Importance of a QA/QC Process Establishing database structures and policies Protecting client confidentiality and handling COIs Defining data

Transcription

1 Data Management Best Management Practices Chris Mickle Cambridge Data Analysis and Statistics Subdiscipline Leader Environmental Data Management Group Leader

2 Today s Topics Importance of a QA/QC Process Establishing database structures and policies Protecting client confidentiality and handling COIs Defining data deliverables and specifying EDD formats Defining and customizing report deliverables Managing users and reports in EQuIS Enterprise

3 What is Site Data Management? Site Data Management The collection, processing, analyzing, and communicating project site data to assist in decision making. A good system s design will promote it s use, and the ultimate value of data are in the data s use rather than in the storage. The real importance of a data management system is to provide the end user with a consistent data set of known quality Long-Term Groundwater Monitoring The State of the Art. The Task Committee on the State of the Art in Long-Term Groundwater Monitoring Design of the Environmental and Water Resources Institute. Reston, Virginia: American Society of Engineers, 2003.

4 Modeling 3-D Visual Data GIS Env. Data Management Water Resources Administration Asset Management Data Management System Public Relations Remediation Document Management Land Use & Management Digital Satellite Data CADD data

5 Benefits of Site Data Management Enhances communications by providing project team access to the data Facilitates rapid data retrieval and analysis Ensures data integrity and control data security Maximize confidence ce and certainty ty associated ated with data Provides for data to be transitioned to clients/owners Complies with contract requirements Provide data that are of known quality for legal and technical defensibility

6 Consequences of Poor Site Data Management Garbage in and garbage out Interpretation of bad data means bad results Low productivity - inefficiencies Lack of process and accountability Limited or no access to data Version control problems Loss of team cohesion and cooperation Impact of reputation as a good data provider

7 Quality Management Defined Quality The totality of features and characteristics of a product or service that bears on its ability to meet the stated or implied needs and expectations of the user. Quality Assurance An integrated system of management activities involving planning, implementation, assessment, reporting, and quality improvement to ensure that a process, item, or service is of the type and quality needed and expected by the client. Guidance for Labeling Externally Validated Laboratory Analytical Data for Superfund Use. U.S. Environmental Protection Agency Office of Solid Waste and Emergency Response. Washington, DC 20460, 13 January 2009.

8 Quality Management Defined Quality Control 1) the overall system of technical activities that measures the attributes and performance of a process, item or service against defined standards to verify that they meet the stated requirements established by the customer. 2) Operational techniques and activities that are used to fulfill requirements for quality. 3) The system of activities and checks used to ensure that measurement systems are maintained within prescribed limits, providing protection against out of control conditions and ensuring that the results are of acceptable quality. Quality System A structured and documented management system describing the policies, objectives, principles, organizational authority, responsibilities, accountability, and implementation plan of an organization for ensuring quality in its work processes, products (items), and services. The quality system provides the framework for planning, implementing, and assessing work performed by the organization and for carrying out required quality assurance (QA) and quality control (QC) activities Guidance for Labeling Externally Validated Laboratory Analytical Data for Superfund Use. U.S. Environmental Protection Agency Office of Solid Waste and Emergency Response. Washington, DC 20460, 13 January 2009.

9 Quality Management Requirements for Successful Data Management Standardized Planning Effective Communication Team Commitment Integrated Quality Controls Advanced Tools To manage data properly requires planning, adequate support, and a long-term commitment to a data management program. A good data management system should be one that is modeled according to how the data are collected and processed, has specifically defined data elements, and is very well documented Long-Term Groundwater Monitoring The State of the Art. The Task Committee on the State of the Art in Long-Term Groundwater Monitoring Design of the Environmental and Water Resources Institute. Reston, Virginia: American Society of Engineers, 2003.

