by Gary Mintchell | Nov 27, 2018 | Manufacturing IT, Operations Management, Software
I’m still deep in cyber security meetings in Germany. A pause here for software and cloud news from the west coast of America—OSIsoft and Amazon Web services. Since PI is used by many industrial companies, these announcements reveal the deep acceptance of cloud technologies.
In short, here are three bullets:
- AWS Quick Starts for PI System: enables industrial customers to quickly deploy and manage the PI System on AWS.
- PI Integrator for Business Analytics: optimized for AWS to reduce time and cost of bringing operational and IoT data to AWS for sharing or advanced analytics.
- Enhanced connectivity and data sharing to accelerate digital transformation and shrink the OT-IT gap.
OSIsoft launched a suite of products today designed to enable manufacturers, utilities, and other industrial customers to run the OSIsoft PI System on Amazon Web Services.
AWS Quick Starts for the PI System consists of AWS CloudFormation templates, scripts, and reference architectures for quickly spinning up and managing a fully functioning PI System on AWS. Customers will use the PI System Quick Starts for moving PI System workloads to the AWS cloud or for providing an aggregate PI System across an enterprise, monitoring remote or isolated assets and enabling data science efforts.
The PI Integrator for Business Analytics, meanwhile, has been optimized to extract, clean and transmit data from PI Systems and reduce data preparation tasks that bog down big data and data science initiatives. Some customers have successfully used PI Integrator technology to reduce the time consumed by data preparation in advanced analytics projects by over 90%.
AWS Quick Starts will be available in 2019. PI Integrator for Business Analytics, previewed at Hannover Messe earlier this year, is available this month.
Under the Hood
Quick Starts are built by AWS solutions architects and partners to help deploy solutions on AWS, based on AWS best practices for security and high availability. These reference deployments implement key technologies automatically on the AWS Cloud, often with a single click and in less than an hour. You can build your test or production environment in a few steps, and start using it immediately.
The PI Integrator for Business Analytics can integrate to Amazon S3, Amazon Redshift, and Amazon Kinesis Data Streams, enabling industrial customers to speed up their data science experiments, combine disparate data sets for business intelligence, and operationalize the outcomes of advanced analytics that augment decision making.
The Life of the PI System
OSIsoft’s PI System transforms the vast number of operational data streams from sensors, devices and industrial processes into rich, real-time insights to help people save money, increase productivity or create connected products and services.
The PI System can be found inside thousands of companies and complex industrial sites around the globe. OSIsoft customers have used PI System technology to predict wind turbine failures, increase output at an iron mine by $120 million in a single year by fine-tuning logistics, reduce the power consumed by a supercomputer center at a national laboratory, deliver water services to millions of new customers in a major metropolitan city, transform how medicines are produced and reduce the time and expense and improve the quality and consistency of beer. Over 1,000 leading utilities, 90% of the world’s largest oil and gas companies and 65% of the Fortune 500 industrial companies rely the PI System in their operations.
“Worldwide, over 2 billion sensor-based data streams are managed by the PI System with some customers monitoring over 25 million data streams.
“Data from operations—the information being generated by chemical reactors, transformers and other industrial devices—is incredibly valuable. Operations data will be the most valuable asset companies have for moving ahead of the competition in the future. Until recently, this data has been mostly confined to the factory floor or production line in part because of the size, scope and complexity of the data generated by operations,” said John Baier, Director of Integration Technologies, Cloud Analytics Practice at OSIsoft. “Working with Amazon Web Services, we want to unlock the value of operations data by eliminating barriers and bringing it to as many people as possible.”
by Gary Mintchell | May 22, 2018 | Internet of Things, Manufacturing IT, Technology
A few of us gathered for a round table discussion of Internet of Things while I was at Dell Technologies World at the beginning of the month. I arrived a little early and had a private round table for several minutes before others arrive and the discussion became broader.
