Subject archive for "it-leaders," page 3

As more companies recognize the need for a [data science platform], more vendors are claiming they have one. Increasingly, we see companies describing their product as a “data science platform” without describing the features that make platforms so valuable. So we wanted to share our vision for the core capabilities a platform should have in order for it to be valuable to data science teams.

“Buy vs build”, “not-invented-here syndrome” and even “invented-here-syndrome” have been written about extensively. I want to share a few reflections on the topic, based on my observations both as an engineering manager (where I had to decide whether to build or buy solutions) and more recently as a founder selling a platform to other companies.

This post describes our engineering philosophy of building an “open” product, i.e., one that supports existing tools and libraries, rather than building our own custom version of existing functionality. Aside from letting our developers be more productive, we’ve found this approach makes our users much more productive — especially power users, who are especially important to us.