We’re honored to share that Push.ai is part of the dbt Semantic Layer inaugural integration partner network. Over the past year we’ve worked with the team as they built MetricFlow to their acquisition and integration to become the dbt Semantic Layer. The newest version expands the modeling capabilities that analytics engineers have been in charge of, allowing them to move beyond tables and towards modeling their business directly. We look forward to collaborating with the dbt team to continue to make analytics more powerful for the engineers behind it and the consumers that use it. 

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Here are a couple of the features we’re most excited about in the new release:

Metrics

Most Semantic Layers are built strictly for Business Intelligence. dbt is the first to build the Semantic Layer for a new generation of data applications by extending to the most centralpart of any business analysis: the metric. Metrics are one of the important innovations because they create specific combinations of measures and time dimensions to establish a consistent “metric_time”. This allows teams to enable anyone to perform time series analysis across any business metric. For a simple example, ecommerce teams trying to get daily revenue may have had to choose between a variety of timestamps that could represent some metric, such as the created date for an order, the delivered date, or one of the other hundreds of possible time dimensions in their data stack. Instead, they can select revenue as the metric they want to analyze.

Dimensions

As we’ve worked with companies to deploy a semantic layer approach to their data workflows, we’ve found that a majority of scale comes from defining dimensions that give users the flexibility to find the exact data they need. This level of personalization is a core part of Push.ai’s mission and dbt’s new Semantic Layer release allows teams to pull dimensions directly from the models they’ve built. Dimensions can power use-cases such as reusing the metric Closed Won Revenue to send relevant data to all levels of a sales team. The department lead can look at the metric by segment and by manager. Each manager can view the metric for each of their team members. Finally each salesperson can see their individual performance. All of these are backed by the same foundational definition and its associations with different dimensions. 

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In Push.ai, this would be as easy as swapping the dimension and destination to customize each report.

Advanced Metric Types

The expansion of metric types will allow teams to fully encapsulate their business model into the calculations and logic that map it to data. One example is the non-aggregating time dimension that allows teams to create specific rules for metrics like ARR that sum within a day but not day over day. This will allow tools such as Push.ai to automatically understand, analyze and distribute key pieces of information to the relevant people in any organization. We’re excited to build these automated analysis capabilities into our application that benefits from the robust set of metric types that dbt is developing.

The Future is Metrics

At Push.ai, we believe that the shift towards analytics engineering has been the most powerful trend in analytics in the past 10 years since the advent of the modern data stack. We have finally hit the point where not only is the data modeled, we can model businesses using the dbt Semantic Layer. Analytics engineers can use dbt as an end-to-end transformation technology that has a dedicated interface for all data consumers. In partnership with tools like Push.ai, organizations can go beyond the chart or dashboard and receive personalized data and insights directly where they work. The future of data is exciting as we can now produce business models, directly from data using the dbt Semantic Layer.