Operational Intelligence in Industrial Environments

Transcription

1 Copyright 2014 Splunk Inc. Operational Intelligence in Industrial Environments Brian Gilmore,

2 Safe Harbor Statement During the course of this presentation, we may make forward looking statements regarding future events or the expected performance of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results could differ materially. For important factors that may cause actual results to differ from those contained in our forward-looking statements, please review our filings with the SEC. The forward-looking statements made in this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, this presentation may not contain current or accurate information. We do not assume any obligation to update any forward looking statements we may make. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only and shall not be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionality described or to include any such feature or functionality in a future release.

3 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

4 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

5 Data Trends Impacting Industry 5

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

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

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

9 IT and OT Data is Machine Data IT GPS, RFID, Hypervisor, Web Servers, , 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,

10 COMPANIES WHO COMPETE USING THIS DATA WILL WIN

11 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

12 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

13 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

14 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

15 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

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

17 Thank You 17