Photo: Tableau

So Bull Moose was able to anticipate demand for then-unknown bands such as Pearl Jam and Limp Bizkit before competitors. “We ordered stuff far better than others,” Wickard says, “but we also returned far less merchandise.” Over the past two decades, Wickard and a handful of programmers working in Portland have expanded the software’s capabilities into a retail-management system that pulls data from various parts of a store’s operations—online sales, loyalty programs, point-of-sale systems, Amazon.com—to make inventory decisions. It instantly places orders and restocks merchandise driven by large demand factors (say, the death of Prince), and smaller ones (one store is selling more Bollywood movies than others). A chainlet with a big data edge. Bloomberg Businessweek, August 29, 2016.

No one understood why I wanted to pull my own data from our management systems. Why couldn’t I just use the standard reports and metrics that everyone else used? Why was I upset when they deleted a whole bunch of “old data” from past projects? Who needed that old data and besides it was just taking up space they retorted.

Most managers, in my experience, either undervalue the data around them or, to put it bluntly, have no idea how to understand and use data. The secret I often teach is that we want to try and find the data that captures what is currently going on in the project (or organization). That data should confirm what we are seeing happening around us and it should also inevitably show us insights about subtler dynamics of the project. The trick is to dig in and coax the insights out of our data that we already have.

For more see How To Get The Data We Need

What data does your organization have that might help your project to succeed?