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How to make Validation Rules into *Validating* Rules

Data fam, we need to have a talk.

It’s time to change the dominant framework of how we use data validation rules. In this blog post, I’m going to write about Salesforce features, but the problem extends beyond just this platform. Even though I’ve gone on record as someone who Loves Error Messages (and I do!), I think it’s time to think twice about introducing error messages into database systems when there are so many other ways to gently guide our colleagues through the data entry experience and into the rainbows-and-lollipops realm of clean data.

In pretty much any data collection system…

… there are ways to check that data are entered in the right format before permanently saving the entry. These “checking methods” can be simple (like, “yellow” is not a valid option, only “green, blue, purple” are allowed) or complex (“is Catthhee a real name?”). The database world is replete with options for “validating” data, but there is a huge missing link in our field, which for me boils down to VALIDATING HUMANS.

Becoming fluent in the tools and strategies of validating data was a huge milestone in my own data literacy journey. When I first got my day job, I set up a bunch of validation rules (Salesforce type) right away! Every time I heard someone say magic words like “always” or “never,” I would dutifully set up a rule that would prevent our system from accepting entries that had “too many deliverables” (maximum of 10!) or not enough budget rows (minimum of 1!). I made fields required, made approval process criteria more stringent, and created a “dashboard of zeros” to monitor data quality. In the beginning, data quality improved, but my team was ready to have a mutiny. The rules impeded them from doing their daily work. For example, if someone was proactively submitting data ahead of time and didn’t yet have a date or a designation that I made required, they would experience a slew of <RED> BOOMING ERROR MESSAGES, which, let’s be honest, none of us enjoy.

I learned the hard way validation rules can quickly become “too much of a good thing.”

Since then, I’ve rolled back most of those early changes, and I have a new philosophy that focuses more on validating humans and less on validating data.

The 3 A’s of validating a human*

*This is made up (by me) and based on personal experience but is not a definition that has caught on outside of this blog.

  1. Acknowledge that the person did something hard
  2. Affirm that this thing is worthwhile and connected to mission/purpose
  3. Applaud the impact this will have on shared goals

Unfortunately, I think validation rules (the way they are currently used) miss the opportunity to uplift our frontline data entry super stars.

From data validation to human validation

Here are 8 methods that you can immediately incorporate into your work!

Sure, at the end of the day, data entry is work, not fun and games. But that doesn’t mean that we can’t incorporate whimsy, purpose, and affirmation into the experience.

If you change anything in your org as a result of this blog post, whether:

please please leave me a comment! It would mean so much to me, and I know I could learn so much from YOU! So don’t be shy! I read and respond to every comment, and this is a conversation that I desperately want to continue. To my fellow travelers in the activist dataverse, I see you! Even if your validation rules are harsh today, we can make them shine tomorrow. Let’s get to work, and I’ll be here to cheer you on along the way.

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