Episode 153: Boosting GA4 with BigQuery, with Johan van de Werken

Boosting GA4 with BigQuery, with Johan van de Werken

Johan van de Werken thrives best at the sweet spot between data, business & technology. 

Graduating with a philosophy degree from the University of Utrect, my guest started his career as a journalist for several Dutch publications, writing about everything from events and  pop culture to media, politics and economics. Around 2014 he switched from letters to numbers, working in CRO for several European e-commerce businesses. That led him to building dashboards and leveraging cloud platforms to turn raw data into usable marketing insights.

Working at an analytics firm that exposed him to BigQuery, he thought about sharing  what he was learning. Seeing that the  domain GA4BigQuery.com was available, he registered it and started posting there as a side gig. It got noticed by Simo Ahava, the founder of Simmer. That led Johan to release the GA4 and BigQuery course on their training platform. As we fast forward to 2023, GA4BigQuery is now a well-known resource for marketers. And its creator is now consulting full-time on data analytics under his own brand, Select Star.  Except for when he’s having fun playing in a punk rock cover band. 

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Johan on Medium

Funnel Reboot episode with “Learning Google Analytics” author Mark Edmondson

Definition of ETLDefinition of an IDE

Episode 152: Data doesn’t lie…or does it? with Yuliia Tkachova

Data Doesn't Lie...or does it?

Data warehouses are amazing things: you can toss all kinds of information into them then pull mind-blowing insights out the other end. This feat can happen because you’re connected to outside systems holding their own database tables. A copy of whatever has recently gone into the table is taken out and shot through a data pipeline and pushed into your data warehouse. But today’s data stacks contain Multiple clouds, hybrid environments, and so many data pipelines the programs in charge of monitoring and logging the flows almost can’t manage them. It becomes overwhelming to manually check and ensure the quality and integrity of the data.  The more sophisticated the systems, the more errors creep into the data. If we rely on flawed data, the outcomes and insights we generate will be equally flawed. This is where data observability comes in.

In this episode you will hear about something called an observability platform. It identifies real-time data anomalies and pipeline errors in data warehouses. Now there’s a twist here because we’re in a cloud computing environment that charges by number of computing cycles. You don’t want an observability tool that’s another pipe accessing client data and running up the meter. The good news is there’s an easier way to detect when data has gone awry, by comparing log files – basically  metadata – they are just as effective at alerting you to problems. 

If you’d like what this is doing described in a completely non-technical way, think of Hans Christian Andersen’s Princess and the Pea. There is a girl who comes to a castle seeking shelter from the rain claiming to be a princess. The queen doubts whether she is truly of noble blood, and offers her a bed, but this bed has twenty mattresses and twenty down-filled comforters on it. A pea is placed underneath the bottom mattress to test if this girl detects anything. The next morning, the princess says that she endured a sleepless night; there must have been something hard in the bed. They realize then and there that she must be a princess, since no one but a real princess could be so delicate.

I spoke with Yuliia Tkachova, the co-founder and CEO of Masthead Data, a company which recently received $1.3M in a pre-seed round. Originally  from Ukraine, Yuliia came to found Masthead after work that convinced her of the need for an observability solution. She had roles as a Product Manager roles at OWOX BI and Boosta, where their data solutions encountered problems. Prior to that, she did marketing for RAGT.  She has Bachelors and Masters degrees from Suma State University, specializing in MIS & Statistics. She also serves as an Organizer at MeasureCamp, a volunteer community where analytics professionals come together to learn.

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Masthead’s YouTube Channel

Connect with Yuliia Tkachova on LinkedIn 



Image credit: Edmund Dulac in Hans Christian Andersen tales

Episode 151: Analytics – worth the investment, with Martin McGarry

Analytics - worth the investment

Analytics is something that everyone says they want, and some brag that they can analyze very well. But few people know what investment’s required to build a quality analytics function, and even fewer are good at justifying its value. 

