Data and AI are two sides of the same coin in the digital age. However, what often goes unnoticed is the role of data in harnessing the potential of AI.
HAS Zero 2024, India’s largest AI conference organized by AIM Media House, Pradeep Gulipalli, Co-Founder & CEO, Tiger Analytics, India, said, “AI, at its core, thrives on data.”
But data doesn’t just arrive in our hands, ready to be analyzed. It is a raw, often unstructured resource that must undergo several processes before becoming valuable.
“This sentiment resonates with many in the industry who understand that a crucial preparatory phase ensures data is usable before data scientists can apply machine learning models. Without this foundation, even the most advanced AI systems would struggle to produce meaningful results,” Gulipalli said.
Gulipalli’s view on the early stages of data processing is simple: “We all know the importance of AI models, but very few appreciate what happens before that. Data ingestion, cleaning, and harmonization often require as much attention as the modeling phase itself.
The complexity of the modern business environment means that data is generated at every stage of an organization’s journey. He pointed out that any company that makes cars or offers financial services produces vast amounts of data at every stage of its operations. Whether sourcing raw materials or collecting financial transactions, data comes from multiple sources and in different formats.
This becomes even more evident when considering large organizations with diverse functions, such as supply chain management, marketing, customer service, and R&D, all of which generate unique data sets.
“The reality is that we have no shortage of data. We often lack a unified system that can make sense of things,” he said.
How to unlock the potential of data?
For Gulipalli, “data products” are the key to unlocking the full potential of data. He explains: “In a world where businesses have hundreds of data sources, it’s no longer about treating each set of data individually. Instead, we need to consider domain data products: consolidated, clean data serving specific business functions.
This shift in mindset is transforming the way organizations manage data. According to Gulipalli, “AI plays a crucial role in analytics and the entire data lifecycle. It facilitates data ingestion, automates cleansing processes, performs harmonization across sources, and even creates domain-specific models.
For example, AI’s role in transforming unstructured data into structured formats has significantly reduced the time and effort traditionally required for data preparation. According to Gulipalli, “We are at a point where AI can handle a lot of the tedious work that was previously manual. It can examine data quality, detect anomalies, suggest corrections and even propose optimal transformations, all in real time.
This automation allows businesses to act more quickly on information, leading to better decision-making. A data scientist may focus on customer segmentation, but with AI, new patterns or insights from unexpected areas within the organization may emerge. “It’s like we have an extra layer of intelligence that constantly improves the quality and usefulness of the data,” Gulipalli points out.
On the other hand, if organizations need data from different sources, it is necessary to make access affordable.
Shekar Sivasubramanian, CEO of Wadhwani AI, said, “Collecting data such as X-rays and MRI scans needed to work on health-related innovations in AI is expensive. An x-ray copy costs INR 100, but taking a photo of the x-ray is free.
Interpreting an x-ray like a photograph may seem unconventional, but it is a legal and practical approach that reduces costs. It is therefore crucial to collect data as an affordable resource for AI innovation.
The Ability of AI to Impact Data Operations at Scale
“Historically, data has been treated as static: a process that involves multiple steps, often executed separately. But with AI, we are moving toward an integrated view where data and AI work together from the beginning of the data pipeline. » This integrated approach not only improves efficiency; it also improves the accuracy of data-driven decision making.
The benefits of adopting this AI-driven approach to data management are becoming clear. He highlighted that businesses now realize that AI is about more than analytics. It’s about automating and optimizing every phase of data processing, from ingestion to cleaning to model creation. It streamlines operations and helps unlock the real value of data.
Overall, this highlights a broader industry trend: the growing recognition that AI is no longer just a tool for advanced analytics, but is becoming the foundation of modern business management. data.
The future lies in treating AI and data as interconnected systems. When organizations adopt this AI-driven mindset, they will unlock levels of efficiency and insight previously unimaginable.
