A new NI survey sheds light on a widening chasm in how enterprises harness data across the product lifecycle. Despite the push toward smarter, faster, and more automated operations, nearly half of surveyed companies still operate with a limited data strategy, relying on product data from only a few functional areas. The findings reveal tangible gaps in manufacturing and engineering data usage, and demonstrate significant performance gaps in time-to-market, innovation, productivity, and manufacturing efficiency when data is not fully integrated across design, validation, production, and use.
Limited vs. Advanced Data Strategy: What the NI Survey Reveals
The NI survey canvassed 300 product innovators across diverse industries to understand how organizations manage data throughout the product lifecycle. The results underscore a clear divide between limited and advanced data strategies. Approximately 47% of respondents reported operating under a limited data strategy, a stance defined by reliance on data from a narrow slice of the enterprise rather than a holistic, lifecycle-wide data view. This fragmentation constrains their ability to extract actionable insights that could accelerate product improvement and operational excellence.
In practical terms, the limited-data group lags behind its advanced counterparts on key business outcomes. In the last year, only 33% of organizations with limited data strategies experienced faster time-to-market, compared with 52% among those with advanced, company-wide data capabilities. That 19-percentage-point gap signals not just a procedural lag, but a broader misalignment between what data is captured and how it informs critical product decisions and acceleration of delivery timelines. Beyond time-to-market, the two groups diverge on innovation and productivity. Innovation rates stood at 51% for the limited group versus 63% for advanced teams, a 12-point differential that reflects how better data integration can fuel creative development, rapid iteration, and more competitive product design.
Employee productivity also mirrors this pattern. The limited-data cohort reported a 50% rate of improved productivity, while advanced-data organizations reached 62%, a 12-point delta that translates into more efficient teams, faster problem resolution, and more time for value-added work. Manufacturing efficiency followed suit, with 41% of limited-data respondents noting improved efficiency against 58% for those employing advanced data strategies—an almost 17-point advantage for entities that centralize and harmonize data across the lifecycle.
These statistics illuminate a broader truth: unlocking data from the entire product lifecycle—design, validation, production, and in-use performance—can refine processes and enable breakthroughs in product quality, speed, and efficiency. In today’s market, where winning products must be delivered at speed and scaled effectively, the ability to aggregate and analyze data across all lifecycle stages is not merely beneficial—it is essential for staying competitive. The divergence between limited and advanced data strategies is not a marginal preference; it is a material factor shaping competitiveness and market outcomes.
The concept of a “limited data strategy” typically points to data silos that prevent cross-functional visibility. Engineering data might be collected in one system, manufacturing data in another, and customer or in-use data in yet another. Without a unified data framework, organizations struggle to connect the dots between product design choices, manufacturing constraints, validation results, and real-world performance. The NI findings underscore that such fragmentation does not just slow downstream analytics; it curtails an organization’s ability to detect patterns, simulate outcomes, and implement improvements that could compound across the lifecycle to yield substantial gains in speed, cost, and reliability.
To better grasp the stakes, consider how a fully integrated data strategy could affect decision-making. When design iterations are informed by real-world production data and field usage signals, teams can identify which features deliver the most value, anticipate failure modes earlier, and optimize validation plans to minimize rework. The lifecycle-wide data view also supports better material selection, process parameter tuning, and quality assurance standards that align with customer needs and regulatory requirements. In short, a mature data strategy acts as a connective tissue that binds disparate domains into a synchronized system for rapid, data-informed action.
The NI survey signals that this broader capability is increasingly within reach but not yet standard practice. While the potential benefits are widely recognized, many organizations remain constrained by data governance challenges, fragmented toolsets, and the organizational inertia that accompanies large-scale changes to data architecture. The implications are clear: without a concerted effort to adopt a unified data strategy that spans design, validation, production, and in-use stages, enterprises risk falling further behind peers who are constructing a more integrated, analytics-driven product ecosystem.
There is also a strategic dimension to the data approach itself. Enterprises with holistic, lifecycle-wide data strategies tend to align their analytics, automation, and digital initiatives more closely with business objectives, enabling faster experimentation, more reliable simulations, and better risk management. Conversely, those with limited data strategies often operate with slower feedback loops, delayed insights, and a higher likelihood of optimizing subcomponents in isolation rather than optimizing the end-to-end product experience. In an era where technology choices such as AI, machine learning, and digital twins are rapidly maturing, the ability to weave data from every phase of the product journey into a coherent decision-making fabric becomes a differentiator rather than a luxury.
Key takeaways from this section include:
- A near-even split exists between limited and advanced data strategies, but the performance gap is pronounced across core metrics.