Ray O’Farrell, CTO of VMware and GM of IoT at Dell Technologies, said the focus of last 6 months since the new Internet of Things organization was announced included these three points:
1. Dell is 7 companies, trying to achieve one cohesive strategy across all; one organization when facing customers.
2. Best way is to work within the ecosystem, that is history of VMWare.
3. Building technology and leverage solutions. This is a complex undertaking as not all challenges within IoT are alike—there are few cookie cutter applications.
The evolution of Internet of Things within Dell to Dell EMC to Dell Technologies constitutes an upward spiraling path encompassing the greater breadth of technologies and organization reflecting the post-merger company. When I first came along, the concept was building an ecosystem around selling an edge device appliance. Now the strategy is much broader bringing the goal of IT/OT convergence closer to reality. As I’ve mentioned before, the IT companies are attacking that convergence from the IT side after years of manufacturing/production oriented suppliers trying to accomplish the same thing from the OT side. Maybe like the old country song we’ll meet in the middle someday.
Everyone talks Artificial Intelligence (AI) these days, and Dell Technologies is not exception. However, AI is not the science fiction doom and gloom predicted by Ray Kurzweil, Elon Musk, and others. Mostly it entails machine learning (ML) from detected patterns in the data.
Or as Dell Technologies says, it is applying AI and ML technology to turn data into intelligent insights, drive a faster time to market, and achieve better business outcomes.
• Dell EMC PowerEdge expands portfolio to accelerate AI-driven workloads, analytics, deployment and efficiency
• Deepens relationship with Intel to advance AI community innovation, machine learning (ML) and deep learning (DL) capabilities with Dell EMC Ready Solutions
• Dell Precision Optimizer 5.0 now enhanced with machine learning algorithms, intelligently tunes the speed and productivity of Dell Precision workstations.
• Dell EMC uses AI, ML and DL to transform support and deployment
14th generation Dell EMC PowerEdge four-socket servers and Dell Precision Optimizer 5.0 are designed to further strengthen AI and ML capabilities.
According to the recently released update of the Enterprise Strategy Group (ESG) 2018 IT Transformation Maturity Curve Index, commissioned by Dell EMC, transformed companies are 18X more likely to make better and faster data-driven decisions than their competition. Additionally, transformed companies are 22X as likely to be ahead of the competition with new products and services to market.
“The Internet of Things is driving an onslaught of data and compute at the edge, requiring organizations to embrace an end-to-end IT infrastructure strategy that can effectively, efficiently and quickly mine all that data into business intelligence gold,” said Jeff Clarke, vice chairman, Products & Operations, Dell. “This is where the power of AI and machine learning becomes real – when organizations can deliver better products, services, solutions and experiences based on data-driven decisions.”
Unlike competitors’ four-socket offerings, these servers also support field programmable gate arrays (FPGAs)3, which excel on data-intensive computations. Both servers feature OpenManage Enterprise to monitor and manage the IT infrastructure, as well as agent-free Integrated Dell Remote Access Controller (iDRAC) for automated, efficient management to improve productivity.
Dell EMC is also announcing its next generation PowerMax storage solution, built with a machine learning engine which makes autonomous storage a reality.
Leveraging predictive analytics and pattern recognition, a single PowerMax system analyzes and forecasts 40 million data sets in real-time per array4, driving six billion decisions per day5 to automatically maximize efficiency and performance of mixed data storage workloads.
The new Dell Precision Optimizer 5.0 uses AI to automatically adjust applications running on Dell Precision workstations to maximize performance by:
• Custom-optimizing applications: Dell Precision Optimizer learns each application’s behavior in the background and uses that data to employ a trained machine learning model that will automatically adjust the system to optimized settings and deliver up to 394% improvement in application performance.
• Automating systems configuration adjustments: Once activated and a supported application is launched, the software automatically adjusts system configurations such as CPU, memory, storage, graphics and operating system settings.
