Unlocking Business Potential Through Data Innovation: Why a Solid Data Foundation Matters

Nasuni’s Vice President of Product Nick Burling offers commentary on why a solid data foundation matters when unlocking business potential through data innovation. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

It’s no secret that data has become the currency for business success. Companies across all sectors are leveraging data innovation to enhance decision-making, drive operational efficiency, and deliver superior customer experiences. From predictive analytics to artificial intelligence (AI) and machine learning (ML), organizations are adopting cutting-edge technologies in hopes of gaining a competitive edge.

With this, IT leaders are being asked to deliver results fast, and the expectation is clear: implement AI now, figure out the rest later. But this backward approach can delay returns on investment, expose organizations to costly errors, and even damage trust in the very technology executives are eager to embrace. Gartner even predicts that at least 30 percent of generative AI (GenAI) projects will be abandoned after proof of concept by the end of 2025, largely due to challenges with data quality and unclear business value.

This May, as we recognized Data Innovation Day, it’s vital to acknowledge that data innovation isn’t just about applauding AI breakthroughs—it’s about acknowledging the slightly less glamorous but absolutely critical backend work that makes these innovations possible. Without a robust and modern data foundation in place, AI will fail to produce ROI.

The High Stakes of Fast-Tracking AI

AI systems are only as intelligent as the data they are trained on. They don’t create insights out of thin air—they identify patterns, anomalies, and relationships based on historical and real-time data inputs. If that data is inaccurate, outdated, siloed, or biased, then the models built on it will reflect those flaws, leading to misguided decisions and unintended consequences.

For example, imagine deploying an AI-driven hiring tool trained on biased historical recruitment data, or launching an AI-based dynamic pricing engine fed by inconsistent sales figures across business units. The results? Misaligned strategies, eroded customer trust, regulatory exposure, and in the worst cases, complete project failure.

These real word examples present clear consequences when rushing AI adoption— and IT leaders are not blind to the risks. A recent study found that out of 1,000 IT leaders surveyed, only 20% strongly agreed that their data is organized, accessible, and ready for AI initiatives. This begs the question of why enterprises continue to pursue flawed AI adoption over comprehensive data innovation. These findings also point to an opportunity to showcase proper AI adoption strategies – starting with best practices for implementing a solid data foundation.

Data Infrastructure: Steps for Implementing a Solid Data Foundation 

Before considering AI, IT leaders should focus on implementing a solid data infrastructure, like hybrid cloud, to ensure:

• Seamless data movement and synchronization between on-premises systems and cloud platforms to avoid silos and maintain data consistency across the enterprise.
• Robust security controls, encryption standards, and compliance frameworks (e.g., GDPR, HIPAA) that span both cloud and on-prem environments to protect sensitive data and meet regulatory requirements.
• Ability to scale storage and compute resources dynamically based on workload demands, without compromising performance.
• Centralized monitoring, governance, and policy enforcement across all environments—making it easier to manage data assets, track usage, and ensure accountability.
• Open standards and interoperable solutions that prevent dependence on a single vendor, enabling greater flexibility and future-proofing a data strategy.

By championing a data-first strategy, IT can guide the company away from reactive tech adoption and toward a more deliberate, resilient, and scalable innovation path. Rather than succumbing to AI urgency, IT leaders must act as strategic advisors. It’s not just about saying “yes” to AI—it’s about asking the right questions: Is our data infrastructure ready? Are we fueling AI with trustworthy information? Are we investing in short-term wins or building long-term capabilities?

Things to Consider as an IT Leader

In the race to implement AI, enterprises must resist the temptation to prioritize speed over substance. The pressure to deliver quick wins can be intense, but without a modern, enterprise-wide data foundation, AI initiatives are likely to fall short of their promises—or worse, backfire. Organizations that rush into AI without first addressing the quality, accessibility, and governance of their data risk not only wasted investments but also eroded stakeholder trust and exposure to regulatory scrutiny.

Instead, IT leaders should use this moment as an opportunity to champion a long-term data innovation strategy. By investing in the infrastructure that enables reliable, unified, and compliant data, businesses can position themselves to unlock AI’s full potential—safely, strategically, and sustainably. The true ROI of AI isn’t found in its rapid deployment, but in its thoughtful implementation—built on a rock-solid data foundation.