We worked to understand the people, processes, and tools affected by NAC to help us build the right solution. On this contract, our goal has been to improve NAC's searching, performance, scalability, and user experience. To achieve this, we first needed to understand NARA’s ecosystem. We kicked things off with a discovery period, performing user research and digging into NARA’s technical landscape. We then used that discovery data to define the ideal user experience (UX), product vision, MVP scope, and roadmap.
We improved efficiencies by supporting our customer’s switch from waterfall to agile. As we were developing the NextGen Catalog, we also coached our customer on agile practices. Adopting new management styles can be daunting, so we worked to make our stakeholders as comfortable as possible. Communicating constantly, we introduced a modified Scrum methodology tailored to NARA’s environment. Thanks to this iterative approach, we were able to rapidly ideate, experiment, test, and implement solutions.
We reduced risk with continuous testing and user research. Through Fearless’ Testing Dojo, we embedded a testing expert onto the team for a period to provide coaching. This empowered the team to start testing early and often and to refine how they assessed quality. We used a combination of user interviews, user acceptance testing, and workshops to gain insights on the catalog’s usage and ensure that users’ needs were considered in our development process. This continuous approach to testing and feedback helped us work more efficiently and effectively, as we didn’t waste time developing features that ultimately wouldn’t work for users.
Thanks to these practices, we rapidly developed and implemented an improved data model. Working iteratively with NARA’s users and stakeholders, we created a new data model that provides a better flow of information. We grouped like elements together, identifying those that weren’t relevant and stripping them from the index. We also provided more context fields. This prevented confusion, made each record more accessible, and improved the search experience. It also led to lower disk space usage, faster API responses, and better quality control.