Speaking of partners and collaboration, Dell Technologies and Microsoft join forces to build secure, intelligent edge-to-cloud solution featuring Dell Edge Gateways, VMware Pulse IoT Center, and Microsoft Azure IoT Edge
• Joint IoT solution helps simplify management, enhances security and help lowers cost of deployment at the edge
• Built on innovative analytics applications, management tools and edge gateways to enable network security from edge devices to the cloud
• Accelerates IoT adoption in industry verticals key to economic growth and development
The joint solution offers an underlying IoT infrastructure, management capabilities, and security for customers looking to deploy IoT for scenarios like predictive maintenance, supply chain visibility and other use cases. The solution will deliver:
• Intelligence at the edge with Microsoft Azure IoT Edge: This application extends cloud intelligence to edge devices so that devices can act locally and leverage the cloud for global coordination and machine learning at scale
• Management and monitoring of edge devices with VMware Pulse IoT Center: This provides more secure, enterprise-grade management and monitoring of diverse, certified edge devices including gateways and connected IoT devices, bios and operating systems. This ecosystem will be built over time involving deeper integration and certification to support customer requirements.
• High-performance, rugged Dell Edge Gateways: IoT devices with powerful dual-core Intel® Atom™ processors connect a variety of wired and wireless devices and systems to aggregate and analyze inputs and send relevant data to the cloud
VMware Pulse IoT Center will serve as the management glue between the hardware (Dell Edge Gateways or other certified edge systems), connected sensors and devices and the Microsoft Azure IoT Edge. Initially, Pulse will help to deploy the Microsoft Azure IoT Edge to the requisite edge systems so that it can start collecting, analyzing and acting on data in real-time.
by Gary Mintchell | May 15, 2018 | Manufacturing IT, News
Walking through one of the Halls at the Hannover Messe, you suddenly find yourself in the Cloud—computing that is. There was Amazon Web Services, Microsoft Azure, and Google Cloud. The Manufacturing IT section just keeps growing. And getting more interesting.
One interesting aspect—I’m beginning to see articles speculating on the “end of Cloud computing.” Wonder what could come next?
Meanwhile, here is one piece of Cloud news I picked up. Amazon Web Services (AWS), an Amazon company, announced the general availability of AWS IoT Analytics, a fully-managed service that makes it easy to run simple and sophisticated analytics on massive volumes of data from IoT devices and sensors, empowering customers to uncover insights that lead to more accurate decisions for their IoT and machine learning applications.
AWS IoT Analytics collects, pre-processes, enriches, stores, and analyzes IoT device data at scale so companies can easily identify things like the average distance traveled for a fleet of connected vehicles, or how many doors are locked after work hours in a smart building, or assess the performance of devices over time to predict maintenance issues and better react to changing environmental conditions. With AWS IoT Analytics, customers don’t have to worry about all the cost and complexity typically required to build their own IoT analytics platform. AWS IoT Analytics is available today in the US East-1 (N. Virginia), US East-2 (Ohio), US West (Oregon), and EU (Ireland) regions, with support for additional regions coming soon.
“AWS IoT Analytics is the easiest way to run analytics on IoT data. Now, customers can act on the large volumes of IoT data generated by their connected devices with powerful analytics capabilities ranging from simple queries to sophisticated machine learning models that are specifically designed for IoT,” said Dirk Didascalou, VP, IoT, AWS. “As the scale of IoT applications continues to grow at a rapid rate, AWS IoT Analytics is designed to provide the best tools for our customers to mine their raw data, gaining insights that lead to intelligent actions.”
AWS IoT Analytics also has features like a built-in SQL query engine to answer specific business questions and more sophisticated analytics, enabling customers to understand the performance of devices, predict device failure, and perform time-series analysis. Also, AWS IoT Analytics offers access to machine learning tools with hosted Jupyter Notebooks through seamless integration with Amazon SageMaker. Customers can directly connect their IoT data to a Jupyter Notebook and build, train, and execute models at any scale right from the AWS IoT Analytics console without having to manage any of the underlying infrastructure.
