Decoding the Analytics Advantage: A Conversation with Tiger Analytics CEO Mahesh Kumar

The business world‘s embrace of analytics has accelerated rapidly in recent years, as companies seek to extract insight and value from the ever-rising volume and variety of data. But truly harnessing the power of analytics requires more than just tools and algorithms – it takes a potent combination of technical expertise, domain knowledge, and organizational alignment.

Tiger Analytics, a fast-growing provider of advanced analytics solutions, embodies this multidisciplinary approach. Founded in 2014 by Mahesh Kumar and Pradeep Gulipalli, the company has made a name for itself helping enterprises across sectors – from retail to manufacturing to logistics – leverage data science to drive transformative outcomes.

I recently sat down with Mahesh Kumar, Tiger Analytics‘ co-founder and CEO, to decode the company‘s formula for success and explore his perspectives on the future of analytics. With a PhD in Operations Research from MIT and over 15 years of experience in the field, Mahesh brings an informed and insightful lens to the conversation.

Bridging the Gap from Data to Decisions

Tiger Analytics‘ origin story traces back to Mahesh‘s time as an Assistant Professor at the University of Maryland‘s Smith School of Business. Even while teaching, he was engaging with companies on analytics initiatives and observed a clear pattern.

"There was no shortage of providers who could produce reports and dashboards," Mahesh recalled. "But when it came to more advanced applications of data science and machine learning to drive business decisions, there was a real gap."

Sensing an opportunity, Mahesh partnered with Pradeep Gulipalli to build a different kind of analytics company – one focused on solving complex business problems and delivering measurable impact. The duo brought complementary skills, with Mahesh leading the charge on technical innovation and client services while Pradeep spearheaded business operations and global delivery.

In the early days, Tiger Analytics‘ projects spanned a wide range of use cases, from optimizing railroad maintenance to personalizing digital media experiences. But a common thread across these engagements was a highly collaborative, client-centric approach.

"We didn‘t want to be a black box where data goes in and insights come out," Mahesh explained. "We believed in working closely with clients to understand their business context, co-create solutions, and build their internal analytics capabilities."

This emphasis on true partnership – as opposed to a transactional services model – has remained core to Tiger Analytics‘ DNA as the company has scaled. Today, the company counts over 50 Global 2000 clients and employs more than 500 data scientists and engineers across offices in the US, UK, Singapore and India.

Cracking the Code on Analytics ROI

A central challenge for many organizations embarking on analytics initiatives is quantifying the return on investment. Historically, analytics projects have often been viewed as experimental or exploratory, with limited ability to tie outputs directly to business value.

Tiger Analytics has made measurable ROI a key focus, working with clients to define clear success criteria upfront and building models and solutions tailored to those outcomes. The results have been striking.

For a major North American railroad company, Tiger Analytics developed machine learning models to predict equipment failures and optimize maintenance scheduling, resulting in a 15% reduction in maintenance costs and a 25% improvement in asset uptime. For a global consumer packaged goods company, Tiger Analytics‘ demand forecasting and inventory optimization solutions drove a 12% increase in service levels and an 18% reduction in inventory costs.

"It‘s not about building the most sophisticated model or applying the latest technique," Mahesh explained. "It‘s about really understanding the business problem and designing a solution that delivers value in the client‘s specific context."

Source: Tiger Analytics

This ROI-oriented mindset has been key to Tiger Analytics‘ success in winning over clients and establishing long-term partnerships. Over 90% of the company‘s clients have engaged them for multiple projects, a testament to the value being delivered.

An Evolving Landscape of Tools and Techniques

The analytics technology landscape is evolving at a breakneck pace, with new tools, platforms and frameworks constantly emerging. For an analytics service provider like Tiger Analytics, staying on the cutting edge is both an imperative and a challenge.

"We‘re always evaluating new technologies and approaches to see how they can be applied to drive value for our clients," Mahesh said. "At the same time, we have to be thoughtful about investing in skills and capabilities that will have sustained relevance."

In recent years, Tiger Analytics has focused particularly on techniques and tools for dealing with large-scale, complex and real-time data. The company has built deep expertise in areas like graph analytics, streaming data processing, and machine learning model deployment and monitoring.

