This presentation was given to an executive MBA session at UCSD in April 2016. The session reviewed big data, internet of things, and how companies are gaining value from location, sensor, manufacturing and other data to make better business decisions.
NTK 2015: Internet of things track (IoT) - Smart HomeAndrej Tozon
Slides from my talk at NT Konferenca 2015 on the Internet of Things (IoT) track. I talked about my "Smart Home" automation - hardware and wiring behind it and connected clients - Windows, including Windows 10 running on Raspberry Pi 2.
Engineering Enablement for Connected and Intelligent SystemsCyient
How can CSPs monitor and manage all passive network components of Cell Towers remotely? Cyient’s Head of IoT and Analytics, Communications, Anji Vaidyula talked about how Cyient’s IoT-enabled Tower Operations Centre could help. Learn more.
IoT Meets Big Data: The Opportunities and Challenges by Syed Hoda of ParStreamgogo6
Download our special report, IoT Tech for the Manager: http://bit.ly/report1-slideshare
IoT Meets Big Data: The Opportunities and Challenges as presented at the IoT Inc Business' Eighth Meetup. See: http://www.iot-inc.com/iot-meets-big-data-the-opportunities-and-challenges/
In our eighth Meetup we have Syed Hoda, Chief Marketing Officer of ParStream presenting “IoT Meets Big Data: The Opportunities and Challenges”. Come meet other business leaders in the IoT ecosystem and discuss the business issues you face in the Internet of Things.
Presentation Abstract
The Internet of Things (IoT) and Big Data have each made press headlines and continue to be board-level priorities. The intersection of IoT and Big Data is a fascinating area of innovation with tremendous scope for business impact. From industrial sensors to vehicles to health monitors, a huge variety of devices connects to the Internet and share information. At the same time, the cost to store data has dropped dramatically while capabilities for analysis have made huge leaps forward. How can analytics drive business benefits from IoT projects? What are the challenges in storing and analyzing huge amounts of real-world information? How can companies generate more value from their data? We will address these questions and also share our perspectives on innovative technologies enabling new IoT use cases.
Managing your Assets with Big Data ToolsMachinePulse
This presentation was given by Karthigai Muthu, Lead Big Data Analyst, at a meetup organized by the group Internet of Everything in March 2015.
Through his presentation, Karthik provided a comprehensive understanding of available ecosystem tools and how they can be used to perform data engineering and data analytics. Karthik covers the following topics in his presentation:
• Establishment of complete data pipeline using big data ecosystem tools.
• Tackling of high velocity streams using various stream processing engines on cloud and performing Real Time analytics.
• Tackling of historical data using big data ecosystem tools and migration of traditional infrastructure to big data environments.
• Integration of big data ecosystem for data analysis using SAMOA , R and Mahout.
• Deployments of big data environments on the cloud.
Powel presenting the Fingrid ELVIS solution at IAM Infrastructure Asset Manag...Jens Dalsgaard
At the IAM Infrastructure Asset Management Exchange 2015 I had the joy of doing a joint presentation with Marcus Stenstrand on the Fingrid ELVIS solution for electric transmission system operators..
NTK 2015: Internet of things track (IoT) - Smart HomeAndrej Tozon
Slides from my talk at NT Konferenca 2015 on the Internet of Things (IoT) track. I talked about my "Smart Home" automation - hardware and wiring behind it and connected clients - Windows, including Windows 10 running on Raspberry Pi 2.
Engineering Enablement for Connected and Intelligent SystemsCyient
How can CSPs monitor and manage all passive network components of Cell Towers remotely? Cyient’s Head of IoT and Analytics, Communications, Anji Vaidyula talked about how Cyient’s IoT-enabled Tower Operations Centre could help. Learn more.
IoT Meets Big Data: The Opportunities and Challenges by Syed Hoda of ParStreamgogo6
Download our special report, IoT Tech for the Manager: http://bit.ly/report1-slideshare
IoT Meets Big Data: The Opportunities and Challenges as presented at the IoT Inc Business' Eighth Meetup. See: http://www.iot-inc.com/iot-meets-big-data-the-opportunities-and-challenges/
In our eighth Meetup we have Syed Hoda, Chief Marketing Officer of ParStream presenting “IoT Meets Big Data: The Opportunities and Challenges”. Come meet other business leaders in the IoT ecosystem and discuss the business issues you face in the Internet of Things.
