Improving Interpretation of Science Writing

Stories of recent fraudulent science seem uncomfortably common. In many of those cases the scientists are blamed, and rightly so. Sometimes criticisms identify more systemic problems like current scientific practice, or scientific institutions like the NSF or a university, or academia in general. Blame is also often laid on pop science and the popular science writers who try to tell a counterintuitive and interesting story, or who are under pressure to write frequently and under a deadline.

Bottom-up creation of data-driven capabilities: show don’t tell

I’ve been writing lately on what to do when people who make decisions in an organization say they want data-driven capabilities but then ignore or attack the results of data-driven analysis for not saying what they think the data ought to say. Some of the most productive things you can do in that situation include automating your work so you can devote more time and attention to more important (and labor-intensive) projects, as well as building support among the organization’s weak actors as a means of garnering positive attention from higher-power stakeholders.

Bottom-up creation of data-driven capabilities: weak supporters *10 = strong support

My previous post addressed the scenario of executives or managers saying they want data-driven capabilities but not accepting data-driven analyses when the findings are presented to them. As I discussed in that post, I think the best first step in such a situation is to regroup and streamline your workflow, automating as much of your more repetitive tasks as possible, in order to free up your time so you can devote more attention to bringing those managers on board. This post focuses on one of many possible next steps: building up support in a wide variety of seemingly trivial areas of your organization.

Big Data of all sizes: how to turn a regular organization into a data-driven organization

Everyone’s talking about Big Data lately. It’s being touted as a “revolution” for organizational decision making. I generally think more reliance on data is a very good thing, and I’m glad that people who traditionally haven’t thought much about data are now thinking about it more. That being said, I’ve been struck at the differences between the ways the actual term Big Data seems to be used by practitioners, as opposed to the ways the term is used by the executives and managers who supposedly want Big Data to work for them.

Another Downside to the Current Journal System

One day I imagine I’ll have a paper that isn’t rejected for publication. When that happens, in the joy of adding a journal-title to my CV, perhaps I’ll wave goodbye to my days of fierce antipathy towards journal conglomerates. In the meantime I’m going to continue to embrace the journal-corporation hating. The system, after all, seems increasingly stupid.