A Data Point defines properties of a specific time-series data, representing a single observation within a sequence of data collected over time. Each data point typically includes a timestamp and one or more attributes that describe the observed values at that particular moment.

Data points serve as the foundational elements for monitoring, analyzing, and optimizing industrial processes. They provide real-time insights into machinery performance, production efficiency, and operational conditions, enabling predictive maintenance, enhanced decision-making and improved overall productivity.

Data points are considered vital elements as they are used by most platform features. The following articles will explain everything you need to know about them, their applications and usages in the UI, as well as using Python with the DSAPI toolkit.

Example of a time-series data

Let's imagine we are monitoring a motor bearing's vibration over time. Each data timestamp in this time-series dataset represents the vibration at a specific moment.

Here's an example of a motor bearing dataset from the sample data file provided:

Each row in the table represents a data point. For instance, on August 7th, 2024 at 00:00:00, the vibration of the motor bearing was 0.0614554421602617N.

By uploading data from our sensors and devices into the Wizata Platform, we can transform them into datapoints to monitor and analyze our systems in real-time, optimizing performance and efficiency. Additionally, by organizing these datapoints with categories and assigning appropriate units, we enhance our ability to filter the data and easily perform unit conversions, making analysis more effective.

Time Series Data & Datapoints: Key Differences

Time-series data and datapoints are closely related concepts, but they serve different purposes within data analysis.

Time-Series Data refers to a collection of data indexed in time order. It represents a sequence of data collected at successive, evenly spaced points in time. This type of data is typically used to track changes, trends, and patterns over a period, making it valuable for forecasting and monitoring. Examples of time-series data include pump pressure and temperature readings, or motor bearing vibrations recorded over time.

Datapoints provide a more detailed focus. Each datapoint captures a specific observation at a given time, detailing the precise state or measurement of the system being monitored.

Additionally, datapoints within the platform can be extensively customized to meet specific needs. You can edit various attributes, such as the business type, name, and category to which the datapoint belongs. You can also specify a minimum value and maximum value, following manufacturer specifications for hardware limits. The frequency can be also set to determine how often the datapoint should emit data.

Further customization includes grouping datapoints under a specific unit of measure, setting a validity period to define how long the Last Known Value (LKV) remains valid, and adding a description. For visual clarity, you can change the color and icon representing the datapoint, and even define its location for contextual information. Additionally, labels can be added to facilitate filtering, and you can link the datapoint to a twin unit and add custom JSON data for advanced properties.

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There are multiple ways of uploading your data points to the Wizata Platform, there is a whole article written regarding this, you can find it by clicking here.