Safety lies in the data
SafetyCulture is a global software company, headquartered in Australia, that creates solutions for safety and operations management in the workplace. Their flagship product, iAuditor, captures data from sensors and reports from frontline workers so companies with distributed workforces can identify common issues, implement processes to prevent them, and make operational improvements.
“We are a data organisation at the core,” said Agnieszka Hatton, VP of Data & Analytics at SafetyCulture. “Good data is essential for our customers to make well informed decisions about their operational and safety processes."
SafetyCulture experienced rapid growth over the last year, 3xing the amount of data processed daily, and they needed to build a high performing team and scalable data architecture to keep up.
A workflow problem
“We were using LookML for all of our transformations, which just wasn't scalable. There's also a limit to what you can do—no testing, no DAG... it required tons of supervision to ensure alignment with existing architecture," said Oscar Lukersmith, Lead Analytics Engineer at SafetyCulture.
At first the team tried using Airflow to schedule and orchestrate LookML models, but quickly ran into accessibility and architecture challenges. “We had to manually build all the dependency graphs, and we ended up with our transformation layer sitting across two different tools. We were just adding more complexity to a system only a few folks had the skills to operate.” said Oscar.
As a result, business stakeholders often didn't know where the source of truth for the data was. “Where to go for the right data was confusing. It resulted in mistrust and a lot of wasted time” added Agnieszka. “From an internal data team perspective, it also meant we had to redo things all the time. We lacked reusability, which dragged out time to insights and affected morale.”
Oscar knew that part of the solution was technology, but there was more to it. “Half of dbt is the tool but the other half is the workflow it enables,” said Oscar.
While onboarding dbt, the team invested in new capability around data modeling. “We brought in an experienced Data Modeler to work closely with Oscar and the rest of the team to design the future state data model. And then we used that data model to rebuild our data," explained Agnieszka.
With the foundations in place, they set 3 objectives for the year:
- Transform SafetyCulture’s internal data analytics capabilities
- Apply advanced analytics to strategic business use cases
- Empower stakeholders to make informed decisions
Transforming SafetyCulture’s internal data capabilities
“Internal data capabilities is the stuff under the waterline, where the work in dbt is setting us up for success. It’s often the things that the business doesn't see because it's below the waterline but they feel the impact,” said Agnieszka.
Redshift infrastructure
The team redesigned their data architecture in Redshift to increase compute by 50% and disk by 100x at the same cost. The re-platforming reduced their average pipeline query time from 17 to 4 minutes, laying the foundation for the speed that’s essential to the data work.
Data model and transformation layer
Consolidating all of their transformation work in one place, the team designed future state conceptual, logical, and physical data models in dbt. “The work we've done using dbt to improve our data model and data transformation approach has meant that people can actually trust the data. We’ve now got these component parts that we can reuse, and as a result, speed has really improved over time,” said Agnieszka.
BI/Visualization
Most of the internal capability work happened within SafetyCulture’s existing data stack, with the exception of migrating from Looker to Tableau. “People love Tableau, and we’ve gotten some great feedback from our stakeholders in the business,” said Agnieszka—in particular calling out multi-use dashboards, an intuitive interface, and faster load times.
Increasing investment in the data team
SafetyCulture’s data team grew from 6 to 15 people over the last year.
"Existing team members at the beginning were frustrated because they could see there were better ways to work. We needed to invest in the system, process and people capabilities” said Agnieszka.
The work involved changing the operating structure, introducing Analytics Engineer and Data Management roles, and defining how to work together. “Shifting into the dbt workflow was a huge shift. But 3-4 weeks in, you could see the moment when Analysts went from learning each component to seeing the big picture,” said Oscar. “Part of the benefit is from dbt itself. But then the other half is the workflow it enables.”
From January to December, the team’s eNPS went from -20 to +69—an 89 point increase.
“The team was part of creating the solution and it was really the Data Analysts and Data Engineers who drove the change. They created the human process together and are really advocates of dbt now,” said Oscar.
Applying advanced analytics to strategic use cases
One of SafetyCulture’s strategic objectives is to broaden its offering to deliver an operations management platform. “We’re branching beyond our initial safety focus and building a platform that empowers our customers to improve their operations management. To do that, we need to understand how customers manage safety and operations today and how our software enables them,” said Agnieszka.
Mapping current customer trends
To understand their customers, SafetyCulture needed to analyse a massive amount of existing customer data. “We're using AI to better understand our customers, looking at about 150 different customer variables from demographics to product usage and behavior,” said Agnieszka. Answering questions like “who are our customers” and “which customers are most likely to expand or churn,” Analysts who previously only focused on one business area collaborated in dbt and identified 7 customer segments. These segments were applied in Salesforce and used by AEs and CSMs for customer expansion and retention.
Understanding obstacles for first time customers
“One of our biggest challenges was that we had a lot of new users coming into our platform but 97% of our first time users dropped off after 28 days. And we didn't really know why” said Agnieszka.
Combining data analysis and customer interview insights, the team worked across the business to identify the key customer pain points and defined five initiatives to improve the customer experience. Six months later, the number of customers staying on beyond 28 days has more than doubled—from 3 to 6.5%. “The increase in new customer retention has compounding effects on our active user numbers, with a projected 40% increase in MAU,” said Agnieszka.
Exploring commercial models
The team also used dbt to explore new pricing models based on how customers derive value from the product and expand usage.
“We built a scenario modeling tool that enabled us to model different pricing constructs across our whole customer base. What if the pricing model was like this? What would that mean for customer expansion? How would it impact our revenue? We combined that with research, working with Qualtrics to add voice of customer data into the model,” said Agnieszka.
“Underneath that, the whole model was built on dbt tables,” added Oscar.
Empowering stakeholders to make informed decisions
To ensure that data was embedded in daily decision making the team focused on empowering stakeholders to make decisions with that data.
Goal tracking and GTM reporting
In the last 12 months the team developed clear goal tracking across the company. “We worked with GTM and Product teams to develop goals and built the Tableau dashboards that they use on a weekly basis to track performance and to take corrective action,” said Agnieszka.
“Building the data underneath in dbt has enabled having a global unified approach to the way that we look at our data. For the first time, there’s now one way of approaching targets and drilling into global sales pipeline across geographies.” The dashboards inform pipeline, onboarding, and customer success tracking, supporting MAU and ARR growth.
What’s next for SafetyCulture
This year, the team’s strategic focus will be exploring 5-star rating and benchmarking so customers can look at how they perform compared to peers in their industry. Over time, the initiative will involve combining product usage and customer data to provide next-best-action recommendations in-app to enable customers to improve their safety and operations management.
Internally, the team is finalizing its migration from Looker to Tableau, implementing a data literacy program across the business, and determining a ‘fit for purpose’ data governance program. As the team continues to build new data assets, dbt is being used to provide a standardised approach to transforming data to ensure quality, speed and reusability.
