data leader

Analytics Leader Digest - 10/11/2022

Insights for analytics leaders we've scooped up this week. Questions to ask before releasing a data product, data friction, re-connecting teams, and more.

How to build a data product that won’t come back to haunt you


Self-explanatory, trustworthy, adaptable, and resilient. These are qualities we’d want in a spouse, a business partner, and especially our production data pipeline. Mighty Canary’s chief data scientist examines what makes a pipeline “good” by evaluating those four traits at three critical chapters in the data journey: “first mile”, “last mile”, and “everything in between”. What follows is a fantastically insightful laundry list of questions to ask yourself about your data pipeline.

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Tell-tale signs your organization is dealing with data friction

Brainstorm against business interface with graphs and data

The verbiage of “pulling” a report never feels more apt than when you’re struggling to download sales figures from the last three years across two different systems because the CFO needs the updated charts right now. What’s on the other side of the metaphorical data “tug of war” pulling against you? Data Friction. Mighty Canary strives to ease this friction - in this article, our own Tabrez Syed gives his tips and tricks to identify and mitigate data friction within your org.

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Is the “modern data stack” a scam?

In the first of what has become a three part series following some feisty tweets from one of the founders of Fivetran, a “data wrangler” peels back the skin of the modern data stack. When so many articles are touting the benefits of dbt + Snowflake, ELT not ETL, etc., it’s refreshing to read a contrarian, if scathing, view of the data industry today. 

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How fateful?

The cutest data science story you’ll ever read, and one that will have you on the edge of your seat waiting to find out whether this data scientist and her romantic partner crossed paths before meeting “for the first time” on a dating app during the pandemic. You almost might not notice the heavy (but clean) technical analysis she fits in - this is a great example exercise for someone wanting to learn about the fundamental steps of a data science analysis, code included.

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What to do when business and data teams are disconnectedclose up of a broken chain and a paper clip on white background

There has been a major focus lately on the tools we use to do our jobs with data. Selecting the right data stack or “toolchain” often supersedes conversations about the processes and, crucially, the language that enables those tools to deliver value. Some of the content in this article may seem basic, or at least not revelatory, but it’s so important it bears repeating. Fundamentals are essential; LeBron still works on his free throws. 

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