The Enterprise Technology Transfer system received an Honorable Mention from the HHS Data Excellence Award in the Distinguished Federal Data Modernization category. 

Creating ETT as a centralized source for NIH technology transfer data has advanced federal data initiatives by enhancing users’ ability to access and analyze comprehensive information in one place. Previously, this valuable information was dispersed across nine standalone databases, leading to challenges in accessing and utilizing the data effectively. This consolidated approach eliminates the need to search through multiple databases, saving valuable time and effort when attempting to harness the existing data in ETT. This has increased transparency between NIH and the public as it allows NIH to easily update its available technologies on the public website, update products licensed from NIH inventions that have received FDA approval, keep current NIH patent listings accurate, and provide data via the annual metrics on all NIH technology transfer activities.

ETT has also increased transparency between ICs within NIH. Previously, the same company would work with multiple ICs as a licensee or collaborator, however, this information would be stored within each legacy database and not readily available to another IC considering a partnership with the same company. Data sharing was more difficult and involved a good deal of manual processing. Now, notes can be kept and shared on the company and valuable information, such as consistent late payments of royalties, can be easily accessed to identify potential red flags. ETT serves as a powerful repository that consolidates all NIH technology transfer data, providing users a centralized location to access the data when making decisions or to retrieve data to convey insights. Our team’s efforts have resulted in enhanced data accessibility and improved user experience.

Finally, having a centralized repository where all users must abide by and agree to record keeping in the same manner has led to increased collaboration between the ICs as they come together to prioritize data governance. For example, standard operating procedures were created for license agreements and a working group convened to identify best practices for non-license agreements that will be used throughout all of NIH technology transfer. This process has helped to streamline record keeping, which in the long-term will make accessing and using this data easier.

Prior to the implementation of ETT, there was no centralized system of record for NIH technology transfer data. The need for a centralized source of information that would allow stakeholders to view all data and relationships between patents, licenses, royalties, and expenses was paramount. Without a centralized source, calls for data were very cumbersome and time consuming, and restricted the NIH’s ability to share knowledge surrounding licensees and collaborators.

ETT was built with analytics reporting abilities far and above what was available to NIH previously. Information about NIH technologies, patents, licenses, and agreements was stored in different record structures, which worked against providing the same set of data elements in response to every inquiry. It was also much more difficult to ensure that all relevant data had been pulled from across NIH, since data would be pulled by different teams and then submitted for manual consolidation. By migrating all legacy data into one system, we can generate reports to answer these data calls much quicker and provide a more complete picture since the data is under one roof. Now, reports can be run within minutes to find all of the technologies a specific company has licensed, all active patents, all agreements from a specific year, the list goes on. These reporting capabilities can also be used to generate forms such as the Royalty Distribution Form to quickly find out what percentage of royalties each inventor on a given license should receive. Additionally, this data powers dashboards that are specific to each user, enhancing both user experience and workload management opportunities. For example, a technology transfer manager can set a dashboard as their home screen that shows them every patent they manage with upcoming filing deadlines. In an effort to increase leadership buy-in and allow data-driven decisions to be made easily, going forward, dashboards will be able to be accessed at the supervisor level. ETT allows for much greater efficiency of data collection and now that technology transfer personnel are working from a common dataset, NIH is able to provide complete and accurate information to external partners and other interested parties.

Previously there were nine stand-alone IC technology transfer databases, and nine ICs using their own business processes. In order to create a centralized database, the business processes which utilize the database need to be standardized to a certain level. Those business processes use database fields, so the use of the fields needed to be standardized. Business process modeling (BPM) breaks down the Technology Transfer business into business areas which are made up of multiple business processes. BPM sessions involved volunteers from all the ICs and OTT who reviewed and standardized over 100 business processes. This was critical in ensuring that we could automate the processes in a single system without forcing substantial changes in current work processes. Without BPM, automation would have been very difficult and without standardizing the use of the fields, the data migration would not have been possible 
Most data migrations are one to one or two to one, however, this was a nine to one migration. Normalizing these individual datasets was a four-year effort due to the size of these databases and the need for substantial cleanup of individual IC and the combined data sets. 7,857 data fields, 591 data tables, and 13,337,463 records were cleaned, consolidated, and migrated. The final migration scripts to combine these databases were carefully coordinated to minimize the blackout period for each IC while ensuring data integrity. 
Migrating this data posed many challenges. Each IC wanted to have their data migrated in a unique way that added to the complexity of this migration. Across each IC there were unique mapping specifications at the field level that required complex migration scripts and significant collaboration.  It took a substantial amount of time and collaboration to walk-through each ICs specific requests for each type of record. 
Each of the nine databases had some data quality inconsistencies that required their data to be cleaned pre-migration. Then, this data needed to be layered and validated to ensure there were no incorrectly mapped data across all nine. Each validation took 7-15 days. A lot of resources were put into de-duplicating this data, especially company and contact data. Each IC was managing this data in a way that made the most sense for them, which often included having duplicate records within their own system. A working-group meets weekly to work through validating these records, keep them up to date, and created a process for controlling these records to ensure that they remain accurate.
To ensure the integrity of the data remains intact, controls had to be put in place to identify who could access, edit, and delete the data. ETT has the ability to restrict records based on their security group and restrict users based on their access group with multiple security levels. This ensures the appropriate people have access to the right data and allows data to remain current and accurate.