10 QC 0 Data Quality Management Process Steps QC 0 QC 1 QC 1 QC 2 Project Planning Sampling Data Confirming and Setup Collection Data Source Data Source QC 2 Database Entry Information QC 5 GIS Spatial Database Check QC 6 Well, Boring, Well Construction QC 7 Field Parameters QC 8 O&M Data QC 4 QC 5,6,7,8 QC 4 Validation, Qualification & Usability Lab EDD Review QC 3 QC 3 Collection of Analytical and Field Sample Information QC 9 Database Quality Control QC 9 QC 10 Data Using and Reporting QC 10

11 QC 0 Project Planning and Setup Project scoping and definition - CSM Identify historical information Identify data quality objectives (DQOs) Define roles and responsibilities with communication and data flow process Develop work plans (QAPP, SAP, DMP, FSP) Develop subcontractor SOWs Develop data validation and evaluation criteria Define deliverables Define data collection methods Plan how the data is going to be managed (EQuIS) Data Quality Management Process Steps QC 0 QC 1 QC 1 Sampling Data Collection Follow WP/SAP/QAPP/DQOs Field Activity Preparation Field Data Collection Field Sample Collection Submitting Samples for Analysis QC 2 Confirming Data Sources Confirm samples shipped correctly Confirm samples arrive at the lab in good condition Identify any deviations from the WP and QAPP Confirm sample information identified correctly QC 2 Database Entry Information QC 5 GIS Spatial Database Check QC 6 Well, Boring, Well Construction QC 7 Field Parameters QC 8 O&M Data QC 4 QC 5,6,7,8 QC 4 Validation, Qualification & Usability Lab EDD Review Data validation and evaluation Data review Evaluate data quality Ensure EDDs are edited correctly Technical review of Data Validation QC 3 QC 3 Collection of Analytical and Field Sample Information Field Sample information forwarded to data manager actual sample/analyses communicated (FTL) Database populated with sample information from the field (DM) Receive EDD data packages from the lab and ensure they go to LC before going g to Data Validator (SC/LC) Track EDDs and data packages for lab analytical completeness (SC/LC) QC 9 Database Quality Control QC data prior to making available Review output tables for completeness Follow-up with end user to resolve any discrepancies Communicate any issue resolutions to the RPT Track comments in the database where possible QC 9 QC 10 Data Using and Report Provide action level criteria Request data tables Review and evaluate data tables generated for accuracy and completeness Provide feedback to DM on changes needed to database QC 10

12 Data Management Plan Objectives Identifies the project objective and required deliverables Describes the data quality and management objectives Summarizes the types of data required by the project Defines roles and responsibilities of the data management team and identifies lines of communication Defines the workflow and data management activities

13 Data Management Plan Objectives cont Provide laboratory and electronic data deliverables (EDD) requirements for transfer of electronic data to a database Standardize data deliverable processes Define EDD formats for field, geological, survey and any other data that needs captured and how it will be delivered Unique station/sample identifications GIS deliverable formats File storage and backup Reporting needs and format

14 Site Data Management Project Team Project Manager (PM) CDM Site Data Management Administrators (SDMA) Project Engineer (PE) & Project Geologist (PG) Field Team Leader (FTL) & Field Team (FT) Sample Coordinator (SC) & Laboratory Coordinator (LC) Project Chemist (PC) & Data Validation Coordinator (DV) Data Manager (DM) GIS GS Leader (GIS) (GS) Project Planning Team (PPT) Report Preparation Team (RPT)

15 Unique Station Naming Convention Primary step in data collection and warehousing Example Field Environmental Station Naming Scheme First Segment Second Segment Facility Site Type Site Number Station Type Station Number Qualifier AA A NNN AA NNN A Note: N = numeric, A = alphanumeric Facility: AA = Facility Name Location Type: S = Site W = Solid Waste Management Unit (SWMU) A = Area of Concern (AOC) Location Number S02 = Site 2 (Former Hazardous Waste Disposal Unit) S05 = Site 5 (Stormwater Drainage Canal) S09 = Site 9 (East River) W32 = SWMU 32 (Metal Shop Sump) W33 = SWMU 33 (Pesticide Shack) A11 = AOC 11 (Hydraulic Fluid Spill) Station Type: MW = Monitoring Well SO = Soil Sample Location SD = Sediment Sample Location SW = Surface Water Sample Location Number: Sequential Station Number Qualifier: S = Shallow D = Deep

16 Laboratory EDD Formats Client contract requirements Regulatory agency requirements EPA Region 2, NYSDEC, NJDEP etc CDM/EarthSoft customized format EZEDD with additional fields included for data validation EDD to store all lab quality control data in the database Include EDD requirements in laboratory SOW and QAPP Management policy for reference values

17 Field EDD Formats Client contract requirements Regulatory agency requirements EPA Region 2, NYSDEC, NJDEP If not required specify CDM format Geology EDD include all location, well, geologic data Import EDD from gint Management policy for reference values

18 Field Data Templates

19 Database Structures and Policies Policies and processes provide standards for facilitating the planning, collection, formatting, and analysis of data to support decision making Database structure requirements How many databases do you need? How many servers do you need? How will client facilities be organized and grouped on the server? How does the grouping of facilities impact reporting? Will data from multiple facilities need to be queried and reported?

