Tableau Pulse is coming — The newest development of the Tableau world, launched and driven by artificial intelligence. Its main goal: To reach that 70% of people within companies that don’t have the word data in their work titles — people who don’t necessarily know how to build a dashboard or have the time to do it. In other words, to keep making date easier for everyone.
The aspiration is to achieve this in a personalized, contextual and smart way by transforming outcomes with metrics that are relevant for our businesses, identifying and communicating insights with AI and infusing data into our flow of work.
How Does Tableau Pulse Work?
Its ground architecture has the following three layers:
- Metrics layer: where important KPIs are located, enriched from a single source.
- Insights Platform: where patterns and changes in metrics are automatically detected.
- Next-Gen Experiences: where contextual insights in our flow are digested.
All of them enhanced by Tableau AI.
Let’s dive a little deeper on each one of these layers:
Metrics Layer
In this layer, we’ll need to define our metrics as our scalable & manageable source of truth.
They possess the following characteristics:
- Measure and Aggregation (and likely some calculations) that hydrate the metric with data.
- Time Dimension and Temporal Level of Detail to track and filter the metric over time.
- Definitional Filters required for the metric to be right (e.g. closed_won = True for “Bookings.”)
- Related Dimensions along which the metric can be meaningfully broken down.
- Additional Metadata and Relationships like “up is good,” or pointers to other metrics or analytics content to help contextualize the metric.
A metric is an easy, understandable analytics artifact. Some examples are:
- Metric Value that provides users “the number” to orient on.
- Sparkline and Trend to show how the metric is performing over time.
- Scope like “this metric last month, broken down by this dimension.”
- Insights that create the “aha! moment” for users.
- Actions that can be taken against that metric, integrated with the tools available across the portfolio.
Changes to a metric definition affects all related metrics.
Insights Layer
This second layer is where the magic really happens. It’s where the analysis of the behavior of a metric takes place. It goes deeper into the data and it’s easy to understand for stakeholders.
It communicates through these tools:
- Insights Generation.
- Insights Ranking: Just the most important insights are communicated to the user.
- Insights Summaries (enhanced by generative AI): Communicates the most important and impactful metrics with business-friendly language for the user.
- Follow-up Questions: Templated questions to guide the user.
Next-Gen Experiences
Finally, the third layer integrates the insights and metrics into our flow of work and, with natural language, communicates the way we want it to (whether it’s email, Slack, mobile or desktop) so that it will be digested when and where it’s needed.
