Industrial companies have long wrestled with data silos. Plants operate on one system, regional offices on another and ERP, MES, SCADA, PLM, and CRM tools barely talk to each other. Mergers and acquisitions add layers of complexity, and compliance requirements make it even trickier to unify data.
The good news? The industry is shifting from a “centralized data platform” mindset to domain-owned data products supported by a shared lakehouse foundation. The payoff is tangible: faster, more reliable decision-making, lower cost-to-serve, fully auditable data and operations ready for AI. Imagine cutting down the time it takes to trace a quality issue from days to hours… that’s the potential here.
68% of companies cite data silos as their main data challenge, despite modern integration and governance solutions.
Silos don’t disappear simply because tools exist they require the right architecture and governance, such as Data Mesh combined with a Lakehouse.
43% of manufacturers already use data integration for predictive maintenance and 39% for demand forecasting.
Increased integration signals that the industry is moving from silos toward a unified architecture as an essential step toward an effective industrial data platform.
According to a recent study, 28% of organizations are deploying elements of Data Mesh and 67% are considering Data Mesh for the long term.
Data Mesh is no longer experimental; a large share of companies sees it as a sustainable strategy for managing data complexity.
The global Data Mesh market is expected to grow at a compound annual growth rate of ~17.8% through 2033
with significant adoption among large-scale enterprises. This growth reflects the rising demand for decentralized architectures that enable large-scale data sharing and util.
Why industrial companies still struggle with data silos
Even with data lakes and BI tools, silos persist. Let’s explore why these gaps remain, the hidden costs of disconnected data and why old “centralized” approaches fall short.
Silo anatomy in industry
At the heart of the problem is the classic OT vs IT divide. Operational Technology (SCADA, MES) is focused on production floor efficiency, while IT systems like ERP and CRM handle business processes. These systems rarely speak natively, leaving gaps.
Multi-site and multi-country operations add complexity. What works in one plant or country may not translate directly to another due to local processes or regulations. Then, consider supplier and partner ecosystems external data often doesn’t integrate seamlessly.
R&D and quality systems like LIMS (Laboratory Information Management System) or QMS (Quality Management System) frequently exist in isolation. So while the data exists, it’s fragmented and inaccessible when decisions need to be made.
What silos cost
Data silos aren’t just an IT headache they hit the business directly:
- Unreliable KPIs: If your data isn’t unified, dashboards tell conflicting stories.
- Duplicated reporting: Teams spend hours reconciling numbers that should already match.
- Slow root-cause analysis: Tracking production issues across multiple systems can take days.
- Under-used AI initiatives: AI models starve without trustworthy, consolidated data.
- Compliance friction: Traceability gaps and unclear lineage increase audit risks.
Why “one central data lake” didn’t fully solve it
The promise of a central data lake sounded great until reality hit. Bottlenecks form as a small central team struggles to ingest and govern all domain data. Lead times are long, and without deep domain knowledge, data quality suffers.
Governance often morphs into policing instead of enabling self-serve analytics.
Data mesh explained
Data Mesh isn’t just a buzzword it’s a new mindset. Here we break down what it is, its four pillars and how it changes both ownership and culture in industrial organizations.
Definition in one sentence
Data mesh is a model where domains own and publish data as products under common standards turning data into something you can trust and consume like a reliable service.
The four pillars
- Domain ownership: Teams closest to the data own it whether production, quality, or supply chain.
- Data as a product: Each dataset is treated like a product with SLAs, documentation and quality metrics.
- Self-Serve data platform: Teams can discover, access and use data without heavy central support.
- Federated governance: Shared standards exist, but autonomy is preserved balance between freedom and compliance.
Cultural shifts
Moving to Data Mesh isn’t just technical it’s human. Teams stop saying “ask the data team” and start consuming certified data products.
The focus shifts from temporary projects to long-lived products and centralized prioritization is replaced by domain roadmaps aligned with business value.
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Lakehouse explained
The lakehouse bridges storage, analytics and AI workloads. In this section, we’ll see why it’s a perfect match for industrial and regulated data and how it differs from traditional lakes or warehouses.
What a lakehouse is (vs data lake vs data warehouse)
A lakehouse combines the best of both worlds:
- Low-cost storage for massive data volumes
- Open formats for flexibility
- BI performance for analytics
- ML/AI workloads for predictive insights
- Governance and lineage baked in
Think of it as the bridge between raw data and actionable intelligence perfect for manufacturing plants, pharma labs or automotive analytics platforms.
