Cristino Ramirez

29 May 2020

Challenge & Selection:

Our government customer was used to relying on proprietary data collection and analysis tools  specifically tailored for the government operating environment.  Over time, the government customer  realized that commercial solutions offerings existed that could provide highly accurate data analysis to  enhance their knowledge and awareness of their operating environment at a very affordable cost.   When compared to designing and acquiring their own proprietary system, the government customer  realized that our commercial solution could have the potential to enhance their capabilities, but were  interested in understanding how our data analysis system could be integrated into their normal daily  operations.

Selection:

I chose the “Learning Launch” tool for this customer, because it allowed our team to work very closely  and collaboratively with the customer in an environment that closely mimicked how the system would  be deployed if the project proved to be a viable prototype that should be scaled.

Application:   Since the customer was already a receiver of raw data from my company, we were able to  generate a high number of data sets over a 6-week period to include disconfirming data in our test  batches.  The cost of the “Learning Launch” was largely limited to labor costs associated with two of our  analysts and software engineers that supported the active contract with the customer for data  collection.  The individuals chosen to support the “Learning Launch” were very disciplined in their  approach, but also had a history of listening very well to customer and tailoring the response to best  meet the customer’s needs.  Since the nature of the project was leveraging real world data that  generated potentially hundreds of datasets daily, we were able to both develop fast feedback cycles and  present real world examples to our customer to garner their feedback.

We built the test around several key assumptions developed in consultation with the customer: (1)  Accuracy of data analysis was the most important attribute of the data collected and analyzed; (2) Speed  of data delivery was the second most important attributed; (3) Number of samples daily was the third  most important attribute.

Insight:  The “Learning Launch” provided to be successful in both walking towards a scalable prototype  to meet the customer’s needs, but also to aide our team in testing assumptions.  Both the customer and  our team were surprise that the order of priority for “Learning Launch” assumptions changed to: (1)  Speed became the most important attribute for the customer; (2) The customer identified the “number  of samples daily” as the second most important attribute; (3) Accuracy, which was the attribute we  assumed was most important, actually became the least important attribute.  The customer realized  that if the data collection and analysis process could happen at the speed they desire with a higher  number of samples daily, they would get a higher level of accuracy in their data over time.  In the end,  their most import outcome was to know very early that the system detected something.  If perceived as  a significant issue, the customer could choose to deploy additional resources to help resolve.

Approach:  The “Learning Launch” tool proved to be an excellent method to test assumptions with real  data in an environment that provided customer collaboration and controlled cost over the 6-week test.   In the end, the scalable prototype met the needs of the customer who has significant “buy in” given they  helped shape the final product.

