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Extract External Data into Analytics

Learning Objectives

After completing this unit, you’ll be able to:
  • Prepare external data before bringing it into Analytics.
  • Upload a CSV file of external data.
  • Monitor and verify an external data upload.

Extract External Data Overview

Time to get that data into Analytics so the sales leadership team can have its performance dashboards. It’s your job to administer Salesforce and set it up for the sales team. So you know that most of the data they want to look at is already there, in the Opportunity, Account, and User objects. But some of it, if you recall, is pulled in from an external source. No problem at all for Analytics! Let’s dig a little deeper.

The Sales Operations team at your company uses a Standard Industry Classification (SIC) code field on each account to identify its industry. Sales leaders have a hard time deciphering these codes, preferring instead a good old-fashioned description. So Sales Operations has provided a CSV file with all the SIC codes they use, complete with descriptions. Here’s a snippet:

SIC Description CSV snippet

Your objective in this unit is to extract this data in Analytics so that it can later be added to the Salesforce data you’re going to extract. Here’s a reminder of where we are in the data journey.

Data journey map with extract external data process highlighted

Prepare Your CSV Data

When you extract data from a CSV file, Analytics makes assumptions about what type of data it is, based on the values it sees in each field. It assigns each field a type of measure, date, or dimension. These types are important because they determine how you interact with a field in an Analytics lens or dashboard.

Analytics does a pretty good job of assigning the correct field type, but sometimes you need to help determine the right type. So let’s walk through the SIC Descriptions CSV file, field by field, to see what you need to do.

SIC Description CSV
Note

Note

We’ve included an Industry Size field here for illustration purposes. It’s not in the CSV file that you are using.

SIC Code
Analytics identifies a numerical field like this as a measure. A measure is a field that you can perform calculations on, such as sum and average. But look out for fields like this that contain numeric values but aren’t actually measures. Nobody wants to do math on an SIC Code, zip code, or Id field, but Analytics thinks you do and identifies fields like these as measures. There’s not a lot you can do about it now, but when you upload the data you get the option to change these fields to dimensions. So for now, just make a note of them.
SIC Description
Any field like this that contains non-numeric characters is identified as a dimension. You can group and filter on dimension fields. Analytics doesn’t usually get tripped up by these fields, so there shouldn’t be anything for you to do!
Industry Size

You probably want to do math on amount fields like this, so this is the perfect candidate for a measure. Analytics recognizes currency symbols, commas, and decimal points in numerical data, but beware. 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.

Last Updated
Analytics recognizes all common date formats and sets a field like this as a date. It even sets the format for you and is smart enough to distinguish between US and European date formats. However, take a moment to review date fields to ensure that values are in one of the supported formats. If Analytics doesn’t recognize the format, it sets the field to a dimension. When this happens, you lose some of the cool date grouping and filtering functionality. Also check that your dates are all in the same format. For example, if Analytics sees a mixture of US and European formats, it uses the European format.
Column Headers
Check that the column headers are correct, because these become field labels in your dataset. You can fix them in the CSV before you proceed. Or you can wait until you upload the file, where you get another chance to change them.

The good news is that you get a chance to preview how your data appears in Analytics, and you can tweak it if you need to.

Upload Your CSV Data

Now that you’ve prepared your CSV file, it’s time to upload it to Analytics using the CSV uploader tool. You also create an app for the new dataset along the way.

Note

Note

When you upload a CSV file, Analytics extracts the data and creates a dataset. You can explore this new dataset right away, but in this case, a long list of SIC codes and descriptions doesn’t make for exciting viewing. Instead, you’ll use this dataset later to prepare the final sales dataset. For the time being, think of it as a holding area for the SIC data.

  1. Download the SIC Descriptions CSV file to your desktop from here.
  2. If you’re not already logged in, log in to the Developer Edition org that you signed up for in the previous unit.
  3. Go to Analytics by first clicking the app launcher (App Launcher icon).
  4. Then click the Analytics Studio tile.
    Analytics Studio tile in the app launcher
  5. In the Analytics Studio, click Create and select App.
  6. You’re creating an empty app, so click Create Blank App.
  7. In the Name your app field, enter Sales Performance Datasets.
  8. Click Create.
  9. At the top of the app page, click Create and select Dataset.
    Tip

    Tip

    Creating a dataset from within an app automatically selects the app name for you. Hey, every little bit helps!

