Transform is building a shared data interface so decisions can be made with confidence and speed. Empower teams to drive your organization in one direction, together.
Transform empowers companies to define their most critical metrics, across all teams. Those metrics are used to analyze experiment results, showing the real impact of A/B tests for the whole company.
With a shared interface for data, teams can spend less time debating data definitions and focus on the metrics that really matter, like revenue.
When important numbers are reported to stakeholders, one mistake can have a catastrophic effect. Re-stating earnings or other key metrics can erode trust with investors. We’ll help you get it right the first time with metrics you can count on.
Harvard Business Review found that knowledge workers spend as much as half their time hunting for data, then finding and correcting errors. What would your data engineers, analysts, and scientists do if they had that time back?
Companies struggle to understand how their metrics are defined, what data are trustworthy to use, and who to contact if something breaks. Simply, people cannot do their jobs because they cannot find data or understand its meaning. Countless hours are wasted trying to find and understand data, instead of deciding how to act on it.
Connect Transform to any data warehouse to create a metrics repository accross all your data systems.
With the growing popularity of pipeline engines and schedulers, data ecosystems have become complex and cluttered with repetitive jobs. Transform streamlines the last-mile of ETL to create efficient metrics and dimension tables, saving you valuable time and money on pipline management.
Use Transform to efficiently compute metrics and dimensions in your warehouse.
One of the most challenging parts about working with data is manually analyzing the cause of metric value spikes. Transform understands how a company’s metrics values are correlated, detects anomalies automatically, and alerts you when things have changed.
Know about changes in your data values before they become a problem.
When one team runs an experiment, they may see positive results but they may unintentionally cannibalize another team’s metrics. In a world of distributed data ownership, having a global set of metrics to understand the full impact of an experiment can have a huge impact to your bottom line.
Create a global metrics repository so experiment results can show the full picture.
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