- Cross-functional data integration across design, validation, production, and in-use phases is a central driver of faster time-to-market, greater innovation, and higher productivity.
- Fragmented data ecosystems hinder the exploitation of advanced analytics and predictive insights, impairing the ability to improve product design and manufacturing outcomes.
- The opportunity lies in establishing centralized, standardized data practices that connect the entire product lifecycle, enabling a true enterprise-wide data strategy.
Gaps in Advanced Data Strategy and Their Consequences
Even among organizations classified as having advanced data strategies, the NI survey reveals notable gaps that curtail the full potential of product-data-driven performance. The data show that only 29% of companies with advanced data strategies were using manufacturing data to improve production processes. This implies that more than two-thirds of these firms are not leveraging manufacturing data to its full potential to streamline operations, optimize throughput, or reduce waste and downtime.
Even more striking is the finding that just 24% of advanced-strategy organizations were synthesizing the full spectrum of their engineering, manufacturing, and in-use data to derive deeper, more actionable insights. In other words, a substantial portion of companies with ostensibly mature data capabilities still operate with partial data completeness when it comes to enabling high-value analytics. The implications are profound: without integrating all relevant data streams, the potential of advanced analytics, predictive maintenance, digital twin simulations, and end-to-end optimization remains underutilized.
Several contributing factors explain these gaps. First, many organizations struggle with data analytics capabilities that do not scale across the enterprise. The volume, variety, and velocity of data generated across design, testing, manufacturing lines, and field performance create complexity that outpaces conventional analytics approaches. Second, there is a tendency to underutilize test data—the information gathered during validation and qualification phases—which can reveal critical design and process improvement opportunities if properly analyzed. Third, data governance and data quality play a significant role; without standardized data definitions, consistent measurement, and trusted data lineage, insights derived from analytics may be unreliable or inconsistent across teams.
These gaps suggest that even the most advanced data strategies require ongoing maturation. A mature data strategy is not a one-off deployment; it is an evolving program that expands the scope of data sources, refines data models, and enhances analytics capabilities to derive increasingly sophisticated insights. When manufacturing data is properly integrated with engineering data and complemented by in-use data, organizations can detect subtle correlations between design choices, process parameters, and real-world performance. Such correlations enable more accurate predictions of product behavior, faster identification of root causes for quality issues, and more informed decision-making for future product generations.
The consequences of these gaps extend beyond operational metrics. They influence competitiveness, risk management, and the ability to adapt quickly to changing market demands. As product lifecycles shorten and customer expectations shift toward more personalized or modular offerings, the value of end-to-end data integration grows correspondingly. When advanced data strategies fail to fully exploit the breadth of available data, organizations miss opportunities to optimize design cycles, improve yield, reduce recalls, and accelerate time-to-value for new capabilities.
In practice, addressing these gaps involves deliberate steps:
- Expand data integration to include design, validation, production, and in-use data in a unified analytics framework.
- Strengthen data governance to ensure consistent definitions, lineage, and quality across domains.
- Invest in scalable analytics platforms and skills that can handle complex, cross-domain datasets.
- Prioritize the collection and analysis of test data as a strategic input for product design and process optimization.
- Foster cross-functional collaboration to align analytics initiatives with product and manufacturing objectives.
As enterprises continue to invest in data-driven transformations, the ability to close these gaps will be a critical determinant of how quickly and effectively they can translate data into tangible improvements in performance, speed, and customer value.
AI Scaling Limits: Power Caps, Costs, and Inference Delays
Enterprise artificial intelligence is expanding rapidly, but it is not without constraints. The NI briefing highlights several constraints that are shaping how organizations scale AI across the enterprise: power caps, rising token costs, and inference delays. These factors collectively influence the practical economics and feasibility of deploying advanced AI systems at scale.
Power constraints are a fundamental consideration as AI workloads grow. Inference, training, and continuous learning demand energy, and data centers or edge deployments must balance throughput with thermal and cost considerations. This reality pushes teams to optimize model architectures and runtime environments for energy efficiency, while also evaluating where processing should occur—on premises, in the cloud, or at edge nodes—to minimize latency and maximize responsiveness. The energy profile of AI workloads directly impacts operating expenses and environmental impact, making sustainable design a strategic priority for enterprise AI initiatives.
Rising token costs add another layer of complexity. As organizations lean on large language models and other transformer-based systems for automation, analytics, and decision support, the cost per inference and per token can become a material running expense. This drives a need for more efficient models, cost-aware deployment strategies, and optimization techniques such as model pruning, quantization, and distillation. It also encourages a careful ROI analysis for AI use cases, ensuring that the value delivered by AI surpasses the recurring costs over the lifecycle of a product or service.
