Now more than ever, we need data to make better decisions.
DataSelf leverages Tableau Ask Data and Power BI Q&A to provide users with natural language query (NLQ), artificial intelligence (AI) and machine learning (ML) capabilities from clean and reliable data feeds. NLQ allows NLQ allows people to get insights by simply conversing with their data. Now you can ask questions of any published data source and get answers in the form of a visualization. It allows you the ability to explore data at the speed of thought.
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Best practices and recommendations for fine-tuning Tableau data sources to make it easier for users.
- Add "
_NLQ" suffix to data source names to indicate that they have been fine-tuned for natural language. - Create data sources dedicated to specific NLQ functions.
- ExExample.: A Sales_NLQ data source only covering sales of stocked items in the domestic market (filtering out Credit Memos, non-stock item sales and intl internal sales).
- Only include needed periods of time. For instance, only the past few months or couple of years.
- NLQ is CPU hungry, putting more data than necessary can dramatically affect its performance.
- Keep the list of dimensions and measures simple. For instance, only include dimensions for Customer, Invoice Date, Items and Salesperson, and measures for Sales and Qty Sold.
- Avoid including more than one date field (for instance, do not include Invoice Date and Order Date). This prevents users from asking something like "sales in 2020" (as in Invoice year = 2020), and the tool rendering sales in Order year = 2020.
- Rename dimensions and measures to be explicit. Ex.Example: Rename "
Doc Date" to "Invoice Date".
How to Run a Query in Tableau Ask Data
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- The portal will open the Ask Data tab and re-optimize the data source for answering questions.
- Simply type in a question in the Ask about fields in this data source box.
- To get used to the natural language query engine, we recommend typing the questions in small pieces.
- For instance, instead of asking "sales by salesperson in 2019 by month", type in "sales by salesperson" (do not press Enter yet) and select the presented option that should give you the correct answer; then type "in 2019" and select the correct option; then "by month" and select the correct option.
This broken down approach allows the user to start with simple questions, validate the results at each step of the way, and then add more context until the complete question is answered. As users get familiar with the NLQ syntax, it becomes easier to ask questions in a way that the engine knows how to answer, or prevent asking questions that the engine still doesn't know how to respond.
- For instance, instead of asking "sales by salesperson in 2019 by month", type in "sales by salesperson" (do not press Enter yet) and select the presented option that should give you the correct answer; then type "in 2019" and select the correct option; then "by month" and select the correct option.
Other Navigation Features:
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