Our guest Martin McGarry is so passionate about analytics, as you’ll see from his  backstory, if anyone can articulate the business value of analytics, it’s him. 

After completing a Bachelor of Science from The University of Manchester and studying at the University of Cambridge as a Doctoral Candidate, our guest worked in the UK analytics practice of a global Management Consultancy.  Due for a change after 6 years of that, he moved to Ottawa Canada and founded his own consultancy so he could offer a more independent approach. A while later started the firm he’s been leading for nearly 15 years, Bronson Analytics

In 2018 he began a recurring event in Ottawa called Beer & Analytics, which draws hundreds from the field together for learning and socializing. In 2022, the event went outside of Ottawa for the first time, being held in Toronto. 

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Martin’s LinkedIn profile

Machine Learning Expert Andiy Burkov

The Hundred-Page ML Book

Synthetic data vendors like DataRobot

Simulation running, via tools like Simul8

Analytics practitioners need to do better at explaining their value

Episode 150: Marketing Analytics Summit – Experts on where we go from here


It’s fitting, given how this is Funnel Reboot’s 150th episode, that we veer off of the standard format and dig into a niche within marketing that’s becoming a de facto part of every marketing function and is dictating new skills that every marketer must learn. I’m talking about marketing analytics. 

This episode is compiled from experts in the world of analytics. It was recorded on location at the Marketing Analytics Summit that was held in Las Vegas. It’s coming  hot off the press from the June 2023 event.

This episode is divided into themes discussed at the summit. Here they are shown with time markers for each chapter:

ChapterStarts atSpeaker (mentioning only first time they appear)
INTRO3:00Jim Sterne
GA5:56Sheena Green
Sara Hoffman
Kelly Anne Wortham
Kenya Gillette
Jenn Kunz
UX/Testing11:58Geddy van Elburg
Deborah O’Malley
Anna Smolina
Process17:53June Dershewitz
Brianna Mersey
People20:19Rachel Heseltine
Ali Groepper
Lina Mikolajczyk
AI’s impact23:50Ryan Levander
Mary Owusu
Facing Change28:48Anil Batra

People and events mentioned in the show: 

Deborah O’Malley, who was the guest on Funnel Reboot Ep 65

The winning analogy of the conference made fun of how loosely people add the term AI to everything. Kenya Gillette used soccer to characterize this. Imagine you were the person who designed the game, documented all the rules and scouted the earth for people to play it. 

Then someone says they can make soccer better…by simply playing the game ON THE MOON. That is the equivalent of saying that any business activity can be made better by adding AI to it.

Episode 139: What Google Analytics 4 Makes Possible, with Mark Edmondson

Mark Edmondson

We’re ending our series on advancing analytics today. We’ll focus on the software that, for many marketers, is at the core of this field: Google Analytics. It was introduced in 2005 as a read-only tool that tracked basic info on websites. The name has stayed the same over these last 18 years, but so much else of Google’s technology landscape has changed: they have released many other tools:  Tag Manager, Google Data Studio, now Looker Studio, ways for API’ing between systems in and outside of Google, and most importantly a place where it can all be managed – Google cloud.  

The new Google Analytics GA4 was born of this environment.It’s been criticized as being immature since it lacks features that were in the old UA interface. However, if judged by how well Google integrated it into their stack and how much those with technical skills can do with it, we would rate it as ready for prime time. Add to that the fact – in a matter of weeks Google teams won’t have to maintain two analytics tools, and they’ll get to focus exclusively on just one – GA4.  

We can debate Google’s motives for tightly integrating GA4 with the whole Google cloud. I’m not wading into how good or evil it is to give away a product and hope users try the paid cloud platform that comes with it. But I’ll say that using Google Analytics with these other pieces lets you do much more with your data that you couldn’t do with the old GA. And when tools directly or indirectly  make money for Google, that incents them to keep those tools and keep improving them. I’ll leave it at that. 

