Vint Cerf made a point a number of years ago that the term internet telephony was doomed to die like the phrase horseless carriage. For today, when it comes to IoT analytics, it’s unclear if some companies are unaware of the value of sensor information for business analytics or are past the point of differentiating.
Certainly, there are some business analytics systems that are totally human centric and represent the lifeblood of human resource analysis. These companies I have not included in our hot list, but should be expected to have connectors into the IoT analytics ecosystem in the near future.
It is a safe assumption that many of the business analytic tools feature as- a-service cloud services with mobile/web dashboards. I have also excluded them from this list if they are not specifically using sensor or machine data.
Focusing on the hot list for IoT then, I have had to make a determination as to whether a company implies IoT analytics in its business intelligence systems or is not involved.
I believe I have curated the list correctly. In some cases these companies have used IoT and machine learning as part of their marketing presence. Where that is the case I will highlight them.
However, once again, if you are a corporation already using business intelligence systems, I believe all of these systems will gather IoT information to allow for better decisions in the future. It is evolutionary and inevitable.
Providing a definition and differentiation is a good first step.
Business intelligence refers to technologies that acquire and transform data into relevant information that can be used to analyze business operations and opportunities.
Analytics is the discovery, interpretation, and communication of meaningful patterns of information. Communication of the information is often done with data visualization techniques.
Analytics applied to IoT spans the gathering of data from sensors and other IoT components to the interpretation of human interaction with the information as part of the workflow of a business process. For example, Uber has IoT analytics that use smartphones for location and little more, while Teradata gathers machine data to reduce manufacturing flaws.
IoT analytics can be differentiated from other analytics and business intelligence only by the absence of data from resources that do not include Internet of Things components – for example, invoicing systems or human resource evaluation tools.
Big Data & Big Companies
IoT Evolution and James Brehm & Associates are conducting a survey of enterprise executives about their use of analytics platforms. We have listed 34 possible platforms plus an “other” column. It’s clear that IoT analytics impacts the use of business intelligence systems, and most enterprises have legacy. Splunk points out that machine-generated data (including websites, communications, networking, and complex IT infrastructures, etc.) hold critical information on user behavior, security risks, capacity consumption, service levels, fraudulent activity, customer experience, etc. Machine data and IoT are the largest portion of big data.
The old rule in IT was that you married your databases, and when it comes to big data there are many large companies that are probably at the heart of your existing big database systems. The company Synchronoss points out that 85 to 90 percent of the big data effort has been in the collection, not in the analysis, of the data. If you have infrastructure from a big company, much of your big data source will come from their systems. Accenture, Amazon, Cisco, Dell, HP, IBM, Intel, Microsoft, and Oracle all have analytics platforms with capabilities that include IoT evaluation tools, machine learning, and security. Salesforce and SAP represent a strong foundation that may be at the heart of your business intelligence, and therefore will be a place where you can integrate IoT analytics.
GE Predix and PTC’s portfolio of Coldlight, Thingworx Neuron, and Windchill have IoT analytics tools based on their experience with industrial IoT processes. (Note: Please read this issue’s piece about PTC.)
For the purpose of this hot list, I have somewhat unfairly treated the large companies as if they are part of your existing legacy. Allowing your legacy leaders to tell you of their capabilities is an action item after reading this article.
Edge (Fog) Computing & Machine Learning
Some IoT analytics are applied to pools of information that have been aggregated over time, while other systems look to ingest and interpret information in real time. Many systems are being designed to take advantage of speed with Hadoop and edge computing solutions. In many cases the use of machine learning related to the patterns and the recognition of anomalies triggers actions without human interaction.
For the purposes of this hot list, I will try to identify who provides analytics with and without machine-learning capabilities.
The discussion of preventive and predictive capabilities of these systems often suggests that real-time information is critical to the analytics ability to support machine learning and pattern detection. Dale Skeen, CTO of Vitria, has pointed out that real-time information always improves the response time.
In general, the way I interpret what companies have said to me is often that preventive systems take advantage of the big data lakes to look for historical patterns beyond specific systems. Data streams have a history of being more purposeful in the analysis with real-time pattern recognition. The general rule of thumb by the industry is that predictive systems are available today that can be used to support virtualization and redundancy.
