Commercial Research

We don't believe good data science comes out of the box. For high quality and discerning value added solutions each problem must be thoroughly understood, the solution then designed to properly address the core problem with specific nuisances and constraints in mind.

To that end a key feature of REA is our ability to fuse academic rigor and discipline with commercial objectives.

OUR KEY PRINCIPLES IN COMMERCIAL RESEARCH

• Commercial research first seeks to deliver real world solutions immediately applicable to the organization.
• Study cycles are structured to ensure a consistent stream of incremental benefit is achieved.
• Research environments are engineered to provide highly controlled and robust examination of the problem.

Your organization will benefit from our experience, where data science technology has been applied to the decision making behind billions of dollars.

Experience you can trust.

What is Commercial Data Science Research?

In general, a business or organisation are interested in using new tools to find commercial value. They are interested in how that new tool may improve the way existing things are done or offer entirely new things that can be done relevant to the objectives of that business.

Commercial Data Science Research is applying skills in machine learning, big data and technology to find solutions to new or existing problems. In this process REA work closely with the organisation to deliver commercial evidence of the value of a Data Science product. The process is broken down into the following steps:

• Problem Analysis - working with the organisation the goal is to understand the objective and make sure there are no obvious show stoppers.
• R&D Theoretical Analysis - REA will typically examine solutions based on artificial data to establish a principled solution to the problem. In this step, if required new theory can be developed if existing technology falls short of the problem challenge. The structure of experiments moves from simple and core aspects of the problem to more detailed refinements.
• Real World Analysis - Supported by theoretical evidence REA will examine the solution under real world data. In some cases the environment will closely resemble the real world and the results will therefore support strong commercial value.
  A large part of this phase will closely study the problem for assumptions that are being made and the impact of those assumptions. Where new data emerges that challenges the theory REA will iterate back to phase b to address those issues.
• Simulation/Live Analysis - The process so far delivers a clear view of the product development to the organisation.