Our real question is how do  we economically benefit from the available tools. And that’s what our guest is going to tell us. His book which came out in early 2023 is called “Learning Google Analytics: Creating Business Impact and Driving Insights”  The business impact spoken of there doesn’t mean using GA4 as a standalone lookup tool. Using it like that and ignoring what’s possible, would make the rest of the whole Google stack seem (to quote from the Movie ‘Contact’, “like an awful waste of space” 

Our guest knows the value of integrating Google’s tools for many world-wide brands, as he’s done through digital agencies on his own as a GA consultant since 2008.  Mark Edmondson has helped turn the out-of-the-box Google Analytics into a package that automatically describes, predicts and activates better marketing outcomes. He currently works at Devoteam as their Principal Data Engineer. 

Mark grew up in Cornwall, UK before gaining his Masters in Physics at Kings College London. He now lives in Copenhagen with his wife and two children, enjoying playing music and cycling around the many lakes.

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Mark’s blog

The ‘jewel in the crown’ of Google Cloud: BigQuery

What it means to implement Server-Side Google Tag Manager 

Episode Reboot. Mark’s takeaway: ‘Moving all the useful data to one place has had the most transformational effects on a client’s digital maturity,’

Episode 138: How Data Pipes Operationalize Analysis, with Noah Learner

Noah Learner

Today we’ll talk about automated marketing data pipelines for reporting and even activation. If that last sentence didn’t make total sense to you, don’t worry, our guest is going to tell us why we need it and how we can set out implementing it. 

The place where Noah Learner got his start was on the island of Nantucket.  It’s around 20km (15 mi) long, which is small enough that most visitors leave their cars on the mainland, come across on the ferry and rent a bike. 

Noah’s journey started with a job at one of the island’s bike rental shops. Over the next decade as he rose to become the company’s GM, he became convinced of the power of SEO for driving traffic. 

He relocated to Colorado, at various points he worked in corporate SEO roles and worked out on his own. He even mashed up skills from his past to serve businesses in the pedal-powered rental market, calling it ‘bike shop SEO.’

In the past 4-5 years he’s built cutting edge SEO tools using Google Cloud technology and has shared how to deploy them by speaking at MozCon, SearchLove and LocalU and in the agencyautomators.com community which he cofounded. he’s just taken on a new role  that has tool-building  baked into his mandate, as Director of Innovation for Sterling Sky

When not at work, he loves doing typical Colorado things like fly fishing and skiing, along with family-friendly activities like hiking and camping where their two dogs can tag along.

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DBT, or  Data Build Tool is a set of scripts or programs that, when deployed, cleans and transforms a data source’s original tables and fields into a usable format for analysis.

VS code environment

Noah’s LinkedIn profile

Two Octobers

Learner’s model: Revenue = Transactions X Conversion Rate X Average Order Value 

Episode 137: Data Storytelling with Looker Studio – Sireesha Pulipati

Sireesha Pulipati

You have strategized and run a program with positive results, you would think that you simply show leadership your data and then you can sit back while they lavish you with praise.

Not quite.

If it isn’t packaged right, it won’t have that hoped-for impact. Remember Maya Angelou said ‘people will forget what you said…what you did….but will never forget how you made them feel.’ That feeling is conveyed through stories. To find and tell those stories, you need business intelligence and data visualization tools. I sought out a Googler who’s an expert in their tool for doing this: Looker Studio.

Sireesha Pulipati is an experienced data analytics and data management professional. She has spent the last decade building and managing data platforms and solutions, and she is passionate about enabling users to leverage data to solve business problems. 

Sireesha holds a master’s degree in business administration and a bachelor’s degree in electrical engineering. Her work history spans multiple industries – healthcare, media, travel, hospitality and high-tech 

She is currently at Google as a technical lead, helping with the business intelligence and analytics strategy for internal teams.

She is the author of the newly-published book Data Storytelling with Looker Studio

People/Products/Concepts Mentioned in Show

Google Looker Studio

Google Looker Studio Pro

This host has loved stories, all the way back to when he read them to his kids.

