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Derived fields let you add more information to a dataset than what is extracted from a data source. For example, add a derived field that categorizes your opportunities into Small, Medium, and Large based on the extracted opportunity amount. Use derived fields to add more context to the data, create groupings, and perform calculations in advance.

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Enrich Your Datasets with Derived Fields

Derived fields let you add more information to a dataset than what is extracted from a data source. For example, add a derived field that categorizes your opportunities into Small, Medium, and Large based on the extracted opportunity amount. Use derived fields to add more context to the data, create groupings, and perform calculations in advance.

Derived fields in a dataset behave just like other fields that are extracted from an input data source—you can query them, filter by them, and group by them. The benefit of generating values in derived fields instead of a query is that the values are immediately available to all users who have access to the dataset. In addition, it saves users time trying to create the queries.

You can add the following transformations to a dataflow to create derived fields. computeExpression

Performs calculations based on fields within the same row of the dataset. You can assign a value to the field based on the value of another field, concatenate multiple text fields, or perform mathematical calculations on numeric fields. For example, you can categorize opportunities based on their amount.

When you define a derived field in the dataflow, you specify the field name, type, and SAQL expression used to calculate its value. The expression can be based on one or more fields from the input data or from other derived fields.

computeRelative

Performs calculations based on the same field in other rows in the dataset. You can calculate the first, previous, current, and next values of a field to determine the changes in value. For example, to analyze sales pipeline trends, you can calculate the changes in the opportunity amount throughout the stages of each opportunity.

When you define a computeRelative transformation, you specify an sfdcDigest transformation as the input, partition the records, and sort the records within each partition. For example, you can use sfdcDigest to extract opportunity history records, and then use computeRelative to calculate changes in each opportunity over time. You can partition opportunity records by opportunity ID, and then chronologically sort records within each partition to correctly identify the previous and next values.

You must specify the computeRelative transformation directly after the associated sfdcDigest transformation in the dataflow JSON. Because Wave merges the logic from computeRelative into the associated sfdcDigest, apply sfdcRegister on sfdcDigest to add the fields from both transformations to the dataset.

Note: You can use this transformation in a replication dataflow. For more information about replication, see Getting Started with Wave Replication.

computeExpression Transformation Example

Let’s look at an example where you group your opportunities into Small, Medium, and Large categories based on amount. First, create an Opportunity dataset by using an sfdcDigest transformation to extract the opportunity name and amount from the Opportunity object. Next, use the computeExpression transformation to add the Opportunity Size derived field to the Opportunity dataset based on the following rules.

• If Amount < $10,000, Opportunity Size = Small

• If Amount >=$10,000 and < $50,000, Opportunity Size = Medium • If Amount >=$50,000, Opportunity Size = Large

To create the derived field, add the following computeExpression transformation to the dataflow. "Create_Opportunity_Size_Derived_Field": { "action": "computeExpression", "parameters": { "source": "Opportunity_Data", "mergeWithSource": true, "computedFields": [ { "name": "Size", "type": "Text",

"label": "Opportunity Size" "saqlExpression":

"case

when Amount < 10000 then \"Small\"

when Amount >= 10000 and Amount < 50000 then \"Medium\" else \"Large\"

end"} ]

} }

computeRelative Transformation Example

Let’s look at an example where you want to analyze your sales pipeline. To start, add an sfdcDigest transformation to extract the following data from the OpportunityHistory object.

Next, add a computeRelative transformation to perform the following tasks. • Partition the extracted records by opportunity ID.

• Within each partition, sort the extracted records by CreatedDate in ascending order. Sorting by CreatedDate ensures that the changes that occur for each opportunity are listed in chronological order.

• Add the following derived fields to the final dataset. OpportunityCreatedDate

Determines the date that the opportunity was first created. You can use this date with the actual close date to determine the number of days required to close the sale. The goal is to shorten the sales cycle to recognize revenue.

ValidToDate

Determines the date to which the state of the opportunity is valid. You can use this field to determine the time period for which the opportunity details are valid in each record.

AmountPrev

Determines the previous amount of the opportunity. You can use this field to determine if the values of opportunities are increasing or decreasing, which can affect whether you hit your sales targets.

CloseDatePrev

Determines the previous expected close date for the opportunity. You can use this field to analyze how the expected close date changes over the sales cycle of the opportunity. If the expected close date keeps getting pushed out, identify the issues that are causing the longer sales cycle.

To build the final dataset with extracted fields from sfdcDigest and derived fields from computeRelative, add an sfdcRegister transformation with sfdcDigest as the input source.

You create the following dataflow. { "extractOppHistory": { "action": "sfdcDigest", "parameters": { "object": "OpportunityHistory", "fields": [ {"name": "OpportunityId"}, {"name": "CreatedDate"}, {"name": "StageName"}, {"name": "Amount"}, {"name": "CloseDate"}

] } }, "computeTrending": { "action": "computeRelative", "parameters": { "partitionBy": ["OpportunityId"], "orderBy": [ { "name":"CreatedDate", "direction":"asc" } ], "computedFields": [ { "name": "OpportunityCreatedDate", "expression": { "sourceField": "CreatedDate", "offset": "first()", "default": "current()" } }, { "name": "ValidToDate", "expression": { "sourceField": "CreatedDate", "offset": "next()", "default": "3000-01-01T00:00:00.000Z" } }, { "name": "AmountPrev", "expression": { "sourceField": "Amount", "offset": "previous()", "default": "0" } }, { "name": "CloseDatePrev", "expression": { "sourceField": "CloseDate", "offset": "previous()", "default": "01/01/1970" } } ], "source": "extractOppHistory" } }, "Register_OppportunityHistory_Dataset": { "action": "sfdcRegister", "parameters": { "alias": "SalesPipelineTrending",

"name": "Sales Pipeline Trending", "source": "extractOppHistory" }

} }

After you run the dataflow, the final dataset looks like this.

Notice that Wave partitions the records by opportunity ID and then sorts the records in ascending order within each partition, based on the CreatedDate field. Wave uses the sort order within each partition to calculate the derived field values.

You can use the final dataset to determine changes to a field values. For example, you can calculate the difference between the Amount and AmountPrev fields to determine the changes in opportunity amounts.