Integrate and Prepare Data for Analysis

You can use CRM Analytics lenses and dashboards to analyze your data. To prepare the data for analysis and increase performance when processing large amounts of data, first load, clean, and transform the data into datasets.

• Data Manager for CRM Analytics
  The data manager is where you monitor your data jobs, prepare datasets with recipes and dataflows, and connect to data.
• Get Started with Data Integration
  In CRM Analytics, data integration involves gathering and preparing the Salesforce and external data you want to analyze. External data is data that resides outside of the Salesforce org that you use for Analytics, such as data from another Salesforce org, outside applications, spreadsheets, and databases. After integrating the data, you prepare it into datasets. Data preparation is the process of transforming your data into a form that’s meaningful and valuable to the people consuming it. For example, you can define data preparation logic that combines data from two data sources and cleans up inconsistencies, such as differently formatted dates and codes. Users then explore and visualize datasets through CRM Analytics lenses and dashboards.
• Prepare and Load Data into Datasets with Recipes and Dataflows
  Use CRM Analytics to prepare, sync, and connect your data. You can also design data set, and run jobs to create and refresh datasets.
• Connect and Sync Your Data to CRM Analytics
  Use the CRM Analytics connectors to connect data inside and outside of Salesforce. You can access data in your local org and external Salesforce orgs, apps, data warehouses, and database services.
• Clean, Transform, and Load Data with Data Prep
  Data Prep provides an intuitive, visual interface in CRM Analytics that allows you to easily point-and-click your way to build recipes that prepare data and load it into a target. Use the graph of a recipe to see at a glance where the data comes from and how it flows through the recipe to the target. To validate the recipe as you build, preview how raw data is transformed at every step of the way.
• Data Prep Access Based on Your User Type
  Not all Data Prep users in CRM Analytics need access to all data prep features. For example, a business analyst doesn’t need to change dataset security to grant themselves and others access to more data. To support advanced and limited Data Prep user types, CRM Analytics comes with two user permissions: Edit Analytics Dataflows and Edit Dataset Recipes.
• Create a Recipe with Data Prep
  Use a Data Prep recipe in CRM Analytics to clean, transform, and enrich data before loading it into one or more targets. Preview your results as you build the recipe to confirm that the logic is correct.
• Preview Results in a Data Prep Recipe
  As a best practice in CRM Analytics, catch mistakes early by reviewing the results of each node and transformation that you add to a Data Prep recipe.
• Profile Columns to Understand Data in a Data Prep Recipe
  In CRM Analytics, run column profiling on sample data to estimate key stats about columns, such as the frequency of values and percent of columns with missing values. Column profiling is especially useful when you are combining data from different sources, where inconsistencies are often introduced. Column profiles are available only for columns shown in preview.
• Limitations When Using Data Prep
  Consider the following Data Prep limitations before building recipes.
• Manage Recipes
  After you create a recipe, use the Recipes tab to edit and delete them.
• Run Data Sync and Recipes to Create and Refresh Datasets
  Whether you use local Salesforce data or pull data from an external source, you must set up CRM Analytics to load the data, make it available to CRM Analytics, and keep it up to date.
• Other Ways to Integrate Data
  Dataflows and recipes can integrate most data from internal to external data sources. However, CRM Analytics provides additional ways to integrate data that are more effective for specific use cases. For example, if you don’t have to transform data before loading it into a dataset, you can import data directly from CSV or Excel files. If you need to transform it, you can upload the file to create an intermediate dataset, and then use a recipe or dataflow to change that data before loading the results into another dataset. Or, to analyze Salesforce report data over time, you can create a dataset and dashboard that trends the report with just a few clicks. With trending, CRM Analytics creates a dataset based on snapshots of your Salesforce report data.
• Get Started Faster with Data Templates
  Salesforce Data Pipelines use purpose built data templates for common use case data tasks to accelerate your ROI. These data templates generate the necessary data connectors, data prep recipes, and queries for apps that can be scheduled to run regularly.
• Manage Datasets
  Edit a dataset to update its data, change its extended metadata, apply row-level security, or restore the dataset to a previous version.
• Data Integration Best Practices
  Review these tips to improve the performance of your recipes, speed up the editing of large recipes, and streamline data sync.
• Data Integration Troubleshooting
  Use these tips to resolve problems with data integration issues in CRM Analytics.