Mukund Raghunath, Founder and CEO of Acies Global, speaks with Della Shea, Head of Operational Risk, Data, and AI at CIBC, in a video interview about his background, what led him to start Acies, the gap that his organization fulfills, and a key offering for clients.

Raghunath has spent over two decades immersed in data, analytics, and technology. His company supports Fortune 1000 clients in navigating their digital transformation efforts. He attributes his MBA from the University of Chicago to refining his approach to problem-solving, shifting his perspective from purely technological to a more business-oriented lens.

After earning his MBA, Raghunath pursued a consulting career, joining ZS Associates, a firm specializing in data-driven strategies for the pharmaceutical and healthcare sectors. There, he worked with prescription data to inform sales and marketing decisions for pharma companies. This experience marked the beginning of his journey at the intersection of technology, business, and data.

Reflecting on his experience before founding Acies, Raghunath highlights his time as a leadership team member at data analytics startup Mu Sigma. Over his 12-year tenure, he observed a recurring challenge among Fortune 500 companies – while significant insights were being generated, their consumption largely occurred through tools like Excel and PowerPoint, rather than being embedded into the operational systems that drive daily business functions.

Raghunath noted a disconnect between the teams working with data analytics and those developing software applications. Analytics professionals often struggle to bridge the gap between insights and their seamless integration into business applications due to limited technical familiarity. Conversely, application developers focused heavily on meeting business requirements from a technological perspective but rarely anticipated the deeper analytical needs that arise as data flows through their systems.

This experience shaped Raghunath’s vision for Acies — to unify data, analytics, and technology in a way that fosters intelligence at scale, enabling businesses to operate more cohesively and efficiently.

Raghunath explains that Acies’ work is structured into three main areas. The first focuses on generating insights through data science, machine learning models, AI applications, and business intelligence reporting. The second area emphasizes scaling these solutions, which involves establishing robust data pipelines and ensuring data flows seamlessly to support continuous system operations, thereby avoiding repetitive analyses. The third area centers on enabling the timely consumption of insights through appropriate tools and applications that drive business decisions.

He points out a disconnect within the organization where data engineers, software engineers, and data scientists often fail to anticipate broader needs. For instance, data engineers might not consider business analytics requirements when building data models, while data scientists may lack the technical expertise to write scalable code for their models. While the individual skillsets are strong, Raghunath highlights the challenge of integrating these roles to create a seamless and continuous workflow, which is the gap his team is striving to address.

Speaking of his approach to dealing with siloed talent, Raghunath stresses the importance of adopting a “deep generalist” mindset within the industry. He advocates for professionals to develop an appreciation for areas beyond their immediate expertise. For example, data scientists should understand the needs and processes of data engineering to enhance their own effectiveness, while data engineers should consider how the data they manage will be consumed and analyzed.

By encouraging individuals to explore multiple domains—such as data science, data engineering, and software engineering — Raghunath highlights the potential to improve collaboration, streamline workflows, and scale intelligence seamlessly across systems. He also stresses the importance of integrating various technical disciplines—data engineering, data science, and software engineering—to create intelligent systems that can scale analytics continuously.

Raghunath mentions that his organization not only helps clients combine these elements effectively but also encourages its team members to broaden their expertise beyond their specific domains.

For instance, data scientists are encouraged to understand the foundational aspects of data engineering and software engineering to ensure their models are scalable. Similarly, data engineers are guided to consider the needs of data scientists and analysts to build data models that are highly functional and aligned with end-user consumption.

From a broader perspective, Raghunath shares that the approach involves tackling problems with a strong focus on business. By asking the right questions and conducting thorough research, his team ensures they understand the problem deeply. Their experience across multiple industries allows them to bring fresh perspectives, often driving innovation.

Accordingly, clients value this comprehensive, proactive approach, appreciating the ability to provide end-to-end solutions without requiring constant direction.

CDO Magazine appreciates Mukund Raghunath for sharing his insights with our global community.