Presentation Abstract
The Internet of Things (IoT) and Big Data have each made press headlines and continue to be board-level priorities. The intersection of IoT and Big Data is a fascinating area of innovation with tremendous scope for business impact. From industrial sensors to vehicles to health monitors, a huge variety of devices connects to the Internet and share information. At the same time, the cost to store data has dropped dramatically while capabilities for analysis have made huge leaps forward. How can analytics drive business benefits from IoT projects? What are the challenges in storing and analyzing huge amounts of real-world information? How can companies generate more value from their data? We will address these questions and also share our perspectives on innovative technologies enabling new IoT use cases.
Managing your Assets with Big Data ToolsMachinePulse
This presentation was given by Karthigai Muthu, Lead Big Data Analyst, at a meetup organized by the group Internet of Everything in March 2015.
Through his presentation, Karthik provided a comprehensive understanding of available ecosystem tools and how they can be used to perform data engineering and data analytics. Karthik covers the following topics in his presentation:
• Establishment of complete data pipeline using big data ecosystem tools.
• Tackling of high velocity streams using various stream processing engines on cloud and performing Real Time analytics.
• Tackling of historical data using big data ecosystem tools and migration of traditional infrastructure to big data environments.
• Integration of big data ecosystem for data analysis using SAMOA , R and Mahout.
• Deployments of big data environments on the cloud.
Powel presenting the Fingrid ELVIS solution at IAM Infrastructure Asset Manag...Jens Dalsgaard
At the IAM Infrastructure Asset Management Exchange 2015 I had the joy of doing a joint presentation with Marcus Stenstrand on the Fingrid ELVIS solution for electric transmission system operators..
Hyperthings is an innovative provider of “Internet of Things” based products and solutions. Interoperability lies at the heart of everything we do and we aim to be the organization which develops solutions to provide more straight forward interaction between physical and virtual world
Splunk for Industrial Data and the Internet of Thingsaliciasyc
The IoT is a natural evolution of the world’s networks. Just as people became more connected by devices and applications during the explosion of the social media revolution, devices, sensors and industrial equipment are also becoming more connected—and are consuming and generating data at an unprecedented pace. Disparate and deployed connected devices can provide a unique touchpoint to real-world operations and conditions. Only few architectures and applications are designed to handle the constant streams of real-time events, sensor readings, user interactions and application data produced by massive numbers of connected devices. Use Splunk to collect, index and harness the power of the machine data generated by connected devices and machines deployed on your local network or around the world.
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2016/05/making-smarter-systems-with-iot-and-analytics/
Many systems today play an increasingly important role in our lives and communities. Systems can learn and adopt by themselves without having to follow a structured, predefined execution flow. They are digitally independant and have become smarter, faster and more reliable. Digital intelligence can be embedded not just in individual components but also across entire systems, impacting everything from traffic flows and electric power to the way our food is grown, processed and delivered. This is achieved by employing the capabilities of multiple disciplines. Devices and systems produce large volume unstructured data. Real-time or historical data can be analyzed to uncover hidden patterns, correlations and other insights and this information is then fed into machine learning algorithms that calculates predictions.
WSO2’s analytics platform together with the WSO2 IoT Server can provide all these capabilities. This webinar aims to
Identify key capabilities needed when composing a smart system
Explore how WSO2’s analytics platform can be used to make a system smarter
Discuss how WSO2 IoT Server manages and enable devices
ConnectM is a leading, global Machine-to-Machine (M2M) Technology Solutions, Business Intelligence (BI) and Services Company headquartered in Bangalore, India. With over 70,000 assets connected, ConnectM's Solution Delivery Platform(SDP) is a robust and proven technology. Our Pre-built applications are leveraged by over 40 customers to manage their assets/machines. ConnectM SDP and applications along with devices from its eco-system partners offer the customer a one-stop-shop for M2M needs.
