Integrating Web Services with Microsoft Excel and Google Sheets
The "Aha" Moment: How to Onboard an API Service and Get Active Users
Introducing Serverless Data Feeds
Share Data Without Sharing Credentials: Introducing Pipe-level Permissions
Just Binge-Listened to 95 SaaStr Podcasts, Here's What I Learned
Lessons from the Data Ecosystem: Part 2
What We've Learned from Exploring the Data Ecosystem: Part 1
We're Reinventing the Query Builder (Well, Kinda)
Talking With Data Journalists: 5 Takeaways from Our Summer Research Project
Data Journalism Survey Results: Crunching the Numbers
Of Unicorns and Clydesdales: Data Science at the Departmental Level
Much electronic ink has been spilled on the rise of the data scientist.
They’ve been called sexy. They’ve been called unicorns. And, we clearly see a direct correlation between this recent adulation and the uptick in searches for “sexy unicorn”. (At least, I hope that’s the reason).
As discussed previously, capturing a unicorn is great if you can find it, but it takes a village to get data stuff done in the enterprise. Michale Mout once put together a Venn diagram based on the Wikipedia definition of data science:
In the response to that article, this quote by Vincent Granville stood out:
This Venn diagram misses the most important circle: domain expertise / business acumen. You can be a data scientist without computer science, statistics or data base (thought it would be very difficult). You can’t be a data scientist without deep domain expertise and horizontal business knowledge.
Business-level domain expertise, of course, tends to be the stuff of the folks down in the trenches of a department or line of business. I like to call them “departmental champions”. They’re the go-to people when you have a data project that involves the details of business operations or will affect line of business policies and procedures. They’re the workhorses, the Clydesdales.
The kind of data analytics performed here isn’t usually asking big questions with big data or building a model for predictive analytics, but using Excel or Access to wrangle together a couple of tables from SAP. These folks have the knowledge, experience and handed-down-departmental-lore to know field “UNIT_ID” is more important than “UNIT_NO” for reconciling inventory and that you better look at “CR_FLAG”, lest you skew your results.
This kind of information is critical and glues together work between departments, IT and third-party vendors. And, of course, this is the data project work that solves day-to-day problems and makes the trains run on time.
We may not see a large number of searches for “sexy clydesdale” anytime soon (let’s hope!), but the domain expertise embedded within the line of business shouldn’t be forgotten while hunting for unicorns.
Image by: N@ncy N@nce