In her previous post, Technical Challenges in Data Science, Amanda Andrei discussed the need for technical vigilance and with experts Dr. Elizabeth Hohman, statistician and group leader within MITRE’s Department of Data Analytics, and Dr. Eric Bloedorn, senior principal artificial intelligence engineer. Tools and models, however carefully managed, tell, of course, only part of the story. Data scientists are people, and they and the tools they use reside within organizational cultures, which may require as much training as the data to hand.—Editor
As Amanda Andrei mentioned in her previous post, Defining, Applying, and Coordinating Data Science at MITRE, we are generating 2.5 million terabytes of data a day, and the need for data science teams and individual contributors is crucial for moving what we find up the spectrum to knowledge that we might usefully….
For a long time I have thought I was a statistician, interested in inferences from the particular to the general, wrote mathematician….
I believe that knowledge management as a discipline developed because technology enabled the deluge of data we began experiencing about 20 years ago. The ways that we used to organize and share our information were no longer adequate to the task and we needed something new. I’ve spent the last five or six years focusing on data, more specifically on helping organizations treat their data as a strategic asset that requires the same stewardship afforded any other valuable resource within the organization.