ConnectM has delivered M2M solutions to various domains including Telecom, Energy, Utilities, Industrial, Construction, IT/ITES Enterprises and Transportation market segments.
ConnectM's solutions are powered by cutting edge M2M technology, and are designed to make significant, sustained and measurable business impact. ConnectM also offers services to its client to build complete solutions. ConnectM's strength is in its ability to offer domain specific analytics on the data collected and deliver actionable business intelligence.
everis joins Telefonica to collaborate in the Internet para Todos Peru initiative, a wholesale telecommunications infrastructures operator that aims to expand the internet connectivity in rural areas of Latin America.
Resulting from this collaboration, everis will be in charge of the deployment and maintenance of the Operation Support Systems (OSS) that enable the creation and sustainment of a reliable and quality network.
The solutions already deployed by everis are based on open models and include advanced network analytics systems, AI and machine learning applications.
At WomenWhoCode, 2019 https://india.womenwhocode.dev/agenda/
Edge Computing: What, Why, How and Where
Edge Analytics: What, Why, benefits, limitations
Edge computing vs Edge Analytics
Edge Analytics use-cases
Big Data for Big Power: How smart is the grid if the infrastructure is stupid?OReillyStrata
Introducing the concept of SLEx (Substation Life Extension) and Intelligent Sensing at teh Edge when it comes to Smart Grid activities. This is a novel concept on truly using sensors to extend substation life, and then dealing with the Big Data that has just been introduced by using state of the art sensing technology. Both SLEx and Intelligent Sensing at the Edge are concepts that have been introduced by Brett Sargent who is CTO and VP/GM of Products at an innovative sensor company located in Silicon Valley.
The Analytics Value Chain - Key to Delivering Business Value in IoTPeter Nguyen
In the IoT, new analytics approaches are needed to handle the time critical nature of IoT challenges.
The first step in designing a new approach is to simplify the process by integrating all the data for an IoT application. That includes all the structured, unstructured, and semi-structured data in your organization.
The second key step in the streamlining process is to unify the analytics layer.
This includes historical analytics (descriptive & diagnostic), real-time streaming analytics, predictive analytics, and prescriptive analytics.
The approach to analytics outlined above is a good first step for IoT. However, it is the ability to execute analytics in real-time across the analytics value chain (streaming, historical, predictive, and prescriptive analytics) with relevant contextual and situational data that addresses the critical “last mile” for timely outcomes.
Then this must be combined with the ability to take the next best action in any particular scenario to create the greatest value.
Jon Longstaff looked at the recent attacks on Ukraine power stations and talked about the security implications of IoT devices in industry and how to secure them.
This slideshare was originally presented at the East Midlands Cyber Security Forum's Summer event on 6th June 2017 at University of Nottingham.
https://emcsf.org.uk/
Why Use Open Source to Gain More Visibility into Network MonitoringDevOps.com
Learn how to use open source solutions for your network monitoring to gain the necessary visibility in the status, performance and responsiveness of your enterprise, cloud or hybrid application environments. Get a faster and easier tool to start collecting data from multiple sources and quickly perform root-cause analysis reducing your MTTR.
Case study: Building a Holistic View of Data - Big Data Expo 2019webwinkelvakdag
Many organisations will be grappling with large, distributed and high-load data systems. Over time they have become complex, hard to maintain, and don’t provide a consolidated view of data available to the organisation. These were the challenges faced by Liberty Global who, together with EPAM, have tackled these issues by creating an Operational Data Hub - a centralized operational data framework to replace multiple monitoring solutions.
Accelerating analytics on the Sensor and IoT Data. Keshav Murthy
Informix Warehouse Accelerator (IWA) has helped traditional
data warehousing performance to improve dramatically. Now,
IWA accelerates analytics over the sensor data stored in relational and timeseries data.
