Empowering Business Through Analytics: Swapnil Srivastava, EVP of Data Analytics at Evalueserve, shares insights on propelling digital ambitions and achieving ROI in our exclusive interview.
Can you tell us about your role as the Executive Vice President & Global Head of Analytics at Evalueserve? What are your main responsibilities?
As the Global Head of Analytics, I run one of the largest lines of business in the company, with more than 500 data science professionals across the globe. Our main responsibility is helping our clients improve their analytics quotient and accelerate their digital ambitions so they can thrive in today’s dynamic and highly competitive business environment. That means solving business problems (or use cases) leveraging data, AI/ML, and technology such that they can generate tangible ROI and create a competitive edge.
Evalueserve offers a range of services and products related to analytics, research, and data management. Can you give us an overview of these offerings and how they can benefit businesses?
Our products and solutions provide actionable insights to help clients accelerate data-driven decisions. We offer analytics capabilities from ideation to implementation:
- Analytical Consultation: We help clients achieve analytics and AI proficiency by engaging with executives to foster strategic alignment. Understanding the path to maturity can play a crucial role in reaching your business objectives.
- Data Infrastructure & Cloud Solutions: A lot of data preparation goes into addressing intricate business challenges and scaling AI adoption. We implement cloud and big data solutions using cutting-edge technology.
- Artificial Intelligence & Advanced Analytics: Our solutions use AI to harness predictive and prescriptive analytics, modeling, simulations, benchmarking, and more. We offer versatile, modular products and accelerators across various industries and functions, enabling rapid deployment and scale.
- Seamless Analytics Delivery: With our Analytics Applications, we provide integrated, precise, and real-time insights directly to your workplace, empowering decision-makers to act immediately.
- Machine Learning Operations & Knowledge Management: We maximize ROI from AI/ML by managing model decay and adhering to best practices. Our solutions help clients establish a robust ML Ops program to monitor model health, employing automation and fine-tuning to maintain optimal AI performance.
Evalueserve has experienced significant growth over the years. What do you attribute this growth to, and what do you see as the main drivers for future growth?
Five years ago, we made a conscious decision to embrace AI technologies as a proactive means to address our client’s data, analytics, and research challenges. We built the AIRA platform (AI for Research and Analytics), which leverages a microservices architecture to scale AI across our solutions in a plug-and-play fashion.
This modular, technology-led approach has been instrumental to the growth of our analytics practice, helping Fortune 500 clients navigate market disruptions and quickly adopt the latest technologies like generative AI.
We’re lucky to innovate alongside some of the world’s largest retailers, quick service restaurants, asset managers, and more – from ideation to implementation. Our talented data science professionals don’t just provide models and algorithms. We partner with leadership teams on business-critical problems, building holistic solutions that accelerate data to actions within the nuances their complex ecosystems.
Looking to the future, we believe our growth will be fueled by continued investment in AI products and accelerators, as well as our commitment to offering full stack capabilities in advisory, data engineering, business intelligence, data science, and product engineering. By embracing technology and focusing on holistic solutions for our clients, we aim to remain at the forefront of the analytics industry.
Aspiring or budding data scientists may be interested in learning from your experience. What advice would you give to someone looking to transition to the field of data science or become a successful data science leader?
There are many roles within data science. One can serve as a data analyst, data engineer, data architect, visualization analyst, or AI/ML engineer. When you start looking at these roles, understand which one you most want to succeed in and master it. Develop domain expertise within your chosen area and a relative industry so you can master a sector and perhaps lead a practice.
Also, be sure to develop strong business skills, critical thinking, problem-solving, communications, leadership, large-scale project management, and, eventually, finance. These will serve you well as your career develops and new opportunities present themselves.
Each new level requires more skills, and so you must become a master in your profession, but then you will need to come to understand how to manage a business. Understanding how to manage a business will only help you understand your client’s needs and how to meet them successfully.
Most importantly, never stop learning. This field revolves around technology, and it is always rapidly evolving. Along with technology, global business environments are also changing, and with them, so are our clients’ businesses. Our solution set must always move with these changes, anticipating them and moving to meet client needs. This is the role of a data science leader, guiding teams through an evolving business environment.
With the field of data science constantly evolving, how does Evalueserve stay ahead of the curve and ensure that its offerings remain relevant and effective?
At Evalueserve, we are acutely aware that the field of data science is dynamic and continually evolving. We solve client problems across multiple industries and actively monitor how trends ebb and flow. As we see demand and gaps in the market, we proactively build capabilities and new technologies.
When we build new capabilities, it’s never about any single cutting-edge use case. We focus on scaling new capabilities across clients and, in some cases, leapfrog from behind. As mentioned earlier, our AIRA platform embodies this vision with its reusable, configurable modules.
Additionally, we believe that our people are our greatest asset. We continuously hire experts from the market who bring subject matter expertise and fresh perspectives. These folks enter our collaborative environment to foster knowledge sharing across functions and verticals. We also invest heavily in in-house training programs offered by Evalueserve University. These programs are designed to upskill our existing employees and train new hires. They cover various topics, from basic data science principles to function-specific training. This emphasis on continuous learning ensures our team stays up-to-date with the latest methods and practices in data science.
Finally, we understand and appreciate that no company can develop all capabilities in-house. Hence, we actively seek strategic partnerships to build solutions that fit client ecosystems.
