Enhancing Data Analysis with Custom Business Context

WisdomAI is engineered to perform a wide range of queries directly on your dataset. If you are seeking to conduct more advanced, customized analysis, the custom business Context feature allows the system to be aligned with your specific operational logic and proprietary terminology. This document outlines an example procedure for leveraging this feature to generate strategic insights.

Level 1: Foundational Data Interrogation

The system is designed to process queries that reference explicit data points within your tables. For example, it can accurately count, group, or filter based on existing columns and values.

When a query includes business-specific terminology not explicitly defined in the dataset, such as “Strategic Account” or “At-Risk”, the system, by design, will prompt for clarification. This ensures that all subsequent analysis is precise and perfectly aligned with the user’s intent.

Level 2: Implementing Custom Logic with Business Context

The Context feature enables the system to autonomously execute complex queries. This is achieved by providing the tool with a set of rules that define your organization’s internal logic.

As an explample, let’s consider that you want to empower the system to identify “At-Risk Strategic Accounts”. To acomplish this, you need to provide the following definitions via the Context feature:

1. Geographical Sales Territories: 
- NA-West (North America West): Includes [Account Names] 
_ NA-East (North America East): Includes [Account Names] 
- EMEA (Europe, Middle East, and Africa): Includes [Account Names] 

2. Account Tier Definitions:** 
- A "Strategic Account" is any customer with a contract duration of 3 years or more. 
- An "At-Risk Strategic Account" is a Strategic Account where Auto Renewal is set to False. 
- A "Secure Strategic Account" is a Strategic Account where Auto Renewal is set to True.

Level 3: Executing Multi-Layered Strategic Analysis

With the business Context supplied, the system no longer needs to prompt for clarification. It is now configured to perform the multi-layered analysis directly.

When presented with the same query:

Generate a list of all 'At-Risk Strategic Accounts' and show me which sales region they belong to.

The system executes the request. It cross-references contract duration, renewal status, and account-to-territory mapping to produce the precise, targeted list required.

This demonstrates the successful application of custom context to yield high-value, strategic business insights.

Level 4: Granular Analysis Using JSON Parsing Context

A key challenge in data analysis is extracting information from fields containing semi-structured text. For example, a single Skus column might contain multiple products, tiers, and quantities in one text string, making direct calculations difficult.

The Context feature resolves this by allowing users to define parsing logic with a JSON input. Without this logic, the system cannot perform calculations on data embedded within text.

The following code snippet shows an example of using JSON context ~~was provided ~~to teach the system how to parse the Skus column:

{
  "example_for_skus_column": {
    "comment": "Parse the 'Skus' column into a structured list.",
    "original_skus_column_value": "Sales Ultimate 15 licenses, Marketing Professional 50 users",
    "expected_parsed_structure": [
      { "product_family": "Sales", "tier": "Ultimate", "quantity": 15, "unit": "licenses" },
      { "product_family": "Marketing", "tier": "Professional", "quantity": 50, "unit": "users" }
    ]
  }
}

With this parsing logic supplied, it is now possible to execute highly specific queries, like the following example:

What is the total quantity of 'Ultimate' tier licenses sold for the 'Sales' product line?

The system can now deconstruct the Skus field for every contract in real-time. It filters for the “Sales” product, identifies the “Ultimate” tier, extracts the corresponding quantity, and aggregates the total, providing a precise numerical answer that was previously impossible to calculate.

Next Steps

• Advanced Data Modeling: Creating Knowledge
• Validate and Test Answers
• Provide Feedback to the System
• Turn Answers into Stories