Hyperthings is an innovative provider of “Internet of Things” based products and solutions. Interoperability lies at the heart of everything we do and we aim to be the organization which develops solutions to provide more straight forward interaction between physical and virtual world
Splunk for Industrial Data and the Internet of Thingsaliciasyc
The IoT is a natural evolution of the world’s networks. Just as people became more connected by devices and applications during the explosion of the social media revolution, devices, sensors and industrial equipment are also becoming more connected—and are consuming and generating data at an unprecedented pace. Disparate and deployed connected devices can provide a unique touchpoint to real-world operations and conditions. Only few architectures and applications are designed to handle the constant streams of real-time events, sensor readings, user interactions and application data produced by massive numbers of connected devices. Use Splunk to collect, index and harness the power of the machine data generated by connected devices and machines deployed on your local network or around the world.
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2016/05/making-smarter-systems-with-iot-and-analytics/
Many systems today play an increasingly important role in our lives and communities. Systems can learn and adopt by themselves without having to follow a structured, predefined execution flow. They are digitally independant and have become smarter, faster and more reliable. Digital intelligence can be embedded not just in individual components but also across entire systems, impacting everything from traffic flows and electric power to the way our food is grown, processed and delivered. This is achieved by employing the capabilities of multiple disciplines. Devices and systems produce large volume unstructured data. Real-time or historical data can be analyzed to uncover hidden patterns, correlations and other insights and this information is then fed into machine learning algorithms that calculates predictions.
WSO2’s analytics platform together with the WSO2 IoT Server can provide all these capabilities. This webinar aims to
Identify key capabilities needed when composing a smart system
Explore how WSO2’s analytics platform can be used to make a system smarter
Discuss how WSO2 IoT Server manages and enable devices
ConnectM is a leading, global Machine-to-Machine (M2M) Technology Solutions, Business Intelligence (BI) and Services Company headquartered in Bangalore, India. With over 70,000 assets connected, ConnectM's Solution Delivery Platform(SDP) is a robust and proven technology. Our Pre-built applications are leveraged by over 40 customers to manage their assets/machines. ConnectM SDP and applications along with devices from its eco-system partners offer the customer a one-stop-shop for M2M needs.
ConnectM has delivered M2M solutions to various domains including Telecom, Energy, Utilities, Industrial, Construction, IT/ITES Enterprises and Transportation market segments.
ConnectM's solutions are powered by cutting edge M2M technology, and are designed to make significant, sustained and measurable business impact. ConnectM also offers services to its client to build complete solutions. ConnectM's strength is in its ability to offer domain specific analytics on the data collected and deliver actionable business intelligence.
everis joins Telefonica to collaborate in the Internet para Todos Peru initiative, a wholesale telecommunications infrastructures operator that aims to expand the internet connectivity in rural areas of Latin America.
Resulting from this collaboration, everis will be in charge of the deployment and maintenance of the Operation Support Systems (OSS) that enable the creation and sustainment of a reliable and quality network.
The solutions already deployed by everis are based on open models and include advanced network analytics systems, AI and machine learning applications.
At WomenWhoCode, 2019 https://india.womenwhocode.dev/agenda/
Edge Computing: What, Why, How and Where
Edge Analytics: What, Why, benefits, limitations
Edge computing vs Edge Analytics
Edge Analytics use-cases
Big Data for Big Power: How smart is the grid if the infrastructure is stupid?OReillyStrata
Introducing the concept of SLEx (Substation Life Extension) and Intelligent Sensing at teh Edge when it comes to Smart Grid activities. This is a novel concept on truly using sensors to extend substation life, and then dealing with the Big Data that has just been introduced by using state of the art sensing technology. Both SLEx and Intelligent Sensing at the Edge are concepts that have been introduced by Brett Sargent who is CTO and VP/GM of Products at an innovative sensor company located in Silicon Valley.
The Analytics Value Chain - Key to Delivering Business Value in IoTPeter Nguyen
In the IoT, new analytics approaches are needed to handle the time critical nature of IoT challenges.
The first step in designing a new approach is to simplify the process by integrating all the data for an IoT application. That includes all the structured, unstructured, and semi-structured data in your organization.
The second key step in the streamlining process is to unify the analytics layer.
This includes historical analytics (descriptive & diagnostic), real-time streaming analytics, predictive analytics, and prescriptive analytics.
