Data is Everywhere
The Internet of Things (IoT) is fast approaching a critical mass of information that will demand a change in how companies process data. With the onslaught of data digital enterprises are now faced with, analyzing incoming data with historical data is becoming increasingly difficult.
The new world of information is widely distributed, streaming, and becoming too big to move. Experts predict that within two to three years, the bulk of analytic processing will take place on the “edge” of information architectures. A new era of information architectures is now unfolding, an infrastructure that is globally distributed and focused on paving the way for much more responsive and agile business models for both IT as well as business operations or operational technology (OT).
Act in a Hyper-connected World
So how can you drive value out of your data in real-time to take immediate action? With the high-velocity streams of live data being produced across your global enterprise – at the edges (routers and switches), in the fog nodes (regional servers), and in the cloud and data center, you need a way to capture, process and act in real-time to business events. However, the variety of data types, in multiple formats, from many distributed source systems poses problems for data management, consistency, and integrity. Instantaneous results of correlations and predictive analytics in this environment require high-performance and in-memory processing because data is created at faster speeds, while petabytes of data traverse your global network every day. Organizations need to make sure they learn and mitigate anomalies or anticipate and detect valuable insights and act on them when and where they happen, instantly. Understanding the data sources but also the context of the event is enabled by processing data closer to the sources, and in some cases this is right at the edge – in a router, in a switch or in a fog node.
The value of the data increases when it can be used proactively and in context to learn and detect in real-time, to prevent or promote actionable events for optimizing and accelerating business processes.
Gain Insights like Never Before and Take Immediate Action
A streaming analytics platform is uniquely suited for this new, federated analytics paradigm. You need a fast streaming engine that’s distributable, open and can scale to process and enrich any type of data that is on the move. Cisco’s Connected Streaming Analytics (CSA) is a very lightweight data platform software that can run disk free and do analytics on the fly. The software engine runs on network devices, fog nodes and in your data center or cloud, complementing your existing big data and transactional systems. CSA is true real-time in the millisecond range for event processing, not near real-time, and brings the analytics processing to your data sources. CSA can process any data streams from sensors, telemetry data from devices and other IT data sources like syslog, simple network management protocol (SNMP) traps and more. CSA can read from and write to enterprise service bus’ to contribute to big data clusters and query transactional systems, enriching streaming data and historical data.
Streaming query processing supports active, continuous in-memory analytics of live data. This provides instantaneous, real-time results and action, as well as efficient use of compute resources and does not require disk for data processing. Disk storage is integrated into CSA, so if historical information is required for computations or reporting CSA has this option as well. CSA can run on an edge device, a fog node, in the data center or in the cloud.
Edge analytics is possible because CSA runs on devices as close to the source as possible and provides an analytics platform that can be used based on the business value and use case driver.
Kim Macpherson presents at the 2015 Data and Analytics Conference.
Delivering High-Performance Analytics for IoT Manufacturing
Cisco’s industry-shaping collaboration with Mazak Corporation, a global leader in the design and manufacturing of machine tools is a great use case for how streaming analytics capabilities can impact an environment. Mazak’s “SmartBox” offering leverages Cisco assets to enable real-time machine data and analytics from Cisco to significantly improve Mazak machine efficiency and efficacy to Mazak’s manufacturing customers. The SmartBox is a product delivering on Mazak’s iSMART Factory concept and takes advantage of our Cisco Connected Machines initiatives to provide insights into machine operations.
How it works:
- Mazak machine data is gathered via the MTConnect adaptors running in the Mazak machine.
- Cisco MT Connect software agents run directly on our ruggedized, industry-leading Cisco Industrial Ethernet (IE) 4000 (IE4K) switch where sensor data streams are sent to CSA, also running on the IE4K providing real-time analytics and machine behavior forecasting providing machine operator visibility and alerting right on the factory floor.
- A Cisco UCS Fog Node also running CSA is collecting data across multiple IE4Ks to provide dashboards and information to Mazak’s other factory systems.
By analyzing this data, manufacturing personnel are able to receive notifications and information to improve machining operations and ultimately product quality. The analytics also provide insights to avoid downtime-related inefficiencies and improve overall equipment utilization and longevity. With real-time analytics on the factory floor, personnel can immediately process hot data coming off the Mazak’s machines and provide real-time insights for operational decisions that impact product quality and production.
The value of the data increases when it can be used proactively, in real time and in context, to process and create action for modern business, IT and OT processes. Streaming analytics can be of extreme value to an organization for revenue generating use cases, operational efficiency and enriching and extending capabilities of existing data platforms.
- Watch this video about the Cisco Connected Machines and Mazak partnership.
- View my presentation from the 2015 Data and Analytics conference.
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