Details Matter: Little Data Trumps Big Data Posted on : Jul 20 - 2019

Have you ever gone into a Starbucks during a busy time and noticed how the staff shifts roles to handle variation in customer volume? When you arrived, there may have been three people in line served by one cashier, two baristas, and a fourth staff member restocking the milk and sugar. When two more customers walk in, the person stocking moves to the second cash register to take orders. Add two more customers and one of the baristas begins taking orders, writing them on cups for her colleague at the espresso machine.

The number of team members in the store is static even as the customer demand rises, yet the speed with which customers experience the first stage of service stays relatively consistent, because the staff members use status data to adjust their processes real-time.

Such fluid adjustment is only possible because the in-store Starbucks team has been trained to observe their processes real-time and make workforce allocation decisions according to defined triggers. Starbucks leaders are never caught off guard about the customer experience because they actively collect—visually, in their case—and use real-time status data to perform more consistently.

Status data reflects how far along a work team is on completing work-in-process (WIP) and whether work queues are shrinking or growing. In my first post in this three-post series about little data trumping big data, I wrote about the importance of accurate data on the customer demand a department, business unit, or entire organization has on an hourly, daily, weekly, or monthly basis. Status data, in contrast, reflects the progress made on addressing that demand through a disciplined process. View More