5 Emerging Data Risks and How CIOs Can Address Them

Modern Data Company’s Srujan Akula offers commentary on emerging data risks and how CIOs can address them. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

AI has fundamentally changed how organizations manage and secure data. As someone who regularly works with CIOs, I’m seeing organizations struggle with a new set of challenges that traditional approaches simply can’t handle. These five emerging data risks represent the most significant barriers to realizing value from enterprise data investments.

1. Fragmented Data Management Blocking Business Value

Your marketing team just copied your customer database into ChatGPT. Your data scientists are feeding proprietary algorithms into open-source models. This isn’t just a security issue – it’s a symptom of fragmented data management that prevents organizations from scaling their data initiatives.

When teams can’t easily access the data they need through governed channels, they create workarounds. These shortcuts not only increase risk but fragment your data ecosystem further, making it impossible to build cohesive customer experiences or derive enterprise-wide insights.

This can be addressed by implementing data products as your logical source of truth. Rather than physically moving or centralizing data, data products create unified access through shared metadata and governance layers. Each data product encapsulates the business logic, quality rules, and access controls for a specific domain—like “Customer 360” or “Product Performance”—while the underlying data can remain in its original systems. This approach enables teams to discover, understand, and consume trusted data assets without the complexity and cost of large-scale data movement, creating a federated yet governed data ecosystem that scales with your AI ambitions.

2. Data Quality Issues Undermining AI Initiatives

Poor data quality remains the number one killer of data initiatives. In traditional analytics, humans could spot and mentally correct inconsistencies. With AI, those small errors amplify into dramatically wrong conclusions that can misdirect entire business strategies.

Here’s what makes this particularly challenging: as data volumes grow, traditional quality approaches collapse. You can’t manually review millions of records, and point solutions that address specific quality issues fail to scale across the enterprise.

Data teams we work with tackle this by fundamentally rethinking data architecture. Data products–managed, curated datasets with clear ownership and built-in quality controls – create reliable foundations for analytics and AI. By embedding quality into the data itself rather than treating it as a separate concern, organizations reduce costs while dramatically improving outcomes. These well-designed data products become valuable business assets that teams can confidently build upon.

3. Data Infrastructure Costs Spiraling Out of Control

“Just run it in the cloud” has become the default answer to data infrastructure challenges, but it’s leading to runaway costs that threaten the ROI of data initiatives. Modern data workloads–particularly around AI–require specialized infrastructure that traditional IT departments struggle to optimize.

The symptoms are everywhere: massive cloud bills with minimal business value, data science teams waiting weeks for resources, and C-suite questions about whether these investments are worth continuing.

The most effective solution I’ve seen is a shift toward right-sized, modular data architecture. CIOs who implement intelligent data orchestration can dramatically reduce costs by matching workloads to the appropriate infrastructure–whether on-premises or cloud—while maintaining a unified governance layer. Organizations using this approach typically see 30-50% cost reductions while actually increasing data utilization. The key is building infrastructure that scales with actual usage rather than provisioning for peak capacity.

4. Data Governance That Blocks Rather Than Enables

Traditionally, organizations approached data governance as a compliance checkbox exercise, resulting in policies that create friction rather than value. As regulatory requirements like GDPR, CCPA, and industry-specific mandates multiply, governance teams often default to restrictive data policies that stifle innovation.

I regularly see organizations where accessing data requires weeks of approval processes, resulting in frustrated business users and missed opportunities. Even worse, these friction-heavy processes actually increase risk as users find workarounds that bypass governance entirely.

Forward-thinking CIOs are implementing what I call “governance by design” – embedding compliance requirements directly into data products and platforms rather than layering them on afterward. With this approach, governance becomes an enabler of innovation rather than a blocker. Automated data discovery, lineage tracking, and policy enforcement reduce compliance costs while accelerating appropriate data use. The companies excelling here make governance invisible to users while maintaining comprehensive audit trails.

5. Siloed Data Teams Creating Redundancy and Waste

As data initiatives multiply across organizations, we’re seeing a troubling pattern: duplicate data teams building similar solutions in different departments with no shared foundation. Marketing creates one customer view, sales builds another, and product teams maintain a third–all using different tools and yielding contradictory insights.

This fragmentation creates obvious inefficiencies, but the bigger cost comes from missed opportunities. Without a unified view of customers, products, and operations, organizations make decisions based on partial information that rarely captures the full business context.

The solution requires an outcome-first approach that optimizes operational expenses by design. Rather than building comprehensive datasets “just in case,” focus on understanding what’s actively consumed by business applications or AI models. This LeanAI principle—minimal viable data with maximum business impact—reduces infrastructure costs while accelerating insights. When teams share governance standards and interfaces, they can collaborate without duplicating efforts, creating immediate OpEx savings and faster time-to-value across AI initiatives.

The Path to Modern Data Management

Organizations pulling ahead aren’t just investing in fancy AI tools–they’re evolving their data foundations to address these fundamental challenges. Every CIO I work with who has successfully navigated these waters shares a common approach: treating data as a product rather than a byproduct of business operations.

This product-oriented mindset changes everything. Data becomes a managed asset with clear ownership, defined quality standards, and measurable business value. Platforms replace point solutions, reducing both cost and complexity while improving outcomes. Governance shifts from restricting access to enabling appropriate use.

The competitive advantage is clear. Organizations with business-focused data product approaches respond to market changes faster, derive more value from AI investments, and scale data initiatives more efficiently than their peers. But getting there requires rethinking fundamental assumptions about how we manage, govern, and leverage enterprise data.

For CIOs looking to lead this transformation, the most critical step is building the organizational capabilities and technical foundations that turn data from a cost center into a strategic asset. Those who succeed will not only avoid these five risks but position their organizations to thrive in an increasingly data-driven economy.