- “Ask” questions and answers. This feature gives users the ability to explore metrics in natural language. For example, a marketer could use this feature to ask, “Which market contributes the most to lead generation in my campaign?” Tableau Pulse will then send information on this metric directly to the executive’s preferred communications platform: Slack, email, mobile device, etc.
- Bootstrapping metrics. This feature allows users to save calculations from a Tableau dashboard directly into Tableau’s metrics layer so they can monitor and track information over time. When a user does this, Tableau Pulse generates and reports information to the user based on the metrics. It could tell the user whether data is moving in a positive direction or what’s driving a trend, for example.
- Metric objectives. This feature allows users to compare progress on a metric with a defined baseline or goal, allowing a sales manager to track their pipeline against goals, for example.
Einstein Copilot for Tableau remains in beta, but Tableau also announced two new features for the AI assistant:
- AI-assisted data transformation. This feature can automate a data transformation pipeline with step-by-step suggestions for preparing data for analysis. For example, a user might ask the co-pilot, “Look through the reviews for this product and help me determine which ones are the most positive.” »
- Einstein Copilot for Tableau Catalog. With this feature, the AI assistant can automatically generate data descriptions that make it easier to find and explore data sources. Tableau says a user working in the hospitality industry could click “Draft with Einstein” to get travel data. The co-pilot would then use the metadata and field names from the data source to provide a detailed description of the data, making it easier for other analysts to reference the information.
“This will not replace the skills and expertise of our data family that creates amazing and beautiful visualizations,” Maxson said. “But it will help you be more productive and really jump-start that raw data analysis by asking questions to help you create visualizations.”
Tableau also highlighted new quality of life improvements to the Tableau platform intended to support its core group of analyst users, including:
- Visualization extensions. This feature extends the visual libraries that Tableau can use to create charts faster, including a new Sankey chart extension to speed up charting. Tableau introduced Viz Extensions as an open API for creating visualization models, allowing Tableau partners to also provide Viz extensions.
- Shared Dimensions And Composable data sources. These features work together to help analysts integrate data from various data sources while spending less time preparing and modeling data. Shared dimensions make it easier to create more complex data models, including those with multiple fact tables, while composable data sources allow analysts to supplement centrally defined data sources with their own additions without changing the model underlying.
Data analysts and curators can use these features to create a single underlying model that can generate dozens or even hundreds of data visualizations. Analysts at a manufacturing company could combine machine learning data from its assembly line, worker data on shifts and holidays, and data on shipments to and from suppliers to create dashboards on supplier management, worker productivity, etc.
