Define “Dirty” Data

Dirty data is defined as inaccurate, incomplete or erroneous data, especially in a computer system or database.

In Salesforce dirty data can manifest in a number of different ways, including:

• Duplicate records (e.g. two leads with the same information, a contact and a lead with the same information, etc.)
• Incomplete records (e.g. a lead without an email address or phone number)
• Inaccurate records (e.g. an opportunity with an inaccurate close date)

Define “Data Quality”

Data quality refers to the usability and accuracy of data (technical definition here).  Dirty data is poor quality.  Completely de-duplicated, properly formatted, populated accurate data is considered clean, and of high quality.

Maintaining Good Data Quality

The standard tools used to create records (import wizards, Data Loader, web-to-lead) are not designed to thoroughly manage data quality:

• Import wizards have limited criteria to match duplicate records (e.g. name, email address) on import.
• Duplicate matching occurs only on one object (e.g. import leads will not match against contact records as well).
• Web-to-lead does not perform any duplicate matching.

Here are a few guidelines that can greatly increase data quality in your org:

• Make sure your import files are clean (removed of duplicates, properly formatted, etc.) prior to importing data.  Admittedly, this is not always possible or practical.
• Use the leads object to store lower quality or unverified data.  Only leads that are qualified and of high data quality should be converted to accounts and contacts.
• Train users to search for existing leads/contacts prior to creating a new lead/contact.
• Train users to search for duplicate records prior to working with unverified data (e.g. web-to-lead submissions).
• Use Data.com Duplicate Management (free as of Spring ’15) or third party tools to prevent duplicate records from being created.
• Use required fields (either via field configuration or page layout), validation rules (look at the REGEX function for complex formatting requirements, such as phone numbers), filtered lookups, and other tools and features to ensure data is entered completely and formatted properly.
• Use Data.com Clean (additional license fee as of Spring ’15), another third party tool, or manually cleanse existing dirty data.

Let’s look at a few examples:

Manually Cleansing Duplicate Records

Salesforce provides several manual tools to merge duplicate records:

Leads

Find Duplicates (Button)

Accounts

Merge Accounts (from the account tab)

Contacts

Merge contacts button (from an account record)

Data.com Duplicate Management

Data.com Duplicate Management identifies duplicate records through the following:

Matching Rules specify which fields are evaluated to determine if a duplicate is detected (e.g. First Name, Last Name, Email).  You can leverage the standard rules for some objects (lead, contact, account), or create your own rules if you want to create your own logic or reference custom objects.  Matching rules can specify fuzzy or exact field matches.

Standard Contact and Lead Matching Rule
[Should / Medium / Salesforce.com]

Understanding Matching Rules
[Should / 5m / Salesforce.com]

Duplicate Rules allow the administrator to specify the matching rule(s) (above) that should be evaluated when a record is created or modified, and what should occur as a result (allow or block the action).

Take Control of Duplicates
[Should / 3m / Salesforce.com]

Once activated, here is what an example duplicate rule looks like from an end user’s perspective.  In this example, the user is attempting to create a new lead, but Data.com has found 2 existing duplicate leads and 2 existing duplicate contacts and will block the creation of this record (the save button issues this error):

To configure this rule, the administrator would create a new duplicate rule (the numbers below correspond to the screenshot):

1. The object determines when the rule will be evaluated.  In this example, the rule will be evaluated when a lead is created or edited.
   • If we also wanted to prevent duplicate contacts from being created or edited, then we would create a second duplicate rule for the contact object.
2. Determine what you want to occur when a duplicate is detected upon edit and creation: block or allow.  This example shows that we would block new duplicate leads from being created, but warn users when a duplicate is detected when an existing lead record is modified.
3. Under matching rules, the administrator can specify which objects and matching rule(s) are used to identify duplicate records.  In this example, we are using the standard matching rule for leads and contacts.
4. The matching rule specifies the individual fields that are compared and methodology for comparison (e.g. exact or fuzzy match).

Data Quality Tools (Third Party)

There are a wide range of products to manage data quality within Salesforce.  Here are a few of the more popular options: