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
Enterprise Data Projects: It Takes a Village
I recently found an article discussing the four different types of Data Scientists. Turns out there’s a quite a bit of wiggle in what the term “Data Scientist” might mean – from business savant to data viz wiz to world class coder to Ph.D. in statistics. A question is posed:
“…for an organization looking to increase its analytical team, firstly have a think about the skillsets of those that currently work for you. Could they perform some of these roles? Could you bring someone senior in to coach them and grow your team organically?”
Without even considering the hunt for unicorns, this is the right approach. With all the complexities, systems and competing priorities in the enterprise, it certaintly takes a team to get data stuff done. Even Superman couldn’t save both New Jersey AND California by himself (well, sans time travel).
This got me thinking about all the other data-related projects happening at the departmental level every day, across every company. Not the glitzy stuff, mind you — we’re talking about the final hammering and nailing that could never be accounted for during a top-down BI implementation.
It’s an ad-hoc-y, collaborative-y process that requires different domain expertise from different people within (and without) the organization. So with that, I give you the four folks, down in the trenches, working together to just get stuff done:
- Line of Business Manager: The idea person; the one that knows how to add value to their part of the company. She’s a logistics pro who needs to pinpoint the 20% of shipping vendors that cause 80% of the delays or the SuperWidgets product manager who wants to compare Twitter references with a mailing list. She uses data to make tangible improvements.
- Departmental Champion: The go-to guy or gal a few cubes away who knows just enough SQL to be dangerous. Typically a master of Excel and Access, they also possess the mad skillz to, at the least, get a prototype up and running. They either have a buddy in IT or can figure out a way to bypass IT altogether. Either way, they can save the hassle of writing up a data request.
- IT Liaison: The keeper of data and security policies; they know where the data lives and how to navigate the proper channels to reduce organizational risk. They’re very smart people who are Completely. Utterly. Buried. Your data request is the 34th request they’ve gotten since 9AM, so you’ll need to take a number.
- Third Party Expert: A lot of data projects may also involve a third-party consultant or vendor. These are the folks that have come up with Magic Toolkit for Big Problem. But, in order for Magic Toolkit to work, they need to get the right data, understand the relevant systems/data elements and navigate the underlying business methodologies used in your IT infrastructure.
To these ad hoc teams that are joining forces to make incremental improvements to their organization each and every day: we salute you. It’s this blue collar “small data science” that makes the trains run on time.
Image by: Frits Ahlefeldt-Laurvig