KDOL Data Management and Data Analysis
NASWA RFP - KDOL Data Management and Data Analytics
Addendum A (10/18/2016) - Change in Questions Deadline and Page Limit Clarification
Addendum B (10/24/2016) - Suggested Technical Proposal Response Structure
Addendum C (10/28/2016) - Change in Proposal Submission Date and Change in Response to Written “Questions & Comments”
Addendum D (11/1/2016)
Addendum E (11/7/2016) - Change in Proposal Submission Date (11/7/2016)
Addendum F (12/8/2016) - Updated Timeline of Events (12/8/2016)
KDOL RFP Clarifications (10/25/2016) - Clarifications
KDOL RFP Additional Clarifications (10/31/2016) - Clarifications
Bidders Questions and Answers Final
(11/1/2016)
Confidentiality and
Non-Disclosure Agreement
CORRECTION (11/2/2016) to Addendum D and Q&As Final: There is an 80% onsite requirement for the key personnel. The Contractor staff must perform Knowledge Transfer onsite. Otherwise, excepting as previously indicated, all other Contractor staff onsite at least 50% of the time.
Circulation Date: October 7, 2016
Bidders Webinar: October 17, 2016; 2:00 PM ET
Q&A
Deadline:
October 24, 2016; 5:00 PM ET
Proposal Submission Date: November 30, 2016 5:00 PM ET
On the behalf of Kansas Department of Labor (KDOL), the National Association of State Workforce Agencies (NASWA) is releasing this RFP to acquire services related to the two major items enumerated below:
1. Perform data management/integration-type activities with the end objective of creating, persisting, and refreshing a single, cleansed, high quality, system of record data store that is frequently fed by the multifarious, current KDOL UI production data bases. One outcome of this activity will be to better position KDOL for full UI IT System Modernization; the second outcome is to enable a quality data repository in which UI Integrity and Fraud analytics and reporting can be executed (see item 2 below).
2. Using the data store created above and a contractor-provided Business Intelligence suite of tools, enhance KDOL's current integrity related discovery and case investigation automation capabilities, and augment with pattern discovery, predictive analysis ranking high risk potential fraud. KDOL requires additional data matching capabilities and data enrichment capabilities to provide further insight into integrity analysis as well as normal benefit adjudication processes. An overall outcome expected from this effort is reducing fraud prior to payments being improperly issued by KDOL.
