Reimagining AI, data management with intelligence at scale

By Dhiraj Goklani

In the ongoing conversation about how quickly enterprises can adopt and utilise the power of Artificial Intelligence (AI), it is easy to assume that organisations with the most advanced AI models will achieve the best business outcomes and come out on top. However, the true differentiator lies in the quality and management of the data that powers the technology.

Data is AI’s fuel. Without clean and trusted data, generating insights, detecting threats, and improving processes to build enterprise resilience becomes nearly impossible.

That said, AI can also improve data management itself. AI can help automate tasks like discovery, classification, and governance. At a strategic organisational level, leaders agree. According to Splunk’s Data Management Report, 73% of respondents believe that AI improves data quality by automating repetitive tasks, while offering new opportunities to identify patterns, trends, and anomalies. Indeed, AI and data management are two sides of the same coin.

The challenge and opportunity for business leaders is clear — to unlock the power of AI with the help of sound data management strategies. By reimagining data management as a strategic pillar, organisations can turn sprawling, fragmented data ecosystems into strategic assets that fuel faster innovation, sharper insights, and stronger security.

Data Chaos is the Silent AI Bottleneck

Enterprise leaders across industries today face an increasingly fragmented and distributed data environment, with data generated and stored across multi-cloud infrastructures, on-premises systems, edge devices, and third-party services.

The report shows that 69% of respondents rank security and compliance as the top challenge in implementing data strategies, followed by 67% who cited data volume and growth as a hurdle. These challenges have significant financial consequences. Splunk’s Global Downtime report estimates that global 2000 companies lose USD 400 billion annually (approximately 9% of their profits) when their digital environments fail unexpectedly.

The stakes are severe. Sixty-two per cent attributed compliance failures to poor data management, 71% reported that it has led to poor decision-making, and 46% confirmed a competitive disadvantage. Without modern tools and practices, organisations end up with fragile and expensive data systems. The report also finds that 73% of respondents attributed rising costs to ballooning data volumes, while 71% cited ever-evolving compliance mandates.

These challenges are further intensified by poor data quality, which undermines the reliability of AI models and significantly increases compute and storage costs.

However, it is equally important to examine how the data readiness gap may impede the effective execution of AI strategies. According to a Cisco report, despite the rising interest in AI, less than a third of organisations report being highly prepared, from a data readiness perspective, to deploy and fully leverage AI technologies. It also highlights that organisations are challenged by persistent issues in data cleaning and pre-processing, as well as difficulties in tracking data origins and data fragmentation.

This gap highlights a significant opportunity for strategic improvement and investment.

Federation Builds Smarter Data Ecosystems

This is why the most forward-thinking organisations are turning to federated data management, a strategy that leverages widely distributed data to their advantage.

Unlike traditional models, federation allows organisations to leave data where it resides, while ensuring it is discoverable, governable, and usable across systems and teams. Organisations adopting this approach have reported faster data access, improved governance, and enhanced compliance, which are critical levers for AI success in regulated and complex environments. Yet federation alone is not enough.

True data leaders adopt holistic practices for smarter, leaner, and more agile data management. These include data pipeline management—designing, orchestrating, and monitoring the data flow from source to destination to reduce latency, prevent drift, and lower infrastructure costs. They also implement data lifecycle management, governing data from creation to deletion by aligning storage and retention with business value to ensure compliance, cut costs, and unlock capacity needed for high-impact analytics.

The survey reveals that 36% of organisations have adopted data lifecycle strategies to reduce storage costs and speed up access to frequently used data. Among those implementing data tiers, 50% cited cost reduction as the top benefit, highlighting how these practices are strategic business enablers, not just operational improvements. The business value is real.

Implications for India’s Digital Ambitions

India’s ambitious goal of a USD 10 trillion digital economy by 2028 is fuelled by an explosion of data generation. This data explosion presents opportunities and challenges, but it holds the potential to accelerate innovation, deepen the democratisation of access, and unlock AI’s full transformative power. However, if the foundational data infrastructure remains fragile, India may struggle to sustain the scale required for meaningful AI deployment and digital expansion, therefore slowing progress towards its broader developmental aspirations.

To address this, Indian enterprises must move beyond ad hoc solutions and towards a more integrated, federated approach to data management. This means treating data as a strategic asset rather than merely an operational by-product, going beyond simple storage and processing.

Ultimately, the AI race will not be won by those with the most algorithms, but by those who master their data.

The author is AVP of Sales for South Asia at Splunk.