Intelecy Data Explorer

Intelecy Data Explorer

Data Explorer provides deep insight into both what is happening in your processes and why it is happening. Data Explorer empowers your operational workforce to test hypotheses, improve their overview, and discover new connections in their data. You can use Data Explorer for ad-hoc process analysis, root-cause analysis and to create flexible and persistent reports.

Unlock insights instantly:

Introducing Data Explorer

Data Explorer is an add-on application to the Intelecy platform giving you access to all tag and machine learning model data, enabling instant data manipulation and visualization.

Enhance efficiency:

Simplify analysis with Data Explorer

To help create the visualizations, you can add tags (time series data) and time ranges to the workspace, creating a shortcut when plotting data. You can also define your own calculated tags based on other tags or simulated data.

Effortless visualization:

Transforming raw data with Data Explorer

You can use Data Explorer on “raw” time series data, but more frequently, Data Explorer is used for "feature engineering" of time series data for analysis and visualization. There are four types of plots available: parallel coordinates, scatter plots, trend and batch analysis. Data Explorer is workspace-based where you create a workspace for analyses that belong together such as a use-case or asset.

Persistent power:

Harnessing Data Explorer for lasting insights

Segmentation is a central part of the tool, and it’s by segmentation you can perform feature engineering. Features are extracted by dividing the data into specific segments of interest. Segments can represent process batches, periods of time, or logical grouping of connected data.

Data Explorer is persistent, meaning once a time series, time range, segment, or analysis is created, it will always be available in your workspace. If you have created a report or visualization once, you can simply set the time to “last week”, “last month” or similar, and re-run the report whenever needed without going through the painstaking process of extracting, manipulating and plotting the data.


Juice Pressure Correlation

Scatter plots

Trending data is good for visualizing data over time, but it can be difficult to compare different tags by looking at a trend. This is where scatter plots become useful.Scatter plots give you a better understanding of correlations between properties when looking at a small number of dimensions at a time. If you have a scatter plot of two tags, and would like to know how this has evolved over time, you can add time as coloring, as you can see above. Coloring can be continuous or categorical. You also have the option to view data in 3D plots

Permeate Pressure Relations

Parallel Coordinate Plots

Quite often data exploration can become challenging. There are multiple variables involved, which can be difficult to visualize. This is where parallel coordinate plots can help. Parallel coordinate plots is an efficient way to visualize and analyze high-dimensional datasets. Parallel coordinate plots are available both for raw time series data and segmented data.

Pressure Drop Overlay

Overlaid segments

This type of plot is specifically tailored to segmented data and enables efficient comparison of time series curves over distinct segments or batches.

A common use case is to observe how a value evolves through production batches over time. This could be any variable of interest, such as temperature, vibration, yield or capacity. The analysis is not limited to batch data; it can also be used e.g. to compare daily power consumption patterns.



Share Data Explorer results with colleagues inside (or outside) your company. Take a snapshot of an analysis, and securely share it with anyone, even if they don’t have an Intelecy account. The exported reports are self-contained (all data is embedded in the report), fully interactive (users can zoom, filter, print) and can even be saved for offline access.

Try for yourself