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There are several features within Shoplogix Analytics Suite that utilizes machine learning to provide insights. The advanced features described below utilize machine learning. The features are described below with data set requirements in order for the feature to function.

Insight Miner

Feature: The Insight Miner Feature automatically searches for data insights, statistics and correlations within the widget data set.

Insight Miner can be access by clicking on the top right corner of a widget:

Under Get Insights, the available data point will be listed to run Insight Miner.

Insight Miner Data Set Requirements:

  • Data is less than 10 MB.

  • Must have at least three columns.

  • One column is numeric or binary.

  • Column titles are clear and descriptive.

  • In a utf-8 unicode format.

  • Indicator widget is not supported.

Explanations

Feature: Explanations will provide possible reasons or correlation for an increase or decrease of a data point in your data set

There are two ways to generate Explanations

  • Explanations can be generated directly from a point in a Time Series

  • Explanations can be generated from the widget by clicking

Explanations analyzes dimensions and combinations of dimensions of the data set in a time series to compare to a previous point in time. Explanations will tell you which dimension drove the behavior which can be a combination of several dimensions.

Explanations Data Set Requirements:

  • Explanations only works on column, line, area, and bar charts.

  • Explanations only works on time series with a single date dimension.

  • Only certain measures are supported.

    • Aggregate functions (SUM, COUNT) are supported

    • Measures based on other Formulas (AVERAGE, MEDIAN, COUNT) aren’t supported

    • Some custom formulas are supported provided that the formula used is using aggregate functions that will provide the same results in partitioned and non-partitioned data

  • Explanations doesn’t work properly when the measure is filtered by a dependent filter.

  • Explanations can only run on a widget when the relevant table is limited to 115K rows (cannot run if the dataset is larger than 115K rows). This limitation may be solved by applying a widget or dashboard filter to reduce the amount of data in the widget.

Forecasting

Feature: Forecast and predict data points in the future based on the current/past trend

Forecasting Settings and Modelling:

A dashboard designer must enable forecasting on the desired widget. To display forecasting the widget value must be enabled for one or more of the values in the chart. Under forecast settings, this must be made visible to display.

The Forecasted Variable is the item that would like to predict. The evaluation period is the data set used for the machine learning algorithm to predict the forecast (at least 30 data points is required).

If there is an evaluation period that should be ignored (for example - an unscheduled time period), this can be modified under the Evaluation Period drop down box.

The algorithm requires continuity in data - meaning we cannot exclude a time period in the middle of a time series.

The forecast period defines the number of values to predict.

The explaining variable is an optional selection that turns the forecast into a multivariate forecast. This will take into account a second variable to that may have a dependency as well.

The AI engine driving this type of forecast uses the following models:

  • Auto Arima

  • Prophet

  • Holt-Winters

  • Random Forest (Random Forest is only used in the ensemble approach; you cannot choose it manually)

These models can be modified in the forecast advanced settings.

Forecasting Data Set Requirements:

  • Forecasting can be applied to the Line chart, Area chart, Column chart widget types

  • Forecasting requires at least 30 historical data points

  • Designers can add forecasting to widgets, Viewers can then use and customize definitions

  • Must be time based variable (with calendar icon) on the X-axis

    • NOTE - selections such as Date Picker or Shift Instance are not using a calendar type variable and would not be a valid forecasting X-axis selection

  • Y-axis must be a numerical value

  • Data cannot have Break-down selected

Exploration Paths

Feature: Exploration Paths will learn from dashboard usage such as filtering choices and drill down data point selections that are made by the dashboard viewers and designers and will learn from the usage to suggest on what to explore.

Once enough data is collected from the dashboard usage and if there are relevant suggestions to be made; the Exploration Paths feature will display a lightbulb icon on the top right corner of the widget

Exploration Paths can be enabled per dashboard. Dashboard owners can enable this setting in the dashboard level setting.

Exploration Paths Data Set Requirements:

  • Dashboard Owners can enable Exploration Path for each dashboard

  • The AI engine requires at least 24 hours of activity to generate suggestions (it might take a couple of days)

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