  10. Click CSV File.
  11. Click Select a file or drag it here.
  12. Navigate to the CSV file that you downloaded earlier, select it, and then click Open.
    Tip

    Tip

    If the file is within dragging distance, you can drag it into the Select a file or drag it here box instead, and save yourself a few seconds.

  13. Click Next.
    Analytics uses the file name as the name for the new dataset, and selects the app.
  14. In the Dataset Name field, change the name to SIC Descriptions.
    Tip

    Tip

    Analytics detects certain properties of your file, such as the delimiter and encoding. You can review these properties in the File Properties Detected box. To make changes, click Menu button in File Properties Detected field and then click Edit.

  15. Click Next.
    The Edit Field Attributes screen appears, giving you a preview of the data you’re about to extract to Analytics.CSV data preview
As previously promised, this is your chance to fix your data before getting it into Analytics. Think back to the things you were looking out for when you prepared the CSV file. You wanted to make sure that fields were set to the correct type: measure, date, or dimension. You also checked that the field names were correct. Take a look at the column headers in the preview to see if Analytics got these right.

The icon in each header tells you the field type:

  • Measure Measure icon
  • Dimension Dimension icon
  • Date Date icon
Tip

Tip

Can’t see the column that you want to check? Use the fields panel on the left to browse or search for the field, then click the field to jump to its column.

If Analytics made a mistake, you can click a column to view and edit its attributes in the Field Attributes panel on the right.

In your data, it looks like Analytics has identified the SIC Code field as a measure. That identification is understandable, but it’s not what you want. If you leave the field type as measure, you can add SIC codes together (not useful), but you can’t group your accounts and opportunities by SIC code (very useful). So let’s change the type.

  1. Click the SIC Code column.

    The field attributes appear in the panel on the right.

    SIC Code field attributes
  2. From the Field Type picklist, select Dimension.

You also checked the date field in the CSV file to ensure Analytics sets the type and format correctly. So let’s check that too.

  1. Click the Last Updated column.

    The field attributes appear in the panel on the right.

    Last Updated field attributes

    Looks like Analytics got the data type right but set the format to European (d/M/yyyy). All the dates in the CSV file are 1/10/2017, which is either January 1 in the U S or October 1 in Europe. Analytics doesn’t know which, so it picks the European format. You want the US format, but that’s OK. You can easily change it here.

  2. From the Date Format picklist, select M/d/yyyy.

As we predicted, Analytics had no trouble correctly identifying the SIC Description field as a dimension. So, it looks like you’re done here. Just click Upload File to finish up.

Analytics uploads the data and gives you a view of progress as it creates the dataset.On screen progress dialog showing progress of dataset creation process

There’s a link here that takes you to the data monitor to see how the dataset is getting on. But let’s take a break here, so just click Close for now.

OK, time for a hot beverage. When you get back, you can check that the dataset has been created.

Monitor the Data Job and Verify the New Dataset

Behind the scenes, Analytics creates a job for your new dataset, which runs in the background. How long this job takes to run depends on how big your CSV file is and how many other jobs Analytics has on the go. It’s easy to check the progress of jobs through the data manager.

  1. In Analytics, click the gear icon (Gear icon) and then click Data Manager.

    The data manager opens in a new browser tab. The first thing you see here is the Monitor tab, with the Jobs subtab selected by default.

  2. Look for your job in the list. The job name starts with the name of the CSV file, SIC_Descriptions.
    Jobs view in monitor
  3. If you’re interested in seeing what Analytics has done to extract your data, click the plus (+) in front of the job.
If all’s well, the job status is Successful. If not, try retracing your steps and loading the data again.

Now it’s time to check the dataset itself. Check such things as field data types and date formats again in the live dataset. So find the dataset and take it for a spin.

  1. On the left of the data manager, click the Data tab.
  2. On the right of the SIC Descriptions dataset, click Dataflow menu button in data manager and select Explore.
  3. On the left of the new lens, under Bar Length, click the Add a measure (+) button.Click plus below Horizontal Axis to add a measure in a lensNo measures here! Remember, we didn’t set any measures in the field attributes.
  4. Under Bars, click the Add a group (+) button. Check that your three fields, Last Updated, SIC Code, and SIC Description, are here. This confirms that Analytics has set them as a date or dimension.

Congratulations! You’ve just turned your CSV file into an Analytics dataset using the CSV uploader tool.

Next up: Bring your Salesforce data into Analytics.

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