In this episode, Kostas Pardalis sits down with Alex Dovenmuehle, head of data engineering for Mattermost, an open-source self-hosted communication tool that optimizes dev workflows in highly secure environments. Kostas and Alex discuss:
- Alex’s background and experience (2:29)
- Data stack Mattermost is using (9:25)
- How Mattermost built their Data Stack (21:05)
- Using data to understand the story of the customer’s journey (24:58)
- Focus on privacy and security (26:33)
- Practical ways Mattermost is using data (37:14)
- What’s next for data analytics at Mattermost and wrap up (42:45)
Mattermost positions itself as a Slack alternative that’s open source and self-hosted with an emphasis on being privacy-conscious.
Alex arrived at Mattermost after four years at Heroku. While at Heroku, he helped steer the company away from using Segment for user analytics and actually created their own homegrown analytics pipeline using Amazon Kinesis that had a lot of traffic going through it.
Upon arriving at Mattermost, Alex noted that they didn’t have much in terms of a data engineering setup so he came in and built that infrastructure.
Mattermost had also been using Segment when he arrived, but prohibitive pricing had forced Mattermost to turn off all but two percent of the events that they were sending to Segment. After he began at Mattermost, Alex said, “I’ve already gotten off Segment once; I’m going to go ahead and do it again.”
He did more research into RudderStack and concluded that this open source Segment alternative was exactly what they needed. He said that it was a simple change and that it opened up a world of possibilities because of how much data Mattermost could send now.
The data from many different latch points is collected into Snowflake, DBT is used for the modeling, and Looker for the visualization.
Privacy and security are essential to Mattermost and ensures that no PII are being sent to RudderStack.
In collecting data, Mattermost unlocks its value by helping map out the customer’s journey, provide financial modeling and improving user experience. Alex discussed how their data is used to inform financial forecasting at an executive level, provide health of the business reports at a board level, and provide answers for product managers to make the best changes. For example, he pointed out how product managers will use very specific data points like how many messages on average are posted under a given thread to help them determine if the feature can be improved.
For Alex, what excites him about the continued collaboration of RudderStack and Mattermost is the constant development of the customer journey mapping and producing an end-to-end view of things that he can help translate to non-analytics people.
The Data Stack Show is a weekly podcast powered by RudderStack. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.
RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.