Inference delays pose a practical barrier to real-time decision-making. In dynamic manufacturing environments, delays in predictions or recommendations can erode the benefits of AI-driven optimization. To mitigate this, enterprises pursue architectures that optimize latency, including faster inference engines, hardware accelerators, and strategic partitioning of workloads between edge and cloud. They seek to design systems that maintain high throughput without sacrificing accuracy or responsiveness, particularly in production environments where timely decisions can prevent quality issues or downtime.
These constraints do not render enterprise AI impractical; rather, they define the frontier where organizations must optimize. The path forward involves a combination of architectural design, hardware optimization, and software efficiency. Leaders are exploring strategies to turn energy efficiency and cost discipline into a competitive advantage: for example, prioritizing use cases with high return on investment, developing reusable AI components, and building scalable pipelines that can adapt to evolving AI models without incurring prohibitive costs. By approaching AI scaling with a disciplined focus on efficiency, organizations can sustain momentum in AI-enabled product development, manufacturing optimization, and decision support while maintaining responsible energy and cost profiles.
In this context, leaders are asked to consider several strategic actions:
- Architect AI systems for throughput and latency, choosing the right mix of edge and centralized processing.
- Invest in model optimization techniques to reduce compute and energy use without sacrificing accuracy.
- Establish governance and cost-tracking mechanisms to monitor AI usage and ROI at the project level.
- Focus on use cases with clear, measurable business impact to maximize the value of AI investments.
- Build a culture of continuous optimization, reconfiguring AI deployments as models evolve and data quality improves.
The takeaway is that enterprise AI is progressing, but its scale is bounded by practical constraints that require thoughtful, cross-functional planning. By treating efficiency, cost, and latency as core design concerns from the outset, organizations can realize sustained benefits from AI while controlling risk and cost.
Organizations Catching Up: The Path from Gaps to Growth
Facing the data gaps and AI scaling realities, many organizations are now actively addressing their shortcomings and pursuing a more comprehensive data posture. Over the past year, a notable 70% of organizations with limited data strategies have prioritized investments in product data and analytics as a first step toward strengthening their data foundation. This shift signals a broad recognition that foundational data capabilities are prerequisites for more advanced analytics, AI, and digital twin initiatives. In parallel, advanced players with established data foundations are now concentrating on deeper, more strategic technologies that promise to elevate performance even further.
For these advanced organizations, the current emphasis is shifting toward cutting-edge technologies, including machine learning (ML), digital twins, and robotic process automation (RPA). This trio represents a higher-order set of capabilities designed to automate, simulate, and optimize complex product lifecycles. The combination of ML for predictive insights, digital twins for dynamic, real-time simulations, and RPA for automating routine tasks creates a powerful toolkit for reducing cycle times, improving quality, and enabling scalable operations.
A leading expert on data strategy—NI fellow Mike Santori—emphasizes a structured approach to maturation: success comes from a deliberate sequence of steps that address the most pressing challenges first, identify the tools required to solve them, and then build a coherent data strategy around those tools. He advocates starting with a clear problem set, acquiring the necessary technologies, and then aligning data governance and architecture to support those solutions. This approach ensures that the data strategy remains focused on concrete issues while remaining scalable across the organization.
Santori also notes that product-centric data will be a central component of a mature data strategy. The idea is to provide the granular visibility needed for the analytics that matter across the lifecycle. A successful plan should be centralized and standardized within the enterprise, enabling all functions to contribute to and benefit from a connected, advanced data strategy throughout the company. This vision of centralization and standardization is seen as essential to achieving enterprise-wide coherence and enabling cross-team collaboration around data-driven insights.
Within the survey, two critical statistics underscore the priorities and perceived risks among respondents. First, 65% of participants believe that a data strategy is essential to optimizing the product lifecycle. This reflects a broad consensus that data governance and architecture are foundational to improving end-to-end product performance. Second, 46% of respondents warned that if outdated product lifecycle processes are not optimized, they will lose market share within two years. This finding signals a strong perceived threat from competitors who are moving faster with integrated data and analytics-driven product development. Together, these numbers illustrate a sense of urgency among organizations to accelerate their data maturity, particularly in the areas that have the greatest impact on speed, quality, and competitive differentiation.
As organizations pursue this path, practical steps emerge:
- Begin with a prioritized assessment of current lifecycle challenges to determine where data enhancements can yield the most immediate benefit.
- Map the required tools and technologies to address those challenges, ensuring alignment with business objectives and ROI expectations.
- Develop a data strategy that is centralized, standardized, and capable of integrating diverse data sources across design, manufacturing, and use.
- Invest in the people and processes necessary to sustain data maturity, including data governance, data literacy, and cross-functional collaboration.
