Dhiraj Goklani
The author is AVP, South Asia, Splunk
In the ongoing conversation on how quickly enterprises can adopt and use 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 power 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 itself can improve data management. 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 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 fuels 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.
Splunk’s 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 $400 billion annually (9% of profits) when their digital environments fail unexpectedly.
The stakes are severe. 62% 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. Splunk’s 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. Cisco reported that despite 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. In fact, it revealed that organisations are challenged by persistent issues in data cleaning and preprocessing, difficulties in tracking data origins and data fragmentation.
This gap highlights a significant opportunity for strategic improvement and investment.
Federation – A Smarter Approach to Data Management
This is why the most forward-thinking organisations are turning to federated data management, a strategy that turns widely distributed data into an 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 have reported faster data access, improved governance, and enhanced compliance – 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.
Splunk’s 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 tiering, 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.
Ultimately, the AI race will not be won by those with the most algorithms, but by those who master their data.
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