Why lakehouse fits industrial data
Industrial data comes in many forms: IoT time series from machines, semi-structured logs, transactional ERP data and unstructured documents. A lakehouse can unify analytics and AI workloads, reducing duplication across separate tools. No more hopping between different systems just to answer a single question.
Key capabilities to highlight
- ACID tables / reliability: ensures transactional integrity
- Schema evolution: adapt as systems change
- Catalog + lineage: track where data came from
- Access control + auditability: essential in regulated industries
- Streaming + batch: support real-time and historical analytics
Data mesh and lakehouse: complementary, not competing
People often confuse architecture and operating model. Here we clarify how Data Mesh and Lakehouse work together and what mistakes to avoid when implementing both.
Target architecture
- Sources: ERP, MES, SCADA, PLM, CRM, QMS, LIMS
- Ingestion: batch or streaming
- Lakehouse storage: open, reliable tables
- Semantic / metrics layer: standardize definitions
- Data products: domain-owned, SLA-driven
- Governance + catalog: federated policies, audit-ready
Where companies get it wrong
- “We bought a lakehouse, so we have Data Mesh”: wrong, owning the platform isn’t enough.
- “We created domains, but no platform standards”: autonomy without standards is chaos.
- “We published data, but not products (no SLA, no trust)”: data without accountability isn’t useful.
Governance in regulated environments
Compliance is a double-edged sword: too much slows teams, too little creates risk. Let’s explore how federated governance keeps industrial and pharma data safe while remaining agile.
The governance paradox
Too much control slows delivery; too little creates risk. Finding the balance is crucial.
Federated governance, concretely
- Centralized: policies, definitions, security patterns, tooling
- Decentralized: ownership, product backlog, domain logic
Minimum governance standards for data products
- Data contracts: schema + meaning
- Data quality KPIs: freshness, completeness, accuracy
- Access tiers: PII, sensitive OT, IP
- Lineage + audit logs
- Documentation + ownership: clear RACI
Implementation roadmap
Transformation is a journey, not a flip of a switch. This section outlines a step-by-step roadmap to go from siloed data to a scalable Data Mesh + Lakehouse ecosystem.
Phase 0 : diagnose (2–6 weeks)
Map domains, identify golden KPIs, spot duplication, assess compliance.
Phase 1 : foundation (6–12 weeks)
Set up the lakehouse core, catalog, access models, reference patterns.
Phase 2 : first data products (8–16 weeks)
Start small: 2–3 domains, 5–10 data products and deliver one “hero use case.” Prove value and trust.
Phase 3 : scale (Ongoing)
Expand domains, standardize the metrics layer and formalize SLAs and platform roadmap.
KPIs to measure success
How do you know if your initiative works? Here we review key metrics for both business outcomes and data quality, so you can track real impact beyond dashboards.
Business KPIs
- Decision cycle time
- Reporting effort reduction
- Downtime / yield improvement
- Audit time reduction
- Lead time improvements
Data / Platform KPIs
- Data product adoption
- SLA compliance
- Data quality score trends
- Time-to-publish new data products
- Security incidents / access review SLA
Conclusion
Breaking industrial data silos isn’t about buying shiny tools it’s about combining the right architecture (lakehouse) with the right operating model (Data Mesh). Start small, standardize early, scale with governance… and suddenly, your industrial data becomes an asset, not a headache.
At Eminence Industry, we guide manufacturers and regulated enterprises through this journey from strategy to execution ensuring data is trusted, auditable and ready for AI-driven insights.
Commonly asked questions FAQ
What is Data Mesh and why is it important for industrial companies?
Data Mesh is a way of organizing data ownership by domains, treating each dataset as a product. For industrial companies, it breaks down silos between OT and IT, enables faster decisions and makes AI and analytics more reliable.
How does a Lakehouse differ from a traditional data lake or data warehouse?
A Lakehouse combines the low-cost, flexible storage of a data lake with the performance and reliability of a data warehouse. It supports batch and streaming data, analytics, AI workloads and strong governance all in one unified platform.
Can regulated industries implement Data Mesh without compromising compliance?
Yes. Through federated governance, clear data contracts, quality KPIs and audit-ready processes, industries like pharma and automotive can adopt Data Mesh while staying fully compliant.
What are the first steps to break industrial data silos effectively?
Start by diagnosing your data landscape: map domains, identify key KPIs, uncover duplication and assess compliance constraints. This foundation makes it easier to implement Data Mesh and Lakehouse successfully.
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