Episode 136: Building Analytics Teams, with John Thompson

Building Analytics Teams

John Thompson is the author of the 2020 book Building Analytics Teams, and a 35-year technology executive in the fields of data, advanced analytics and artificial intelligence (AI). 

He is Global Head, Artificial Intelligence (AI) at EY.  John was an Executive Partner at Gartner. He was the global advanced analytics and AI function at  biopharmaceutical company CSL Behring, where he led an analytical applications team.

John has built start-up organizations from the ground up and he has reengineered business units of Fortune 500 firms to reach their potential. He has directly managed and run – sales, marketing, consulting, support and product development organizations.

He has been a technology leader with expertise and experience spanning all operational areas with a focus on strategy, product innovation, growth and efficient execution.

Thompson holds a Bachelor of Science degree in Computer Science from Ferris State University and a MBA in Marketing from DePaul University.

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Tom Davenport

Data Science modules on Coursera by U Michigan’s Dr Charles “Chuck” Severance

Shawn Rogers

Diagram showing work of analytics being approached in either an artisanal or a factory style; used with permission

Episode Reboot. John’s book  “Data for All” published in 2023

Episode 135: Data Clean Rooms, with Puneet Gangrade

Data Clean Rooms

Disclaimer:When I bring technology vendors on the show, you should know that they are not sponsors or affiliates. They’re simply here to give you a broader perspective.

Puneet Gangrade is a Customer Success Engineer, an active amateur soccer and badminton player and a resident of New York City who originally hails from Indore, India

He works at Habu, a data clean room software company, with the goal of creating reliable data and media strategies for all of his clients. He is responsible to work with Habu’s Engineering, Product teams, and clients on end-to-end workflows including clean room adoption, onboarding, planning, implementation, and growth. Habu works across distributed data platforms that allow agencies, publishers, advertisers, and their partners to truly unlock the potential of data and collaborate in a privacy-safe manner. 

He has been a data clean room enthusiast since the beginning of 2018. Previously, he worked as a Digital Analytics Specialist at Time Inc till Oct 2017 and as a Data Science Team Lead at MightyHive, an adtech agency from early 2018 to mid-2022. He has a BS in Computer Science and earned his MS in Marketing Intelligence from Fordham University Gabelli School of Business, with a concentration in Marketing and Analytics.

Amid the rise of advanced measurement techniques in media and other industries, he likes to keep learning about various data clean room technologies and constantly think of use cases in media, finance, CPG, pharma, etc. where data clean rooms could be leveraged. He continues his passion for everything related to data clean rooms and data collaboration at Habu.

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Puneet’s LinkedIn Profile

Puneet’s Twitter Profile

Company where Puneet works: Habu

Description of Walled Gardens

Concept of an Escrow (example given with finance can also be applied to data)

Episode Reboot: Read this background article: Data clean room: A silver bullet to a post-cookie transition?

Episode 121: Looker Studio 101, with JJ Reynolds

No about analytics is complete without talking about how to visualize data. A picture’s worth a thousand words, right? In the past, when data was in a spreadsheet, it only took hitting that ‘chart’ button to render some numbers visually. But for many of us, this experience has moved to a browser where we build our own report, either in an interface like GA or in a standalone visualization tool. 

I’m talking with someone who’s really good at a visualization tool, but came by his power-user status in a roundabout way. My guest was born and raised in Hawaii.  After he got his Marketing degree, he worked at an ad agency where he did everything from videography to FB and G-ads writing. That, and also building a few websites, stoked his curiosity for how the tagging and the analytics behind all these things worked. He didn’t just want to get at raw data, he wanted actionable data.  He felt that to optimize his marketing, if he only knew how to present visitor behaviour data visually, the answer would be apparent – even obvious.  

He went down YouTube rabbit holes, asked around at conferences, and eventually landed on the beta of Google Data Studio, now called Looker Studio.  And that’s the Data Visualization tool we’re talking about with JJ Reynolds, who joins us from Reno NV.

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