We’re generating more data than ever — but without the right strategy, that abundance quickly becomes a burden. Data only drives value when it’s actively managed, governed, and used to fuel business decisions.
Yet across industries, data management remains an afterthought. Despite managing enormous volumes — 64 percent of organisations handle at least one petabyte, and 41 percent exceed 500 petabytes — many still lack a cohesive approach.
As AI accelerates digital transformation, data has become the essential fuel for innovation, automation, and competitive differentiation. But without a scalable, intelligent framework to harness it, organisations risk falling behind — on compliance, agility, and market relevance.
The data dragon is growing but our tools are falling outdated
With data volumes surging, the challenge of storing, managing, and securing information is only intensifying — especially as regulatory pressure mounts. Traditional data management methods are quickly becoming obsolete and ill-equipped to handle this scale and complexity.
According to Splunk’s latest Data Management Report, 67 percent of organisations cite data volumes and growth as a major hurdle in implementing their data strategy, and an even greater 69% point to maintaining security and compliance standards as even more difficult.
The consequences are already evident: poor data management has led to compliance failures in 62 percent of surveyed organisations, exposing them to regulatory penalties, reputational damage, and operational setbacks. Beyond compliance, 71 percent report that fragmented or inaccessible data has hampered decision-making, and 46% say it has weakened their competitive edge.
At the core of this issue is an outdated playbook. Traditional methods often force businesses into one of two flawed extremes: centralising all data in one place — which drives up costs and reduces agility — or allowing data to exist in scattered silos that create gaps in visibility and inefficiency.
Neither model can support a dynamic, digital-first business. Constant data transfers increase exposure to breaches and violations, while inconsistent governance leaves leaders without control. What’s needed is a modern, scalable strategy, one that turns complexity into clarity.
A smarter way forward: Strategy that scales
To truly tame the data dragon, organisations must move beyond outdated management models and adopt a forward-thinking strategy — one that not only controls data sprawl but also unlocks business value.
The key lies in two fundamental pillars: lifecycle management and data pipeline management.
- Lifecycle management ensures data is effectively monitored, governed, and adapted throughout its entire journey — from creation to storage to eventual deletion.
- Data pipeline management focuses on structuring how data is collected, prepared, and delivered, ensuring high-quality input for training, deploying, and refining AI models.
Crucially, businesses must move beyond treating all data equally. Strategies like data tiering, data reuse, and data federation help organisations prioritise high-value information for accessibility and decision-making, while continuing to manage less critical data efficiently in the background.
This not only improves operational agility but also ensures businesses are equipped to scale intelligently without being buried in digital noise.
AI and data: A symbiotic relationship
AI cannot function without high-quality, well-managed data and increasingly, data management depends on AI to maintain control at scale. When built on a solid foundation, the relationship between AI and data becomes transformational.
Across industries, businesses are seeing the tangible benefits of aligning AI with a strong data management strategy. Our report shows that:
- 85 percent of organizations report that their data strategy provides AI with the necessary volume and variety to generate meaningful insights.
- 74 percent of businesses say that their strong data practices help eliminate bias in the data sets that AI models learn from, improving fairness and reliability.
- 82 percent confirm that their data strategy has enhanced the accuracy of their machine learning models — laying a strong foundation for competitive advantage in an increasingly AI-driven market.
The takeaway is clear: AI and data are two sides of the same coin. When organisations invest in AI-ready data management, they unlock the full potential of both — enabling faster decisions, smarter innovation, and stronger long-term resilience.
Turn chaos into competitive advantage
Gaining full visibility into data is the critical first step toward building a winning data management strategy. Businesses that implement the trifecta — lifecycle management, pipeline management, and data federation — are better positioned to turn chaos into clarity and data into a true driver of growth.
The data dragon is only growing, and ignoring it no longer just means missing opportunities. It means exposing your business to security, compliance, and operational risks.
The path forward isn’t about controlling data for control’s sake. It’s about shaping it to fuel smarter decisions, faster innovation, and more resilient businesses.
Christine Low brings over two decades of strategic leadership and expertise in technology sales and business development across the APJC region. Currently serving as the Head of Observability for APAC at Splunk, Christine is instrumental in driving the adoption of observability solutions that empower organisations and government agencies to gain actionable insights into their digital environments. Previously, Christine held key roles at Cisco, IBM, Logicalis, and Telstra, where she provided her expertise in sales leadership, partner ecosystem management, account management, sales enablement, and solution development.
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