on the collected data. Also, to protect human and ecological receptors from negative impacts when making decisions based on analytical data.

4. EPA General Assessment Factors (EPA QA/G-4)
What is Data Quality?
Definition
EPA General Assessment Factors (EPA QA/G-4)
Soundness: The extent to which the scientific and technical procedures, measures, methods or models employed to generate the information are reasonable for, and consistent with, the intended application.
Applicability and Utility: The extent to which the information is relevant for the Agency’s intended use.
Clarity and Completeness: The degree of clarity and completeness with which the data, assumptions, methods, quality assurance, sponsoring organizations and analyses employed to generate the information are documented.
Uncertainty and Variability: The extent to which the variability and uncertainty (quantitative and qualitative) in the information or the procedures, measures, methods or models are evaluated and characterized.
Evaluation and Review: The extent of independent verification, validation, and peer review of the information or of the procedures, measures, methods or models.
These are the headers for DQOs

5. Laboratory Perspective
What is Data Quality?
Laboratory Perspective
Soundness
Audits
Certification
Applicability/Utility
Client Expectations
Usability
Clarity and Completeness
Uncertainty and Variability
Evaluation and Review
These are the headers for DQOs

6. Consultant and End User Perspective
What is Data Quality?
Consultant and End User Perspective
Soundness
Regulatory Scrutiny Intended Use of Data
Applicability/Utility
USABILITY
CURRENT/FUTURE USE OF DATA
Clarity and Completeness
TIME AND BUDGET
EASY TO INTERPRET AND USE
Uncertainty and Variability
REGULATORY SCRUTINEY
DATA VALIDATION TIME/BUDGET
Evaluation and Review
REGULATORY SCRUTINY
These are the headers for DQOs

7. Regulator Perspective
What is Data Quality?
REGULATOR
Regulator Perspective
Soundness
Regulatory Scrutiny
Intended Use of Data
Applicability/Utility
Usability
Current/Future Use of Data
Clarity and Completeness
Easy to Interpret and Use
Easy to Explain
Uncertainty and Variability
Public Scrutiny
Decision Making
Evaluation and Review
These are the headers for DQOs

8. Steps to Ensure Data Quality
Field
Use approved collection methods
Keep things clean and organized
Follow the preservation requirements
Use updated instrumentation
Keep track of your samples and field results
Be thorough in your documentation

9. Steps to Ensure Data Quality
Field
Strict chain-of-custody
Know your primary field contaminants and target analytes
Be diligent in following approved collection method
Use the bottles required for each method
Take safety seriously

10. Steps to Ensure Data Quality
Laboratory
Use approved/peer reviewed methodology
Address audit concerns
Maintain and update instrumentation
Take client questions and concerns seriously
Run control charts
Ask for regular feedback on performance
Keep things clean and organized

11. Steps to Ensure Data Quality
Laboratory
Be thorough in your documentation
Keep strict chain-of- custody
Conduct a thorough review of the data
Understand your client’s end goals
Take safety seriously

12. Data Validation Plan how Data will be Managed
Determine how Records will be kept
Plan the sample and location names
Naming conventions – ORIGINAL, SHORT, MEANINGFUL

13. Validator’s Duties May Include:
Review the chain-of-custody
Review compliance of field and/or laboratory methodology
Review results in comparison to expected results
Determine result bias or inconsistencies
Determine instrumentation problems
Result: Communication of the Data Quality to the End User via Report, Qualifiers, and/or Narrative

14. Future Use Looking Ahead
How is the data going to be used in the future, and what is the best way to maintain it?
Looking Ahead
Other than the current intended purpose, how can this data be used in the future?
What should this data NOT be used for?
How long is the data usable for?
What would cause this data to not be usable in the future?

15. Maintaining Data Quality

16. Storage Store data so that the: Field data Field notes
Final signed (legal) laboratory report
Final chain-of-custody
Final EDD
Final data validation
Data qualifiers

17. Storage Example

18. Resulting Data Qualifiers
Store qualifiers so that they are used when the data is used
Store reasons for qualifiers with the final data records
Make sure that end users can understand why qualifiers were added. It may affect the final use of the data

19. Qualification Storage

20. Report the Data
Figures
Tables
Reports
Appendices
Laboratory Reports

21. Analytical Report

23. Why Audit?? Multiple laboratory reports and versioning EDDs
Accidental change
Validation report and flagging versioning

24. How Do We Maintain Quality?
DEFINE: Determine what quality data look like for your project and how is it obtained?
PLAN: Plan for quality data
Put a plan into place
Communicate what quality data will involve to field/laboratory/consultant/etc…
Know all and any potential future uses of the data (other than your current intentions)
STORAGE: Keep all of the data evidence together in one place.
Field Notes, Data, Laboratory Reports, EDDs, etc…
REPORTING: Maintain qualifications and reasons in figures and tables.

25. How Do We Maintain It?
AUDITING: Determine a method to track changes to data and files.
Multiple lab reports
EDDs
Unintentional Changes
Data Validations
ACCESS: Make sure that any end-users have access to the data and the qualifications.

26. Questions?
WHAT
WHY
WHERE
WHEN
WHO
HOW