Edition #32 – May 4, 2020
Originally sent via Mailchimp
Good morning product analytics friends 👋
Well I’m not going to reveal anything that you don’t already know, but the folks at Fishtown Analytics raised their first round of money to keep up with this growing community of annoyingly-intense dbt practitioners. I just keep on talking about it, and with quarantine in effect, my kids are looking forward to going back to school.
Anyways, I got a longer take on that below, but I also wanted to share that I’ve started a meetup for the dbt community of Ottawa-Gatineau (that’s in Canada btw). The idea is to of course talk about dbt, but also everything that is within the realm of analytics engineering and analysis using modular and sometimes open-source tools. I’ve started
begging inviting people in my region to join, but this is going to be a labor of love as I think I might just be the sole practitioner of dbt in my region.
Anyways, a first meeting has been scheduled for May 21st at 3pm EST. This is to be a virtual event (duh!) and I do want to extend the invitation to all of you who are interested in learning more about dbt and want to join a community of analytics practitioners who are curious about dbt, and also the practices, tools and techniques that are involved in a modern approach to analytics. I’m also opening up the invitation to the seasoned veterans who are interested in helping out getting this thing off the ground.
The agenda is loosely defined, but it’ll probably involve a quick intro, demo and conversation about dbt and ecosystem. I’m also having a few conversations with some of those veteran dbt practitioners to join us and correct all the false claims I’ll be making.
With that, on with the 32nd edition of the Product Analytics newsletter!
What has been my highlight?
Fishtown Analytics, makers of dbt, just announced their first round of fundraising – $12.9 million led by Andreessen Horowitz. This is important news because dbt adoption is growing exponentially and the good folks at Fishtown had a challenge of keeping up with such excitement.
As Tristan Handy, co-founder and CEO of Fishtown Analytics says, this announcement is not about growing their company, but fueling their mission to elevate the analytics profession. “Fishtown Analytics exists to support the dbt community, not the other way around”. Seeing how that team has put together and nurtured such a great community around this amazing product, I’m excited about the future!
As a dbt practitioner myself for the past 2-3 years, this is wonderful news because I know how transformative it is whenever we at Lantrns Analytics introduces dbt into a client’s data stack. It makes data integration and transformation powerful, flexible AND accessible. It also introduces a lot of the devops practices that makes your data warehouse reliable, easy to change and fast to deploy.
I’m also very proud that Lantrns Analytics is a partner of dbt. Our focus is on product analytics, but there is such a great ecosystem of partners that can support your needs, whatever they are. Not only is adopting dbt great in itself, but you also get to be part of an amazing community of really smart data practitioners who are just itching to help everyone get the most value from their data.
Growing your product with the help of data.
Just reading the title bored me out of my mind. But I fought through the first few paragraphs and finally understood where this piece was going… and a little lightbulb lighted up.
I’ve talked about data privacy in general before and GDPR specifically, and I always thought that the main benefit of enforcing strict data privacy was to empower your users. I still do. But there’s another advantage, that of data discovery.
With regulations such as GDPR, you “need to track the data [you] have, where it came from, who was allowed to modify it, and how it was modified. If [your] dataset merges multiple data sources, [you] have to track those other sources. [You] need to be able to find and delete data on short notice if a customer requests it.”
Turns out that data scientists need the same thing. Data discovery is having transparency into your data, the same way you should have transparency to enforce data privacy.
The author then talks about the open source project Amundsen which was started to enable data discovery at Lyft. I have zero knowledge about Amundsen, but if you’re like me and that topic did switch on a little lightbulb in your head, I guess having a look at this project would be the next logical step.
Factory operations to transform data into analytics.
I know, I know, I’ve been writing a lot about Great Expectations lately. I do feel like this provides additional coverage to analytical stacks and I’m still trying to wrap my head around how to best use it in my projects. It’s still a early-stage project and the community is slowly building, and this article is a great example of how this project and community is maturing as it was contributed by someone outside the Great Expectation’s core team.
If you’re in the same boat as I and are looking for a good clean example of how to use GE from end-to-end, than this is a well-written and super-clear tutorial by Ian.
Deriving insights from your product’s data.
There’s been plenty of articles on attribution modeling lately (from dbt and Rittman Analytics) and this one covers another angle. I don’t have experience with Snowplow, but if that’s your thing, then this Snowplow-specific approach to attribution modeling could be your thing.
Snowplow puts you in control of your attributing marketing spend to outcomes. You decide what data goes into your attribution model, you decide what counts as a marketing touch or a conversion across all your channels and platforms, you decide what attribution mechanism you want to use, and you decide how this information is displayed to the various stakeholders in your business.
I’ve worked on an attribution modeling project this week and it was a breeze using dbt’s Segment package and their attribution modeling guide. I’ve talked about this with some other data folks, and it seems to me like we’ll start seeing the emergence of a cool ecosystem of packages and guides to cover all sorts of data integrations and analysis. This one is another recipe to add to your book.
What’s happening the product analytics market.
by Jan Kyri
This list is extensive to say the least. I thought I had a somewhat good overview of BI products out there, but turns out, I really don’t. If you want to look at alternatives to some of the tools you’ve adopted half-heartedly, then there’s probably a better option out there.