By Arvind Subramanian, Executive Vice President, Managing Director for India at Iron Mountain
India is in the midst of a digital revolution, with data and AI poised to inject a staggering $450-$500 billion into its GDP by the end of 2025. While AI is rapidly transforming industries through automation and insight generation, its efficacy hinges entirely on the quality and relevance of the data it consumes.
Historically, structured data has been the bedrock of analytics. Now many organizations are grappling with an explosion of unstructured data emails, social media, images, audio, and video that according to Gartner, constitutes 80-90% of all new enterprise data and is growing at an unprecedented rate. Adding to this complexity, invaluable information often remains trapped in physical formats, awaiting digitization. The consequences of poor data integrity are stark: a recent Iron Mountain report, in partnership with FT Longitude, revealed that large organizations globally lost nearly $390,000 in the past year due to such issues. To combat this, businesses are heavily investing in developing unstructured data capabilities through strategic partnerships and internal upskilling, with a strong emphasis on enhancing data literacy. Crucially, AI is not merely the beneficiary of this transformation but a pivotal enabler in refining this vast pool of unstructured data.
Driving Better Decisions with AI-Powered Analytics
Indian organizations are increasingly leveraging AI to facilitate sharper, faster decision-making. By integrating predictive models into their workflows, companies are better equipped to respond intelligently and efficiently to dynamic market shifts. The Iron Mountain report highlighted that Indian organizations prioritize AI-powered decision-making and agility as the primary objective for their unstructured data initiatives over the next two years. Notably, 45% of Indian organizations placed the highest emphasis on enhancing decision-making and response agility through AI and predictive models, surpassing Germany (32%), Brazil (40%), and the U.S. (39%). Furthermore, 53% of Indian organizations identified AI-powered analytics for data quality control and assurance as the most beneficial tactic for improving their unstructured data thus far. These tools are instrumental in validating and ensuring the accuracy of unstructured data, thereby establishing a foundation for more trustworthy and actionable insights.
The next frontier for many businesses involves deepening their use of AI to scrutinize data integrity and reliability at scale. By automating the detection of inconsistencies and errors, companies can significantly mitigate the risks associated with flawed inputs – issues that frequently translate into tangible financial losses. With AI readiness being a top objective for unstructured data, many organizations’ data strategies are completing a full circle, with AI itself becoming instrumental in making unstructured data more AI-ready.
Centralising and Curating Data for AI Readiness
To optimize datasets for AI, organizations are actively consolidating unstructured data onto cloud-based platforms. This centralization provides AI systems with streamlined access to unified data pools, fostering consistent standardization and reliable quality control across inputs.
Automating data classification is also gaining significant momentum. As the volume of incoming data accelerates, rapid and accurate tagging ensures that AI tools can swiftly locate and apply the correct data to the appropriate task. This structured approach not only enhances processing efficiency but also contributes to the relevance and reliability of AI outputs.
Metadata strategies are becoming increasingly sophisticated. Organizations are implementing mechanisms such as ‘AI nutrition labels’ to indicate the trustworthiness of a given dataset for decision-making. These labels help differentiate verified, authoritative data from unverified sources, which may be better suited for contextual understanding. By embedding trust signals directly into the data, teams can make more informed judgments when evaluating AI-generated insights.
Keeping Humans in the Loop for Responsible AI
Even the most advanced AI systems necessitate human oversight; they’re not infallible and require continuous training and validation. Humans are crucial for ensuring AI systems align with ethical standards and business goals, assessing data credibility, and understanding AI-generated outputs. As organizations push forward with AI adoption, embedding explainability and oversight into every process layer ensures innovation is matched by accountability.
Now is the time for Indian businesses and leaders to champion responsible AI development. Partner with experts to build an AI ecosystem that’s powerful, efficient, trustworthy, and human-centric, securing India’s digital future responsibly.
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