business-requirements
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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