Using AWS IoT Analytics, customers can apply machine learning algorithms to device data to produce a health score for each device in a fleet, prevent fraud and cyber intrusion by detecting anomalies on IoT devices, predict device failures, segment fleets of devices, and identify other rare events that may have great significance but are hard to find without analytics. And, by using Amazon QuickSight, a fast, cloud-powered business analytics service, in conjunction with AWS IoT Analytics, it is easy for customers to surface insights in easy-to-build visualizations and dashboards.
AWS IoT Analytics can accept data from any source, including external sources using an ingestion API, and integrates fully with AWS IoT Core. Launched in 2015, AWS IoT Core is a managed cloud platform that lets connected devices easily and securely interact with cloud applications and other devices. AWS IoT Analytics also stores the data for analysis, while providing customers the ability to set data retention policies.
Modjoul, Georgia Pacific, Teralytic, Siemens, OSIsoft, Pentair, 47Lining, Domo, NetFoundry, and Laird Technologies are just a few of the customers and Amazon Partner Network members using AWS IoT Analytics to uncover valuable insights within their data and use those findings to innovate across their specialized businesses.
Modjoul is a data invention company for wearable technology that is focused on keeping employees safe. “Our mission is to keep industrial workers safe, whether they’re working in or out of a vehicle,” said Eric Martinez, CEO and Founder, Modjoul. “In an eight-hour shift, we collect data 28,800 times per day from our connected activity tracker worn by each of our employees that includes 40 metrics including heart rate and activity level. With AWS IoT Analytics, we not only analyze all that health data, but also enrich it with location and environmental data, such as outdoor temperature, to get accurate analytics that prevent injuries and save lives. Today, we’re operating better and faster.”
Georgia Pacific is one of the world’s leading makers of tissue, pulp, paper, packaging, building products, and related chemicals. “At Georgia Pacific, our industry-leading dispensers allow us to deliver solutions to customers, not just sell products,” said Erik Cordsen, IoT Program Architect and Product Leader, Georgia-Pacific. “Now we are focused on making our dispensers ‘smart’ by adding sensors and connectivity that allow us to improve customer experience by providing real-time information about product levels and other statistics. With thousands of endpoints continuously feeding in data, we are using AWS IoT Analytics to enrich messages with location and product metadata in order to calculate platform health and value to our customers. AWS lets my team focus on solving the business problem instead of wrestling with technology.”
Teralytic is a soil health company focused on improving farmer’s yield by monitoring and improving the condition of their soils. “We have a network of soil-sensing IoT devices embedded in the soil from which data are collected, fed, and analyzed for us to understand the health of our customers’ agricultural ecosystems,” said Dan Casson, Vice President of Engineering, Teralytic. “We chose AWS IoT Analytics for its ability to filter outlier readings from our calculations and proactively detect issues as they arise so we can resolve them faster. In some cases, we’re able to identify and prevent issues before they occur. With AWS IoT Analytics, we use Machine Learning models to help detect situations where nutrients in the soil are at risk of leeching into ground water or runoff into surface water so the farmer can adjust the watering schedule, if needed. In addition to the environmental benefits, these machine learning models can help reduce a farmer’s costs as well as potentially increasing their yield.”
47Lining develops big data solutions and delivers big data managed services — built from underlying AWS building blocks like Amazon Redshift, Kinesis, Amazon Simple Storage Service (Amazon S3), and Amazon DynamoDB — to help customers manage their data across a variety of verticals including energy, life sciences, gaming, and financial services. “Because AWS IoT Analytics is designed around time-series data, it’s a great fit for our customers in industrial, energy, and oil & gas, who seek real-time decision support and process optimization,” said Mick Bass, Senior Vice President, Big Data Practice, 47Lining.