Can you share any recent success stories or notable achievements that Evalueserve has had in the analytics space?
We recently launched MagnifAI, a platform co-created by data scientists, data engineers, and domain specialists. It serves as a catalyst, accelerating the process of gaining insights and taking action in B2B customer and campaign analytics. This is achieved by:
- Providing a suite of technology accelerators tailored for principal use cases
- Incorporating partner plug-ins
- Offering low-code modules that consolidate fragmented workflows for standard use cases, which can be adapted to client settings within a few weeks
We see MagnifAI becoming an essential part of the customer analytics funnel for large companies. Our first customers have liked what they have seen and are contributing to the product’s long-term road map.
The plan is to introduce modules for related go-to-market use cases such as digital analytics, Voice of Customer (VoC), branding, pricing, and forecasting. Furthermore, MagnifAI integrates with the Usecasehub governance platform, ensuring adherence to business and strategic best practices.
How does Evalueserve approach data privacy and security, and what measures do you have in place to protect your clients’ data?
Strong cybersecurity systems and policies are needed to guarantee the integrity of data, analytics, and intelligence used and generated by automation and AI. We know that all automation and AI technologies must comply with the most robust corporate standards in security, privacy, data management, regulatory adherence, governance, transparency, and accountability. We have those policies and cybersecurity technologies in place and continue to evolve our solutions so we can remain vigilant.
Further, protecting individuals’ rights to privacy and general well-being is critical to data analytics and AI. From protecting and guaranteeing data to ensuring our AI models produce outcomes based on clean data sources without bias, we commit to working with our clients to provide accurate and fair information and protect human rights. In addition to our own policies, we adhere strictly to GDPR, PCI, and HIPAA norms while managing customer-sensitive data.
How do you see the field of data science evolving in the next 5-10 years, and what implications do you think this will have for businesses and organizations?
It seems pretty clear to me that we are moving to a business environment where data science professionals increasingly govern, guide, and deploy strategy. We are seeing a combination of more powerful AI for coding, data cleansing, synthetic data creation, reporting (generative), automation, and more. Further, we are finding that more and more tools use low-code or no-code interfaces for humans, making it even easier for data scientists to work quickly.
Overall, this is a great trend. Much of data science is quite rote. Just consider how tedious cleaning data has been traditionally. The more we can use these tools to address routine tasks, the better our teams will be. We can focus on strategic work and creative thinking to develop new approaches for data analysis.
Further, everyone knows how in-demand data science professionals are. Technology evolutions will ease some of that resource strain, allowing humans to work on more of the in-demand data science strategy, architecture, governance, model training, and deployment.
Evalueserve has a global presence with offices around the world. How do you ensure that your services and products are tailored to meet the unique needs of clients in different regions?
Well, fortunately, globalization and the use of teams across the world is not new. We’ve been in business for over 20 years and find that maintaining customer relationships in-country or in-region, in the case of Europe, is best.
We also maintain operations centers in each region where we maintain significant business engagements. This allows us to build teams that can get work done in real-time for clients as needed. In Asia, we have our India and China operation centers; in Europe, there is Romania; and in the Americas, we have Chile, Canada, and the United States.
Some business operations must continue, no matter where the client is or the nature of their business. We are proud of our major operations centers and our ability to work on a client project 24 hours a day, five days a week.
Of course, our team uses the most recent knowledge management tools to ensure success. And we have our own proprietary internal tools to help the research and data analytics teams offer superior and unique services.
Finally, what sets Evalueserve apart from other analytics and research providers, and why should businesses choose to work with you?
Our approach revolves around the concept of domain-specificity. It’s based on the idea that companies have unique knowledge about their industries and customers that a generic ML model wouldn’t possess. This insight translates into domain-specific analytics and AI solutions developed by industry experts.
These domain-oriented models are trained using industry-specific inputs, queries, datasets, terminologies, and experiences. When these models are applied to a meaningful use case, the results can be substantial and fast, making it a worthwhile initiative. This applies to any form of AI automation or analytics, including generative AI.
This methodology recognizes a simple truth: a generalized model is insufficient. It takes individuals with specific domain expertise to generate beneficial business outcomes. From crafting prompts to data organization to response validation, human input is crucial for the success of domain-oriented AI.
Businesses want to work with us because we understand what it takes to deliver a substantive and impactful use case for them. Our experience shows that use cases are integral in defining outcomes. They lay out business goals, pinpoint challenges to be addressed, and outline potential benefits or returns on investment. However, a solid use case also gauges feasibility, risks, measurement parameters, and possible proofs of concept.
When these factors are well-defined, businesses can prioritize the use cases with the greatest potential for impact and long-term, reusable solutions. They want our unique approach and experience in deploying successful models, so they too become successful in their efforts.
Swapnil Srivastava
Executive Vice President, Data Analytics at Evalueserve
Entrepreneurial and research-oriented Data Science leader with over 15 years of experience in strategy and analytics consulting.
Proven expertise in helping clients achieve strategic organizational objectives such as (i) revenue and margin improvement (ii) operations and asset utilization optimization (iii) establishing analytics centers of excellence and (iv) accelerating digital transformation.
Industry Experience: Logistics and Distribution, Industrial Manufacturing, Retail and CPG, Technology, and BFSI Functional Experience: Sales & Marketing, Supply Chain and Manufacturing, Pricing, and FP&A