The NI perspective integrates technical guidance with organizational strategy, highlighting that product-centric data should form a core piece of the enterprise data strategy. By aligning data initiatives with the lifecycle stages and ensuring consistent data definitions and governance, companies can unlock the deeper insights needed to optimize design choices, tune manufacturing processes, and anticipate in-use performance issues before they affect customers.
Industry Outlook: Optimizing the Product Lifecycle through Data-Driven Leadership
The convergence of data strategy and lifecycle optimization is increasingly seen as central to sustaining competitive advantage. The NI findings point to a future in which data-driven product development—and the governance that supports it—becomes a primary differentiator. Organizations that successfully centralize and standardize data across design, validation, production, and in-use stages will be better positioned to harness analytics, ML, digital twins, and other advanced technologies to accelerate innovation, reduce time-to-market, and improve overall efficiency.
A practical implication of these insights is that leadership must prioritize not only technology investments but also organizational readiness. Data governance, stewardship, and a culture of data-driven decision-making are foundational to turning rich data into reliable insights. This means establishing clear ownership models for data assets, defining standard data schemas and metadata practices, and building analytics capabilities that scale with the organization’s growth. It also means aligning incentives so that teams across engineering, manufacturing, quality assurance, and product management collaborate toward shared data goals rather than operating in silos.
The path to a truly lifecycle-spanning data strategy requires deliberate, iterative steps. It begins with a comprehensive understanding of the data landscape—what data exists, where it resides, its quality and lineage, and how it is used today. The next phase involves designing an architecture that can unify disparate data sources and support consistent, repeatable analytics workflows. Finally, the organization must operationalize governance and analytics practices that ensure data remains accurate, accessible, and secure as the enterprise scales.
From a technology perspective, several capabilities are central to this vision:
- A centralized data platform that integrates data across design, validation, production, and in-use performance.
- Advanced analytics, including ML-driven insights, to identify trends, predict failures, and optimize processes.
- Digital twin capabilities that simulate product performance under a range of real-world conditions.
- Robotic process automation (RPA) to automate routine data-handling and repetitive tasks that drain time and resources.
- Real-time data streams and edge-to-cloud architectures that enable timely decision-making in manufacturing environments.
- Data governance frameworks that ensure data quality, consistency, and security across the enterprise.
The implications for industry leadership are clear. Organizations that act now to standardize data practices, invest in scalable analytics, and adopt advanced technologies that leverage lifecycle data will be better prepared to win in a rapidly evolving market. They will be more capable of delivering advanced products at speed, while maintaining reliability, quality, and customer satisfaction. The NI survey underscores a compelling call to action: evolve from isolated data pockets to a connected, lifecycle-spanning data strategy that aligns with business priorities and accelerates value realization.
Key leadership takeaways include:
- Treat data strategy as a strategic asset, not merely a technical project. Its impact spans product design, manufacturing, and customer experience.
- Prioritize cross-functional data integration to unlock end-to-end visibility and enable holistic optimization.
- Invest in technologies and capabilities that scale with the lifecycle, including ML, digital twins, and RPA, integrated within a governed data framework.
- Build organizational capacity for data maturity through governance, culture, and skills development.
- Monitor and measure progress with clear KPIs tied to time-to-market, innovation, productivity, and manufacturing efficiency.
Conclusion
The NI survey presents a clear, data-backed picture of the current state of data strategies in product-centric organizations. A substantial share of enterprises operate with limited data strategies, which correlates with slower time-to-market, lower innovation, reduced productivity, and diminished manufacturing efficiency when compared with advanced, lifecycle-spanning data strategies. Even among the advanced data setups, gaps persist—manufacturing data and the full cross-domain integration of engineering, manufacturing, and in-use data remain areas needing attention to unlock deeper insights and higher performance.
The path forward is defined by the dual goals of widening data coverage across the product lifecycle and elevating the sophistication of analytics and AI deployment. Leaders must pursue centralized, standardized data architectures that enable a connected, enterprise-wide data strategy. They should also acknowledge and address the practical constraints shaping AI scaling—power, cost, and latency—by adopting efficient architectures and practicing disciplined, value-driven deployment.
As organizations strive to close these gaps, the emphasis on ML, digital twins, and automation will intensify, with a growing expectation that data-driven approaches can drive faster time-to-market, stronger innovation, and greater overall efficiency. The most successful players will be those who treat data strategy as a cornerstone of business strategy, invest in the necessary capabilities, and cultivate a culture that treats data as a shared, strategic asset. By doing so, enterprises can transform from fragmented data pockets into a cohesive, lifecycle-spanning data ecosystem that empowers smarter decisions, resilient operations, and sustained competitive advantage.
