Fixing Integration Errors in Data Can Be Astonishingly Easy

Fixing data errors is not an easy task. And when it is, it might not be a quick one. You’re either losing effort or time, which we know is not ideal.

Alerts and monitoring in the data stack

It’s no surprise that your life revolves around data when you are a data engineer or when you work in Revenue Operations. Data going one way or the other is what you see every day. Integrating two systems becomes a daily activity and keeping your organization’s data pipeline in order is your highest priority.

It’s a hard job, and reaching perfection is quite difficult, not to say almost impossible. And just like any technical employee or a person working closely with any type of software, you are most likely going to run into some bugs in the process. Of course, the data engineers are not to blame. Data is complex, and business systems have different requirements when it comes to the data that is ingested from other sources.

In the data pipeline, these bugs are called “errors”, but they are exactly the same thing. They are tiny, and at many times unnoticeable to the inexperienced eyes. That said, they can cause some serious damage when it comes to an organization’s data. It might cause metrics to be miscalculated or not updated, or it may result in a lack of data visibility for some sections of the organization. I’m sure no fintech (or any other type of company, for that matter) wants that!

And these errors, as tiny as they are, can be really tricky. First, data engineers/RevOps don’t have an easy way to notice that their data syncs have errors (i.e bugs). There is no system that alerts them in a timely fashion, and they tend to notice them when it’s too late.

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Additionally, fixing those errors is not an easy task. And when it is, it might not be a quick one. As we mentioned earlier, each business system has different requirements and works differently. Plus, documentation varies from software to software. There are some companies, like HubSpot, that have amazing documentation, but for some others, documentation can be an impossible and time consuming task, making the discovery process of the root cause of the error to be quite painful.

There are things you can do to be one step ahead, of course. Errors tend to fall in the same four or five categories, and once you’re able to determine that category, you get closer to fixing them. Also, you can learn the data models of the systems you’re working with, that can be helpful too. But is that the best use of your time?

While learning all your system’s ins and outs seems like a grueling process for a human being —especially one that already has important tasks to take care of—, for a machine is really not too much of an issue.

With the rise of technologies that help identify and fix errors automatically, such as AI applications, a whole new world of possibilities is unlocked. The hours and brain power that are normally spent by the engineering team to handle these issues can be done with the help of machine learning in a much faster and cleaner way. By doing this, engineers can focus on more thorough, complex work, instead of looking for hours for the documentation of whatever tool that suddenly stopped working.

Best thing about it: It’s actually not something crazy to think about. In the end, it’s just a model that takes the guesswork out of the equation by classifying each error into the four or five categories we mentioned and by system. But you shouldn’t even worry about that…

That’s where HelloGuru comes in.

Book a call here to learn more about it!