Take a 30-year-old system (let us call it the ‘rating engine’) that already boasts a 99.7% rate of automation and rewrite it in upgraded technologies without losing any automation. On top of that, somehow reorganize the UI to improve the user’s speed of decision making for the 0.3% that require manual intervention. No documentation existed, and each user interacted with the system differently.
Part of the challenge was understanding how to improve user’s efficiency with software when we do not know how they use the software to make their decisions.
To gain insight, we enlisted the help from the professionals of research: graduate and PhD students. Unidev partnered with the local university and their lab staff to study how users interacted with the 30-year-old software.
Background of Knowledge Work:
Work that requires individuals to interact with computer data to make decisions is called knowledge work. At the time of this project, no formal process for evaluating knowledge work existed. Traditional labor had tried and true practices for evaluating work because the work could be easily observed and measured. You could watch someone pick up a box 50 times in a day. You could measure the weight of the boxes to determine the maximum weight was 50 pounds. You could observe and time how long someone needed to be on their feet to complete the job. These benefits of observation and measurement allowed management to clearly define job requirements and reorganize operations to improve efficiency.
With knowledge work, observation and measurement are not as simple as watching a task. Thus, the research question: how can Unidev improve user’s efficiency with software when we do not know how they use the software to make their decisions?