Are we truly leveraging the advancements in our data platforms - the seamless data ingestion, the comprehensive consolidation and the reliable production of assets like metrics?
If we find ourselves still writing numerous ad-hoc queries, managing a clutter of dashboards, or frequently exporting data to spreadsheets for quick analysis, it’s a sign that our data consumption practices and tools haven’t kept pace with the sophistication of our data infrastructure. At times, operating on data feels like driving a Ferrari to buy milk.
So, how can we evolve our data consumption? The first step is understanding user needs and use cases in alignment with organizational priorities.
Common use cases include:
- monitoring metrics, periodic business reviews, root-causing
- slicing into segments and across metrics to uncover causal patterns
- designing and analyzing experiments, launches, ongoing campaigns
- sizing opportunities, forecasting, planning
Just as data production has evolved through the use of directed acyclic graphs (DAGs) to manage raw data assets and transformations, we can similarly utilize metric trees to map the business processes that consumers want to optimize.
Then, metric trees could streamline, even automate the common diagnostic analyses for business reviews. We could map and analyze experimental test and control segments to well-understood metric relationships. A metric tree that captures the growth equation could assess the impact of changing input levers.
In essence, tools that natively understand these metric trees can convert consumer requests and workflows into actionable data and algorithms.
Looking ahead, the next decade will likely witness the maturation of metric trees and its intricate interplay with the "data DAG” that has been central to data production over the past decade.
