# Presentation: 310758

## NIH Contract DataVerification and Validation Plan

- Presentation to AMC
- May 27, 2008

## 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.

## 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.

## 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.

## 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.

## 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.

## 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.