Conclusion: Most companies are just at the fledging stage of their IoT implementation, with either one existing legacy system being converted or a greenfield having been deployed and tested. IoT analytics as part of your business intelligence then has been driven by this implementation. Whichever group you are in, it’s probably worthwhile to explore the advantages of these hot list companies and your existing vendors.
In many enterprises, the adoption of IoT has been driven by operations requirements, and IT has been informed later or still not allowed to ingest the information into the business intelligence systems. Big data is very much an IT-driven opportunity, and IoT analytics represents a junction of operations and IT’s need to manage information effectively.
It’s unlikely you can’t integrate the tools from a new solution provider to your legacy business intelligence, but depending on the nature of the implementation you may find the systems stand alone well and deliver capabilities at the edge that outweigh the need for integration.
The IoT Analytics Hot List
Actian http://www.actian.com “empowers companies of all sizes to connect to data of any type, size, or location; analyze it quickly wherever it resides; and take immediate action on accurate insights gained to delight their customers, gain competitive advantage, manage risk, and find new sources of income. With Actian Vector in Hadoop, we have delivered the world’s first end-to-end analytics platform built to run 100 percent natively in Hadoop.”
Angoss http://www.angoss.com/ “is a global leader in delivering powerful predictive analytics that help businesses discover valuable insights and intelligence in their data, while providing clear and detailed recommendations to improve risk, marketing and sales performance.”
Alpine Data www.alpinedata.com “enables organizations to create a culture of analytics at scale by providing the most comprehensive platform for advanced analytics. Using Alpine, organizations can manage the entire analytic lifecycle in one environment, and enable people to build, deploy, and consume analytic applications and insights in an agile and collaborative manner. Leaders in all industries, from financial services to health care, use Alpine Chorus to outsmart their competition.”
Appian http://www.appian.com provides business process management and case management software for enterprise in a variety of industries including insurance and wearables. The platform unites users with all their data, processes, and collaborations – in one environment, on any mobile device, through a simple social interface either on-premises and in the cloud.
Cerner http://www.cerner.com delivers “intelligent solutions for the health care industry. Our technologies connect people and systems at more than 18,000 facilities worldwide, and our wide range of services support the clinical, financial, and operational needs of organizations of every size…. From the beginning, we have innovated at the intersection of health care and information technology. Our mission remains to contribute to the systemic improvement of health care delivery and the health of communities.”
Cloudera http://cloudera.com “delivers the modern data management and analytics platform built on Apache Hadoop and the latest open source technologies.... This highly customizable big data framework collects, stores, and analyzes a variety of sensor and log data from Internet of Things deployments. Industry applications in the IoT space (e.g., predictive maintenance, machine diagnostics, telematics processing, remote monitoring, and early warning systems for automobiles) are built on this framework.
Devicify http://www.devicify.net looks to create a new form of business intelligence by tying analytics into the processes that are performed. These processes are interpreted with basic questions of who has security privileges for what functions. This includes where and when users are allowed to access the systems. “Devicify uniformly applies business context across a wide variety of technologies and platforms, building a smarter business, not just smarter connected products. “
Hortonworks http://hortonworks.com/ “provides data and analytics solutions for enterprises, [and] is demonstrating the simulation of bi-directional data communication between an on-vehicle platform and the cloud. The demo will show Hortonworks Data Flow technologies, which shows a use case of a connected Qualcomm platform [that] could deliver critical capabilities for vehicle communication. Data and analytics on HDF prioritize key data (speed, geo-location, and airbag deployment) and determine how and when to share it through a cloud platform. Through Qualcomm Technologies modem solutions built into the design, on-device processing and transmission to the cloud can happen in real time through an LTE connection (or through Wi-Fi if a hotspot is available), ensuring the flow of data is prioritized and reliable.”
KNIME http://www. knime.com is an analytics platform that is an “open solution for data-driven innovation, designed for discovering the potential hidden in data, mining for fresh insights, or predicting new futures. For over a decade, a thriving community of data scientists in over 60 countries has been working with our platform on every kind of data: from numbers to images, molecules to humans, signals to complex networks, and simple statistics to big data analytics.”
