How actionable visibility helped a healthcare giant improve feature velocity

Supporting an aggressive business growth plan with limited IT resources

For the IT staff at one of the largest providers of specialized hospitals and rehabilitation facilities for outpatient care in the U.S., supporting rapid growth with a relatively small core team is a daily challenge.

One product value stream was under particular pressure, as they developed and supported the systems used by tens of thousands of patient care specialists. While the business often complained feature delivery was too slow, the developers were either drowning in unplanned support work or blocked while waiting on input or approval from the business.

They turned to Viz and the five Flow Metrics to better understand where their work was slowing down. They sought to discover which investments in people, process and technology would help them go faster. And they wanted clear data visualizations to share with their leadership and peers to advocate for those investments.

• 2x increase in feature Flow Velocity
• Massive reduction in support ticket volumes
• Identified precisely where to invest to remove impediments to value delivery

In August 2019, the enterprise began using Viz to generate Flow Metrics from their development work—managed in Microsoft Azure DevOps—and support work—executed in ServiceNow. The insights began surfacing within a couple of weeks.

• An experiment was run in September to use feature flagging to artificially slow down the release of UX changes to production from once a week to once a month. The monthly release was accompanied by a newsletter and training.
• An improved Flow Distribution in September confirmed the hypothesis, and month after month the ratio of defects declined while the Flow Velocity for features increased.
• The results were shared with other product value streams, who soon adopted the revised release schedule to similarly favorable results.
• The value stream used the data to make a case for adding additional states to Azure DevOps to more accurately reflect their workflow.
• Once implemented, the Flow Efficiency metric in November adjusted to show a much lower efficiency, around 60%. The work was found to be waiting for business analysts, not for developers.
• The Flow Efficiency metric was taken to the CIO, who immediately approved the hiring of additional business analysts, knowing that this would clear the impediment to faster feature delivery.

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