Reduce Cost of Producing Insight

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The Before Scenario, and the Negative Consequences of it.

Quality data engineers are hard to find and expensive → The cost to build and maintain data products is too high

The After Scenario, and the Positive Business Outcomes from it.

More people across the organization can be part of the data development process → Increased operational efficiency of data team

The Required Capabilities, and the Metrics to prove success.

Leverage universal language: SQL or Python → Cost of producing a data product (headcount, tools, middleware, compute divided by number of data assets/products produced)

How dbt Labs Does It, and how we do it Better.

Reduce Effort to Ship/Deliver – Benefit: Reallocate existing resources to higher priority work, expand data transformation workload across a wider array of human capital → Reducing bottlenecks and overreliance upon individuals

Proof Points

Whatnot | Problem: Previous stack required knowledge of complex frameworks and languages, which was hindering growth and scale | Solution: Analysts, engineers, and machine learning all working with common data models to enable them work more efficiently. | Result: 10x decrease in maintenance costs | Quote: “We could have the same setup with Airflow DAGs. But I’d need a bigger, more specialized, expensive team and our maintenance costs would be 10 or 20 times higher.”

Discovery Questions

• Do your business users self-serve analytics? What is the impact of them being self-sufficient? 

• Describe the makeup of your data team. Do you have plans to expand the data team? What skillsets are you looking for?

• Do you have more analysts or engineers?

• How quick is the turnaround on data requests?

• We’ve seen these common goals with many of our other customers. How would you prioritize these goals? (Build Trust in Data and Data Teams, Ship Data Products Faster, Reduce Cost of Producing Insight/Running)