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2. Reason for Plan. A March 9, 2007 memorandum from the Administrator, Office of Federal Procurement Policy (OFPP) requires each Executive agency to: Establish agency-wide requirement for statistically valid verification and validation of data submitted to FPDS-NGProvide annual certification of d
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1. 1 NIH Contract DataVerification and Validation Plan Presentation to AMC
May 27, 2008
2. 2 Reason for Plan A March 9, 2007 memorandum from the Administrator, Office of Federal Procurement Policy (OFPP) requires each Executive agency to:
Establish agency-wide requirement for statistically valid verification and validation of data submitted to FPDS-NG
Provide annual certification of data accuracy and completeness to the General Services Administration (GSA) (Initial annual statement of data verification and validation was provided to OFPP 12/15/2007)
Assign clear responsibilities for data verification
Modify policies, procedures, and training as necessary to ensure quality data quality
The OFPP requirement works in combination with the Federal Funding Accountability and Transparency Act (FFATA) requirements for full disclosure of all federal award information.
3. 3 HHS Implementation A review of a statistical sample of data was done by LMI on behalf of DHHS in August and October of 2007.
The review resulted in findings of weaknesses in the data completeness and accuracy.
The HHS Deputy Assistant Secretary for Acquisition Management and Policy asked OPDIVs to provide by February 15 their FY 2008 V&V plans for effecting improvements in contract data accuracy.
The plans not only set out how the OPDIVs will verify and validate their data in FY 08, but will be used by HHS to develop & implement a Department-wide data improvement strategy.
NIH submitted its plan as required. It was shared with the OAs for comment. As a result the plan changed. A requirement for DCIS data entry through the PRISM portal before award was deleted.
4. 4 Objectives of NIH Plan In general, the objective is to enter all award data on time and accurately. We fall short on this objective today.
Enter all data by October 15 (this may change to a later date).
Learn the correct entry for each data field and to significantly reduce number of errors
Focus acquisition staff on the fields identified by LMI as having the most errors; however, all required fields still must be completed.
Learn from our approach this year to make future improvements.
5. 5 Implementation of NIH Plan To implement the plan, the following specific steps are being taken:
Memo to the acquisition community with accompanying instructions.
Meet with AMC and the Intramural and Extramural AOs to discuss the memo and instructions.
Distribute memo and instructions to acquisition community.
Develop training materials and train both OA staff and staff in the delegated community. This will be a “train the trainer” approach.
Develop method to compare DCIS and nVision reports to identify awards not entered into DCIS.
Work with DCIS Configuration Control Board to bring about edits, changes, and improved descriptions of fields as needed.
6. 6 Implementation of NIH Plan(continuation of previous slide) Continuation of specific steps:
Certification from OA Directors and Lead AOs that the training and dissemination of the information on the NIH FY 08 plan has been provided to all Buyers in their organizations.
Place print out of DCIS data entry in the award file for CO/Award Approver review
Review prior to award by CO/Award Approving Official of DCIS data entered.
DCIS data from BPA calls and task and delivery orders against NIH “parent” contracts, e.g., NITAAC, is entered by the Buyer placing the order or call.
7. 7 Benefits to NIH of Successful Implementation Complete and accurate data provides a complete picture of our workload and accomplishments.
Complete and accurate data allows OAs to inform their customers and accurately capture costs and resources in annual SLAs.
Complete and accurate data makes reports to NIH management, DHHS, OMB, Congress and the public more reliable. We can trust our data and act accordingly.
Complete and accurate data results in more complete and accurate workload and staffing analyses. Experience has shown that these analyses support current staffing levels and more.
Complete and accurate data ultimately results in less time spent on responding to data calls.