The approach to analytics outlined above is a good first step for IoT. However, it is the ability to execute analytics in real-time across the analytics value chain (streaming, historical, predictive, and prescriptive analytics) with relevant contextual and situational data that addresses the critical “last mile” for timely outcomes.
Then this must be combined with the ability to take the next best action in any particular scenario to create the greatest value.
Jon Longstaff looked at the recent attacks on Ukraine power stations and talked about the security implications of IoT devices in industry and how to secure them.
This slideshare was originally presented at the East Midlands Cyber Security Forum's Summer event on 6th June 2017 at University of Nottingham.
https://emcsf.org.uk/
Why Use Open Source to Gain More Visibility into Network MonitoringDevOps.com
Learn how to use open source solutions for your network monitoring to gain the necessary visibility in the status, performance and responsiveness of your enterprise, cloud or hybrid application environments. Get a faster and easier tool to start collecting data from multiple sources and quickly perform root-cause analysis reducing your MTTR.
Case study: Building a Holistic View of Data - Big Data Expo 2019webwinkelvakdag
Many organisations will be grappling with large, distributed and high-load data systems. Over time they have become complex, hard to maintain, and don’t provide a consolidated view of data available to the organisation. These were the challenges faced by Liberty Global who, together with EPAM, have tackled these issues by creating an Operational Data Hub - a centralized operational data framework to replace multiple monitoring solutions.
Accelerating analytics on the Sensor and IoT Data. Keshav Murthy
Informix Warehouse Accelerator (IWA) has helped traditional
data warehousing performance to improve dramatically. Now,
IWA accelerates analytics over the sensor data stored in relational and timeseries data.
COFES 2015: Product lifecycle, supply chain and data networksOleg Shilovitsky
My presentation at COFES 2015 Design and Sustainability Symposium speaks about complexity of product lifecycle for supply chain. More specifically, I'm talking about potential of data networks for solving decision workflows in complex data environments.
This presentation aims to create awareness for IoT device makers on the various aspects they might encounter with their products. Security challenges which need to be addressed are listed to try to guide developers along the right path.
How M2M / IoT Architecture changes the Vending market and scales for smaller ...Eurotech
How modern M2M / IoT Architecture Building Blocks change the Vending market and scale for smaller companies.
M2M Innovation World Congress
Marseille, September 2014
Webinar - Transforming Manufacturing with IoTHARMAN Services
The Manufacturing industry is realizing the tremendous benefits in the “Internet of Things” (IoT), an inevitable evolution to traditional M2M solutions. Innovations across embedded devices, advanced analytics, and enriched user experiences all powered by cloud, has enabled new opportunities for both perpetual revenue and perpetual customer value. In this session we will break down benefits of IoT for Manufacturing with real-world examples.
Internet is no longer just a global network for people to communicate with one another using computers, but it is also a platform for devices to communicate electronically with the world around them. The result is a world that is alive with information as data flows from one device to another and is shared and reused for a multitude of purposes. Harnessing the potential of all of this data for economic and social good is one of the primary challenges and opportunities. IoT enabled data driven predictive maintenance is becoming relevant in all the major industries as it can drive efficiency by providing higher levels of safety and quality at a fraction of the current costs. Thanks to Big Data, Analytics and IoT devices, predicting potential failures is going to be a real capability…but what happens after a failure is predicted, the need for maintenance is detected or a part replacement is required? Even if you can predict failures, dynamic technician scheduling associated with equipment maintenance management requires insight into real-time held inventory, technician location and estimated service completion time. Establishing an ecosystem where customers, equipment producers, service companies and all other digital service providers can collaborate is the right answer.
Presentation of José Manuel Caramés, Global Head of M2M Transport at Telefónica about the M2M benefits for Fleet Management solutions at TM Forum Live! in Nice ..
The workshop "Platforms for connected Factories of the Future" took place on 5 and 6 of October 2015 in Brussels. Main objective on the first workshop day was to give an overview on existing activities in industries and research regarding platforms for the manufacturing area.
A reference architecture for the internet of thingsCharles Gibbons
A reference architecture for the internet of things: including Devices, Protocols, massively Distributed Service Layer, Business Support Systems, Channels, Device Management and Identity Management.
