Data Science in R&D

The ability to innovate drives the biggest differentiation companies to try and build across various industries. The process of innovation can be used to:

Enhance existing products and services.

Launch brand new products or services.

In today’s world, innovation has a component of data science associated with it in some shape or form. Artificial Learning/Machine Learning (AI/ML) as the industry widely refers to, has become an integral part of every R&D team across each discipline and industry.

The use of data science to innovate is now an established practice – starting with clinical biology to marketing and sales.

Why the Importance?

The power of using data for innovation is suddenly exposed with the advent of automation through computerization and the ability of collecting vast amounts of data through digitization, coupled with cheap computing. From a Research & Development (R&D) point of view, Data Science has the following advantages:

Provides valuable insights to design a correct hypothesis

Improves productivity of projects across effort and time

Optimizes resources over costs and time constraints

Manages large and complex iterations at high speed

Simulates outcomes for options with high chance of success

Why Work with a Partner?

AI/ML is complex, risky, and requires high levels of investment as well as change in the way an organization operates. This transformation needs time and money and might set an organization back with respect to competition.

AI/ML requires specialized talent, the availability of which globally is limited. Even though most educational institutes across the world now provide AI/ML courses, the lack of experience of the new talent makes availability scarce.

AI/ML demands a very deep understanding of mathematics and data domains. Even though this discipline thrives on experimentation and failure in the lab, these failures in the real world can have drastic consequences.

With time, the complexity of challenges in the areas of ML has increased, requiring a high degree of skill and experience. In many cases, it is just sheer experience that comes to the rescue of understanding and solving problems.

A lot of work in AI/ML is research-based and is further divided into core research and applied research. Organizations need to manage investment risks in research by creating a healthy balance between core research and applied research.

What do We Offer?

iQGateway has been in the data science business for more than twice the time of an average data science company currently in operation. Experience has taught us many lessons that are now available to our clients as a service, allowing them to reduce or eliminate the learning curve and costs associated with it. Our R&D services provide our clients the following instant benefits:


Our team is built primarily to focus on applied research. The team comes with varied experiences from across diverse technology platforms, problem statements and industry verticals.


Our experience spans across 10 industry verticals – some unique and some similar – with multiple projects executed across each vertical allowing for cross-domain exchange of ideas.


Our unique process framework provides our clients complete transparency on all projects that we undertake. This ensures our clients receive immediate returns on their investments.


Our experience has evolved into a library of reusable components. The use of these reusable components and Intellectual Property (IP) allows us to build solutions faster for our clients.