Moment Shines With A Panoply Powered Data Stack

 

With Looker and Panoply, Moment was able to migrate off RJMetrics and build a robust, scalable business intelligence platform. Now data is democratized across the entire company for better daily business decisions.

Moment is a company comprised of photographers, filmmakers, designers, and engineers who believe the world is a better place when you get lost and follow your camera. As Moment has grown from a small company to a booming gear and experience vendor, data has become a realized value that the company needed to harness.

For the last few years Moment had been using RJMetrics but was starting to feel constrained and frustrated with the flexibility of the tool. The Moment team was collecting more data and had more product offerings than ever before. It was imperative that Moment restrategize their data program to include a more effective and accommodating business intelligence tool. Moment needed to find a new cloud-first, scalable solution. For that, they turned to Panoply and Looker.

We sat down with David Hahn, Technical Lead at Moment and Andrew Stoner, Commerce Lead at Moment, and Aron Clymer of Data Clymer to explore the journey that has led Moment to have a mature Looker and Panoply-powered data stack in place.

What is Moment?

We started on Kickstarter making lenses for phones. Now we do much more than that.

We share our best tips on where to go, what to carry, and how to up your photography (or filmmaker) game. We host photo adventures with pro guides to the places you need to visit. We sell the gear that creatives recommend when traveling, shooting, or hanging in the city. We ship for free (worldwide) and deliver before you take off. We set-up your gear, with pro guides, even if you didn’t buy it from us.

What was the before/after in terms of the data stack you had in place before your present solution?

Previously, Moment’s data stack was based on RJMetrics, which served as our company’s ETL, data pipeline solution and business intelligence tool. To start, a clear win for us was separating those components of the data stack into Panoply, as the data pipeline, and Looker as the business intelligence / data visualization tool we always knew we needed.

Isolating these two key aspects of our data stack was a huge win for us. Our biggest challenge with RJMetrics was not having control over the modeling layer and basically everything - every change was a matter of weeks and we would end up in their queue.

Removing that hurdle empowers us to quickly create dashboards, get insights and make critical business decisions. Ultimately, we outgrew RJMetrics - and as our company became more data-literate, Panoply and Looker fit the bill.

We can now zero-in on our data needs and facilitate anyone at our company who works with data aggregation to build business intelligence off of the data we collect. Everyone can easily jump into the data explorer and dig into sets of data without needing a data scientist on hand.

Before we started our data stack project, we saw the customer story of Shinesty and how that company made the leap from RJMetrics to Panoply/Looker. Shinesty’s success and our similar frustrations with RJmetrics limitations were a major encouragement in making this change.

What about Panoply did you like - and what other vendors did you consider and why didn't you choose them?

For Panoply in particular - it was the price that drew us in. As for Looker - we were already paying for RJMetrics and we were looking to keep prices from inflating too much. We decided against solutions such as Fivetran because Panoply had the built-in connectors we needed at a reasonable price.

Which teams inside Moment use data?

Everyone. On a weekly cadence we have each person at Moment answer the question, “What did you learn this week?” It’s our forcing function to keep each team at Moment tied into what’s happening across the company.

What have been Moment’s big wins from the new Panoply-based data stack?

There’s really two core wins with Panoply and Looker coming from RJMetrics: Panoply had significantly more built-in connectors than RJmetrics, so integrating with more of our data sources was an easy win to give us a more complete picture with our data.

One of the more game-changing Panoply connectors we’re using right now is Google Sheets - that has cut down on a lot of file uploads we used to have in RJMetrics. While we still do some file uploads (which Panoply also supports), we’ve been able to drastically reduce the number of file uploads we rely on and improve efficiency in getting access to our data.

Describe your onboarding with Looker and Panoply. You had Data Clymer as an integration expert - tell us how easy it was to get started.

For us, having Data Clymer lead the initial buildout and be available to answer questions was super helpful. Getting our team on custom training calls with Data Clymer’s analysts really helped to speed up the transition off of RJMetrics.

For anyone looking to explore a new data stack - Panoply/Looker is a very intuitive platform and the added insight into the modeling layer and data warehouse is very helpful to move quickly in exploring new data sets across your customers journey.

 

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