It even sets the format for you and is smart enough to distinguish between US and European date formats. If it sees any other non-numeric characters, it sets the field to a dimension and you can’t do math on it! So review numeric fields like this and remove any other non-numeric characters if you want the fields to be measures.ĬRM Analytics recognizes all common date formats and sets a field like this as a date. CRM Analytics recognizes currency symbols, commas, and decimal points in numerical data, but beware. You probably want to do math on amount fields like this, so this is the perfect candidate for a measure. CRM Analytics doesn’t usually get tripped up by these fields, so there shouldn’t be anything for you to do! You can group and filter on dimension fields. So for now, just make a note of them.Īny field like this that contains non-numeric characters is identified as a dimension. There’s not a lot you can do about it now, but when you upload the data you get the option to change these field types to dimensions. Nobody wants to do math on an SIC code, zip code, or ID field, but CRM Analytics thinks you do and identifies fields like these as measures. But look out for fields like this that contain numeric values but aren’t actually measures. A measure is a field that you can perform calculations on, such as sum and average. It’s not in the CSV file that you are using.ĬRM Analytics identifies a numerical field like this as a measure. We’ve included an Industry Size field here for illustration purposes. So let’s walk through the SIC Descriptions CSV file, field by field, to see what you need to do. These types are important because they determine how you interact with a field in a CRM Analytics lens or dashboard.ĬRM Analytics does a pretty good job of assigning the correct field type, but sometimes you need to help determine the right type. It assigns each field a type of measure, date, or dimension. When you extract data from a CSV file, CRM Analytics makes assumptions about what type of data it is, based on the values it sees in each field. Your objective in this unit is to extract this data in CRM Analytics so that it can be added to the Salesforce data you’re going to extract. So Sales Operations has provided a CSV file with all the SIC codes they use, complete with descriptions. Sales leaders have a hard time deciphering these codes, preferring instead a good old-fashioned description. The Sales Operations team at your company uses a Standard Industry Classification (SIC) code field on each account to identify its industry. No problem at all for CRM Analytics! Let’s dig a little deeper. But some of it, if you recall, is pulled in from an external source. You know that most of the data they want to look at is already there, in the Opportunity, Account, and User objects. It’s your job to administer Salesforce and set it up for the sales team. It’s time to get that data into CRM Analytics so the sales leadership team can have its performance dashboards. Monitor and verify an external data upload.Prepare external data before bringing it into CRM Analytics.After completing this unit, you’ll be able to:
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