The Product Analytics Newsletter

Data Engineering For All

In this 15th edition of the Product Analytics newsletter, we explore how there seems to be a trend to democratize data engineering tools and allow less technically-inclined analysts (and product owners) set up their analytical stack and get access to rich product insights.

(Source: Coriers)

Good morning product owners!

I hope you are all enjoying the Summer The rhythm is a little slower, people are more relax and it’s usually a good time to try new things, to experiment.

This is our 15th edition of the Product Analytics newsletter and preparing those newsletters is probably the most enjoyable part of my week (professionally that is 🙃). But I do want to keep this thing interesting for you and I think it’s also important that this newsletter evolves.

To that point, I’m doing things different today. I’m just focusing on one subject that caught my attention this week. Not sure if that’s to be the new format, but I just felt like giving it a try 🤷‍♂️

Like it? Hate it? Let me know by hitting the Reply button and giving me your feedback.

With that, on with the 15th edition of the Product Analytics newsletter.

Olivier
@olivierdupuis

Going Beyond Single SaaS Tools

I’m part of two awesome PM Slack communities: Mind The Product and Product Collective.

It’s not uncommon for me to see questions such as: “Hi! From your experience what do you think which are the best Analytic tool for Product Managers?”. Most common answers are, you guessed it: Amplitude, Mixpanel, Heap and of course Google Analytics.

Inevitably the discussion turns to how they differ from one another, how there ain’t one tool that answers all questions, how some of them are better for a specific stage of your product’s growth (e.g. Google Analytics is a great tool to understand acquisition, that’s it), etc.

Debate follows, duals are declared, riots ignite, eternal chaos sets in, etc.

I think that many of you have already experimented and adopted out-of-the box product analytics solutions such as those BI tools. We all have our favorites. But as you are scaling your product, they leave you wanting for insights that aligns more with your strategy and drives your product improvement process.

This is where you start considering building your own analytical stack

Building Your Product Analytics Stack

We’ve talked at length about building your stack. In fact this is what we at Lantrns Analytics do all day. You can definitely learn more by reading our guide on product analytics and have a look at how our Product Analytics Core packaged service works.

As our own product analytical stack shows…

…there are a few key elements required to start having access to rich data-driven insights. At the core of it are the Data Loader and Data Warehouse layers where data is being extracted from your product’s systems, transformed and stored for consumption.

Those are really the 2 main layers where data engineers are involved in. And this is where some of the tools that live in that space are starting to open themselves up to more than just the data engineers.

Product Roadmaps of Segment and dbt

First encounter I had with this was while I attended Segment’s product roadmap presentation given by Tido Carriero, their Chief Product Development Officer.

(As a side note, not sure if I can share this, but here’s a recording of that presentation )

Besides a bunch of goodies that made the geek in me drool with anticipation (privacy classification, Personas workflows, Protocols tracking plan improvements, etc.), one of them caught my eye – Visual Tagger.

I couldn’t find any further info about this “announcement” on the world wide web, but from that presentation (starts at 39:11 in the video link above), it seems to be a Chrome extension that will allow non-technical people set up tags by not having to integrate javascript directly within the page themselves. How that works exactly is unclear to me, but that’s the promise.

It is still a bit subtle a move from Segment to definitely say that they want to open up access of their tool to less technically-inclined analysts, but it is interesting that they recognize that need and are building such a tool to make Segment accessible to all.

Another product that is more boldly taking that route is dbt. I know I often talk of dbt, but to me this is a game-changing tool in analytics as they are making the important ETL function (it goes beyond ETL, but that’s besides the point) easy to do, scalable and enjoyable.

To be honest, I didn’t see it coming when I started reading the 0.14 release announcement from Drew Banin (co-founder and data scientist at Fishtown Analytics).

Here’s what their Utopian view of dbt and analytical ecosystem looks like…

In their own words:

“If we’re going to continue to elevate the analytics profession, then we need to make dbt more accessible to the power users of the future — data analysts.”

For them, that means start offering tools through their dbt cloud platform that will allow less technical analysts to start building ETL easily, but also that will allow teams to more effectively collaborate.

A Trend?

I feel like this is awesome news for product owners!

There are multiple milestones in the product analytics journey. But going beyond SaaS product analytics tools such as Amplitude represents a milestone that is hard to reach. You can hire for that data engineering talent or work with external agencies such as ours, but there should be a way for product owners to start discovering the benefits of more advanced product analytics without going All In just yet.

We see other companies identifying that trend and wanting to make advanced analytics more accessible. Tableau’s Data Prep and Conductor is already investing in that space. And with Tableau being acquired by Salesforce, that’s a big market of users that could benefit from easy-to-use, powerful data engineering tools.

How will other companies that are recognizing the changing landscape of analytics and offering new tools will react to that trend? Of course they should start by offering tools that will be adopted by data engineers, but how will they plan to go beyond those user’s adoption? This is important as if they don’t, someone else will.

What do you think? Are we starting to witness a “democratic” revolution in the analytics world? Are you, as  product owners, excited about those possibilities? Do you see this as an opportunity to start investigating and maybe investing in more rich product analytics?

Would love to hear about your thoughts. Just shoot me an email at odupuis@lantrns.co.

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