RuffleButts Fashions a Modern Solution for Faster Data Insights

When this growing children’s apparel retailer wanted to redo its data wardrobe, it found the perfect fit with Snowflake, Fivetran, and Coalesce

Flower Mound, TX
Top Results:
to build first star schema with Coalesce
to achieve production data warehouse of 3 subject areas, including dashboards
to productionize first subject area vs. competing bid’s 3 month estimate

“I love everything in our stack, but Coalesce is the secret sauce because it allows us to change things very quickly.”

Matt Tischler
CFO, RuffleButts

Say the name “RuffleButts” aloud in a room full of people and, in addition to the inevitable giggles, you are sure to get nods of recognition from many parents. Founded in 2007, the popular children’s apparel company sells its clothing for babies, toddlers, kids, and adults on its website, marketplaces like Amazon and Target, and through large national retailers, such as Nordstrom.

The company name comes from its original signature item: the RuffleButt diaper cover, which founder Amber Schaub based on the classic ruffled bloomers that were fashionable for children many years ago, and that she was frustrated she could find nowhere else. Schaub and her husband Mark eventually expanded the company to sell all types of girls’ apparel, then added another dedicated clothing line for boys called RuggedButts. The company’s data journey is a unique one, as it was the chief financial officer—not normally a technical role—who was instrumental in developing the data foundation RuffleButts needed to grow.

Building a new data foundation


Data locked in the ERP system, with reports manually updated
No easy up-to-date views on sales or inventory data
Difficult to make data-driven decisions because data was slow to acquire and impossible to visualize in Excel

Matt Tischler is the Chief Financial Officer of RuffleButts, and has been with the company for two and half years. When he first started, there was no real data analytics program to speak of beyond a few spreadsheets updated from CSV files. But the company had an extensive product catalog even then, anywhere from 8,000 to 10,000 active SKUs, meaning there was a lot of data to parse in order to better understand the business.

The company had all its sales data in NetSuite, which Tischler used to monitor the day-to-day business. “The first question I always wanted to know was, how much did I sell yesterday across all channels of distribution?” he says. Tischler also needed to know how the business was doing in other areas, and realized he needed to modernize the company’s data stack in order to do the kind of data analytics the business required to grow.

Having already been involved in a number of large-scale data projects, Tischler knew he needed solutions that could easily scale. “We wanted to start small and build a platform that we could then bolt on to,” he says. “I think of it as taking a ‘Lego’ approach—let’s build something to start, and then we can attach more things to that, or interchange them over time. So that’s exactly what we did.”

Working prototype in a weekend


Brought in technology consulting firm Resultant to help with data strategy
Developed early prototype using Snowflake, Coalesce, Fivetran, and Tableau
Tested new solution on a number of pressing problems to get some quick wins

To get started, Tischler connected with Michael Tantrum, National Sales Director with Resultant, a consulting firm that works with each of its clients to strategize the best technology solutions to solve their specific business challenges. “One of the first things that Matt said to me was ‘I’m a bit skeptical because I’ve seen this go bad before,” Tantrum recalls. “‘My IT team is just two people—do not make their life hard. I don’t want you adding to their workload.’”

“I’ve learned from what I’ve seen in the past to avoid a prolonged project that’s going to take forever with no results,” says Tischler. “That was the last thing I wanted. I needed a quick and nimble solution, all in the cloud, that was dummy-proof because it was going to be me running it for the next year until we could hire someone with a data engineering background.”

Tantrum knew what they needed was a SaaS solution to make things as easy as possible. Tischler had already spoken with another consulting firm, which had quoted him as long as three months to get a solution spun up and ready to start using. But Tantrum assured Tischler that the light, iterative solution he had in mind could be ready in under a week.

Tantrum’s plan was to build a data stack for RuffleButts using only plug-and-play SaaS solutions: Snowflake as the data platform, Fivetran for ingestion, Coalesce for data modeling, and Tableau for reporting and dashboards. He spent the weekend pulling together a quick-and-easy prototype that he and Tischler could take for a test drive and see if they could use it to address some pressing problems.

Winning over company leadership


Built first star schema with Coalesce in just 7 hours
Achieved a production data warehouse of 3 subject areas, including dashboards, in 300 hours
Productionized first subject area in just 1 week versus 3 months offered by a competing bid

The prototype showed promise right out of the gate. “From turning on Snowflake, Coalesce, and Fivetran to having the first star schema took just seven hours—of which five hours was spent trying to find the right data in NetSuite,” recalls Tantrum.

In the beginning, Tischler didn’t know everything he was going to need, so he and Tantrum approached it subject area by subject area, the first being sales. They got started with a project reporting on Fourth of July weekend sales, got a quick first win, and moved on to the next area where they could have a positive impact. “It’s better to have those quick wins and keep things very modular than to say, ‘We’re going to spend three years in a cave and kind of have a big bang at the end,’” says Tischler. “You need to show value along the way. Most projects are multi-year, but as I worked with the Resultant team it was more like multi-week.”

Even though he was not a technical data engineer himself—just a CFO who understood data—Tischler was soon able to take over the project and do everything himself. It took him just 300 hours to build out the full production data warehouse he needed, including the Tableau dashboards. “The total cost for the entire build out was less than the other guys quoted me just to build the first module,” he says.

By starting with this low-risk, low-cost approach, Tischler was able to help ease any anxiety the company’s leadership team might have had in investing in a new solution. He recalls one experienced board member warning him, “I’ve done this wrong in the past and it cost me two to three years, so I just want to make sure that you’re doing it properly.” Tantrum concurs, adding that “If you get it wrong, data warehouse projects can be the iceberg on which CIO careers die.” His best advice for mitigating risk? “Prototype, iterate, fail fast.”

Before the new data stack was in place, Tischler found that questions about business metrics he was asked in board meetings could take him up to a week to answer; today, he’s able to respond to these types of questions immediately with accurate, real-time data. “At the end of the day, I needed this thing to be useful,” he says. “We had a lot of questions people in the business—including myself—were asking, and we needed a very structured result very quickly.”

“Now the board can get numbers at the pace they need them in order to measure and manage the business,” says Tantrum. “All for a small investment of 300 hours, some software, and a bit of Snowflake compute—as opposed to loading ongoing overhead onto the IT team, employing more staff, and building more infrastructure.”

In the end, Tischler appreciated not just the flexibility of the solution Resultant helped him put in place, but also the nature of their working relationship itself. “It’s important to have the right partner who understands the iterative nature [of this kind of project], and who’s not going to get annoyed with my never-ending requests for changes because I didn’t get the requirements right the first time,” he says. “I think the more flexible you can be in every situation, the better.”

As for what the future holds for RuffleButts, the company plans to continue expanding its offerings beyond children’s clothing into adult apparel. And the robust, new solution in place will allow the company to add more data, more subject areas, and more analyses for wider business questions, and to do all that in a highly time-efficient and cost-efficient manner, saving the company money—something that any CFO would smile about.

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