Operational Intelligence in Industrial Environments

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Challenges in Industrial Landscape Data Collection & Analytics Ad hoc Analysis of OT Data Correlate Data Across Application/Infrastructur e Silos Batch Oriented/ Rear-View Approach CHALLENGES IT/OT Convergence Security and Privacy

Technology Trends Impacting Industry Hybrid: Point of Origin and Storage Location is TRANSPARENT Edge Fog Datacenter Cloud Components Sensors Actuators Micro-Compute Gateways Controllers Routers Servers Applications Infrastructure Virtualization Containers IaaS PaaS SaaS 4

Data Trends Impacting Industry 5

Storage Trends Impacting Industry Traditional New Schema at Write Schema at Read SQL Search ETL Universal Indexing Structured RDBMS Unstructured Volume Velocity Variety 6

Make machine data accessible, usable and valuable to everyone. 7

Industrial Data Explosion The NEXT WAVE STRUCTURED DATA INDUSTRIAL DATA & THE INTERNET OF THINGS MACHINE DATA

IT and OT Data is Machine Data IT GPS, RFID, Hypervisor, Web Servers, Email, Messaging, Clickstreams, Mobile, Telephony, IVR, Databases, Sensors, Telematics, Storage, OT Sensors, Historians RTU,PLC,HMI, Pumps, HVAC, Drills, Pipelines, Conveyor Belts, Transformers, Generators, UPS, Telematics, Turbines, Fuel Cells, Telemedicine, Windmills, Valves,

COMPANIES WHO COMPETE USING THIS DATA WILL WIN

Fully Integrated Enterprise Platform Collect Data Index Data Search & Explore Alert & Action Enrich Data Report & Visualize Analyze & Predict Scale HA / DR Admin Data Security Apps SDKs/API

Enables Real-time Industrial Insights Industrial Assets Native Inputs TCP, UDP, Logs, Scripts, Wire, Mobile Consumer and Mobile Devices SDKs and APIs Java, JS, C#, Python, Ruby, PHP OT Modular Inputs MQTT, AMQP, COAP, REST, JMS Real-time HTTP Event Collector Token Authenticated Events External Lookups/Enrichment IT Technology Partnerships Custom Alert Actions Asset Info Maintenance Info Data Stores Kepware, ThingWorx, Cisco, Palo Alto

In addition to this lack of precision and granularity, businesses often store sensor data in relational database tables, one sensor per table. Charts and other visualizations are sometimes primitive and require printouts and transparencies for correlation. 13

Optimizing Manufacturing & Engineering Processes Aggregate data from devices(printers), test data, test history Product performance over time to improve engineering process Insights into production data to improve yields and reduce cost Improve Customer Satisfaction, Reduce Production Cost and Optimize Engineering Process

Improving Quality in Manufacturing Big Data QA Data Platform 1. Dynamic ETL Platform 2. Big Data, Real-time Statistical Analysis 3. Create Long-term Summaries 4. Apply Advanced Machine Learning Algorithms Statistical Engine Search Language Summarization Layer (Element, Objective, Time) Element Based Objective Based Data Mart Layer (Structuring, Normalization) Time Based DFS DFS DFS DFS Unstructured QA Data Manufacturing Line Equipment QA 1 QA 2 QA 3 QA 4 QA 5

Easy to Try and Get Started Free Cloud Trial Free Software Download Free Enterprise Security IT Service Intelligence Sandbox 1 2 3

Thank You 17