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Our Approach
Our Approach
An opportunity is a problem worth solving. The solution comes later.
Our Frameworks
Our Frameworks
Decision Opportunity Representation (DOOR) is a framework for formally representing decision opportunities—the situations in which an agent can deliberately impact the environment.
The quality of a problem statement determines the quality of the analyses, decisions, and solutions that follow. The Problem Statement Quality Framework (PSQF) proposes that problem statement quality can be understood through six fundamental dimensions: Conciseness, Cross-Functionality, Knowledge-Driven Representation, Atomicity, Value-Centricity, and Measurability. Together, these dimensions characterize the quality of a problem statement and identify opportunities for improvement.
The Data Solution Life Cycle (DSLC) is a framework for designing and delivering end-to-end data solutions. It emphasizes that successful AI and analytics projects begin with identifying the right problem—not selecting the right algorithm—and that each stage builds upon the previous one.
The framework consists of five stages: Problem Understanding, Data Acquisition & Integration, Data Preparation, Modeling & Validation, and Deployment.
Complexity Adaptation Framework (CAF) explains why a situation becomes difficult for an individual, team, or organization. Complexity is not defined by size alone. A situation becomes complex when the information being handled exceeds the current capability of the agent handling it.
The Investment–Time–Value (ITV) Framework is a general framework for classifying systems based on the tradeoffs they are willing to make. Whether designing an AI system, managing a business, optimizing a manufacturing process, or making personal decisions, every system operates under the three fundamental dimensions.
The 3V Framework, introduced by Doug Laney (2001), characterizes big data through three dimensions: Volume (the amount of data), Variety (the diversity of data types and sources), and Velocity (the speed at which data is generated and processed). Together, these dimensions explain how increasing data complexity can outstrip existing capabilities and necessitate new technologies, methods, or organizational approaches to extract value.
ICE stands for Impact, Confidence, and Ease — three factors used to score and rank experiment ideas on a scale of 1 to 10.
Impact — How much will this move your target metric?
Confidence — How sure are you it will work?
Ease — How quickly can you ship it?
The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely adopted methodology that provides a structured, domain-independent framework for planning and executing data mining and analytics projects. It organizes the data mining lifecycle into six iterative phases—business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
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