March 1, 2024

Help! My Salesforce Data is Driving me Nuts!

At the end of the day, Salesforce is a database.

Yes, that database is the lifeblood of revenue and customer relationships, but a database nevertheless.

So if the data in said database ain't all that great, bad stuff happens. Execs can't manage the business effectively, users lose trust and abandon, customer experiences suffer, etc. etc.

Ay caramba!

The good news? Well-established methods exist to measure and fix data quality issues.

The bad news? It's takes resources and commitment.

Of course no one ever says they're okay with bad Salesforce data, but not everyone is willing to invest in a solution. But first, let's understand what we mean by bad data by defining  good data

  • Uniqueness: each real-world entity is represented by one record. In other words, the absence of duplicates. One Contact for one person, one Account for one company, one Lead for one prospective customer human.
  • Accuracy: information is correct for each record, such as emails, phone numbers, and addresses.
  • Completeness: first, a given record - such as an Account - has all the necessary information, such as Industry and Annual Revenue. Second, all the records exist in Salesforce - such as having an Account record for all current and past customers.
  • Validity: is the data in the correct and usable format. For example, without having State and County Picklists enabled the chances that any 10 given Accounts from California all being listed as "California" vs "CA" vs "Cal." vs. "Cali", is virtually non-existent. Are Contacts listed in ALL CAPS? If so, good luck with your merge fields.
  • Age: data decay is real. The older your data is, the more likely it is to be inaccurate. Job changes are more frequent than ever and is a leading cause of inaccurate contact data, for example.
  • Usage: this one gets frequently overlooked but is just as important. Data that's collected but not used in reports, models, prompts, automations, etc. is wasting resources. Wasting time if entered manually, wasting storage space (Salesforce storage limits are a real thing once your business takes off), wasting space on an input form. It's not a good habit to collect data "just in case".

Still with me? Cool. Now that we understand good vs bad data, let's talk about the most common causes of bad data.

  • Lack of Data Governance and Ownership: does it surprise you to hear that the primary cause of data issues is people issues and not technology? Without clear strategy, roles and responsibilities, and ownership bad data just happens. How many of your users are SysAdmins? How many checkboxes do you have for the same data points? I recently discovered a customer tracking Email System Used (ie Google vs Microsoft) in both the Opportunity and Account. So which one is the source of truth? Does it surprise you even more to learn that the picklist values
  • Integrations:   APIs can do things to Salesforce no mere mortal can, such as creating duplicates, Contacts without Accounts, overwrite good data with bad, and other maladies. This results from incorrect mappings, triggers, and no designation of systems of records vs systems of truth.
  • Poor User Experience (UX): when Admins, Developers, and Architects implement changes and requirements without the end user in mind, users rebel. Imagine the end of a Fiscal Quarter and a Sales Rep is pushing their final Opportunities to Closed Won, but is met with a barrage of required fields and validation rules. The Sales Rep has never received any value from completing those fields and making quota is on the line. How likely is it they'll complete those fields thoughtfully, completely, and accurately?
  • Lack of tools to address: quite simply, Salesforce does not provide the necessary tools for effective data management. Matching rules don't work well, they retired their own enrichment product, and calculating field usage requires significant customizations. This is where third party products shine, such as Cloudingo for duplicate prevention, 6Sense for completeness and accuracy, and for usage.

In my next post, I'll provide proven recipes for successful Salesforce Data Management.

P.S. Need help getting started? Check out our Salesforce Data Assessment package for complete support and implementation of a data-centric culture.

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