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We’re excited to announce that Velir is now a partner and "Preferred Consulting Provider" of DBT Labs, creators of the open source framework that has defined the modern data stack movement over the last three years. We’ve seen DBT’s power to make sophisticated data solutions easy to build and maintain first-hand with our client johnnie-O, a men’s apparel retailer. Working with DBT, we provided johnnie-O, with a 360-degree view of their customers and ad spend. The same data that drives johnnie-O's reports and dashboards is also used to drive personalized, cross-channel experiences thanks to DBT's integration with Segment CDP.

While the DBT Labs brand was just started in 2021, their open-source Data Build Tool (DBT), has been around much longer and has become the glue between the various components of the modern data stack. Many of the vendors behind those components, such as Fivetran and Snowflake, are also Velir partners.

DBT's recent seed funding round and valuation of $1.5B demonstrates the widely-held belief that businesses are ready to invest in and mature their data infrastructure. The capacity for data to generate new revenue streams and become the next domain of competition between businesses explains why so many have updated the old adage, "Software is eating the world" to "Data is eating the world."

Unlocking 360-Degree Customer Data for Retailer johnnie-O

How we built an enterprise-class customer data warehouse and integrations to give johnnie-O advanced analytics and personalization capabilities.

What is DBT?

Describing the role of DBT in a data solution is notoriously difficult. It exists in its own product category and has introduced a new way of working which leaves us with few benchmarks. One way to appreciate its potential is to think about the impact that containerized shipping had on the world. The concept was simple, "Let's put our goods in containers!", but it reduced manual labor, upended supply chains, and made it possible to order a $100 flat-screen TV with the click of a button.

Similarly, DBT reduces friction in the data supply chain and has an outsized impact on data-consuming products such as analytics dashboards, marketing automation campaigns, and machine-learning models. As we've written about before, mid-sized companies using DBT are now able to produce data-driven solutions on par with ones used by major enterprises such as Netflix, AirBnB, and Uber.

A graphic showing how DBT works. Raw data is moved to data platforms where it's developed, tested, and documented into datasets. From datasets, the information can be used in business intelligence tools, machine learning models, and operational analytics.
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DBT's features include the following:

  • Version control for data transformation
  • Scheduled orchestration of data transformation tasks
  • Automated documentation
  • Automated data integrity testing and alerts
  • Code reuse through packages and macros

However, DBT is more than the sum of its features. When combined, they allow data teams to tackle more challenging projects at scale. Looking beyond features, DBT’s business outcomes include:

  • Giving analysts the capacity to work like a data engineers, which reduces headcount without sacrificing solution outcomes
  • Ensuring data integrity, which mitigates the data death spiral where business users lose faith in analytics systems due to data discrepancies
  • Allowing businesses to tackle unique requirements rather than conforming to one-size-fits-all SaaS solutions
  • Providing a central repository documenting the business rules that govern analytics metrics and dimensions
  • Accelerating time to value for data-driven initiatives such as customer data platforms (CDPs), personalization, and ML/AI

If you'd like to read more about how DBT can help accelerate data-driven initiatives, take a look at our case study with the apparel retailer, johnnie-O. With the deployment of DBT, we were able to meet all johnnie-O's expectations around data quality, analytics dashboards, customer segmentation, personalization results without the use of Python or a data engineer. If your organization is hoping to achieve more with data but doesn't have a team of data engineers, reach out to see if DBT can help.


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