20 Database Structures and Policies cont Will your database structure mitigate conflicts of interest and comply with contract confidentiality agreements? Will you be hosting EQuIS for your clients? What type of licensing will you implement with EarthSoft and your clients? How often will you back up your databases? How often will you shrink your databases?

21 EQuIS 5 Server Proposed Database Structure Shared Reference & Business Rules Database FSG (Federal Projects) Shared Reference & Business Rules Database NSG (Private Sector) Shared Reference & Business Rules Database Project Specific Region 2 Site 1 Site 2 Region 3 Client X Facility 1 Facility 2 Client Z Facility 1 Facility 1 Building A Building B Facility 2 Phase 1 Phase 2

22 Aligns with CDM Core Values Excellence superior performance as viewed through the eyes of our clients, employees, and shareholders. h Initiative - anticipating and taking action, focused on what counts most. Teamwork - working together to achieve results and build positive relationships. Shared Commitment - mutual relationship between CDM and employees based on shared goals, trust, and respect. Integrity -dealing honestly and respectfully with clients, g y g y p y, employees, shareholder, business associates, and the community; conducting business consistent with laws and standards.

23 Populating EQulS Field Data Collection Drilling, Soil Sampling, Well Installation, Analytical Sampling Analytical Lab Analytical Results, QA/QC EDP EQuIS Management of Data Location, Sample, Result; Lithology, Water Level, Geophysical, EDP Logs, Reports, Contours, Cross-Sections, Models, Statistics, EDP = EQuIS Data Processor

24 EQuIS Data Workflow Process Lab Data EQuIS Data Processor (EDP) (Standalone) Automated Data Review (ADR) GIS Borings Cross Sections EQuIS Sample Planning Sample EDGE EDDs Enterprise EDP EQuIS Interface e Plumes Statistics Field Data Automated Data Checks & Loading Trend Plots View and repair data before it enters database. 90% of Level 3 Validation Reports

25 Define Report Deliverables Grid Report Sample Inventory Full summary table Hits only table Statistics table CDM CARSTAT and ProUCL data tables Reporting Detection Limit table Field Duplicate Report Completeness Report

26 Inorganic Contaminate Summary Table

27 GIS Used to Create Contour Map Spatial Analyst used interactively to create figure

28 Web Portals Use web portals to share information, handbooks,,g guides, templates, and training material and amongst your team

29 Web Portals

30 Data Analysis and Statistics Technical Resource Groups

31 Web Portals

32 A Change Management Process John Kotter Harvard Business School professor graduated from MIT Best-selling author of Leading Change #1 Leadership Guru in America (Business Week survey)

33 A Change Management Process Kotter s 8-step process developed based on years of research with real organizations Illustrated the process through a penguin fable, Our Iceberg is Melting

34 A Change Management Process Create a Sense of Urgency Help others see the need for immediate action Establish a Guiding Team A powerful group with: Leadership skills, Bias for action, Credibility, Communication ability, Authority, Analytical skills Develop Change Vision and Strategy - Clarify how future will be different and how you will make that future a reality Communicate for Understanding and Buy-in Get as many as possible to accept the vision and strategy Empower Others to Act - Remove barriers for those who want to help Produce Short Term Wins - Visible, unambiguous successes ASAP Don t Let Up - Press harder and faster for successes; Be relentless until vision is a reality Create a New Culture - Hold on to the new ways of behaving Create a New Culture Hold on to the new ways of behaving and encourage, reward behavior

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36 Conclusion Integrate Site Data Management concepts into projects at the planning stage which include: Site data management process Identifying the quality control steps Assign responsibilities and hold staff accountable Software and databases assist in achieving project goals (EQuIS, GIS, others) Proper data management is important to the success of any environmental cleanup project. The value of high-quality data for making informed decisions is critical. Integrating proper data management throughout all phases of a program results in a system highly valued for its completeness and accuracy. Long-Term Groundwater Monitoring The State of the Art. The Task Committee on the State of the Art in Long-Term Groundwater Monitoring Design of the Environmental and Water Resources Institute. Reston, Virginia: American Society of Engineers, 2003.

37 Thanks for your time today