Audience
- This playbook is designed for beginner CRM administrators.
Playbook objective
The objectives of this playbook are to:
- Identify and scope malformed data problems
- Fix or delete malformed data
- Read our data management strategy playbook for help with creating a data management strategy.
- The DemandTools modules referenced in this playbook are for versions 5.X.X. Please be sure to update your software.
- We recommend standardizing your data first as it can fix many malformed data problems.
- We recommend setting up a sandbox environment to test data manipulations prior to implementing them in a production environment. You don’t want to make changes that adversely affect your CRM data quality.
- If you are trying to solve a specific data problem, review the product training documentation in the Validity Help center or get answers to your questions from Validity’s data experts during office hours.
- For technical issues regarding your software, please contact Validity support.
A clear data management strategy will help to improve your CRM data quality and support achieving your desired business outcomes. With a clean CRM database, many businesses achieve better:
- Accuracy in sales forecasts and reporting
- Data privacy compliance
- Targeting for marketing
- Operational efficiency
- Preventing malformed data
- Malformed data methodology
- Selecting a frequency
- Assessing malformed data
- Addressing malformed data
- Automating malformed scenarios
- What to do next
- Data governance: Developing and implementing data policies and procedures to support business goals.
- Data quality: Accurate, complete, reliable, and actionable data.
- Data standardization: Applying a common and consistent data format.
- Data hygiene: The process of cleaning data to reduce errors and improve data quality.
Preventing malformed data
Proactive data management is a key strategy for maintaining a clean database. One of the most effective ways to prevent malformed data is to reduce manual data entry by implementing configurable data types and business rules in your CRM or within a web form.
- Configurable data types include picklists, using check boxes to affirm a selection, or to provide multiple choice options and radio buttons to limit a selection.
- Business rules may be an email address syntax check and validation on a web form or populating the state/province and country based on the entered zip/postal code.
- Standardizing your data upon import can help prevent some malformed data problems.
Malformed data methodology
Before you start fixing your malformed data, ensure you have a plan in place to help you manage activities. A plan can help you split up the amount of work you do into manageable sizes and help ensure you identify and address all malformed data.
Frequency
Decide on the frequency you need to address malformed data. How frequently you address malformed data depends on the rate at which you are introducing new records into your organization as well as the overall size of your database. Determine the frequency based on your needs. No matter what frequency is required, automate your data cleaning scenarios using DemandTools.
- Daily: If you have hundreds of new records coming in daily from different channels, you probably need to run malformed jobs daily.
- Weekly or monthly: If you have records that trickle in each week or come in less frequently, then you probably need to run malformed jobs weekly or monthly.
- Quarterly: Each quarter, schedule a comprehensive malformed data review and cleanse to ensure all records are addressed as expected.
- Address malformed data upon import: DemandTools allows you to standardize and fix malformed data during import. Be sure to train staff responsible for importing on any requirements and procedures.
Data may be input or changed manually in your CRM by staff in your sales, marketing, customer success, and finance teams, so you may need to adjust frequency even if your data intake rate is low.
Assess malformed data
Assess your malformed data problems using the Assess, Tune or Export modules to understand the scope of work and potential malformed data sources.
- Review your Assess results in my.validity.com to get a high-level view of malformed data by object and prioritize work based on severity. The Assess feature breaks down malformed data by object and categorizes it as Malformed.
- For each object, perform additional analysis using the Tune or Export modules to identify specific malformed data types or data fields.
- With your data governance team, determine what data is most important to your business and create a prioritized list based on the object and/or data type. Some malformed data may be very old or unactionable, in which case deleting the data may be the right course of action instead of trying to populate it with the correct value.
Below is a list of common malformed data to look for during your assessment.
- Email addresses
- Missing username (e.g. @sampledomain.com)
- Bad syntax (no @ sign or top-level domain (e.g., .com, .net, .org))
- Misspelled domains (e.g., gmial.com and yahooo.com)
- Typos
- Misspelled states, countries, and street identifiers (e.g., Avwnue, Streeet)
- Unwanted text
- Account names containing words or phrases like formerly known as (fka), doing business as (dba), delete, dupe, old, gone, city or region of the business
- Lead or Contact names containing the words delete, dupe, old, or gone. Or they contain nicknames or have the last name included with the first name in the first name field.
- Addresses
- PO box included with address on a single line
- Incomplete zip or postal codes
- Phone numbers
- Incomplete or incorrectly formatted numbers (e.g., 303555129_, (30)35551298)
- Picklist fields
- Data inserted into your CRM database has an invalid picklist value for a specific field.
- For example, your picklist values are “Red, Yellow, Green” and the data inserted into your database is “Yello”
- Data inserted into your CRM database has an invalid picklist value for a specific field.
Address malformed data
Now you have a prioritized list of malformed data to search for and address. Use DemandTools to populate the correct value, delete, or to populate a malformed data field identifier that the sales, marketing, customer success, or finance teams can use to identify and fill in malformed data.
We recommend standardizing your data first as it can fix many malformed data problems.
When you finish standardizing your data, address the other types of identified malformed data.
Automate your malformed data scenarios
Schedule your malformed scenarios to run automatically at a frequency you define. We recommend testing scenario automations in a sandbox prior to implementing the automations in a production environment.
Data governance team alignment
As you work through addressing missing data, talk to your data governance team about sources of malformed data and recommend improvements such as employee training or system enhancements to proactively reduce data errors.
- Continue cleaning your data using our other data playbooks.
- Assess and Verify playbook (do this one first)
- Dedupe playbook
- Missing data playbook
- Data standardization playbook
