Use Splunk for Industrial IoT Data to Ensure Up-time and Reduce Cost
Drive performance and availability of critical industrial assets and control systems with real-time monitoring, personalized alerts and asset grouping
Mature your maintenance and mitigate failure with built-in AI (artificial intelligence) and ML (machine learning) capabilities
Secure OT Operations
Safeguard ICS (industrial control systems) against threats without disturbing service with advanced analytics and actionable intelligence
A unique place to manage, analyze and secure all your industrial data
Real-Time Diagnostics and Monitoring of Industrial Controls and Assets
Get an integrated insights into the health of your critical assets and control systems, and perform advanced analytics with no programming.
Minimize your routine preventative maintenance that introduces risk and decreases availability, set early warnings and trigger real-time actions with alerts and dashboards. You can now rapidly identify and diagnose issues and improve availability and performance.
Splunk for Industrial IoT Sandbox installs rapidly and makes it easy to visualize asset performance metrics and build dashboards with click and drag functionality. Register to gain access to the sandbox environment when ready. You’ll:
- Get a simple insights of complex industrial data by seamlessly by integrating data across Industrial Control Systems, sensors and applications.
- Quick analysis of large-scale data investigation and discovery with real-time event collection, searching and storage.
- Set alerts and notifications on the performance of critical industrial assets and share insights with peers in real-time via reports.
Protect Critical Assets with Analytics-Driven Security
Utilize custom correlations or out-of-the-box, searches and visualizations of all your disparate data in real time and gather all the context you need to perform rapid investigations and response. Get a view of your industrial asset security posture so you can act quick and effectively.
Benefits of Better Monitoring and Diagnostics
Getting a real-time and unified insights into the health, availability and performance of highly distributed industrial assets and complex control systems is an uphill battle. These systems often use protocols that frequently operate in silos in your environment.
Analyzing and unifying the machine data generated in industrial environments can help you to:
- Monitor multiple disparate systems from a single tool
- Ensure equipment is operating as intended
- Monitor, track and avoid unplanned asset downtime
- Quickly perform root cause analysis and pinpoint costly operational issues
- Get the cause of failures and improve efficiency and availability
Predictive Analytics for Industrial Operations
Use the power of ML to understand performance baselines and predict deviations. Get intelligent recommendations that relate to asset maintenance, production and supply chain management. This helps to predict and prevent imminent outages without any affecting production downtime.
The introduction of Cloud, IoT, Advanced Analytics and ML are changing the landscape of industrial machine maintenance. The convergence of these new technologies is now helping companies pivot from reactive maintenance strategies to predictive maintenance.
Optimize OEE (overall equipment effectiveness) with real-time operational visibility across IT, OT and IoT sources
Energy and Utilities
Improve operator insights and drive machine uptime with a consolidated view of your industrial data
Drive efficiency across your main operations to improve margins and better serve your customers.
Integrating IoT solutions in industries is going to be essential to match with the increasing needs of the digital world. If you are willing to digitize your industry services, then Splunk for Industrial IoT should be your better choice. Contact us to know more about different Splunk IoT Industry solutions and Splunk IoT Industry applications and we also provide services like Splunk IoT Training, Splunk IoT Customization and Splunk Iot Staffing.