SmartSense by Digi, a global leader in highly-scalable IoT solutions, was formed through the acquisition and integration of 4 companies, and employs 135 people. Their Engineering organization is made up of 50 engineers across multiple globally distributed teams. The IoT platform and solutions they produce help their customers understand the physical environments that they care about to ensure safety, compliance, and efficiency. And they do this at scale by collecting over 24B data points across more than 61K customer sites.
As a company, SmartSense by Digi made a strategic decision to develop a new unified platform, combining best in class features from the various acquired companies. In order to deliver this new product, Dan Bachiochi, CTO at SmartSense by Digi, and the executive team needed to make trade-off decisions, including a significant shift of resources from legacy products. To make these decisions, Bachiochi and his team needed metrics to understand how much of the engineering team could focus on the new platform while still supporting legacy customers. They also needed to measure whether the engineering teams were actually shifting their work to meet their new platform objective. “I spent a lot of time, nights, weekends, sifting through information, and systems, talking to people to pull the reports needed to have informed discussions. In addition to being labor intensive, some of the data was inaccurate because it was based on what I thought was happening.” Bachiochi stated.
Given the heterogeneous makeup of the teams from 4 acquired companies, Bachiochi was also trying to solve an efficiency challenge. So he goaled his organization with aligning on standards to drive consistency across engineering, “We needed to use the same tools and processes across distributed engineering teams. And I needed a way to measure that this was happening.”
Bachiochi recalls discussing these accuracy challenges with many of his industry peers, “We did not want to settle for ‘this is as good as it’s going to get.’ We knew we could do better and be more strategically metric driven at the team and executive level.” Bachiochi and his team needed these measures to be connected to the work of individuals, but did not want to focus solely on individual metrics. “Focusing solely on individual performance metrics leads to bad org dynamics where you are drawing battle lines between Engineers and the Management team,” Bachiochi commented.
Bachiochi was about to write code himself to solve the problem, but this would have just automated sub-par reporting. Then he came across Jellyfish. Bachiochi recalls, “During our search, I remember thinking, ‘finally, here’s a company in Jellyfish trying to solve the problem I have, the way I want to solve it.'”
Bachiochi remembers the impact of Jellyfish as soon as he started using it, “I was immediately able to achieve one of my objectives to clean up our process and tool usage. By tapping directly into Jira and other system data, Jellyfish made process and tool standardization measurable.” This sped up the process of standardization across heterogenous teams, and let them optimize their teams while maintaining a lean management structure. Bachiochi has only four people on the team dedicated to management, and Jellyfish provides him with the tools he needs to focus on leading teams that are as productive as possible.
Jellyfish also provided the metrics-driven insights required to drive strategic decisions related to release of their new unified platform product. “Jellyfish revealed that we were nowhere near where we wanted to be with allocation of resources to the new platform,” Bachiochi said. In the first couple months, the data revealed that engineering was between 40% and 55% allocated to developing the new platform, well below the 70% they felt they needed to release the new platform. Armed with this newly accurate data, Bachiochi had productive conversations with the executive team related to trade-offs between roadmap, maintenance and support. They decided to shift the majority of resources to the new product while still supporting their existing customers, and invested in a development partner. Jellyfish showed these decisions boosted engineering resource allocation to the new platform to a high of 76%, a net gain of 36%. And they have consistently seen an average gain of 21% since implementing Jellyfish.
Jellyfish helps Bachiochi get the most out of his engineering teams while staying aligned with the executive staff, “My executive team was already asking for the type of insight that Jellyfish provides. I had thought about the DIY approach, but it would have taken longer and been less accurate. Jellyfish was made for this.” Bachiochi concluded.
SmartSense by Digi has successfully released their new unified IoT platform to the market much sooner than they would have without the insights Jellyfish provided.
Bachiochi now has the tools in place to continuously improve their engineering operations. These continuous improvements include the re-allocation of resources to the new platform, bringing on a partner development team in South America, and most recently moving to full-stack teams. All of these changes are aimed at improving engineering performance against strategic objectives, and Jellyfish provides the measures that tell Bachiochi if it’s actually working, “I am able to rapidly make changes, and see the positive (or negative) impact within weeks rather than months.”
Going forward, Bachiochi and the other executives at SmartSense by Digi have set a strategic objective for Engineering to be more predictable. They will use Jellyfish to help plan and predict future work, validating that they are delivering on strategic priorities, “To be predictable, we need to know that what we’re doing is scaling, and actually working, and quickly identify when it isn’t working. Jellyfish not only quickly shows us what’s working, but also gives us confidence to scale the new ideas that work,” Bachiochi concluded.