Predixion https://www.predixionsoftware.com “The Internet of Things is changing industries across the world. As millions of devices each day connect to the Internet, it becomes impossible to send all the data generated at the edge to the cloud. The concept of the data lake is dying because most of the data generated at the edge never makes it to the cloud. You need real-time insights, and to get them you need analytics to occur at the edge, on the device or gateway. Enter Predixion RIOT, the fully self-contained edge analytics engine for the Internet of Things. With Predixion RIOT, OEMs can embed advanced analytics directly onto their devices and machines to make their devices smarter, and enterprise customers can add intelligence to every level of their networks to solve expensive problems like unplanned downtime. “
RapidMiner https://rapidminer.com is an open source predictive analytics platform “empowering organizations to include predictive analytics in any business process – closing the loop between insight and action. RapidMiner’s effortless solution makes predictive analytics lightning fast for today’s modern analysts, radically reducing the time to unearth opportunities and risks.”
SAS http://www.sas.com enables you to manage “your data is at the edge, in motion or at rest, make fast, confident decisions – while reducing data transport and storage costs. SAS Analytics for IoT covers the full IoT analytics life cycle – from data capture and integration to analytics and deployment…. Our proven event stream processing engine can handle huge volumes of data at very high rates (e.g., millions per second), with extremely low latency (in milliseconds). This enables real-time data management on IoT data to make it analytics-ready. Intelligent filtering separates what’s relevant from all the noise, so you’ll know what data to store and what you can ignore.”
Synchronoss http://synchronoss.com/ “Analytics for IoT is a highly scalable software-as-a-service platform that connects previously disparate data from across your M2M devices to create actionable insights. It then turns these insights into revenue by helping carriers create productized services for external business partners and clients. Synchronoss Analytics for the Internet of Things delivers predictive, prescriptive, and automated insights that will enable your company to not only collect data, but leverage it to jointly bring assets to market that create new revenue streams.”
Splunk http://www.splunk.com “enables the curious to look closely at what others ignore – machine data – and find what others never see: insights that can help make your company more productive, profitable, competitive and secure.... Machine-generated data is one of the fastest growing and complex areas of big data. It’s also one of the most valuable, containing a definitive record of all user transactions, customer behavior, machine behavior, security threats, fraudulent activity, and more. Splunk turns machine data into valuable insights no matter what business you’re in. It’s what we call operational intelligence. Operational intelligence gives you a real-time understanding of what’s happening across your IT systems and technology infrastructure so you can make informed decisions.”
Teradata http://www.teradata.com gets more value from data with “its portfolio of big data analytic solutions, integrated marketing applications, and services [that] can help
organizations gain a sustainable competitive advantage with data. Teradata Aster Analytics answers the powerful why did this happen question using IoT data. The pre-built analytic functions include new IoT data preparation capabilities and machine learning techniques to quickly understand and detect patterns in machine behavior. This can be used to mitigate risk, reduce maintenance cost and downtime, and increase productivity. Aster Analytics makes it easier and faster to find meaningful and relevant insights hidden in massive volumes of IoT data with millisecond performance. Many of the machine learning models generated can be easily ported to run on virtually any operational environment that can run Java. The Teradata Aster Scoring SDK allows analysts to easily deploy Aster IoT analytic models into virtually any IoT edge servers, public clouds, and in the data center. Teradata Listener makes it easier to acquire and distribute streaming sensor data for analysis. Capturing and managing continuous streams of data is normally complex and labor intensive.”
TIBCO http://www.tibco.com Spotfire is analytics specifically designed to answer questions as fast as possible. Computer-integrated manufacturing systems are rapidly being equipped with sensors, controllers, and other embedded devices that are able to manage production, equipment maintenance, product quality, inventory, and supply chain operations. Manufacturers that are able to collect real-time sensor data and mashups of external data sources can use predictive analytics to identify potential equipment failures and operational discrepancies. This information enables manufacturing leaders to take proactive steps to prevent costly downtime and improve the operational efficiency of plant floor activities.
Vitria http://www.vitria.com The company’s “IoT Analytics Platform-as-a-Service for IoT provides a fast path to insights and actions that empower you to transform your business operations and applications for Internet of Things applications. The platform enables business analysts, operations managers, and other business-centric users to get up and running and get results in minutes, not months. It includes a unified analytics engine that was designed for the demanding business and technical requirements of the IoT era.”
Edited by
Ken Briodagh
