Ajeet Singh is the co-founder and Executive Chairman at ThoughtSpot, a company revolutionizing analytics with search and AI. Driven by his passion for creation, Ajeet has built two multibillion-dollar technology companies from the ground up – ThoughtSpot and Nutanix. Prior to starting ThoughtSpot, he was co-founder and Chief Products Officer at Nutanix, the leader in the enterprise cloud industry and largest tech IPO of 2016. Prior to Nutanix, Ajeet learned the ropes of enterprise technology startups as the Senior Director of Product Management at Aster Data, later acquired by Teradata for $300M. Ajeet has also held business and technical roles at Oracle, where he was part of the team that first launched Oracle Database to the Amazon EC2 cloud. Ajeet holds an MBA from the Indian Institute of Management, Calcutta, and a Bachelor of Science in Chemical Engineering from the Indian Institute of Technology, Kanpur, where he graduated at the top of his class.
- What is the scenario of Business Analytics in today’s times?
Business analytics has never been more important for organizations than today. Everywhere you look, companies are facing disruption and challenge. The pandemic has fundamentally rewritten the rules on how businesses operate, requiring them to be more agile and adaptive than ever before.
Even before the pandemic, the currents of digital transformation were reshaping the economic landscape. Customer wants, needs, and expectations rapidly changed. New business models emerged that created real competitive differentiation. The businesses that could keep up did so by leveraging data at every level, not just at the executive team or C-suite. This pervasive use of data-driven insights, powered by consumer-grade analytics, has become an imperative for businesses.
- How has the invasion of Technology made Business Analytics seamless?
Analytics technologies have been around for decades with the same promise of democratizing data and improving decision-making. Despite advances like data visualization, the process of getting insights from analytics historically required technical skills. Users needed to know SQL or learn complex tools. For the average business user, this made leveraging analytics tools unfeasible.
The real shift has come from the emergence of consumer-grade technology to engage with analytics. My company ThoughtSpot pioneered search, a paradigm we all know from our personal lives, as a new experience to engage with data, find answers, and make decisions. The industry at large has followed suit with their own by bolting on search products to their offerings. We added AI, making it possible for users to get answers to questions they care about, but wouldn’t even know to ask. The cloud has made it possible for analytics to look at all a company’s data, not just a subset, meaning finally getting a single source of truth is possible.
It’s this consumer experience, powered by search, cloud, and AI technology, that is revolutionizing analytics and finally making it possible for any person, not just the technically proficient, to leverage business analytics.
- Could you throw some light on the importance of AI in Business Analytics?
We live in a world where there’s massive amounts of data, with more and more being generated every day from apps, mobile, IoT, and more. With the cloud, storing all this data has become simple and cost-effective.
Pouring through all this data to find relevant insights is a task quickly outpacing what humans alone can achieve. With AI infused into analytics, the system itself can use algorithms to detect changes, anomalies, trends, and patterns from all this data. Our customers are using this to unearth insights to people without them even knowing where to start. All of this can be done in an automated way, pushing insights to people whenever and wherever they make decisions.
- What are some trends Business Analytics will see in the coming days?
One of the biggest trends coming for business analytics is operationalizing insights. Right now, analytics often exists as a separate workstream, where a business person goes to answer a question they have. We’re already starting to see some of our customers take this a step further, directly connecting analytics to their other apps or tools so they can use insights to automatically trigger actions, processes, or workflows. This, combined with a consumer-grade experience, means organizations can operationalize insights at an unprecedented scale.
- What is the future of embedded analytics?
I believe we’ll start seeing analytics embedded in all different kinds of products and services. Today, these are all powered by data. Adding analytics directly makes it possible to make the work done in these tools far more effective and efficient.
Perhaps even more exciting, however, is the opportunity for companies when it comes to building data apps. These are apps where data and insights themselves are the product.
There are two reasons companies can capitalize on embedded analytics and data apps in a new way. First, with new low-code tools like ThoughtSpot Everywhere, product leaders and developers can easily build, test, and iterate on their apps without long production times or heavy overhead. This gives them the unprecedented opportunity to create product experiences that stick, monetize data in new ways, and harness data right within existing tools.
Second, the simplicity and ease of use of modern analytics give end-users the ability to go beyond the traditional, static dashboards offered by other embeddable analytics solutions. They’re empowered to endlessly explore and interact with data through simple natural language searches. They can answer any question that arises without being limited by a predefined or pre-built report or dashboard. With AI capabilities, the system goes further, automatically monitors the underlying data to flag relevant changes, without users having to even ask a question.
- How have organizations used data during the pandemic?
Data has been the backbone of companies looking to navigate the pandemic. It all comes down to having a real source of truth about the situation as it evolves. We’ve seen customers like Canadian Tire use analytics to move to an eCommerce business as stores shut down, manage inventory as supply chains ruptured, ensure they could supply PPE, all while ensuring the health of their business and their customers.
The core is making sure this data is accessible and in the hands of business people at all levels of the organization. These people, often on the frontlines engaging with customers, vendors, and partners, must be empowered to engage with the data, and use it to make decisions.
- What is the way forward for companies in India?
Organizations, in general, are becoming much more digital in India as commerce becomes digitized in both the B2B and B2C. This creates an opportunity for businesses to leapfrog a generation when it comes to how they leverage data and analytics. Instead of being held back by legacy infrastructure or technology like dashboards, companies in India can build digital transformation initiatives that take advantage of cutting-edge technologies across the entire data ecosystem. This will let them build scalable infrastructures powered by the cloud, find rich insights with analytics and AI, and put them in the hands of every business person through search.
- What are the key skills needed to be a data scientist in BI?
Many leaders will tell you data scientists need to learn skills like python, R or how to build and train the best model. Those are all important, but the value of data science is only realized when it is solving a real business problem. This means data scientists need to have business acumen so they can understand the domain, where there are issues, and how data science can be applied to solve them.
The best data scientists go even further and have the soft skills like communication, storytelling, and relationship building, to become a trusted partner for their business counterparts. These skills are essential in identifying the right problem to solve, building trust, and driving the real adoption of AI and ML.