Data Challenges
Overcoming data quality, integration, and security obstacles
Your Progress
Section 4 of 5Obstacles to Data-Driven Operations
Industrial data systems promise transformation but face real-world friction. Data quality is the foundational challenge: Missing readings (sensor failures), inaccurate values (calibration drift), and erroneous spikes (electromagnetic interference) corrupt analytics. "Garbage in, garbage out"—a predictive maintenance model trained on bad data makes worse decisions than no model. Data silos fragment visibility: Energy data lives in SCADA, production in ERP, emissions in Excel spreadsheets. Insights require integration, but legacy systems resist APIs, formats don't align, and departments guard data ownership. Security vulnerabilities multiply attack surfaces: Every IoT sensor is a potential entry point for ransomware. The 2021 Colonial Pipeline shutdown started with a compromised VPN password—operational technology (OT) cybersecurity lags IT by a decade. Cost and complexity scale nonlinearly: Moving from 100 sensors to 10,000 sensors increases data volume 100×, but integration complexity 10,000× (every sensor needs calibration, connectivity, processing). Organizational inertia is the final barrier: Data reveals uncomfortable truths (that "efficient" process actually wastes 20% of energy). Operators resist recommendations from "black box" algorithms. Getting humans to trust and act on data insights often harder than building the system.
Interactive Challenge Severity Matrix
Explore data challenges by severity and frequency, with mitigation strategies
Severity × Frequency Matrix
💡 Key Insight
Perfection is the enemy of progress. Many plants delay data initiatives waiting for perfect data quality or complete integration. Start messy: 80% data quality enables 60% of value. Fix critical issues (security, calibration), accept minor gaps (occasional missing readings), and iterate. A dashboard with 5 imperfect metrics beats a spreadsheet with zero metrics.