1. DOE EM-5 DQO Training Workshop - Day 1
Appendix A
Evolution of the Data Quality Objectives Concept
From qualitative concept to practical implementation.

2. Evolution of the DQO Concept
Objectives:
To illustrate how the DQO Process has matured over time from a qualitative concept to practical implementation.
To reinforce DOE’s requirement for integrating the DQO Process into all environmental sampling programs.
To dispel the misconception that DQOs are the PARCC parameters.

3. EPA QAMS-005/80
DQO concept first defined in terms of the PARCC parameters:
Precision
Accuracy
Representativeness
Completeness
Comparability
The emphasis was on lab data quality objectives, which are just a subset of the overall DQOs. Why: the early emphasis of EPA was to get a handle on the analytical aspects of RI/FS & RD/RA work, including reducing analytical and sample collection/handling error.
Result was that the PARCC parameters became standard boilerplate text for quality assurance project plans. Virtually every QAPjP or FSP had/has some reference to DQOS. Projects really believe that they had done "DQOs" as a result (NOT!)
EPA, 1983, Interim Guidelines and Specifications for Preparing Quality Assurance Project Plans, QAMS-005/80, February

4. EPA/540/G-87/003 EPA/540/G-87/004 1987 Defined DQOs as:
“…qualitative and quantitative statements which specify the quality of the data required to support the Agency decisions during remedial response activities”
EPA, 1987, Data Quality Objectives for Remedial Response Activities, EPA/540/G-87/003, March
EPA, 1987, Data Quality Objectives for Remedial Response Activities: Example Scenario, EPA/540/G-87/004, March

5. EPA/540/G-87/003 EPA/540/G-87/004 1987 Major Elements:
Analytical Levels I - IV
PARCC Parameters
Three stage DQO Process:
Stage 1: Identify decision types
Stage 2: Identify data uses and needs
Stage 3: Design data collection program
Stage 3: Design data collection program
EPA, 1987, Data Quality Objectives for Remedial Response Activities, EPA/540/G-87/003, March
EPA, 1987, Data Quality Objectives for Remedial Response Activities: Example Scenario, EPA/540/G-87/004, March

6. EPA QA/G-4 1994 Supercedes previous DQO guidance. Defined DQOs as:
“…a systematic planning tool based on the Scientific Method for establishing criteria for data quality and for developing data collection designs”
EPA, 1994, Guidance for the Data Quality Objectives Process, EPA QA/G-4, September

7. EPA QA/G-4 1994 Presents a new 7-Step DQO Process.
Step 1: State the Problem
Presents a new 7-Step DQO Process.
Step 2: Identify Decisions
Step 3: Identify Inputs
Step 4: Specify Boundaries
Step 5: Define Decision Rules
Step 6: Specify Error Tolerances
Step 7: Optimize Sample Design
EPA, 1994, Guidance for the Data Quality Objectives Process, EPA QA/G-4, September

8. DQOs PQOs SPOs DMOs Misconception
The term Data Quality Objectives is misleading since “data quality” is only one component of the DQO process.
This underplays the role of DQOs as a Planning Process
More appropriate terms would be:
Planning Quality Objectives (PQOs)
Systematic Planning Objectives (SPOs)
Decision Making Objectives
(DMOs)
DQOs
Badly named. Perhaps Decision Quality objectives or the others noted
Decision Making obj, etc.
PQOs
SPOs
DMOs

9. Opinion
DQO guidance should be housed in a non-data section of EPA. This would help eliminate the misconception that the DQO Process is simply the PARCC parameters.

10. } DOE-HQ September 7, 1994 Thomas Grumbly memo:
“…it is the policy of…(EM) to apply up-front planning…to ensure safer, better, faster, and cheaper environmental sampling…It is EM policy that the…(DQO) process be used in all environmental projects...” }
DOE Letter, DOE EM-263 to all Field Offices, 1994, Institutionalizing the Data Quality Objectives Process, September

11. Implement DQOs . . . Easier said than done
Grumbly memo directs sites to do DQOs, but...
No guidance for an implementation mechanism.
Lack of a uniform approach results in an unwieldy process.
No guidance on documentation/format.
Lack of documentation format guidance yields variable products (defensibility?).

12. Impact
DOE Cleanup decisions are vulnerable to criticism - if not rejection.
Non-standard approach/documentation often lack clearly stated:
Decision Statements (Principal Study Questions)
Decision Rules
Error Tolerances
Sample Design
These shortcomings are revealed in the Data Quality Assessment Process.

13. Challenges at Hanford Unstructured approach to DQOs Cultural barrier
proves to be quite unmanageable.
aggravates acceptance.
Perception that DQOs are waste of time and money.
Cultural barrier
SAPs are well understood.
DQOs are not.

14. ?*!!
EPA
QA-G4
Certification of
DQO Training
DQO
SOP

16. Challenges at Hanford (continued)
Reality:
DQOs are not the problem.
Flawed approach is the problem.
More was needed.
Merely giving Projects QA/G-4 - not enough.

17. Highly structured, tactical approach to implementing the overall DQO Process.
Identify Projects requiring DQOs.
Begins with Scoping - a key element.
Gets early input from regulatory agencies and key decision makers.
EPA OIG approved process
Obtains regulator input up front
everyone agrees upon DQO projects at the beginning of the year
helps us to project resource requirements (previously, projects would come in at the 11th hr and dump on us)
Makes efficient use of resources.
Up front scoping. Makes better use of decision maker time
global issue identification resolution prior to initiating DQO
Workbook enables us to present everyone with a structured outline up front. Keeps things in focus. Has significantly streamlined the process.
Utilizes a facilitator to coordinate everything
Global Issues identified and resolved prior to DQOs.

18. Tools further streamline the implementation . . .
Scoping Checklist to ensure a good start.
Workbook captures the inputs/outputs of the 7-Step Process. 1st Draft provides Strawman
Visual Sample Plan used in DQO meeting to what-if sample designs.
EPA OIG approved process
Obtains regulator input up front
everyone agrees upon DQO projects at the beginning of the year
helps us to project resource requirements (previously, projects would come in at the 11th hr and dump on us)
Makes efficient use of resources.
Up front scoping. Makes better use of decision maker time
global issue identification resolution prior to initiating DQO
Workbook enables us to present everyone with a structured outline up front. Keeps things in focus. Has significantly streamlined the process.
More details to come . . .
(Module 9)

19. History Summary PARCC 3 Stage Process 7 Step Process
ERC DQO Implementation Process
ERC DQO Tools

20. Contacts: Sebastian C. Tindall Bechtel Hanford Inc.
George Washington Way, HO-02
Richland, WA 99352
Elizabeth M. (Liz) Bowers Department of Energy
Jadwin Avenue, A2-15
Richland, WA 99352
James R. Davidson, Jr Davidson & Davidson, Inc.
Gage Blvd., Suite 205
Kennewick, WA 99336

21. End of Module