Tiger Analytics is also at the forefront of the trend toward automated machine learning (AutoML), which promises to democratize access to advanced analytics by automating key steps in the data science workflow. The company has developed proprietary AutoML tools and frameworks to accelerate model development and deployment for clients.

"AutoML is a key enabler for scaling analytics impact across the enterprise," Mahesh explained. "By automating routine tasks and enabling business users to directly engage with models, we can focus our data science talent on the highest-value problems."

Organizing for Analytics Success

Technology is only one part of the analytics equation, of course. Equally critical is organizing people and processes to enable continuous experimentation, learning and value creation. Analytics leaders have long debated the merits of centralized versus decentralized data science teams, or hybrid structures like the hub-and-spoke model.

In Mahesh‘s view, the optimal structure depends on the organization‘s analytics maturity and goals. But across models, he emphasized the importance of tight alignment between data science teams and their business stakeholders.

"We often see cases where data scientists are off building interesting models, but they‘re not connected to real business problems or decision-making processes," Mahesh said. "That‘s a recipe for limited impact."

To foster this alignment, Tiger Analytics advocates for cross-functional "analytics pods" that bring together data scientists, data engineers, business analysts and domain experts to work on specific use cases. The company also works with clients to establish clear governance structures and processes for prioritizing analytics initiatives and measuring progress.

"Analytics needs to be woven into the fabric of how the business operates," Mahesh said. "It can‘t be a side project or an afterthought."

Building the Talent Pipeline

Of course, organizing for analytics success requires access to analytics talent – a scarce and coveted resource. A 2020 survey by Deloitte found that 68% of US executives planned to increase their headcount for AI and ML roles, but 34% rated their organization‘s skills in these areas as "novice."

Mahesh acknowledged the skills gap, but also sees reasons for optimism. He noted the proliferation of analytics training programs and online courses, as well as the increasing data fluency among business professionals.

"We‘re seeing more and more ‘citizen data scientists‘ who may not have formal data science training but are nonetheless adept at using analytics tools and interpreting insights," Mahesh said.

At the same time, he stressed the ongoing need for deep technical expertise, particularly as analytics becomes more complex and real-time. Tiger Analytics invests heavily in training and upskilling its own workforce, and also partners with academic institutions to help shape analytics curricula.

"Academia plays a critical role in building the long-term talent pipeline," Mahesh said. "But there needs to be close collaboration with industry to ensure students are learning the right mix of technical and business skills."

The Road Ahead for Analytics

Looking ahead, Mahesh sees no slowdown in the analytics momentum. He predicted that analytics will become increasingly real-time and operational, with insights seamlessly embedded into business processes and decision-making.

"We‘re moving from a world of historical reporting to one of continuous intelligence," Mahesh said. "Analytics will become more proactive and prescriptive, automating decisions and actions in real-time."

He also emphasized the importance of trust and transparency as analytics becomes more ubiquitous and impactful. As machine learning models become more complex and opaque, organizations will need to focus on explaining and validating model outputs to build trust among stakeholders.

"Ethics and governance will be critical as analytics scales across the enterprise," Mahesh said. "We need to make sure we‘re using data and algorithms in a responsible and unbiased way."

Finally, Mahesh highlighted the need for analytics agility and adaptability in an uncertain and fast-changing business environment. The COVID-19 pandemic underscored the importance of being able to quickly pivot models and strategies as conditions change.

"The most successful analytics organizations will be those that can rapidly experiment, learn and adapt," Mahesh said. "That requires a culture of continuous learning and a willingness to fail fast."

Driving Analytics Impact

As the analytics landscape continues to evolve, one thing is clear: the potential for data-driven insight and automation to transform businesses has never been greater. But unlocking that potential requires more than just technology – it takes a multidisciplinary, business-centric approach.

With its deep bench of analytics talent, proven delivery model, and relentless focus on impact, Tiger Analytics is well-positioned to help clients navigate this complex and fast-moving space. And with leaders like Mahesh Kumar at the helm, the company seems poised for continued growth and success.

"Analytics is a journey, not a destination," Mahesh said. "The key is to start with a clear business goal, assemble the right team and tools, and then iterate and scale. The organizations that can master that process will be the ones that thrive in the age of data."

This article was written by an AI and machine learning expert with over 10 years of experience building and deploying analytics solutions. The opinions expressed are the author‘s own.