Data Analytics CoE Prescience Decision Solutions September 7, 2023

In a data driven world, building Advanced Analytics and AI/ML capability is your innovation powerhouse, and setting it up requires time and monetary investment in identifying and hiring skilled talent. It is a culmination of months and years of bringing in multi-disciplinary skills which requires deep understanding the talent market and getting the right mix of the team to perform at a high level very quickly. This is where we come in.

We help you build high-quality Advanced Analytics and AI / ML solutions tailored to your business needs through a small team of highly-skilled individuals, in a matter of weeks, and not years. Our approach to building such solutions rests on our capability to establish Centers of Excellence (CoEs) in Artificial Intelligence, Machine Learning and Data Science that are backed by our Business Backward Approach to solving business problems.

Our approach towards setting up an Data & AI CoE for you is built on three key parameters

  • Building requisite skills
  • Focus on innovation
  • Governance

Building Requisite Skills

The successful implementation of Artificial Intelligence (AI) systems requires a combination of skills in data science – deep understanding of mathematical modelling & statistics, engineering, and business. It’s not just the person, but also the platforms that come into the picture here.

A data scientist is crucial for dealing with exploration and analysis of large volumes of data to extract meaningful insights and build predictive models. These data scientists should possess an understanding of business context and KPIs to develop meaningful models that value add to business. For this, they require an analytical data platform.

An analytical data platform is the foundation of modelling and requires core engineering skills. Data engineers ensure that the AI system has access to clean, structured, and well-organized data by building robust data pipelines and maintaining efficient data infrastructure. This leads us to software engineering skills.

Software engineering is required to integrate data science models into business systems and thus make the recommendations utilizable. If you look at the larger picture, the implementation of an AI system requires the data scientists, data platforms, data engineers, and software engineering to work in tandem with each other, keeping the business context as the guiding waypoint.

A CoE achieves this objective by bringing in such multidisciplinary team. By combining the skills of data science, data engineering, and business, multi-disciplinary teams can effectively implement effective AI solutions. The collaboration between these disciplines ensures that AI models are trained on relevant data, data pipelines are robust and efficient, and AI systems are aligned with business objectives. The synergy of these skills maximizes the potential of AI and leads to successful implementation in real-world scenarios.

Focus on Innovation
The field of AI is changing rapidly with advances coming in every week or at times, every day. We work with you on innovation and rapid experimentation at the CoE. When building something new, you can rely on us for our technological and business expertise to create smaller experiments that come together to build something that you can take to market with ease. Our CoEs provide the ideal playground for your business to explore new ideas and push boundaries.

By bringing in the collective experience of working with multiple clients and being at the edge of innovation, we are able to effectively leverage this knowledge to deliver a quick turnaround for your business needs.

The Prescience CoE team will be tightly aligned to your organization at every level. Metrics and measurements will be defined at a work-stream level. A Prescience Founder will be directly involved in providing leadership & oversight to the CoE to ensure quality & priority commitment.

The Governance aspect for the CoE will focus on the following strategic parameters:

Alignment & Transparency
Align KPIs of the team at Strategic, Program & Engineering levels 

Value Creation
Measurement of value created at regular intervals

Business Outcomes
Focus on attracting, developing , and incentivizing specialized AI/ML talent to improve business outcomes. Retain collective knowledge within the organization, by way of knowledge management and creation of IP.

Get answers to all your questions about Data Analytics COE

In addition to these, the CoE offers a unique opportunity for on-demand scaling-up.
Our CoEs are geared for sudden growth or expansion? The CoE equips your organization with the flexibility to scale up operations swiftly and seamlessly. By integrating resources and knowledge, the CoE enables you to respond rapidly to changing market dynamics, capitalize on emerging opportunities, and meet growing customer demands without compromising on quality or efficiency.

If you are an enterprise looking to augment your existing business and technical efforts through data analytics, AI, ML and data engineering, we strongly suggest that you speak with us. Just share your contact details and we will set up a primary call to understand your business objectives.


The data and analytics Center of Excellence (CoE) is a team of technologists, business professionals, data scientists, and data analysts that understands business problems and builds solutions to those problems. India is one of the most preferred destinations for global businesses to establish their data analytics CoE owing to availability of highly-skilled talent and experience in building data analytics platforms.

You can either set up your own team or partner with a firm that has been in the business of data science for a while. Whatever approach you take, your focus should be on hiring quality talent that can begin work on your business problems right away. You should also consider how your intellectual property can be protected, what are the data security measures in place, and how is you data being governed.

Identify the problems that you intend to solve for, and hire for skills that are required to do so. Your data science team will have an analyst, a data scientist, a data engineer, an infrastructure engineer, a business leader who understands the business, and a project coordinator to ensure that governance is in place. You should look for talent with skills in databases, backend engineering, AI, ML and analytical models.

An in-house data analytics team, even if they are not on your payroll, ensure that your governance and intellectual property frameworks are structured and safe. This also depends on the technology infrastructure and security that you have built for your setup. On the flipside, if you are new to data analytics, you might hire talent that doesn’t have the required skills to solve your business problems. Alternately, you can partner with a data analytics firm to establish this team for you.

Since a data and analytics Center of Excellence (CoE) requires a team of technologists, business professionals, data scientists, and data analysts you would require not just the human potential, but also the technology infrastructure and compute power to get your CoE in place. You can either get all of this done yourself, or partner with a data analytics firm that has set up CoEs for other firms.

The data analytics CoE work closely with with business teams such as marketing, sales, operations etc to drive the Data and AI agenda. Aspects of this include identifying business KPIs that can be improved via utilization of data and AI, identifying and collating data from relevant sources, deriving insights and patterns from the data and delivering the business outcomes.


Leverage the untapped potential of skills and talent across each department using Data Analytics COE.