Data Quality Starts At The Source

Summary

The most important gauge of success for a data platform is the level of trust in the accuracy of the information that it provides. In order to build and maintain that trust it is necessary to invest in defining, monitoring, and enforcing data quality metrics. In this episode Michael Harper advocates for proactive data quality and starting with the source, rather than being reactive and having to work backwards from when a problem is found.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I’m interviewing Michael Harper about definitions of data quality and where to define and enforce it in the data platform

Interview

• Introduction
• How did you get involved in the area of data management?
• What is your definition for the term "data quality" and what are the implied goals that it embodies?
  • What are some ways that different stakeholders and participants in the data lifecycle might disagree about the definitions and manifestations of data quality?
• The market for "data quality tools" has been growing and gaining attention recently. How would you categorize the different approaches taken by open source and commercial options in the ecosystem?
  • What are the tradeoffs that you see in each approach? (e.g. data warehouse as a chokepoint vs quality checks on extract)
• What are the difficulties that engineers and stakeholders encounter when identifying and defining information that is necessary to identify issues in their workflows?
• Can you describe some examples of adding data quality checks to the beginning stages of a data workflow and the kinds of issues that can be identified?
  • What are some ways that quality and observability metrics can be aggregated across multiple pipeline stages to identify more complex issues?
• In application observability the metrics across multiple processes are often associated with a given service. What is the equivalent concept in data platform observabiliity?
• In your work at Databand what are some of the ways that your ideas and assumptions around data quality have been challenged or changed?
• What are the most interesting, innovative, or unexpected ways that you have seen Databand used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working at Databand?
• When is Databand the wrong choice?
• What do you have planned for the future of Databand?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

• Databand
• Clean Architecture (affiliate link)
• Great Expectations
• Deequ

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA