More than one million small businesses use ecommerce platform Shopify to reach a global audience of consumers. That includes direct-to-consumer (DTC) all-stars like Allbirds, Rothy’s and Beefcake Swimwear.
But online sellers like these are also ingesting data from platforms like Google Analytics, Klaviyo, Attentive and Facebook Ads, which quickly complicates weekly reporting.
That’s where data transformation comes in.
dbt and Preql
As the name implies, data transformation tools help convert data from its raw format to clean, usable data that enables analytics and reporting. Centralizing and storing data is easier than it’s ever been, but creating reporting-ready datasets requires aligning on business definitions, designing output tables, and encoding logic into a series of interdependent SQL scripts, or “transformations.” Businesses are making significant investments in data infrastructure tooling, such as ingestion tools, data storage, and visualization/BI without having the internal expertise to transform their data effectively. But they quickly learn if you can’t effectively structure your data for reporting, they won’t get value from the data they’re storing—or the investment they’ve made.
The space includes two major players: dbt and startups.
Founded in 2016, dbt “built the primary tool in the analytics engineering toolbox,” as the company says, and it is now used by more than 9,000 companies—and it is backed by more than $414 million.
But dbt is a tool for developers at companies with established analytics engineering teams.
Preql, on the other hand, is a startup building no-code data transformation tool that targets business users who might not have expertise in programming languages but who nevertheless need trusted, accessible data.
Preql’s goal is to automate the hardest, most time-intensive steps in the data transformation process so businesses can be up and running within days as opposed to the six- to 12-month window for other tools.
“We built Preql because the transformation layer is the most critical part of the data stack, but the resources and talent required to manage it make reliable reporting and analytics inaccessible for companies without large data functions,” said Gabi Steele, co-founder and co-CEO of Preql.
The startup is therefore positioning itself as an alternative to hiring full analytics engineering teams solely to model and manage business definitions—especially among early-stage companies that are first building out their data capabilities.
In other words, Preql is the buffer between the engineering team and the people who actually need to use the data.
“Data teams tend to be highly reactive. The business is constantly asking for data to guide decision making, but in the current transformation ecosystem, even small changes to data models require time and expertise. If business users can truly manage their own metrics, data talent will be able to step out of the constant back and forth of fulfilling reporting requests and focus on more sophisticated analyses,” said Leah Weiss, co-founder and co-CEO of Preql.
But that’s not to say dbt and Preql are bitter rivals. In fact, they are part of the same data transformation community—and there’s a forthcoming integration.
“One way to think about it is we want to help the organizations get up and running really quickly and get the time to value from the data they’re already collecting and storing without having to have the specialized talent that’s really well versed in dbt,” Steele added. “But as these companies become more sophisticated, we will be outputting dbt, so they can leverage it if that’s the tool that they’re most comfortable with.”
A Closer Look at Preql
The startup raised a $7 million seed round in May, led by Bessemer Venture Partners, with participation from Felicis.
Preql collects business context and metric definitions and then abstracts away the data transformation process. It helps organizations get up and running with a central source of truth for reporting without having a data team or writing SQL.
Preql reads in data from the warehouse and writes back clean, reporting-ready schemas. It partners with data ingestion tools that move data from source applications into the warehouse such as Airbyte and Fivetran and cloud data warehouses like Snowflake, Redshift and BigQuery. For businesses who consume data in BI tools, it also partners with Looker, Tableau and Sigma Computing.
DTC Target
Preql is initially focused on the DTC market in part because the metrics, such as cost of customer acquisition (CAC), conversion rate and life-time value (LTV), are standardized. They also tend to have lean operations.
“We’ve found that these companies are working really hard to download data from disparate sources—third-party platforms that they use, Shopify, their paid marketing platforms—in order to get a sense of even basic business health and performance,” Weiss said.
They also tend to use manual reporting processes, which means “it’s often an operations person who’s downloading data from a bunch of sources, consolidating that in spreadsheets, making a bunch of manual interventions and then outputting weekly reporting or quarterly reporting,” she added.
But much of what these companies want to measure about performance is consistent and a lot of the data sources are structured the same way.
“With Preql, we were able to make some assumptions about what we wanted to measure with the flexibility to customize a few of those definitions that are specific to our business,” added Cynthia Plotch, co-founder at Stix, a women’s health essentials ecommerce site. “Preql gave us clean, usable data for reporting. We were up and running with weekly reporting within days, saving us months of effort if we had to invest in data engineering teams.”
Data Transformation in 2027
Steele and Weiss believe the next five years will be about “delivering on the promise of the modern data stack.”
In other words, answering questions like: Now that we have scalable storage and ingestion, how can we make sure we can actually leverage data for decision making? And how can we build trust in reporting so we can build workflows around it and act on it?
This is because a lot of companies struggle to move on to predictive analytics and machine learning because they never solved the fundamental issue of creating trusted, accessible data.
What’s more, Preql believes the next phase of tools will go beyond building infrastructure to deliver more value as data talent sits closer and closer to the business.
“Data analytics will only get more complicated because the number of data sources is growing, along with their complexity, and the need is becoming more acute for real time results. And the more data you have, the more granular the questions become and even more is expected of it,” Amit Karp, partner at Bessemer Venture Partners added. “I think we’re in the very early innings of what’s going to be a very long wave—five, ten or even 20 years down the road. It’s a giant market.”
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