Giving every gardener a green thumb
Connected IoT Devices
SOLUTION: Personalization, predictive maintenance
PLATFORM USE CASE: Lakehouse, Delta Lake, data science, machine learning
“Databricks Lakehouse has been the key to gathering new insights of our autonomous mowers and also to predict maintenance issues and ensure our service teams are equipped with the right insights to fix problems before they significantly impact our customers.”
— Linus Wallin, Data Engineer, Husqvarna AI Lab
Whether you’re a commercial landscaper maintaining a football field or a homeowner working in the garden, the task of keeping a lawn perfectly trimmed can be a pain. Husqvarna is focused on reshaping that experience by providing peace of mind for all who rely on powered landscaping equipment. With such a varied customer base and hundreds of thousands of pieces of IoT-equipped machinery — from lawn mowers to chainsaws — the company struggled to harness their data to deliver on a range of use cases, from self-driving mowers and predictive maintenance of equipment to personalized customer support and more. With Databricks Lakehouse, Husqvarna has started to transform their business by using data and AI, resulting in new product innovations and personalized customer experiences that are revolutionizing the industry one lawn at a time.
Overcoming obstacles to AI innovation
Industries of all kinds have felt the impact of AI — including the agricultural sector, where individual and commercial gardens alike are prime candidates for automation, better equipment care and personalized customer experiences. At Husqvarna, their mission is to modernize landscaping by tapping into data insights and AI to optimize the performance of their wide variety of outdoor power products — including chainsaws, trimmers, brushcutters, cultivators, garden tractors and autonomous lawn mowers — and to improve the overall customer experience.
As they embarked on their data-driven journey, Husqvarna lacked the resources and data infrastructure needed to not only access and unify their data, but also share and analyze it to support the business solutions they envisioned. With the native AWS solutions they were using, maintaining compute resources was highly complex and resource-intensive. And Jupyter notebooks running off individual laptops weren’t scalable, making it challenging for data teams to explore data at scale, and to collaborate and share insights with the wider team. As a result, decisions were made in silos and on gut feeling rather than by depending on the data.
Additionally, disparate data meant that professionals at Husqvarna were limited in what they could do with it — and the little they could do was expensive to maintain. Often, along with siloed data came siloed skill sets, preventing the type of collaboration and streamlined processes needed to continue seeing success.
“A lot of people felt blind,” explained Linus Wallin, Data Engineer at Husqvarna AI Lab. “When we thought about what to do with a product, the outcome was often opinion based, or what was at least seemingly common sense. It wasn’t a strategic way to operate and was not at all scalable. But we didn’t have the ability to democratize our data for other teams to explore themselves.”
For many reasons, the Husqvarna team knew their data architecture was in need of an overhaul. “Our old system was extremely difficult to maintain,” said Wallin. “Everything took an incredible number of man-hours to do, so managing the cost of our clusters when they got too large, bringing those clusters back up when people needed them in a timely manner, and doing all of that manually — it was exhausting and horrible. We needed a better path forward.”
Data democratization drives innovation
Once Husqvarna integrated Databricks Lakehouse Platform into their data workflows, ultimately centralizing their various data sources into one location, a number of issues seemed to evaporate, and the benefits exceeded the team’s expectations.
For starters, Husqvarna was able to create more personalized experiences for their customers — ones that are better tailored to their needs based on the environment and the uniqueness of green spaces — and it will eventually mean predictive maintenance. And this allows them to come up with new solutions to improve customer experiences with their products. For example, Husqvarna now has a solid data pipeline that prepares data for map generation of a green space, based on collected runs completed by their autonomous lawn mowers. This enables the company to gather enough data points to generate zones for their autonomous mowers to help them avoid obstacles or non-grass areas and adjust for cutting frequency, slopes, cut heights, etc.
“Databricks Lakehouse has been the key to gathering new insights of our autonomous lawn mowers,” added Wallin. “And now we can predict maintenance issues and ensure our service teams are equipped with the right insights to fix problems before they significantly impact our customers.”
Now that Husqvarna is able to make better use of their data overall, they are expanding their roadmap for data and AI to also improve business operations and supply chains. For example, management now has an overview of everything from production volumes to service contracts to IoT through an easy-to-understand visual interface, which wasn’t possible to produce with the company’s former technology.
The future of data-driven manufacturing
Databricks has had a huge impact on Husqvarna’s business, including delivering the ability to set meaningful KPIs such as the number of connected devices.
“Now we actually know how many machines are connected via Bluetooth on the fleet side at any given time,” said Mats Persson, Systems Architect at Husqvarna. “We can track our machines very closely and use the insights to understand how to improve all facets of our business from product development to customer service.”
The successful use of Databricks Lakehouse has enabled Husqvarna to not only build and sell products that are desirable to people, but also to focus on creating a more engaging and targeted experience overall. “We are still early in our data-driven journey, but we are finally in a position where we have gone from making product decisions based on gut feeling to making informed decisions that we can be confident in,” concluded Wallin.