Manufacturers are generating massive amounts of data, but maximizing its benefits
requires effectively capturing, connecting and using that information. To create a
competitive advantage, industry leaders are moving beyond basic data collection toward
integrated industrial intelligence strategies that link operations directly to business
outcomes.
The following recommendations are practical, evidence-based ways manufacturers can
unlock value from data while managing cost, complexity, and long-term uncertainty:
● Focus on capturing contextual data, not just machine signals: High-
value insights emerge when machine telemetry is combined with process
parameters, material inputs, operator actions, and quality outcomes. Context-rich
data enables root-cause analysis and improves the reliability of predictive
models.
● Break down data silos across systems and departments:
Disconnected Manufacturing Execution Systems, Enterprise Resource Planning,
quality, and maintenance systems limit visibility into how manufacturing decisions
interact across the enterprise. An integrated data environment improves
coordination and decision-making across operations, engineering, and supply
chains.
● Tie analytics initiatives directly to business and manufacturing
outcomes: Robust ROI comes from analytics projects linked to clear metrics
such as downtime, scrap, throughput, or labor productivity. Use cases should be
defined by the technology and operational decisions they enable.
● Begin with predictive use cases before moving to prescriptive
systems. Predictive maintenance and quality forecasting offer quicker and more
measurable value than fully prescriptive or autonomous solutions. Early
successes establish trust and internal capability for more advanced analytics.
● Use targeted pilots to evaluate ROI before scaling: Narrowly focused
pilots tied to critical assets or high-impact production lines provide crucial data for
broader deployment decisions, reducing risk and aiding in justifying larger
investments.
● Evaluate long-term strategic value alongside short-term payback:
Some benefits – such as improved resilience, faster decision-making, and AI readiness accrue over time instead of immediately. Firms need to account for these compounding advantages when evaluating data investments.
● Invest in quality data, interoperability, and standards early: Low-
quality data and incompatible formats compromise analytics and inhibit
scalability. Aligning with recognized standards reduces technical barriers and
supports long-term system interoperability.
● Build cross-functional ownership of data initiatives: Industrial
intelligence succeeds when operations, engineering, quality, maintenance, and
IT share responsibility for data strategy. Cross-functional practices ensure
insights translate into action.
● Data strategy is part of a successful workforce strategy: Analytics
tools only create value when workers understand them and have the knowledge
and ability to use them effectively. Upskilling and reskilling programs help embed
data-driven decision-making and reduce resistance to change. Proper
onboarding of new employees is also crucial to a successful strategy.
● Plan for scalability: Data pipelines, governance models, and analytics
platforms should be designed as long-term infrastructure. Scalable building
designs and factory floor planning allow for easier adoption of advanced
technology such as AI, digital twins, IoT and augmented/virtual reality.