“If you look at the evolution of data, the earliest uses were basic. People captured data in spreadsheets and notes to make decisions. What has evolved are the techniques and organisational literacy around leveraging it,” said Sharad Kumar, CTO of Qlik, while describing the evolution of data.
Data is no longer just columns and rows; it has moved on from being a unidimensional fact and figure to something more dynamic. Today, almost every aspect of our life is governed by data, and we have arrived at a point where data is enabling decision-making for organisations. On the sidelines of the recently held Qlik Connect 2025 in Orlando, indianexpress.com caught up with Kumar, who shared his insights on how AI is shaping data integration and modern business strategy.
3 major transformations in data analytics
During the conversation, Kumar outlined three major transformations in data analytics over the years. He shared that it all began with the centralisation phase with data warehousing. “When we started building data warehouses like Teradata decades ago, it was the first transformational change. We focused on pulling data once, centralising it in one place, and making it easier for people to access. This gave us a backward view of data, which we call descriptive analytics”.
The next phase was predictive analytics. Kumar revealed that this was the phase when machines were being trained and building machine learning algorithms on the same data. Later the world moved from a historical view to a forward-looking view that could predict outcomes for smarter decisions. “Think about recommendation engines on Amazon or Netflix—that’s machine learning in action.”
According to Kumar, the recent transformation came with the generative AI wave. “Suddenly having access to ChatGPT two years ago completely changed the landscape.” What fundamentally changed was how humans interacted with data. Now it’s not about searching for information; it’s about getting answers—a fundamental switch,” he explained, adding that the evolution continues at an accelerating pace.
Kumar went on to state that the next wave is already here: agentic AI. With agentic AI, it is not about asking; Kumar feels that one can express their intent, and agents will determine which processes to deploy and in what sequence. “Going from warehousing to predictive took a long time, but the transitions from predictive to generative and from generative to agentic are happening much faster. The pace of change is compressing,” Kumar said.
Gen AI and enterprise
As generative AI has become a buzzword across the spectrum, we asked Kumar what was hype and what was real when it came to its enterprise use cases. The Qlik executive acknowledged that while generative AI has captured the attention of the C-suite, its implementation hasn’t been an easy one for many.
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Kumar also said that the ground realities are different. “When you talk to data and AI practitioners, you find that the data is not ready. It’s messy, siloed, low quality, not timely, and often can’t be trusted. If you build AI systems on bad data, they will fail,” he said, adding that this was indicative of why success rates remain modest. “Only about 25 per cent of AI projects are truly succeeding in delivering business value. The biggest challenge is the data foundation,” he said.
When asked how the gap can be closed, Kumar recommended a two-pronged approach. “Enterprises that are succeeding are starting with narrow AI use cases that are contained and less risky. At the same time, they’re focusing on getting their data foundation right, which is the only way to scale AI effectively,” he said.
From data to business outcomes
On being asked how Qlik’s platform supports the journey from raw data to business outcomes, Kumar explained that the platform offers a wholesome assistance to businesses through their data journeys. The executive said that the journey begins with data collection. “First, we provide capabilities to get data from anywhere—databases, SaaS applications, complex systems like mainframe and SAP, files, and streams—at high velocity in near real-time.”
Data collection is followed by integration. Kumar said that Qlik allows businesses to join and integrate siloed data. “Unless you can join data together, you cannot get a complete picture. If customer information is in one system, purchases in another, and return information in a third, you need to connect these to understand your customer.”
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After integration, building trust in data follows. The company helps businesses by helping them assess data quality, preserving the lineage of data to trace their roots. Later, the Qlik platform enables multiple types of analytics. “Once you have a trusted data foundation, you can build BI visualisation dashboards for descriptive analytics, machine learning models for predictive analytics, and conversational agents for generative AI,” he explained. Kumar added that finally Qlik enables action, as it allows customers to take insights and automate actions on them.
Challenges faced by enterprises
When it came to challenges faced by enterprises in modernising their data, Kumar revealed that there are three primary challenges, such as data migration, skill gaps, and funding. Data migration is a challenge, as most data today, according to Kumar, continues to be in on-premise systems. Getting this data onto the cloud is a considerable challenge for many.
On the other hand, with many organisations moving to cloud and AI, Kumar feels that most of them often lack the necessary skills, especially for AI implementation. Lastly, with funding, most companies think that they don’t need much budget for AI, as ChatGPT gives the perception that you can quickly apply models. “What we’re finding is that you need a significant budget to fix your data foundation, which is a heavy lift,” he noted.
When asked what his recommendations would be for organisations, Kumar said, “Funding for data foundation should be rolled into their overall AI initiative funding. If you don’t properly fund your data initiatives and have the right technology and the right skills, you’ll face challenges.”
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Lastly, on being asked what excites him the most about the future of data and AI, the Qlik executive said that potential applications of AI to streamline data workflows are something that he looks forward to. More broadly, he sees AI transforming every aspect of business and daily life.
