Thursday 1st March, 2012
4:50pm to 5:30pm
Measuring productivity remains a notoriously difficult problem, nowhere more so perhaps than in innovation. Feedback on the progress of projects and the performance of workers is scant, highly uncertain, and collected either too infrequently or too slowly. Yet such information is indispensable to the efficient allocation of resources to innovation projects. These challenges are all the more acute for companies involved in complex product development, where performance hinges critically on an organization’s capacity to constantly and consistently innovate. At the same time, information captured by enterprises has generally gone from scarce to superabundant, affording them an unprecedented opportunity to monitor information flows, observe worker interactions and organizational structures, and estimate individual and organizational performance.
We will discuss how companies are using data to obtain sharper, more timely insights. Specifically, we will present how real-time information about engineering collaborations are being leveraged to measure, model, and ultimately forecast organizational productivity and project performance with a level of accuracy and timeliness heretofore impossible. Over the past couple of years, QuantumBlack has developed and deployed an analytics tool to help companies in a variety of industries, from aerospace and automotive to software and semiconductor manufacturing, improve the yield of their project investments. The software tracks and analyses real-time communication and collaboration data, as well as data on performance metrics related to tasks and projects under assessment, to forecast organizational productivity, predict the success or failure of projects, identify performance bottlenecks and drivers, and ultimately help optimize resource and work allocation strategies.
The talk will center on case studies involving successful deployments at several Formula One (F1) teams. We will show how we were able to forecast the productivity of innovation teams, improve investment yields by as much as 15%, and raise productivity by nearly 20%. Certainly, this is no free lunch and we will dwell on some of the more important difficulties: the technological and computing challenges associated with machine-learning and real-time analysis of a transient data set that can grow at the rate of several terabytes per day, some of the privacy issues associated with trawling employee communications even if by machine-only readers, and finally some of the cultural and management challenges that we and our clients faced in deploying a capability that forecasts individual and organizational performance. By the same token, there is a great deal that enterprises can do to help build and facilitate the adoption of analytical capabilities within their ranks. After all, and as we will show, the returns certainly warrant the effort.
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