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Metric-driven data architectures

A trend we’re noticing at Thoughtworks is people increasingly looking to address the challenge of business metric fragmentation. It is common for organisations to use multiple BI tools, such as Tableau and Power BI, in order to meet the specific reporting needs of different teams. However, when crucial business metrics (such as monthly recurring revenue and customer churn rate) are defined and maintained in multiple locations, serious risks surrounding their discoverability, agreement, and correctness can arise. Creating one interface for defining and computing metrics, with individual metrics still being owned by the relevant team, reduces data quality risks and presents opportunities for publishing to other consumers such as ML products and SaaS integrations, consistent with data mesh principles. The recent coinage of the term metrics layer describes this pattern well. If you’re interested in discussing how we could help you with these challenges, or want to chat about how you’re tackling them, we’d love to hear from you!

Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.

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