Transform is a business intelligence tool that enables data conversations and data collaboration. It is designed to address the gaps in data knowledge felt across the business, so that organizations can build context and insights around data, together.
It does so by refocusing a company’s data ecosystem around metrics – which are the central object that data teams and business teams need to align on. When metrics are defined in a single place, the business can properly align around its definition, share context and lineage at the metric level. This makes metrics the perfect starting point for analysis: end users can quickly get acquainted with all the relevant metadata around a metric, ensure that they gain the proper context and history around metric changes, and then start slicing and dicing to answer their own questions without need for further guidance by a data analyst.
Data teams will leverage our MetricFlow framework to define metrics. MetricFlow is a semantic layer designed to abstract SQL queries away into bite-size chunks as metrics. Organizations can plug their data sources into MetricFlow, allowing the framework to manage the compilation and execution of complex SQL against their warehouse.
Benefits & Value proposition
Transform is a powerful combination of a semantic layer that scales with your business, ensuring proper data governance practices, and an intuitive UI that exposes consistently defined metrics, to enable broad consumption by the business. This centralization of knowledge around metrics streamlines an organization’s decision-making.
With MetricFlow, users can:
Construct metrics from powerful semantic models: It constructs a relational graph of your underlying data model, giving you the power to construct and aggregate even your most complex metrics to dimensional granularities across your data warehouse. It also helps you avoid the fan trap and chasm trap in your joins (which are common in other semantic layers).
Avoid code duplication and disjointed metric logic: MetricFlow uses a semantic model, generating SQL under the hood as you query your metrics. MetricFlow is optimized for DRY – to help you avoid code duplication in key workflows that can lead to technical debt and disjointed metric logic.
Leverage optimized queries to any data warehouse: MetricFlow supports—and even optimizes—queries to any data warehouse. This is built on a flexible interface to define new database connectors and optimizations.
Easily generate and maintain metrics in your warehouse: Once you define your metrics in MetricFlow, you can easily and flexibly generate denormalized tables back to the warehouse.
Seamlessly exchange metric models between tools: MetricFlow parses the YAML files that follow the MetricFlow spec into standardized Python objects that can easily be translated to and from a variety of metric specs.
With Transform, users can:
Trust that they are using the correct data: Assigning proper ownership for metrics ensures that a single point of contact is responsible for the maintenance and accuracy of a metric.
Find and learn about the data their organization has available: End users can easily get acquainted with metrics that have been curated by the data team, review trends over time and explore metadata around metrics.
Answer their own questions: Slice and dice, and explore the power of the semantic data model expressed in MetricFlow.
Build context, insights, and collaborate on metrics: Enable seamless collaboration between data producers and business stakeholders about metrics definitions and metric performance. Bring context to every metric with a record of questions and answers. Annotate your metric charts to describe spikes or dips in a metric.
Create and save new content: Save slices and dices of your data as queries and pin them to a metric page. Or, go a step further with Boards, to compile several metrics or queries as charts in a single place that can easily be filtered and further sliced and diced. These Boards can be easily shared and accessed by your colleagues.
Keep track of your metrics and never miss a beat: Subscriptions and notifications increase timely communication and understanding.
Work where they want to: With connectors to major tools where most analysis happens today, users don’t have to change their workflows to use the trustworthy datasets analysts curate in Transform.
Transform brings everyone to the same page (literally) around how metrics are defined.
Where does Transform sit in the data stack?
Transform sits conveniently in between an organization’s data warehouse, and other downstream tools. Essentially, it acts as a proxy to the warehouse, translating metric requests into SQL queries to the warehouse.
In this way, transformation occurs at the 'metric level' — consistently defined in code, accessible to all downstream tools, and centrally governed to maximize insights.
Who should use Transform?
The biggest challenge facing organizations today is how to organize and structure teams around data. Tools in the data stack abound, but are usually sequestered by organization or job function, reducing the ability for teams cross-functionally to collaborate and generate insights around data. Transform is a solution for organizations that truly want to leverage self-serve BI, and it is built for data people and non-data people alike. From a more tactical standpoint, a few different personas or types of individuals would interact with the tool:
A data engineer, data analyst or data scientist would be well-suited to set up MetricFlow – the semantic layer where metric definitions are authored in YAML for future downstream consumption by end users. They are typically familiar with the underlying warehouse and data structures, write SQL, and generally charged with transforming raw data assets into usable, meaningful data insights for the end user. They want to reduce the time spent writing duplicative SQL, and leveraging a semantic model like MetricFlow ensures that metrics are governed and accessible for their stakeholders to self-serve.
A business analyst or any sort of business subject-matter expert charged with interpreting what the data means - in the form of reporting or dashboards, would also interact with Transform. With MetricFlow-defined metrics as the starting point for curiosity, these folks can quickly get acquainted with definitions and owners, ensure that they gain the proper context and history around metric changes, and then start slicing and dicing to answer their own bespoke questions.
Ideally, these two distinct groups of users will collaborate together within Transform to ensure the most up-to-date, accurate and useful metrics.