Preparation Recipe
In CRM Analytics (formerly Tableau CRM, originally Einstein Analytics), a Preparation Recipe is a data preparation step that transforms, cleans, and combines datasets before analysis.
Definition
In CRM Analytics (formerly Tableau CRM, originally Einstein Analytics), a Preparation Recipe is a data preparation step that transforms, cleans, and combines datasets before analysis. Built in a visual recipe editor, it lets analysts define filters, joins, formulas, and aggregations without writing code - shaping raw source data into the dataset that powers dashboards and lenses.
In plain English
“A Preparation Recipe is CRM Analytics' way of cleaning and combining data before you chart it. You line up steps visually - join this table, filter out that group, compute a new column - and the recipe runs, producing a dataset your dashboards read from. Same tool worked under the older names Tableau CRM and Einstein Analytics.”
Worked example
A business analyst at Glasspine Code. needs a "Top-100 accounts by revenue, with most-recent open Case" dataset for a weekly executive dashboard. They build a Preparation Recipe that joins Account to Opportunity (rolling up Closed-Won Amount), left-joins to Case (filtered to Status ≠ Closed), computes an "Open Case Age" formula column, aggregates to one row per Account, and outputs the dataset. The recipe runs nightly; the dashboard refreshes against a clean, pre-joined dataset instead of recomputing joins in every lens.
Why Preparation Recipe matters
In CRM Analytics (previously called Tableau CRM and originally Einstein Analytics - Salesforce renamed it to CRM Analytics in 2022), a Preparation Recipe is a data preparation step that transforms, cleans, and combines datasets before analysis. The visual recipe editor exposes nodes for input, join, transform, filter, aggregate, and output, letting analysts shape source data into the form their dashboards and lenses need.
Recipes sit in the middle of the CRM Analytics pipeline: connect source data (Salesforce, uploaded CSVs, or external connectors), design recipes to prepare the data, run them to produce datasets, then build dashboards and lenses on top. Mature CRM Analytics teams invest in recipe design because dataset quality directly shapes what analysis is possible - well-structured datasets enable rich exploration, while poorly shaped data forces constant workarounds in every downstream dashboard.
How organizations use Preparation Recipe
Built recipes to combine Salesforce CRM data with marketing platform exports, producing unified datasets that power cross-channel attribution dashboards.
Maintains a library of reusable recipes for common prep patterns (monthly cohort builds, account-hierarchy flattening) so new dashboards plug into existing datasets instead of duplicating prep logic.
Treats recipe design as first-class data engineering, with naming conventions, documentation, and code review so analysts can modify each other's recipes safely.
About the Author
Dipojjal Chakrabarti is a B2C Solution Architect with 29 Salesforce certifications and over 13 years in the Salesforce ecosystem. He runs salesforcedictionary.com to help admins, developers, architects, and cert/interview candidates sharpen their fundamentals. More about Dipojjal.
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