The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — equipment on production lines, sensors at customer facilities, sales data, and much more. Harvesting insights means filtering out the noise to arrive at actionable intelligence.
This report shows how to craft a strategy to gain a competitive edge. It explains how to evaluate IIoT solutions, including what to look for in end-to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.
IT organizations using machine data platforms like Splunk recognize the importance of consolidating disparate data types for top-down visibility, and to quickly respond to critical business needs. Machine data is often underused and undervalued, and is particularly useful when managing infrastructure data coming from AWS, sensors and server logs.
Download “The Essential Guide to Infrastructure Machine Data” for:
The benefits of machine data for network, remote, web, cloud and server monitoring
IT infrastructure monitoring data sources to include in your machine data platform
Machine data best practices
The Internet of Everything (IoE) is a continuous interaction among people, processes, data, and things. Sensors, networks, and smart devices are ubiquitous, providing a torrent of streaming data or big data. The Internet of Things (IoT), which is a network of physical objects accessed through the Internet that can sense and communicate, is a component of IoE.
Cisco is helping customers and strategic partners leverage the full potential of IoE to achieve radical results across all sectors and industries. Indeed, IoE is capable of helping public safety and justice agencies increase cost efficiency, improve safety and security, provide better response times, and increase productivity.
The transformation of supply chain management is happening now. IoT is driving that change, but supply chain analytics is instrumental in taming the massive amounts of data generated by IoT sensors, devices and objects and turning it into insight and into a competitive edge. Smart companies recognize this.
Part 3 in our Partnering with Certainty Webinar Series, "Customer Demands at the Edge."
As distributed edge environments become more critical, physical security becomes more important. Nobody would leave their data center wide open for anyone to enter, but that’s exactly how many organizations treat their edge computing sites. Often, they consist of a rack or two of gear in a non-dedicated location, perhaps a janitor’s closet, with little to no physical security.
Fill out your information and click "Register" to watch the third event in our Partnering with Certainty Webinar Series, “Customer Demands at the Edge: Protect me from Downtime!” This webinar originally aired on November 9th, 2017.
In this webinar, we discuss physical security best practices, including environmental issues such as temperature and humidity monitoring. We also update partners on the physical security features of the latest APC racks and the NetBotz line of security and environmental appliances, cameras and sensors.
With the proliferation of health and fitness data due to personal fitness trackers, medical devices and other sensors that collect real-time information, cognitive computing is becoming more and more important. Cognitive computing systems, with the ability to understand, reason and learn while interacting with human-generated data, enable providers to find meaningful patterns in vast seas of information. IBM Watson Health is leveraging the power of cognitive computing to help providers make data-driven decisions to improve and save lives worldwide, while controlling healthcare costs. Read our whitepaper and learn about the new era of cognitive computing and how it can improve health outcomes, optimize care and engage individuals in making healthy choices.
Published By: Forrester
Published Date: May 10, 2012
In the never-ending race to stay ahead of the competition, companies are developing advanced capabilities to store, process, and analyze vast amounts of data from social networks, sensors, IT systems, and other sources to improve business intelligence and decisioning capabilities.This report will help security and risk professionals understand how to control and properly protect sensitive information in this era of big data.
By processing real-time data from machine sensors using artificial intelligence and machine learning, it’s possible to predict critical events and take preventive action to avoid problems. TIBCO helps manufacturers around the world predict issues with greater accuracy, reduce downtime, increase quality, and improve yield.
Read about our top data science best practices for becoming a smart manufacturer.
The Internet of Things (IoT) didn’t just connect everything everywhere; It laid the groundwork for the next industrial revolution.
Connected devices sending data was only one achievement of the IoT—but one that helped solve the problem of data spread across countless silos that was not collected because it was too voluminous and/or too expensive to analyze.
Now, with advances in cloud computing and analytics, cheaper and more scalable factory solutions are available. This, in combination with the cost and size of sensors continuously being reduced, supplies the other achievement: the possibility for every organization to digitally transform.
Using a Smart Factory system, all relevant data is aggregated, analyzed, and acted upon. Sensors, devices, people, and processes are part of a connected ecosystem providing:
• Reduced downtime
• Minimized surplus and defects • Deep insights
• End-to-end real-time visibility
Protect sensitive information in emerging computing models such as virtualized environments. Learn how to leverage the same security architecture to provide more effective and more efficient data security across dedicated database servers as well.
