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Jamie Quint explains his ideal analytics stack for founders

Sylvain Giuliani
Feb 25, 2021 min read
Feb 25, 2021 min read
Syl is the Head of Growth & Operations at Census. He's a revenue leader and mentor with a decade of experience building go-to-market strategies for developer tools.

Jamie Quint knows a thing or two about analytics. He’s one of the minds behind Reddit’s coin system, the creator of Notion’s analytics stack, and an alum of many household-name startups.

Last November, he tweeted about which tools make up the best analytics stack for founders.

Best stack is:@Amplitude_HQ @ModeAnalytics @segment @fivetran @SnowflakeDB @getdbt @getcensus
— Jamie Quint (@jamiequint) November 24, 2020

“Over the years, I've used probably basically every conceivable database or data system that's reasonably popular to use at companies of zero to 500 people,” he said.

To get a bit more insight into how Jamie used these seven tools —  Amplitude, Mode, Segment, Fivetran, Snowflake, dbt, and Census – we sat down with him and discussed his underlying philosophy behind choosing this stack, and how he sees analytics stacks changing in the future.

Jamie’s Framework for Picking the Right Analytics Stack

When Jamie started at Notion, the company was running its analytics on Amplitude. While Quint said it’s an “amazing product” that most companies should be using, he added that people often get into trouble when they try to stretch it past its limits.

“Once you need to dive deep into, say, a retention question … that doesn’t really fit what Amplitude is designed to do,” he said.

To answer this type of question, you’d need to combine different types of data, like purchasing data from Stripe and support data from Intercom, which Amplitude isn’t designed to do.

With these limitations in mind, Jamie thought about the functions he needed from his analytics stack. He describes his thought process as a question-and-answer format, based on different needs.

Need: We need a place to combine different types of data.
Jamie’s Question: How can we store our customer data in a way that allows it to be combined with the other data and queried directly?
Answer: We need a data warehouse that can store structured and semistructured data. Snowflake is our best option.

Need: We need a way to send different types of data to the data warehouse.
Jamie’s Question: How do we import all the data we need into the data warehouse in a way that allows us to transform it all later?
Answer: We need an ELT (extract, load, transform) tool that can import all our data. Fivetran can suit this purpose.

Need: We need a way to get our combined data out of that place.
Jamie’s Question: How can we push that data back out to Salesforce for light attribution modeling, Facebook and Google for custom audiences, or Google Sheets for growth modeling?
Answer: We need a tool that makes it simple to automate the process of pulling data out of our warehouse and putting it where it needs to go. Census is what we need here.

He repeated this process of identifying needs and functions for about three months, and at the end, was left with the exact analytics stack he wanted and needed. He said his combined growth-marketing and engineering background helped in his process, but that it’s not a prerequisite for anyone looking to do the same.

“At Notion, I built the entire analytics stack myself. I was the person doing almost all of the analysis and the person doing most of the marketing,” Jamie says.

“Building the analytics stack was singularly encapsulated in my job, but I don't think it's some huge technical undertaking. Usually, someone on the marketing side is going to have to partner with somebody who's a little bit more technical to figure out some of this stuff.”

The marketer presents the need, the technical person presents the question, and they both find the answer.

For example, if a growth-marketing-oriented founder is trying to find out what the upgrade rates are for customers who call support, they might know that that they would need to combine Stripe and Intercom data, but they’d have to their technical partner to make it happen. The engineer, in turn would turn that need into a more specific question, like, “If you want to combine those types of data, shouldn’t we combine all customer data and make it easy to query?” From there, you both set out to find an answer — a tool that serves the specific function you need in order to answer your retention question.

The Seven Tools in Jamie's Analytics Stack

Jamie has honed his ideal modern data stack down to Amplitude, Mode, Segment, Fivetran, Snowflake, dbt, and Census. Right now, he says these are the best tools to fulfill the analytics functions every founder needs.

“You’ve got to get the data in, got to transform it to be useful, got to be able to analyze it and then have to be able to get it out into other platforms, where it can be used and utilized to add value to the business,” he said, describing the core needs this stack serves.

He broke his thoughts down for us tool by tool:

  1. Amplitude fulfills the need for easy, flexible data analysis. “People need to have simple analysis that doesn't require writing a bunch of SQL.”
  2. Mode fulfills the need for more complex analysis. “People also need visualization for analysis that's written in SQL.”
  3. Segment serves as a “meta analytics layer” which serves to “federate data across all the different tools ... without having to include a bunch of JavaScripts on the page.”
  4. Snowflake is the central place to store all your data. It’s best suited for this function because if its scalability and general ease of use.
  5. dbt helps you prepare this centralized data to make it easier to use. Jamie says dbt is “the transform layer, ... so people can just load all the data into the database/data warehouse and then figure out what to do with it.”
  6. Fivetran sends data into your data warehouse. “Everyone's using a lot more SaaS tools and they need to get data into their data warehouse.”
  7. Census helps you put your data to use. According to Jamie, people need to “get their data out of the warehouse and back into your other tools so you can make it usable.”

As comprehensive as this analytics stack is, Jamie is quick to admit that there is room for improvement. He says that data-quality monitoring is the one big gap that needs to get filled soon.

“There are a number of startups like Iteratively that are looking at doing stuff like that. Companies like Segment and Amplitude are also trying to do that at the same time.”

The Future of This Analytics Stack

Right now, Jamie’s analytics stack is the ideal stack for founders. But it may not always be that way. The key is understanding how Jamie landed on this particular set of tools so you can adapt as technology advances and changes.

“The functionality of all of these tools in my stack is something that people are going to continue to need for an indefinite amount of time,” Jamie says. “I think the question is always 'will something come along that just blows the other ones away?'”

Jamie has a lot more to say on the future of data and his experience working with analytics throughout the years. We think learning from the past is one of the best ways to prepare for the future, especially in an industry that moves as fast as the data and analytics industry.

“I don't know enough to make a prediction about where analytics tools will be in five years,” Jamie says. “I don't think five years ago anyone would have predicted that Google's BigQuery and Amazon Redshift would get beaten by Snowflake.

For the foreseeable future, Jamie’s seven tools—Amplitude, Mode, Segment, Fivetran, Snowflake, dbt, and Census—are what you need. Schedule a demo with Census today and we’ll help you get started on your analytics stack.

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