Tag Management

Section under 'Settings' where you manage all your measuring points, their configuration and hierarchy.

At the heart of the application is the data itself. Every insight, dashboard, or analysis starts with the raw time series: chronological sequences of measurements. To make this data manageable, we organize it using tags.

What is a Time Series?

A time series is a raw stream of data coming from meters, APIs, or imports. Each data point in the series is a value tied to a specific timestamp.

Example: Imagine you have a meter installed at a production site. This meter sends data for two variables:

• Gas consumption in m(g)³: e.g., 2025-09-26 10:00 = 15.2 m(g)³, 2025-09-26 11:00 = 14.7 m(g)³, …

• Gas consumption in kWh: e.g., 2025-09-26 10:00 = 52.8 kWh, 2025-09-26 11:00 = 48.1 kWh, …

Each of these variables is stored as its own time series.

What is a Tag?

• A tag is a flexible layer built on top of time series.

• Each tag represents a specific measurement, metric, or sensor reading (e.g., temperature, energy consumption, production output).

• Each tag maps to one or more time series sequentially in time.

• At any given moment a tag is supplied by one time series only — the mapped series must cover non-overlapping time intervals.

• While the time series contains the actual data, the tag provides context and structure, making the data usable across the system.

• A tag can also be included in the composition of another tag, allowing you to build flexible hierarchies.

Why tags?

Tags let you represent a single continuous entity on a timeline, even if the underlying data source changes over time (e.g., after a meter replacement). By keeping the tag’s composition up to date, you can:

• Maintain consistent charts and reports without reconfiguring.

• Reuse the same tag across dashboards and analyses.

• Ensure historical and new data stay linked under one reference.

Virtual tags

• A virtual tag is a computed tag created by applying formulas to tags (sum, difference, average, if else statements etc.).

• Like regular tags, virtual tags can also be included in tag compositions.

Raw, Normalised and Renditions of Data

Enelyzer can capture data in many different units, but all raw data is normalised into a predefined unit based on the category and quantity of the time series.

Raw Data

• Enelyzer provides a wide range of units to capture raw data.

• For raw data not registered in standard units (e.g., 0.3 kWh instead of 1 kWh), you can set a factor during creation or acceptance of the time series.

Normalisation

• All raw data is normalised to Enelyzer's standard unit for further processing.

• Normalisation on time series may require a conversion factor.

• Tags can only be created in the normalised unit.

• Once normalised, data can be rendered in different units for visualisation or alerts, while preserving the integrity of the underlying values, using conversion factors.

Data Renditions

In the near future, it will be possible to create renditions of data to support specific use cases, such as:

• Applying manual overrides at the tag level while keeping the original data unchanged.

• Automatically filling gaps in the time series.

The original data always remains intact - renditions are additional views or modifications applied on top of it.