30th November 2018

Hello! 👋

Organizations are ever-evolving, and so are the functions and responsibilities within organizations. Gartner predicts that by 2019, 90% of Global Enterprises will have a Chief Data Officer (CDO). While marketing, sales, operations, and product development have long been a part of the core of an organization, 'Data' is the new kid on the block trying to fit in. 

I tried to enlist all the possible roles that exist in data teams today (our dear Humans of Data) and ended up with a long list of 70+ titles 🤯. You would probably even spot your own role in the list below.


Being data-first is still a new adventure that enterprises are embarking on. To demystify the roles, responsibilities, and challenges of the new-age data team, and that of a CDO (and they are as ambiguous as the CDOs of the 2008 financial crisis 💸), I draw parallels between data science and the other well-established functions in an organization in this week's issue

P.S: I like to think that I am data-driven just like many of you and would love your views on this week's issue!

Have a nice day!

Divyansh (@navydish) from Atlan

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Automated Testing in the Modern Data Warehouse

Josh Temple Data Engineering and Analytics, Milk Bar

If data issues have lasting consequences, why are we less sophisticated at testing than our software developing counterparts? Josh argues that a unified testing is absent in data science workflows and provides a framework to understand where to incorporate tests and how to automate them.

Read more here!

The purpose of testing is to validate our assumptions. Good tests catch bad assumptions. Every time you make an assumption about the state of your data or the result of a query, you should write a test to confirm that assumption.


Avoiding being a 'trophy' 🏆 Data Scientist

Peadar Coyle

This article compiles a long list of 'anti-patterns' and observations that have helped Peadar understand and prioritize (or de-prioritize) data science workflows in a team. Terms like leadership buy-in, data audits, and cultural mismatches are the less-talked-about terms in Data and Peadar provides a solution to traverse through some of these anti-patterns.

Read more here!

Data science projects return on a power law-like distribution, much like Venture Capital. I despite having 8 years experience of doing data analysis and machine learning, and 4 of those are in industry have no idea before-hand which projects will have the best ROI. I’ve learned to do several projects in a 6-12 month period, and learned when to cut projects that aren’t working for whatever reason. An organisation needs to admit to failure, and be honest about reality for this to happen.


Imposter Syndrome in Data Science

Caitlin Hudon | Haystacks

Data is overwhelming. Caitlin puts down her thoughts on the rapidly changing field and how that can lead to an imposter syndrome for the Humans of Data. To be able to navigate this journey, she enlists certain values and qualities that a team should promote to be able to function effectively. Some of those are rare gems. 💎

Read more here!

DJ Patil and Jeff Hammerbacher were the first titled “data scientists” only about 7(!) years ago (around 2011). Since then, as we’ve all been figuring out what data science *is*, differing definitions of “data scientist” have led to some confusion around what a data scientist should be (or know).

That's all, folks! 👋

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