data-activation
If you haven’t already, we recommend that you read last week’s Meet the Family: the Communicators & and the Translators for reference before diving into this blog.
We're back to explore more family dynamics under the roof of AI for associations. It’s time to meet the whimsical wonders of the AI brood. Our AI generators and revivers are the poets, the musicians, and the artists of the family. They’re the proverbial renaissance men and women. They’re skilled in multiple musical instruments, are multi-disciplinary in their art, have published a few novels, and occassionally dabble in poetry.
These creators, who fall squarely in the category of Generative AI, have the ability to produce something entirely new, or revise based on something that already exists.
Let’s get to know them.
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Families are connected. They're committed. They're complicated. They have the right intentions but can come up short at times. But in the end, they represent support. And while family members may take on different roles and drive different dynamics within the brood, they're still best when they're functioning together as a unit.
Most wouldn't necessarily see AI as just another family member but I do think it's fair to say, that AI—regardless of the attributes and the functions it serves—is best leveraged when perceived as smaller parts that fit into a much larger picture.
For the month of April and May, HighRoad's blogs will be dedicated to AI for associations. You'll get the skinny on their roles, sometimes their dysfunctions. But most importantly, specific uses cases that are meaningful for associations.
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Hungry Hungry Hippo was a cool game. The strategy was pretty basic. You had four different hippos all chomping down on plastic marbles. Each player had a hippo. The goal was to hit the hungry hippo at just the right time to gobble up the marbles. But, as kids, that translated to hitting the hippo lever obsessively to get as much circular yumminess as possible. Those with the fullest hippos at the end of the clock, won the round. And so on and so forth.
GenAI is a little similar. Yes, per usual, I’m oversimplifying. I’m comparing one of the most powerful and evolving technological advances in history, to a $15 Milton Bradley game from the late 70s. Yes, this is indeed happening.
But roll with me here.
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Data integrations are our specialty. And while we’d love to say that they’re 100% accurate all of the time, in the end, technology is technology. It’s only as good as the people behind it. When errors come up, most of the time, it isn’t about the technology that is ‘integration’.
No, that technology will continue running the same way it was designed to run. It will remain that gate that opens once signaled, allowing data to flow from one system (AMS, EMS and future association systems) to another system (HubSpot). Then the field, field attributes, values, and lists that flow through are based on the business rules and requirements dictated by its owners—the associations and nonprofits that we serve.
When an integration isn’t working, about 99% of the time, it’s:
- The accuracy (or lack thereof) of their data
- The way the data is configured within the AMS
- The way tech assets are delivered
- Staff’s lack of understanding around their data or how to activate on it
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Data is abstract. Complex. Multi dimensional. It has wide-spread application and needs a lot of grooming for performance. It’s no wonder there’s often confusion and malaise around it.
From an organizational perspective, however, all staff should have an optimal level of data literacy in order to put their data to action. Literacy doesn’t just apply to Data Analysts or Data Operations Managers. No, every individual within an organization should have a base level understanding of what data is available, how it’s organized, how it works, and how to upkeep it according to governed standards.
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There are a lot of terms thrown around when it comes to identifying the right data to sync from one system to another. Requirements gathering, requirements capturing, requirements elicitation. They’re all different ways of saying that the same thing—"let's identify the data that's going to move our association in the right direction."
In the end, it’s about digging deep on what your organization needs to grow, sustain, or change and following that trail to the end of the data line. Come to think of it, identifying the right data to bring from one system to another isn't unlike making chicken soup. Yes, you heard that right. Let's dig into that.
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