Solution Roadmaping

Our Approach

A roadmap is not a timeline. It is a stochastic sequence of actions.

Our Frameworks

Data Solution Life Cycle (DSLC)

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. 

Dependency Structure Matrix (DSM)

A square matrix representation of a system that captures dependencies among its elements, enabling analysis of system architecture, sequencing, modularity, clustering, and structural complexity.

CRISP-DM

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. 

Probabilistic Decision Tree

A tree-structured representation of sequential decisions and uncertain events in which decision nodes represent alternative courses of action, chance nodes represent probabilistic outcomes, and terminal nodes represent resulting consequences. Decision trees support evaluation of alternative strategies under uncertainty through probability and expected value analysis.

Expected Value of Perfect Information (EVPI)

EVPI is the maximum amount a decision-maker should be willing to pay to obtain perfect information before making a decision.