Unboxing a VIDX People Counting Sensor
February 8, 2024
Watch: Metadata Properties Explained
Understanding your data starts with understanding its structure. In this short video, we break down what metadata properties are, how they’re applied, and why they play a critical role in turning raw sensor data into meaningful insights.
What Are Metadata Properties?
Metadata properties are the descriptive fields attached to a data point that provide context beyond the raw value itself.
Instead of just collecting a number, like a person count, metadata adds meaning by answering questions like:
- Where was this data collected?
- When did the event occur?
- What device or sensor captured it?
- What conditions or filters apply?
In other words, metadata transforms raw data into usable, structured information. They’re especially helpful when you’re working with large amounts of data and need better ways to organize, classify, and find what you’re looking for.
These examples are automatically available in Vea. But when you have a specific need for categorizing location-based data, that’s where custom metadata properties come in.
How Custom Metadata Properties are Used in Vea
The great thing about Custom Fields is how flexible they are, you can set them up in whatever way makes the most sense for how you want to analyze your data.
For example, let’s say you’re the Operations Director for a retailer with 100 locations. Some stores are in shopping malls, some are in strip plazas, and others are stand-alone buildings. You have people counters installed at every location, but you want to analyze traffic based on the type of property.
Without metadata tags, your options are pretty limited. You could look at each location individually, or manually select a group of stores to calculate totals or averages, but both of those approaches take time and a lot of effort.
In Vea, you can easily add custom metadata tags to categorize your locations. Using our example, you would tag all of the stores located in shopping malls, and then tag those in strip plazas, and so on. Then when running a report, select the new property type filter to quickly group and compare data.
If I’m the Operations Director, I know traffic patterns can vary a lot between malls, strip plazas, and stand-alone locations. Comparing this store to others with a similar setup gives me a much more accurate, apples-to-apples view than comparing it to the average of every store combined.
Turning Data Into Decisions
Metadata properties aren’t just a way to organize your data, they’re what make your data usable at scale.
Inside Vea, they give you the flexibility to structure information around how your business actually operates. Whether you’re comparing store types, analyzing performance across regions, or isolating specific trends, metadata ensures you’re always working with the right context.
The result? Faster analysis, more accurate comparisons, and insights you can actually act on.
If you’re already collecting data, the next step isn’t more data… it’s better structure. And that’s exactly what metadata properties in Vea are built to deliver.