APPROACH

DATA-BUSINESS-CALIBRATION FRAMEWORK


This involves a systematic process of calibration and alignment between business objectives and available data resources as below:

Define Business Objectives:

Identify Key Performance Indicators (KPIs):

Assess Current Data Landscape:

Data Governance and Compliance:

Feasibility Analysis:

Cross-Functional Collaboration:

Prioritization and Roadmapping:

Iterative Feedback Loop:

The specific details of such a framework can vary depending on the industry, organizational structure, and the nature of business objectives. Additionally, the field of data science evolves, and new methodologies frameworks emerge over time but the alignment with the business objectives remains the crucial part of the framework.

CAPABILITIES

Team members collaborate to address the multifaceted challenges involved in extracting value from data. Some of the skill sets are as below: 



R&D

Research and development (R&D) in data science for solving business problems involves the exploration, creation, and application of innovative approaches, techniques, and technologies to address specific challenges faced by businesses. Here are key aspects of how R&D in data science is applied to solve business problems:


We stay at the forefront of technological advancements, experimenting with novel ideas, and adapt solutions to the unique challenges presented by different industries and organizational contexts. It is a dynamic and ongoing process that contributes to the evolution of data science practices and their impact on solving real-world business problems.