Prevent data collection errors before they enter your code

For our last topic in our introduction to Customer Data Platforms for Engineers, we’re going to dive into how to ensure that the data entering your internal systems is consistent across various points of collection, and explore how CDP developer tools can help you accomplish this by automating the process of generating data collection libraries for multiple platforms. 

Let’s consider an all-too-typical workflow for implementing an update to a data collection plan. First, marketers or product managers decide what customer data should be collected, and represent these attributes in a static document such as a spreadsheet. They then hand this reference over to your team, at which point you are tasked with manually translating the attributes and values in this plan into the code that collects these data points. If you have native apps on multiple platforms, this might entail iOS, Android and Web developers coordinating with each other to ensure that attribute names and data types are consistent. 

This process is highly error-prone, even for the most detailed-oriented developers. For instance, it is easy to imagine how CustomerName in one library could become Customer_Name in another, making these data points impossible to combine into a unified profile. When errors like this occur, your team is left with the responsibility of reviewing and correcting every data event on every platform where the customer name is captured which, like every customer data-related task, cuts into the time you spend building new product features. 

It doesn’t have to be this way. They say necessity is the mother of invention, and the need for a better, more seamless, and less time-intensive way to translate data plans into code led to Smartype. This robust data quality feature from mParticle translates the properties, values, and types comprising any JSON schema into usable, statically-typed code for iOS, Android and Web platforms. 

If the scenario above sounds like something you encounter regularly, you may want to consider getting started with Smartype today. You don’t have to be working with an mParticle data plan to take advantage of Smartype’s data quality benefits––its generate command, which translates a raw data structure into usable code, works on any JSON schema. These docs will help you get started using Smartype in your projects, and this video shows Smartype in action in a web application. 

That concludes our educational series on CDPs for engineers––I hope you found value in learning about different ways in which CDPs can benefit technical teams. If these messages inspired you to take control of your data in ways that only a best-in-class CDP can allow, here are some further resources for you to peruse:

Feel free to visit any installment of this series here at your leisure.

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