Demystifying GA4. Lessons Learned on Designing a Robust Data Model for Actionable Insights
This is a deep dive into building a robust data pipeline for the Google Analytics 4 (GA4) data coming from Big Query to extract actionable insights at scale. This is a real-world case study of our practical application, offering a roadmap for organizations seeking to leverage GA4 for informed decision-making.
Some of the challenges we overcame include
- Leveraging Snowflake to make data ingestion more cost-effective and performant (moving off Matilion)
- Identifying and overcoming inconsistencies with UTM tagging over time
- Leveraging attribution to account for excessive direct traffic
- Harmonizing advertising platform data with Google Analytics session data
The Fastest Way to ACTUALLY Get Hired as a Data Scientist with NO Work Experience
Are you struggling to launch your career in AI/ML/DS?
Are you tired of being told you need more experience?
You’re not alone. Many hiring managers will not even consider candidates who don't have years of experience. It can be tough to get your foot in the door.
Hey, my name is Rho! I'm a full-stack senior analytics engineer. I have worked as a data scientist for several years using a multitude of technologies, with practical experience in several industries. That wasn't always the case.
In the beginning I was frustrated. I had the skills and talent; I won a data science competition; I used natural language processing to solve for real problems at work. No one cared.
I couldn't get an interview let alone an offer. I struggled. I cried. And I prayed, that if I succeed I would reach back to help others struggling as I was.
I want to share with you how I overcame my own inexperience.
Confession of an Analytics Professional
I find myself wearing the expert hat, not because I earned it, but because everyone who knows better is gone.
Now, let me tell you about the state of our data. At the time, we didn’t know. That was the issue. It was a black box. Our data model was opaque with logic scattered all across the data stack. As we pick around the edges a picture starts to form, a dense, thorny briar patch, each thicket a tangled mess of information. That's how I see it—unruly, interlacing, and chaotic. In this session, you will learn firsthand how we leveraged the modern data stack to lead our team out of the briar patch.
Use Cases for this session include:
- Data Engineers looking to better understand data modeling beyond star schemas, maybe even just getting started with DBT.
- Mid to Senior Analysts who need to better understand how the modern data stack and data modeling (data mesh primarily) empower analysts to build better insights at scale
- Analytics engineers who need help to "sell" analysts and decision-makers on the promise of thoughtful data architecture and data modeling.
**Capabilities:** I will showcase how we used Streamlit to streamline processes at Purple to address data quality concerns with our marketing data. It will lean pretty heavily on dbt and Snowflake. From a data perspective, it will skew pretty heavily towards marketing data needs. The transition we made in Snowflake was from development in one environment (production schema) to a three-tier environment (dev / QA / Prod) as well as the introduction of CICD into our process.
The modality is a story (not a lecture), to leverage the human element of experience with the intent to motivate and inspire. In this session, I am going to tell you the story of Serge Sookram, an analytics engineer with an appetite for anything that will kill him, fast food, fast cars, & fast women. Learn firsthand how he leveraged the modern data stack to lead his team out of the briar patch.
Bull Doze Thru Bull
I was tired. Tired of being broke. Tired of all the crap in my life. Too much nagging. Not enough sex. Too much weight. Not enough exercise. Too much work. Not enough results. Too much month at the end of the money. And my kids...my only reason to endure, were killing me.
I dreaded the weekend. Another reminder of how much my life sucked. At least when I went to work, I could focus on work and take my mind off things.
My wife's had it. "It's never enough," she says, "You're never happy." My wife is tired. Tired of seeing me like this. She is right of course. I keep repeating the same line: the idea that when I graduate college, get my first job, get a new job, make a certain amount of money . . . then I'll be happy. But that isn't true at all! She calls me out on it. I keep moving the goal posts because each step doesn't make me happier, it just frustrates me more that I am not where I want to be.
As an analytics engineer I'm immersed in a culture where the terms "data-driven" and "single source of truth" are constantly tossed around in conversation. The truth is the most vital data that shapes our decisions can't be pulled from an API or summarized in a report. This data resides within us, in the deepest chambers of our hearts, and carried on our backs.
In this session I endeavor to take you through a framework of thought that not only helped me conquer my own inner battles but also became a sturdy foundation of support for those around me. It's a genuine exploration of the human element within the data-driven world we inhabit, one that I hope will resonate with you.
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