Domo is a computer software company that specializes in business intelligence tools and data visualization. “Since our inception in 2010, AWS has been a trusted service provider that keeps up with the demands of our dynamic business,” said Jay Heglar, Chief Strategy Officer, Domo. “We extended our relationship with AWS to IoT Analytics because we wanted a flexible option to enable faster access to machine-generated data for our customers. Through our proprietary connector to AWS IoT Analytics, we are ensuring our customers have access to one of the most innovative solutions, allowing them to leverage machine-generated data at scale.”
Laird Technologies designs, develops, manufactures, and supports wireless systems solutions and performance materials for wireless and other advanced electronics applications. “By combining our long range wireless sensor and gateway products with AWS IoT, our customers have been able to quickly and securely get data from their devices into the cloud,” said Paul Elvikis, Business Development Director for Industrial, Laird Technologies. “Unfortunately, they would often get overwhelmed with the amount of sensor data that would start coming in. Customers would struggle to figure out how to do anything with it. AWS IoT Analytics has been a great help in extending our capabilities to solve that issue for our customers.”
NetFoundry gives its customers and their applications control of their networks without any telco, hardware, or private circuit constraints. “The capabilities of AWS IoT Analytics in enabling the transformation of vast amounts of data into actionable information, without the high costs and steep learning curve of other IoT platforms, enables NetFoundry’s IoT customers to get the ROI they need,” said Michael Kochanik, Co-founder and Global Head of Channel Revenue, NetFoundry.“With AWS IoT Analytics, we can integrate IoT networking capabilities to provide our IoT customers with ‘one-stop shopping’ including data collection, networking, analysis, transformations, storage and visualization. Partnering with AWS enables our customers to get integrated, end-to-end agility, security, performance and cost efficiency at scale.”
AWS offers over 125 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 54 Availability Zones (AZs) within 18 geographic regions and one Local Region around the world, spanning the U.S., Australia, Brazil, Canada, China, France, Germany, India, Ireland, Japan, Korea, Singapore, and the UK.
by Gary Mintchell | Mar 21, 2018 | Data Management
Manufacturing technology professionals have been working with data of many types for years. Our sensors, instrumentation, and control systems yield terabytes of data. Then we bury them in historians or other databases on servers we know not where.
Companies are popping up like mushrooms after a spring rain with a variety of approaches for handling, using, analyzing, and finding all this data. Try on this one.
Io-Tahoe LLC, a pioneer in machine learning-driven smart data discovery products that span a wide range of heterogeneous technology platforms, from traditional databases and data warehouses to data lakes and other modern repositories, announced the General Availability (GA) launch of the Io-Tahoe smart data discovery platform.
The GA version includes the addition of Data Catalog, a new feature that allows data owners and data stewards to utilize a machine learning-based smart catalog to create, maintain and search business rules; define policies and provide governance workflow functionality. Io-Tahoe’s data discovery capability provides complete business rule management and enrichment. It enables a business user to govern the rules and define policies for critical data elements. It allows data-driven enterprises to enhance information about data automatically, regardless of the underlying technology and build a data catalog.
“Today’s digital business is driving new requirements for data discovery,” said Stewart Bond, Director Data Integration and Integrity Software Research, IDC. “Now more than ever enterprises are demanding effective, and comprehensive, access to their data – regardless of where it is retained – with a clear view into more than its metadata, but its contents as well. Io-Tahoe is delivering a robust platform for data discovery to empower governance and compliance with a deeper view and understanding into data and its relationships.”
“Io-Tahoe is unique as it allows the organization to conduct data discovery across heterogeneous enterprise landscapes, ranging from databases, data warehouses and data lakes, bringing disparate data worlds together into a common view which will lead to a universal metadata store,” said Oksana Sokolovsky, CEO, Io-Tahoe. “This enables organizations to have full insight into their data, in order to better achieve their business goals, drive data analytics, enhance data governance and meet regulatory demands required in advance of regulations such as GDPR.”
Increasing governance and compliance demands have created a dramatic opportunity for data discovery. According to MarketsandMarkets, the data discovery market is estimated to grow from $4.33 billion USD in 2016 to $10.66 billion USD in 2021. This is driven by the increasing importance of data-driven decision making and self-service business intelligence (BI) tools. However, the challenge of integrating the growing number of disparate platforms, databases, data lakes and other silos of data has prevented the comprehensive governance, and use, of enterprise data.
