Whether machine learning, robotics, artificial intelligence, or any other buzzing technology, almost every road in digitopia leads to data. Every entrepreneur constantly asks the question; how do I improve my business using technology? If data was an enabler before, today, data & analytics is increasingly becoming the cornerstone of business decision-making worldwide. Not convinced yet? If you love football, you will probably remember the German national football team’s unbeaten run in the 2014 FIFA World Cup. Following a 7-1 victory against Brazil in the semi-finals, Germany brought Argentina to their knees in a nail-biting final that went on to win 1-0 in extra time. But the real hero, the 12th man, during their dream run, was big data analytics. Using SAP HANA, Germany recognized their average ball possession time and reduced it from 3.4 seconds to 1.1 seconds. If big data analytics can help a national football team win the world cup in 2014, imagine the limitless possibilities of data in 2023.
As data-driven solutions continue to transform businesses worldwide, Rajan Sethuraman, CEO, LatentView Analytics, walks us through the happenings in the data analytics industry. With more than three decades of professional experience, Rajan clutches an illustrious history in the management consulting industry. He holds an MBA in Business Administration, Management, and Operations from Indian Institute of Management, Calcutta.
In conversation with Rajan Sethuraman, CEO, LatentView Analytics
Even though the concept of data and analytics has been around for a while now, it has really started to kick on over the past few years. How do you perceive this evolution and the latest trends?
As a company, we have been around for 16 years. Even during 2006, organizations were starting to realize the potential of data generated by the ever-increasing IT systems and applications, including ERPs, transaction processing systems, operating systems, HRMS, and whatnot. Today, organizations have moved significantly in terms of deploying technology and applications across their processes, generating tons of data. But despite most organizations swimming in the sea of data, they are starved for actionable information and insights that can improve the quality of their decision-making. That is the strong trend that sets us off right in this direction. Data analytics can be crucial for organizations to make high-quality business decisions and optimize their processes. That’s been our founding philosophy. In fact, that trend itself is still playing out today. However, most organizations are still scratching the surface regarding the quantum of data they analyze and leverage. For instance, today, data generation has sky-rocketed with the increasing level of online interactions between organizational stakeholders via digital mediums. This leaves much room for analyzing this data and producing actionable insights on both sides of the business.
One of the biggest challenges with data analytics is ensuring people’s privacy and their data’s confidentiality. Whether I am walking into a company as an employee, a retail (or e-tail) outlet as a customer, or a hospital as a patient, I am always in pursuit of personalized experiences.
On the other hand, processing data has gotten simpler over the years. Barring a few high-quality, sophisticated algorithms being developed, many of the current algorithms have been around for decades. However, the ease with which data can be stored and manipulated has changed. The cost of data storage, computation, memory, and processing has decreased significantly. This has enabled more organizations to leverage the power of data. In some sense, we are talking about a bunch of secular trends. However, most organizations are yet to leverage the potential of data comprehensively, and it creates enormous demand as well.
How does LatentView cater to this trend? What are the areas that you focus on?
A lot of our work, for example, is around understanding human behavior in various shapes or forms, whether consumer, employee, patient, or citizen. However, human behavior is a fairly complex topic. Although we generate a lot of data around human interactions, we still have a long way to go in terms of understanding the nuances of the data and uncovering the mysteries of human behavior. Hence, we are still in this space’s nascent stages of evolution. The good thing is that there is a lot of focus and attention from the industry. Several organizations are doing extensive research in the area. We all know about generative AI and GPT. All of these initiatives are coming together to enable high-quality decision-making.
As the industry whisks efforts to drive data-powered transformation, how should leaders develop working structures that attract and retain talent to score organizational and people goals?
“In God we trust. All others must bring data.” These words, made by W. Edwards Deming, an American engineer, today have more relevance than ever. The most vital aspect of data transformation is to believe in data and act upon the idea that data & analytics can make a huge difference in the organization’s decision-making. We can call it the data analytics maturity of an organization. The organizations on the higher side of this maturity index equip themselves with high-quality data & analytics and act up the actionable information across the functional departments, organizational policies, and customer engagements, among others. Unfortunately, the organizations that reside low on this meter still resort to traditional ways and end up making crucial business decisions based on gut feeling and experience. They don’t trust the data. That’s a challenge organizations must overcome to succeed in the digital era.
On the other hand, organizations must also ensure access to high-quality data analytics platforms because data is often scattered across the enterprise—multiple silos and systems, different functional departments, and geographical boundaries, among others. It takes world-class analytics systems to combine data from different platforms and churn out high-quality, actionable insights to help you make well-informed decisions. Additionally, you have to plant, cultivate, and firmly root the philosophy of bringing data to the center of decision-making within the organization. There will be instances wherein you cannot be 100 percent objective, which is all right. The important aspect is having a general philosophy around objectivity and understanding.
How do we create a balance between leveraging digital advances and fulfilling ethical responsibilities to lead an organization effectively in the digital age?
One of the biggest challenges with data analytics is ensuring people’s privacy and their data’s confidentiality. Whether I am walking into a company as an employee, a retail (or e-tail) outlet as a customer, or a hospital as a patient, I am always in pursuit of personalized experiences. I expect them to tailor-make services and products. But they can’t provide a personalized experience unless they know about you and get access to your data. At the same time, everybody rightly wishes to control revealing information about them and give access and revoke it when necessary. The challenge for organizations is to crack the ideal balance between offering personalized experiences and the privacy of people & confidentiality of their data.
The latest artificial intelligence and machine learning models are also beginning to drag in many challenges, especially the Black box AI systems. It is like a child growing up amidst a violent home environment. The child is definitely going to inherit some of those behaviors. We can go back and rewire the Black box AI systems, either. It is possible that we might be passing on some of our bad qualities as humans to these models. This uncertainty around it also poses a challenge.
What does the future look like for the industry, and what areas does LatentView focus on?
The data analytics industry is in a nascent stage and still a fragmented space. Large enterprises are ramping up their investments and efforts in the space. Investors are increasingly interested in the space because it is a fast-growing area, and many smaller companies are making their mark. It’s a sure bet that the data analytics industry is poised to sky-rocket soon.
We at LatentView have identified five important pillars of growth for us. From a geography standpoint, we have always been heavily focused on the US. Over the past six months, we have been expanding our footprint across Europe and doubling in the European market. We are exploring opportunities and having conversations with clients and prospects. We are also running a small experiment in India. However, the US will always remain an essential geography for us.
On the other hand, from an industry standpoint, we have been doing most of our work in technology and the digital native space. We are now adjoining more focus areas, especially BFSI and Retail segments. We see these two areas as crucial future prospects.
From a capability standpoint, there are two areas that we are pushing hard on—supply chain analytics and data engineering. We are also stepping up our efforts in the marketing analytics space—around full-funnel marketing or growth marketing. We are investing a lot of resources into advanced analytics and data science, including graph theory and applying graph theory around natural language generation & processing and image analytics.
What would be your advice to budding industry leaders hacking new opportunities?
While there may be 200 things that might seem interesting, it is crucial to pick and focus on a few things that you believe can change the game in terms of quality decision-making within the organization. Any organization or leader embarking on analytics initiatives should first analyze what the data that they already have access to is, what kind of use cases and problem statements are my internal clients coming to me with, whether they can evaluate with the available data and help clients address the pain points or opportunities, and what will be the likely business impact while using the available information. People want to see tangible benefits without any delays. Hence, you need to show proof that data analytics-driven decision-making can be a lot more impactful, generate more revenue, unlock more value, and save costs.