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At a time when manufacturing has become global but remains rooted in outdated, time-consuming, and cost-intensive processes, Fictiv’s digital manufacturing ecosystem, data-driven approach has proven to be a disruptive force. Used by Silicon Valley innovators as well as big brands in robotics, healthcare, automotive, aerospace, and consumer electronics industries, Fictiv has become a trusted partner to bring new products to market faster.
As a data-driven organization, Fictiv relies on product and system data to surface customer insights and gain greater visibility into its vast network of manufacturing partners. These insights help steer product improvements and business strategy, improve system design, and increase workflow efficiency.
Fictiv’s data, business operations, and strategy teams work together to manage a wide range of data responsibilities daily. These range from centralizing data from disparate systems for more in-depth analysis, to developing predictive algorithms, to analyzing qualitative data to gut-check employee intuition and drive data-driven decision making across business units.
As a cloud-first company, Fictiv’s centralized Snowflake data warehouse stores information from dozens of sources and systems that its business teams rely on each day. This includes a CRM, ERP, and its product. Despite this central source of data truth, Fictiv has a relatively small data team that wasn’t able to keep up with the demand for dashboards, reports, and ad hoc analyses using its previous data analytics platform, Domo.
The number of resources required to maintain the platform and underlying data models while simultaneously delivering analytics and business intelligence left business teams constantly waiting for answers.
90% of our users had to export raw data from our old analytics tool to work with it in Google sheets. When you look at the cost of the tools we had in place, it became clear that it was a poor investment.
Jim Ruga
Chief Architect at Fictiv
Because Domo wasn’t a true self-service solution and required a deep technical skillset to uncover answers to everyday questions, 90% of business users had to export data to spreadsheets for analysis. Not only did this scenario diminish the ROI from Fictiv’s investment in its data platform, but it also left the team open to security risks that come from exporting data and sharing spreadsheets outside the data warehouse and the data team’s purview.
In 2019, it became clear that the investment wasn’t paying off. The human resources required to set up, configure, and manage its infrastructure continually wasn’t scalable. Ficitv’s Chief Architect, Jim Ruga, set out to build a more modern data analytics stack. His goal was to simplify analytics workflows, reduce the number of human resources required, and provide an analytics tool that delivered the same spreadsheet experience business teams were comfortable using.
Jim and his team wanted to deploy a new analytics tool that would open up Fictiv’s Snowflake warehouse so business users could quickly find relevant data to explore, analyze, and visualize. Because Fictiv’s business teams generally used Google Sheets for independent analysis in the past, Jim knew he needed to find a cloud-based analytics solution that took a similar spreadsheet approach without requiring the use of SQL to ask questions.
That’s when he discovered Sigma. “Most often, our users took data offline to a spreadsheet to do their analyses,” says Jim. “Sigma offers a simple and intuitive analytics interface based on a spreadsheet. Everyone knows how to use a spreadsheet, so it was natural for them.”
“Sigma offers a simple and intuitive analytics interface based on a spreadsheet. Everyone knows how to use a spreadsheet, so it was natural for them”
Jim Ruga
Chief Architect at Fictiv
Jim and his team love Sigma’s native integration with Snowflake. This simplified the set up and maintenance required to manage the infrastructure, and ensures that queries get run quickly so business teams can get answers on-demand in real-time, without waiting for a data team member to run a report or build a dashboard.
And because Sigma’s visual interface automatically generates SQL, business users that don’t possess coding knowledge aren’t held back from submitting complex queries — such as parsing nested JSON for deeper analysis.
When comparing solutions, another aspect that stood out to Jim and his team was Sigma’s visual data modeling capabilities. This made it easy for the data team to quickly align underlying data models in supporting systems with Sigma, and update as needed without the significant efforts required by other tools.
Jim and his team have seen fast success with the Sigma deployment. Sigma has been quickly adopted by business users, thanks to the spreadsheet interface. It’s also drastically reduced the number of ad hoc requests every week — freeing data team members to focus on higher priorities, like system integration, and the development of algorithms to catch design flaws early.
“Thanks to Sigma, I’ve gotten back 50% of my time so I can tackle bigger data problems and move new initiatives forward. The ability for any user to rapidly answer questions without leaning on the data team has been transformative.”
Franklin Pfaller
Business Analyst on the Business Ops team at Fictiv
With Sigma, Fictiv benefits from deeper transparency and integration across their diverse network of systems. From product usage metrics to vendor performance, material characteristics to industry trends — data from a diverse and wide range of sources is able to be analyzed holistically by the Fictiv team to fuel critical business analysis, develop key success metrics, and shape overall strategy.
“Today, we have an ETL service, Snowflake, and Sigma,” says Ruga. “Our stack is easier to manage with pre-built adapters and tools that optimize the ingestion of SQL and unstructured JSON data with minimum effort. We’ve reduced the number of human resources required and found the renewed focus to deliver critical insights that drive business growth and success.”
Sigma also helps Fictiv continually improve the development of algorithms that prioritize new product features and identify design flaws inherent in its manufacturers’ network. The data insights uncovered in Sigma get fed into the machine learning tools that create the algorithms.
“Sigma insights are the source of a lot of our ability to rapidly discover manufacturability flaws in design. We examine hundreds of thousands —sometimes millions— of different design permutations to discover manufacturability flaws in design rapidly,” says Jim.
These insights and algorithms assist users, help them better understand designs, and ultimately allow Fictiv to drive down manufacturing costs. These cost savings are crucial to the success of Fictiv’s digital manufacturing model and keep Fictiv customers coming back time and again.
“Sigma has ultimately helped us achieve a number of key business outcomes, including the ability to lower manufacturing costs for customers and more effectively prioritize product features based on demand,” shares Jim. “Actionable insights like these make a big difference in customer satisfaction and Fictiv’s overall success.”
