Workday has made a continuous investment to be a data-driven business. In our early years most of this investment went to developing systems to gather alerts from our technology infrastructure, capture performance data, and track application access for security purposes. During those years we were probably better at gathering data than using it to optimize our operations.
That has changed. We’ve upped our maturity level with respect to data analysis, thanks to the efforts of development leaders who understood that to keep improving our service we needed to comb through the data. I thought it would be interesting to share a couple examples of our analysis efforts, highlight how a data-driven approach has helped our service delivery, and describe our ideas for continuing to gain more insights from our data.
I’ll start with an effort initiated by Jim Stratton, who heads our Technology Architecture Group. Upon joining Workday three years ago, Jim made a major improvement to our understanding of the performance experienced by our customers by looking broadly at our application log data held in a system we call Stats Warehouse (SWH).
Specifically, Jim analyzed performance for thousands of Workday transaction types across all of our customers for a period of two months. He categorized transactions into 50th percentile and 90th percentile performers. Jim was able to use the results to guide application developers to the areas of each product where improved performance would help the most. He started application service-owner meetings to regularly review and improve poorly behaving transactions.
These focused development efforts have helped Workday improve our response times even as the volume of requests continue to increase. Jim tells me he uses SWH every day and still is amazed at how the data leads him to areas for potential improvement.