Hari Saravanabhavan: Leading Strategic Practices for High-growth Organizations

Concentrix is a leading global provider of customer experience (CX) solutions and technology. It Reimagines everything CX to improve business performance for some of the world's best brands and the ones that are changing the world. Concentrix Designs, Builds, and Runs CX daily for over 130 Fortune Global 500 and 125 new economy clients. Whether it's a specific solution or the whole end-to-end journey, the company covers it. Across 40 countries and six continents, Concentrix provides services across key industry verticals: technology & consumer electronics; retail, travel & e-commerce; banking, financial services & insurance; healthcare; communications & media; automotive; and energy & public sector.

With deep industry expertise, the Concentrix Global Data & Analytics team inspires intelligent change by infusing the "Analytics First" philosophy into everything. The company delivers best-in-class analytics to organizations in different industries and helps them wield data and analytics as a competitive armor, operational accelerant, and innovation catalyst. Concentrix Global Data & Analytics provide a wealth of analytics offerings & solutions, including data engineering, data modernization, data sciences, and business intelligence, along with industry-specific solutions, all powered by a self-serve augmented analytics platform – 'Concentrix Insights Platform' to drive positive business impacts for its customers.

Building and Delivering the Company's Global Analytics Brand

Hari Saravanabhavan is a DSAI (Data Science, Analytics & AI) leader with over 25 years of experience successfully leading strategic practices for high-growth organizations. He is a market creator, Ph.D. scholar, thought leader, AI & analytics evangelist, board advisor, and holder of two patents.

Hari is an innovative business executive with an unparalleled vision for creating cutting-edge technology services leveraging data science, applied analytics, and Artificial Intelligence (AI), with a strong focus on digital transformation imperatives.

At Concentrix, he is a global leader for the Data and Analytics practice responsible for building and delivering the company's global analytics brand across multiple constituent audiences, including clients, partners, analysts, and employees.

Importance of Starting Early

Hari considers himself extremely fortunate to have worked with data and business analytics in the early stages of his career. According to Hari, his career has gone through the natural trajectory of business intelligence/insights at the beginning evolving into advanced analytics and culminating towards data sciences and AI, now including Gen AI. This has given him a foundational perspective on the practice and various aspects of the sciences, which he values as key learnings in his career. Also, the opportunity to serve in leadership roles across business, practice management, and delivery has provided an all-encompassing view of competency across various global markets.

Organizations Being Data – Led/ Analytics Driven

Hari has witnessed quite a few challenges in the industry over the last two decades; a few still exist, albeit in a different form. Being a data-led or analytics-first culture was a key opportunity area to address in the initial years. However, most organizations have matured on this journey and changed their perspectives. However, the inclusion of unstructured data in the last decade, and more recently synthetic data and the inclusion of AI models, has reset the requirements for enterprises to be focused on this aspect. Enterprises are still catching up on being data led/analytics-driven, given the new sources of data & inclusion of technology capabilities, with Gen AI being a good case in point.

Talent with the right analytical skills used to be a challenge, and over time, as that was achieved, there emerged a gap in business/domain skills. Today, with advanced levels of analytics maturity, the industry has overcome basic technical skills, but there still exists a gap in data science & AI skills. These will get further accentuated with new technologies/techniques like data mesh and neural nets, and, as a result, the talent shortage continues to be a challenge.

In the initial years, they had challenges with data translators that evolved into the need for analytics translators. While this has been solved to a certain extent, the industry's progress into niche areas warrants a combination of data, analytics & specific domain translators that are still opportunity areas to pursue.

Understanding of Needs and Principles

As they undertake data science initiatives and projects, leaders must understand what needs to be achieved from an outcome/value perspective. A strong foundation and understanding of data management principles, analysis, machine learning techniques, and technology skills are critical for a successful data science endeavor.

Hari believes it is important to collaborate with the internal functional organization for various requirements, including understanding of data availability/reliability, domain expertise, insights, and action orchestration, as a few examples. Data science leaders must have strong program management and communication skills despite being in a technical role. This is typically a development area for technical competencies and hence needs to be a focus.

Data science can help people innovate and think beyond current mindsets using a powerful combination of data, technology, and domain expertise. Still, it needs a curious leadership mindset to follow an exploratory path. Given the data and technology play, governance around multiple areas is a natural ask; for example, data governance, privacy, security, regulatory requirements, and program management are important attributes to possess.

New Age Technologies Allow for Better Insights and Intelligence

Disruptive technologies like Artificial Intelligence (AI), data science, big data, and cloud computing have an enduring and highly relevant impact on innovation across various industries. These new-age technologies allow for better data management practices, insight generation, and highly scalable orchestration of the desired actions. Techniques like Machine Learning enable continuous feedback and constant learning within the enterprise. Together, these technologies have significantly decreased the barriers to entry, enabled rapid experimentation, and facilitated the development of innovative solutions.

Given the rapid changes, leaders need to orient (or reorient) towards a data-driven and technology-enabled culture, building processes around a collaborative ecosystem, in many cases including external partners. There is a need to strike the right balance of outcomes and governance/compliance requirements (privacy, ethics, and regulatory) to gain the technology benefits successfully.

Delivering Unique Successful Experiences

Data, analytics, data science & AI are the key fundamentals for any transformation, including digital transformation. Hari opined that concepts like data democratization, Gen AI, and ML ops are real game changers that will revolutionize the way business is done globally.

Concentrix is focused on delivering unique successful experiences, including technical experiences (Tx), using Concentrix's innovative "Design, Build, Run" methodology that drives the desired outcomes for its internal and external stakeholders.

"Design, Build, Run" strikes the right balance by encompassing a comprehensive strategic view. A high-level perspective would include the following:

• Defining the vision, assessing current maturity, and prioritizing the right use cases in the design phase.
• Orchestrating the data and technology strategy, enabling a seamless data flow, and developing the right partnerships and governance.
• Delivering at scale, optimizing with the defined automation, and innovating with best practices & frameworks.

Focus on the Way

It is easy for a professional or an organization to get carried away with abundant data, new techniques, and path-breaking technology; however, it is important to ask, "Why are we here? What are we solving for?"

All professionals are ultimately solving real problems; it is important to understand the functional dimensions and technical expertise needed to solve for these problems. Respect the data, start at that point versus diving into the analysis/ findings and learn to differentiate between outputs & outcomes.

Data science is exploratory and needs inputs/feedback from multiple quarters, and collaboration is key to success. Additionally, data science continues to evolve, and we must augment ourselves; lifelong learning is a key trait for promising leaders. Storytelling allows complex analysis to be presented more appealingly and is a critical success factor for data science success in an organization.