How to Maximise the Value of Your Salesforce Data

Learn how Fivetran can help you get the most out of an extremely popular data source.
August 5, 2019

Salesforce is a cloud-based customer relationship management (CRM) tool that helps companies connect with customers, partners and potential customers.

Salesforce objects allow you to store data specific to your organisation. Standard objects within Salesforce include account, contact, lead and opportunity. You can also create custom objects to store information specific to your business or industry. For instance, if you were an ice-cream business, you could have a custom object called "flavor" to keep track of your most popular ice-cream flavors per account or customer. Then you could make a calculation to determine your most popular flavor.

To get the most out of Salesforce, you will want to:

1. Analyse All of Your Salesforce Data

Fivetran pulls all the underlying data from Salesforce into a SQL-enabled environment. With access to the full data set, you will be able to transform and display the data, enabling you to deeply understand your CRM operations. Business intelligence (BI) tools such as Looker and Tableau make this easy. When you select fields such as opportunity start and end date, the BI tool performs transformations under the hood to make the data legible to you. You can also apply some logic to handle missing or incomplete data — using Fivetran Transformations, you can build a transformation that pulls start and end dates from the contract object (if available), and the CPQ quote object (if the contract has not been finalized yet).

Another reason to comprehensively replicate Salesforce data is that the data set provides calculated values in formula fields, but the underlying calculations are not carried over when you replicate the data, and the fields will not update to reflect changes to the underlying data. In order to faithfully recreate those formula fields, you should replicate the fields used to calculate the formula fields and recreate those calculations within your BI tool or using Fivetran Transformations.

2. Combine Your Salesforce Data With Data From Other Sources

Data from Salesforce only represents part of your organization’s workflow. You can combine Salesforce data with other sources to build a comprehensive picture of how your customers interact with your products and services. For instance, you can blend Salesforce data with data from support desk software like Zendesk in order to see the most revenue-intensive support tickets and assign priorities to your engineers; and you can combine Salesforce data with event tracking data from apps like Mixpanel to identify the behaviours and customers that generate the most revenue. To measure the success of your digital advertising funnel, you can combine Salesforce data with advertising data from Facebook, Google, Bing and more.

Using Fivetran connectors, you can also link your Salesforce data with data from other sources under the broader Salesforce umbrella, including Salesforce Marketing Cloud, Salesforce Support Cloud and Pardot.

3. Increase Reliability and Ease of Use With Fivetran

The Salesforce API is very complicated, with a huge range of tables with different update and delete mechanisms. Building and maintaining your own connector to integrate data from Salesforce and other sources is tricky and time-consuming. The Fivetran Salesforce connector is the first one we ever built, which means we’ve discovered and worked around every edge case across hundreds of customers. Moreover, it takes only a few minutes to set up, can replicate data on a continuous basis, and automatically migrates schema changes.

Other features Fivetran offers include control over individual tables and fields that are synced, column hashing for sensitive data, incremental updates that preserve your API call quota, and comprehensive logging of every API call and SQL transformation.

At Fivetran, we understand that data scientists and data analysts begin their roles with the intention of generating insights and developing predictive models, but they often spend the majority of their time extracting and wrangling data. The tips in this article should help you skip the grunt work and get back to what you really care about.

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