What Preql Is Transforming Data Transformation

What Preql is Transforming Data Transformation

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by Alan Jackson — 2 months ago in Review 4 min. read
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Shopify is used by more than 1 million small businesses to reach a worldwide audience of customers. This includes all-stars in direct-to-consumer DTC (direct-to-consumer) like Allbirds, Rothy’s, and Beefcake Swimwear.

Online sellers such as these also consume data from platforms such as Google Analytics, Klaviyo, and Attentive — which can quickly complicate weekly reporting.

Preql Transforms that Data in This Video.

Preql and Dbt

Data transformation tools, as the name suggests, convert data from raw format into usable data for analytics and reporting. Although centralizing and storing data has never been easier, creating reporting-ready datasets involves aligning business definitions and designing output tables. Encoding logic is then converted into a series of interdependent SQL scripts or “transformations”.

Companies are investing in data infrastructure tools such as data storage and visualization/BI without the necessary expertise to transform their data. They quickly realize that if your data isn’t structured correctly for reporting, you won’t get any value from it.

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Two Major Players are in The Space: Startups and Dbt.

Founded in 2016, the company dbt “built a primary tool in the analysis engineering toolbox,” and is used by over 9,000 companies. It is also supported by more $414 million.

However, dbt can be used by developers in companies that have established analytics engineering departments.

Preql is a startup that creates no-code data transformation tools for business users. It targets people who don’t have programming skills but still need reliable, accessible data.

Preql’s goal is to automate the most difficult and time-consuming steps in data transformation so businesses can get up and running in days, rather than the six- to twelve-month window for other tools.

Gabi Steele, co-founder, and co-CEO of Preql, stated, “We built Preql as the transformation layer was the most important part of the data stack. However, the resources and talent needed to manage it make reliable analytics and reporting inaccessible for companies with large data functions.”

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.

Preql, in other words is the buffer between engineers and those who will actually use the data.

“Data teams are often reactive. Although the business constantly needs data to guide its decision-making, it is difficult to make small adjustments to existing data models in this current environment.

Leah Weiss, Co-CEO and co-founder of Preql, stated that if business users can manage their own metrics, then data talent will be free to focus on more complex analyses and move away from the back-and-forth of fulfilling reporting requests.”

However, Preql and dbt are not bitter rivals. They are actually part of the same data-transformation community, and there is a planned integration.

Steele said, “One way to look at it is that we want to help organizations get up and running really quickly and get time and value out of the data they’re already collecting” As these companies get more sophisticated, we will be able to output dbt so that they can use it if it’s the most familiar tool.



A Closer Look at Preql

In May, a seed round of $7 million was raised by the startup, which was led by Felicis and Bessemer Venture Partners.

Preql gathers the business context and metric definitions and then abstracts away any data transformation. Preql helps organizations establish a central source of the truth that can be used to report without the need for a data team.

Preql can read in data from the warehouse, and then write back clean, report-ready schemas. It works with data ingestion software that moves data from source applications to the warehouse, such as Airbyte and Fivetran. It also partners with Sigma Computing, Tableau, and Looker for businesses that use BI tools.

DTC Target

Preql’s initial focus is on the DTC market because metrics such as cost per customer acquisition (CAC), conversion rates, and life-time values (LTV) are standard. They are also known for having lean operations.

Weiss stated that companies have been working hard to obtain data from different sources, Shopify and third-party platforms they use. This is to gain a sense of basic business health, performance, and profitability.

They also use manual reporting, which is often done by operations people who download data from many sources, combine it in spreadsheets, make manual interventions, and then output weekly or quarterly reports.”

However, a lot of the performance metrics these companies are looking for are consistent and many of the data sources are the same.

Cynthia Plotch, the cofounder of Stix, an eCommerce site for women’s health products, said, “With Preql we were able to make some assumptions about the things we want to measure with the flexibility to customize a few definitions that are particular to our business.” Preql provided us with clear, useful data for reporting. We had weekly reporting up and running in days. This saved us months of effort if we needed to hire data engineers.



Data Transformation in 2027

Steele and Weiss think that the next five years will be all about “delivering on our promise of the modern data platform.”

Also, this means answering questions such as: How can we ensure that data can be used for decision-making, now that we have scalable storage? How can we trust reporting to build workflows and take action?

Because many companies have struggled to transition to predictive analytics or machine learning, they didn’t solve the fundamental problem of creating trustworthy and accessible data.

Preql is also convinced that the next generation of tools will not only build infrastructure but deliver more value to the business as data talent becomes closer and closer.

Data analytics will become more complex as the data sources are increasing in complexity and data is available. This means that real-time results are becoming more urgent. Amit Karp, the partner with Bessemer Venture Partners, said that the more data you have, then the more specific the questions will become and the more it is expected to do so. “I believe we are in the very early stages of what will be a very long wave, five, ten or even twenty years down the line. It’s a huge market.

Alan Jackson

Alan is content editor manager of The Next Tech. He loves to share his technology knowledge with write blog and article. Besides this, He is fond of reading books, writing short stories, EDM music and football lover.

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