by Angela Guess
Kalev Leetaru recently wrote in Forbes, “As I’ve come to work with an ever-widening swath of the data sciences and “big data” communities, I have been struck by how narrowly focused much of its practitioner base is on statistics and computational expertise as opposed to a solid understanding of the domain being analyzed. Data scientists are deployed to almost every task imaginable at the companies and governmental and NGO agencies I’ve worked with, yet many started their careers as statisticians, computer scientists or in the computational hard sciences. How is this lack of domain knowledge affecting the world of big data and data analytics today?”
Leetaru continues, “Precious few data scientists that I’ve met have deep backgrounds or rigorous training in the disciplines and domain areas in which they find themselves presently deployed. In many of the organizations I’ve worked with, data scientists are treated as spot problem solvers, moved rapidly across the entirety of the organization’s practices, analyzing deeply technical and nuanced problems in one domain and then addressing deeply complex issues in an entirely different domain the following day. Often spreadsheets are tossed over the fence to the data sciences group each morning and the results of the final model emailed back that afternoon with little interaction or communication among the producer and consumer of the data analytic pipeline.”
Photo credit: Flickr/ Danny Nicholson