Just as tools for data ingestion and data modeling streamlined data production, tools for metrics modeling and metrics workflows will be key to streamlining data consumption, enabling true democratization and driving operational rigor across the enterprise.
What do these two metrics-first components look like?
1) Metrics Modeling
As the name implies, this refers to modeling the right set of metrics that capture the nuances of the business model.
But, it extends beyond just capturing and cataloging the metric definitions. For a robust system, the ability to flexibly compute various metric cuts should be table stakes. Metrics layers are a step in this direction.
Moreover, the relationships between metrics have to be modeled as a first class citizen. Trace is leading the way in designing and implementing these metric relationships.
2) Metrics Workflows
Metrics modeling alone serves as just a "back-end." As mentioned earlier, the emerging crop of metric layers includes some modeling capabilities, barring the robust modeling of metric relationships.
The second crucial piece to streamlining data consumption involves applications that utilize this back-end to drive repeatable workflows for users across the organization.
These applications should ideally be low/no-code, empowering users from both data and business teams to extract meaningful insights on business performance and operate effectively.
Combining robust metrics modeling with streamlined metrics workflows creates a powerful framework that enhances data consumption by democratizing insights and driving operational clarity and rigor across the organization.