Higher education has come under increasing scrutiny as never before due to rising costs, changes in future job requirements, and new forms of learning opportunities offered by non-traditional companies and institutions. Students and parents are rightfully questioning the value of higher education based on perceived outcomes as well as staggering student loans that in some cases could take a lifetime to pay back. While the value equation debate rages on, there is another phenomenon taking place. It is nothing short of a revolution regarding the advances in technology and how institutions of higher learning along with nontraditional organizations are utilizing these powerful new tools. These new tools include new mobile devices, enhanced and feature-rich learning management systems, data-feeding sensors, 3D printers, smart classrooms, smart buildings, and collaboration tools allowing students and faculty to collaborate just about anywhere face-to-face, virtually.
"The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — equipment on production lines, sensors at customer facilities, sales data, and much more. Harvesting insights means filtering out the noise to arrive at actionable intelligence. This report shows how to craft a strategy to gain a competitive edge. It explains how to evaluate IIoT solutions, including what to look for in end-to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.
The Internet of Things (IoT) is composed of sensor-embedded devices and machines
that exchange data with each other and the cloud through a secure network.
Often referred to as “things” or “edge devices”, these intelligent machines
connect to the internet either directly or through an IoT gateway,
enabling them to send data to the cloud. Analyzing this data can reveal
valuable insights about these objects and the business processes
they’re part of, helping enterprises optimize their operations.
Devices in IoT deployments can span nearly any industry or use case.
Each one is equipped with sensors, processing power, connectivity,
and software, enabling asset control and other remote interactions
over the internet. Unlike traditional IT assets, these edge devices are
resource-constrained (either by bandwidth, storage, or processing
power) and are typically found outside of a data center, creating unique
security and management considerations.
Symantec has established some of the most comprehensive sources of Internet threat data in the world through the Symantec Global Intelligence Network, which is made up of more than 64.6 million attack sensors and records thousands per second.
Published By: SnowFlake
Published Date: Jul 08, 2016
Today’s data, and how that data is used, have changed dramatically in the past few years. Data now comes from everywhere—not just enterprise applications, but also websites, log files, social media, sensors, web services, and more. Organizations want to make that data available to all of their analysts as quickly as possible, not limit access to only a few highly-skilled data scientists. However, these efforts are quickly frustrated by the limitations of current data warehouse technologies. These systems simply were not built to handle the diversity of today’s data and analytics. They are based on decades-old architectures designed for a different world, a world where data was limited, users of data were few, and all processing was done in on-premises data centers.
Location analytics is the process of
integrating geographical data into business intelligence (BI) and analytics-led decision
making. Location analytics creates meaningful insight from relationships found in
geospatial data to solve a broad variety of business and social problems.
Location data is found everywhere – with an item or a device, in a conversation or
behavior, in machines or sensors, tied to a customer or competitor, attached to a
database record or recorded from vehicles or other moving objects. Organizations
want to take advantage of location data to improve decisions, create better customer
engagement and experiences, reduce risks and automate business processes.
The Internet of Things (IoT) is rapidly emerging as a core transformational technology of the digital era. The ability to gather data from sensors embedded throughout an enterprise can drive insights and operational efficiencies from the supply chain to the customer. But IoT and Industrial IoT (IIoT) implementations require high degrees of IT/OT convergence - collaboration and integration between information technology and operational technology groups - to succeed.
These two groups, however, often have different goals, performance metrics, and perspectives on both the collaboration and the outcome. This SAS/HPE-sponsored paper helps readers get a better understanding of the relationship, either real or perceived, between these two groups. Futurum Research surveyed the state of the relationship between IT and OT teams as it pertains to the design, implementation, and creation of value through IoT technologies.
Your network doesn’t just transport data. It serves a myriad of apps and endpoints—mobile devices, sensors, servers, machines, cameras, wearables—and all the employees, customers, and processes that use them. Which means it can produce invaluable contextual intelligence based on real-time analytics to help you navigate the growing demands of business, security, operations, and IT.
Cisco DNA delivers crystal-clear visibility across your network so you can enhance mobile experiences and make business decisions quickly and accurately.
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