Data conversations are overwhelming. Based on the tone in the media there is a broad assumption that we are all either geniuses in computer programming or strumming our lips behind potted plants in a dark corner. I hope you can appreciate the range of options in my presented scenario--and will consider a voice slightly left of the center.
Several of my current clients are not part of organizations with data scientists on staff or even embedded in a data rich culture or work environment. One thing I continue to learn through collaborations? We need to know where to start. As Maya Angelou famously said, “I did then what I knew how to do. Now that I know better, I do better.”
Collaboration in data science also requires a decent proficiency across a broad spectrum of project support. The more frequently you engage UX design, analytics, programming, or whatever your unique mix of people, data, and resources might be--the better the outcome.
A recent favorite data podcast, Linear Digressions (link below), discussed a recent white paper published by Katie Malone and Skipper Seabold titled, How to Make the Right Decisions About How You Make Decisions. The italicized comments are my own...
Experienced data scientists know that a great dataset, combined with simple analytical methods, is more likely to yield great results than a mediocre dataset and complex methods.
You will need to answer the following questions--or guide your clients appropriately:
You have data--where is your strategy? This chart is from the white paper--paper is free but you need to enter your email address. In my opinion, the juice is worth the squeeze.
Even if you aren't planning on launching your own data strategy you need to understand the context of decisions around data you consume.
We can no longer ignore or plead ignorance while our professional ecosystem is awash in large volumes of data.
There are many points of entry to understanding data--but first you need to know where to begin.