March 2013 | byCaitlin Rosenthal
Daniel McCallum’s 1854 organizational design for the New York and Erie Railroad resembles a tree rather than a pyramid. It empowered frontline managers by clarifying data flows.
In 1854, Daniel McCallum took charge of the operations of the New York and Erie Railroad. With nearly 500 miles of track, it was one of the world’s longest systems, but not one of the most efficient. In fact, McCallum found that far from rendering operations more efficient, the scale of the railroad exponentially increased its complexity.
The problem was not a lack of information: the growing use of the telegraph gave the company an unprecedented supply of nearly real-time data, including reports of accidents and train delays.2 Rather, the difficulty was putting that data to use, and it led McCallum to develop one of the era’s great low-tech management innovations: the organization chart. This article presents that long-lost chart (see sidebar, “Tracking a missing org chart”) and shows how aligning data with operations and strategy—the quintessential modern management challenge—is a problem that spans the ages.
Continue Reading ->
Posted on January 29, 2014 by Saga Briggs
When learners interact with content in your course, they leave behind ‘digital breadcrumbs,’ so to speak, which offer clues about the learning process. We’re now able to collect and track this data through learning management systems (LMSs), social networks, and other media that measure how students interpret, consider, and arrive at conclusions about course material.
The good news is that this information–called Big Data–can do wonders for personalized instruction, especially within the e-learning industry. The not-so-good news is that the rise of Big Data brings with it many risks and ethical dilemmas, all of which need to be addressed before we move forward with this new approach.
Continue Reading >
Posted on November 27, 2013 by Kevin McFarthing
Big Data is receiving a lot of attention and investment at the moment, with the combination of technology and analytics generating enormous possibility. As Greg Satell rightly points out, businesses that neglect Big Data will be left behind.
Data, and smart ways of generation, can potentially identify new patterns as well as test new hypotheses and simulations. So data are extremely useful, and Big Data will help enormously; but it’s not the concept itself, it’s what it will enable companies to do.
It’s also essential to bring qualitative knowledge and understanding to complement quantitative data; indeed some of the richest sources come from ethnography, observing what customers actually do. This should be a core activity but, as Jorge Barba suggests, it may not be widespread very soon.