See the Future with Pyramid's AI-driven Explain Feature

Pyramid’s AI-driven “Explain” feature allows users to dig directly into their source data with a single click, providing faster insights into key drivers and influencers through automated analysis with minimal effort.

Real insight into data is usually only gained after traversing and mining large amounts of data until valuable findings are discovered. Instead of manually drilling into the data or relying on intuition to choose dimensional elements, “Explain” automatically determines the elements that contribute to a data point’s value.

The Problem

A significant amount of time is typically spent sourcing data, manipulating it, and arriving at insights. Analysts often need to explore numerous permutations of hierarchies and metrics, searching for drivers influencing high concentrations or average values. An automated AI function would be an ideal solution to this challenge. Many BI tools lack such capabilities directly on any data source, often requiring data ingestion first and limiting automated analysis to predefined metrics and hierarchies, which reduces self-service and on-demand functionality.

Pyramid’s Solution

Pyramid’s “Explain” feature performs automated analysis in a single, on-demand click without requiring any data preparation or complex development. Insights can be obtained immediately against any data source. An advanced machine learning algorithm automatically traverses all hierarchies and metrics, identifying the most significant drivers and influencers behind the values in question.

The Explain feature uses a two-pronged approach, analyzing both high concentrations of values and influencing factors, revealing the most significant items and metrics.

The feature combines automatically generated textual commentary with graphical representations. An advanced interface provides a larger set of contextual reports and AI-generated explanations for each selected node.

The depth and intensity of the algorithm can be customized to suit user requirements in an intuitive, user-friendly interface. All generated insights, reports, and dashboards can be saved as standard artifacts and further enhanced using Pyramid’s Analytics platform.

Business Case

Amy, a business analyst at XYZ Telco, uses Pyramid to analyze data from her SAP system using HANA Calculation Views. During her analysis, she discovers that the monthly charge for customers with multiple phone lines and online security is $1,044. She wants to understand why it’s so high and what other items in the data best explain its value.

Amy right-clicks on the data point and selects “Explain,” triggering the underlying query engine to retrieve all the details for that data point across the entire data model and present it to the Explain machine learning engine for automated analysis.

Within a few seconds, Amy can start to review the results. First, she reviews the tab that explains what items dominate the value drivers. It shows that customers with streaming TV (StreamingTV = “Yes”) are the most significant element and account for $884.25. Within that, two-year contracts account for $638.90, and within that, $463 is accounted for by customers who have technical support. By clicking on the middle oval, she can also see textual commentary describing the breakdown, with a contextual column chart depicting how the two-year contract monthly charges compare with other members in the contract hierarchy.

Next, Amy wants to determine what factors are pushing the average value of monthly charges higher for the specified data point. This may give her a sense of the relative sizing versus absolute value concentrations.

She clicks on the influencers tab, where the AI engine traverses all hierarchies and other metrics in the data model, using average values to determine the impact rather than a simple summation. Here it shows that when the contract tenure is greater than 63 days, it has an impact of just 1, while within that, a fiber optic internet service has a 1.2 impact (meaning those customers with this option pay 20% more on average in monthly charges). Finally, any customers with streaming members impact the average value of monthly charges by 30% or 1.3. (Factors over 1.0 contribute to increasing average values, while factors below 1.0 contribute to decreasing average values).

Amy then increases the depth and breadth of her analysis by moving to a more advanced interface. She selects three branches and four levels in the search parameters and views the results in a tree. On the first level, she can now view a two-year contract and partner = “Yes”; on the second level, she can view Partner = “Yes” and Tech Support = “Yes” within the context of Streaming TV = “Yes”, and so on. The tree stretches down to a fourth level where she can view Online Backup = “Yes” and Payment method = “Credit card automatic” within the context of TechSupport = “Yes.”

Finally, Amy performs a detailed analysis of the Online backup node on the fourth level. By clicking on it, the advanced interface exposes a dashboard containing four contextual reports with automatically generated explanations.

The Explain feature has allowed Amy to perform detailed analysis with a single click in a few minutes, directly against her SAP HANA data source, without requiring any ETL, ingestion, or data manipulations.

Summary

Pyramid’s “Explain” feature performs automated analysis directly against any data source in a single, on-demand click. “Explain” finds the most significant drivers and influencers for a selected data point by automatically traversing all hierarchies and metrics. It analyzes high concentrations of value and influencing factors for increasing or decreasing average values, combining textual commentary with graphical representations to highlight results.