Io-Tahoe’s smart data discovery platform features a unique algorithmic approach to auto-discover rich information about data and data relationships. Its machine learning technology looks beyond metadata, at the data itself for greater insight and visibility into complex data sets, across the enterprise. Built to scale for even the largest of enterprises, Io-Tahoe makes data available to everyone in the organization, untangling the complex maze of data relationships and enabling applications such as data science, data analytics, data governance and data management.
The technology-agnostic platform spans silos of data and creates a centralized repository of discovered data upon which users can enable Io-Tahoe’s Data Catalog to search and govern. Through convenient self-service features, users can bolster team engagement through the simplified and accurate sharing of data knowledge, business rules and reports. Here users have a greater ability to analyze, visualize and leverage business intelligence and other tools, all of which have become the foundation to power data processes.
by Gary Mintchell | Sep 28, 2017 | Internet of Things, Manufacturing IT, Operations Management
Two research studies have crossed my inbox recently regarding management knowledge of and actions toward Digital Transformation and the Industrial Internet of Things. Suffice to say that there is a disconnect.
Get smart: Humans have perceived for millennia the disconnect between knowing and doing. These research surveys show that even when managers acknowledge the importance of modern digital technologies they cannot get the job done.
Big Thought: Implementers have realized significant cost reductions and increased speed of product development.
The first study was conducted by enterprise business solutions provider, HSO. It found 54% of managers in the manufacturing industry believe that their company is not effectively using predictive engineering technology, despite the technology being billed as a leading industry trend.
In an era that has been dominated by the rise of IoT and predictive analytics technology, it was also surprising to find that only 15.2% of those polled placed predictive engineering as a business priority for the next five years. In addition to this, a quarter of the 250 managers involved in the study feel that a lack of integrated technology across different departments is the main reasons that firms do not implement predictive engineering.
However, the study did reveal that more than four in ten managers in manufacturing feel that the rise of IoT technologies is crucial to help drive predictive engineering, with artificial intelligence and machine learning also being rated as important factors.
Out of the manufacturers that are using predictive engineering to help make their processes more efficient, over half (55.6%) stated that they are benefitting from significant cost reductions while 44.8% are seeing an increase in the speed of product development.
A second study by IFS, and enterprise applications provider, found lack of integration stands between companies and digital transformation benefits of IoT. According to a survey of 200 IoT decision makers at industrial companies in North America, only 16 percent of respondents consume IoT data in enterprise resource planning (ERP) software. That means 84 percent of industrial companies face a disconnect between data from connected devices and strategic decision making and operations, limiting the digital transformation potential of IoT.
The study posed questions about companies’ degree of IoT sophistication. Respondents were divided into groups including IoT Leaders and IoT Laggards, depending on how well their enterprise software prepared them to consume IoT data—as well as Digital Transformation Leaders and Digital Transformation Laggards depending on how well their enterprise software prepared them for digital transformation.
The two Leaders groups overlapped, with 88 percent of Digital Transformation Leaders also qualifying as IoT Leaders, suggesting IoT is a technology that underpins the loose concept of digital transformation. Digital Transformation Leaders made more complete use of IoT data than Digital Transformation Laggards; Leaders are almost three times as likely to use IoT data for corporate business intelligence or to monitor performance against service level agreements.
Digital Transformation Leaders were more likely than Digital Transformation Laggards to be able to access IoT data in applications used beyond the plant floor. They were more than four times as likely to have access to IoT data in enterprise asset management software, twice as likely than Digital Transformation Laggards to be able to access IoT data in high-value asset performance management software, and almost twice as likely to be able to be able to use IoT data in ERP.
The data suggests a real need for more IoT-enabled enterprise applications designed to put data from networks of connected devices into the context of the business.