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Industrial Data Management Platform

Industrial Enterprise Management

Solution Group

Industry

Manufacturing

PoC Duration

Iotellect Match

10-40 days
↓
AI powered Low Code IoT/IIoT Platform

Enable reliable delivery of industrial and IT data between various systems within an enterprise. Implement a high-performance data storage facility allowing to design a unified corporate data lake. Scale to many millions of samples per second, providing operational intelligence for very large infrastructures. Collect and publish data via a hundred communication protocols, implementing a company-wide Enterprise Data Bus (EDB) and Enterprise Service Bus (ESB). Upstream data from remote facilities to the HQ while filtering values at any level, ensuring that more valuable events get higher while less important ones get stored at the low-level platform servers or at least aggregated before their escalation.

Iotellect is a low-code platform for building custom industrial data management solutions that connect machines, PLCs, SCADA/MES systems, historians, databases, and enterprise applications in one environment. It helps teams collect, contextualize, store, and distribute industrial and IT data at scale, then build dashboards, alerts, analytics, and workflows for operational visibility, data integration, and enterprise-wide decision-making.

Build Your IoT Application

Connect Your

Industrial Data Sources

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PLCs
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Industrial PCs
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Smart meters
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Edge computing devices
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IIoT gateways
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Network switches and routers
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Barcode scanners and RFID readers
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Data loggers
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Connected industrial equipment
such as CNCs
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Human-Machine Interfaces (HMIs)
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SCADA/MES systems
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Databases and historians
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Time-Sensitive Networking (TSN) devices
Connect Devices

Connectivity & Management

  • Enabling of data exchange between sensors, controllers, and systems through widely used communication protocols such as MQTT, CoAP, modbus, OPC-UA, profibus, etc.
  • Provision of localized data processing close to the source (sensors, machines), reducing the need to send all raw data to a central system or cloud, thus optimizing bandwidth and response times for real-time operations
  • Facilitation of direct communication between devices without needing a central server, enabling faster data sharing and decentralized decision-making
  • Integration of industrial data into broader business systems, managing resources, production schedules, and inventory in coordination with real-time operations
  • Automation of regulatory reporting, making it easier to comply with industry standards (e.g., ISO, OSHA) and governmental regulations
  • Continuous monitoring of machine health by analyzing sensor data, predicting potential failures, and initiating preventive maintenance to avoid downtime
  • Failover Mechanism to ensure continuous operations by switching to backup devices or systems automatically in case of a network disruptions or system downtime

Integration

  • Low code integration with Supply Chain Management (SCM) systems
  • Low code integration with Product Lifecycle Management (PLM) systems
  • Low code integration with SCADA systems
  • Low code integration with digital twin platform
  • Low code integration with predictive maintenance platforms
  • Low code integration with Manufacturing Execution Systems (MES)
  • Low code integration with Quality Management Systems (QMS)
  • Low code integration with Human-Machine Interface (HMI) systems

Analytics

  • Identification of specific events or anomalies in real time, such as equipment malfunctions, quality issues, or safety violations, and triggers automated actions or alerts
  • Usage of historical data, machine learning, and AI algorithms to predict equipment failures before they occur
  • Prediction of future production requirements, optimizing inventory levels and resource allocation based on historical trends and market data
  • Specific recommendations for improving processes, adjusting production schedules, or reallocating resources to enhance efficiency and minimize costs
  • Identification of the underlying causes of equipment failures, quality issues, or production bottlenecks by analyzing historical and real-time data
  • Detection of deviations from normal operational patterns using statistical analysis and machine learning, allowing quick identification and resolution of potential problems before they escalate
  • Extraction of valuable patterns and correlations from vast datasets collected from industrial systems, sensors, and machines, helping to discover hidden insights for process optimization or product development
  • Usage of deep learning or neural networks for complex problems, such as image recognition in quality control or optimizing multi-variable processes
  • Determination of the cause-and-effect relationships between operational parameters and production results, helping industries to fine-tune processes for optimal performance

UI/UX

  • Visual representations of key performance indicators (KPIs), operational statuses, and data trends, helping managers make informed decisions quickly
  • Virtual modelling of physical industrial assets or processes that are continuously updated with real-time data
  • Usage of charts, graphs, and other visual tools to display data in an easy-to-understand format, making it simpler for operators and decision-makers to identify trends, patterns, or issues at a glance
  • 3D visualizations of machinery, workflows, or plant layouts provide real-time visual monitoring and analysis of industrial environments
  • Heat mapping of machine performance or operational data spatially, highlighting areas with high activity, downtime, or energy usage
  • Progressive revealing of more detailed data layers, allowing users to go from high-level overviews to detailed analytics without leaving the dashboard
  • Interaction with system diagrams, such as flow charts or machine layouts, to visualize and control processes directly from the interface
  • Feature to replay historical data over a specific time period to analyze past performance, identify trends, or investigate the root cause of an issue

Key Features for Your
Industrial Data Management System

Customers and Partners

  • System Integrators
  • Original Device Manufacturers
  • Independent Software Vendors
  • Engineering Companies
  • Manufacturing Firms
  • Small and Medium Businesses
  • Consulting Companies

Solution Users and Developers

  • Dedicated low code developers
  • Industrial automation engineers
  • Business and system analysts
  • Industrial process engineers
  • Industry 4.0 and IIoT solution architects
  • BI professionals
Customer success team
Community
Online training

Assistance

Hydropower Predictive Diagnostics

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Related Case Studies

Hydropower Predictive Diagnostics
An Automated Predictive Diagnostics System was deployed at a major hydroelectric power station on the Iotellect platform.

Customer
Under NDA
Country
Under NDA
Industry
Power Engineering
Solution Type
Industrial Automation

Common Questions

What is an industrial data management platform?

An industrial data management platform helps companies collect, structure, store, and distribute data from industrial equipment, automation systems, databases, and enterprise software. It creates a reliable foundation for operational visibility, analytics, reporting, and integration across OT and IT environments.

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