How do APIs and IoT relate? The answer is not as simple as merely adding an API on top of a dumb device, but rather about understanding the architectural patterns for implementing an IoT fabric. There are typically two or three trends:
Exposing the device to a management framework
Exposing that management framework to a business centric logic
Exposing that business layer and data to end users.
This last trend is the IoT stack, which involves a new shift in the separation of what stuff happens, where data lives and where the interface lies. For instance, it's a mix of architectural styles between cloud, APIs and native hardware/software configurations.
More and more people in mega cities, more sensors, more apps, Smart is everywhere for smart living. but what's about security, what's about the people. How to deliver better living, happy living. HPE provides IoT solutions with connectivity management, processing at the edge and in the cloud, security, data management, etc to help industry verticals, telecom operators deliver secured trusted IoT solutions
Fin fest 2014 - Internet of Things and APIsRobert Greiner
An overview of the core concepts behind the ultra-hyped Internet of Things. We start the presentation with an overview and slight re-classification of what the Internet of Things is. Then, we jump into how to *serve* the internet of things - discussing a homebrew project using the RaspberryPi and Microsoft Azure.
Fabio De Santi e Thiago Urtaran - Smart cities: um caso real, a arquitetura d...DevCamp Campinas
Palestra DevCamp 2018 - Fabio De Santi e Thiago Urtaran Abbruzzese: Smart cities: um caso real, a arquitetura de IoT e os desafios para a implementação
A presentation pertaining to the integration of real-time data to the cloud with significant potential in the areas of Industrial IT,Real-time sensor information processing and Smart grids applied to various vertical industries. This is related to my blog post at www.cloudshoring.in
NetFlow Auditor Anomaly Detection Plus Forensics February 2010 08NetFlowAuditor
NetFlow Auditor software uses NetFlow and sFlow to detect anomalies & analyze full network traffic forensics. The objective of our software is to provide easy to use full-featured anomaly detection and analysis of Flows to quickly identify who is doing what, where, when, with whom and for how long on a network and provide alerts, scheduled reports, SNMP Traps and or filter lists. It allows organizations to quickly identify and alert on network anomalies to help resolve performance problems and manage network security and compliance across business services and applications, dramatically reducing the risk of potential downtime.
This deck presents some basic concepts of IoT and some more advanced concepts, reviews the current market players and future of IoT as well as the key ingredients and architecture for success.
The Fog or Edge Computing model complements Cloud Computing with small, typically sensor-enabled and IOT connected devices that process distributed data at its source. As this model matures, we see an uptake on a 3-tier architecture with Intelligent Gateways to aggregate sensor input before communicating with data centers or a Cloud. Two forces will drive the practice of distributing Intelligence (Understanding/Reasoning/Learning) to the Gateway. The first is the presence of the Gateway itself, which enables a standards-based approach to distributing intelligence and moving it closer to the edge. The second is the trend for simplifying system requirements by processing training data or model validation with big data prior to deployment, and using small footprint devices for operational systems.
This webinar will present an overview of the relevant technologies and trends. Participants will learn about the state of the art today, and how to identify apps in their own environment that would be good candidates for Intelligent Edge solutions.
DELL Technologies - The IoT Value Chain - Solutions for the Smart World - Del...Smarter.World
In this presentation we will introduce various aspects of the Internet of Things, Industry 4.0 and the associated challenges in implementing new digital services.
We also refer to IoT / Industry 4.0 terminology, market developments, factors and drivers, IoT platform components, but also to the differentiation and similarities of the Internet of Things and Industry 4.0.
Using various application examples, we will outline the range of DELL Technologies offerings.
Here, however, we remain at an overview level for the first time without paying attention to the details of the deployable DELL-EMC products and solutions.
We would continue this in downstream discussions depending on the identified topic segment.
Everyone brings unique insights, experience, and knowledge from different cultural backgrounds. Sometimes you have to zoom in/out to understand the diversity of human experiences.
UCSD: Building a Big Data Culture - It Takes a VillagePaul Barsch
Companies talk about the need to make decisions based on analytics, but there are people, process, technology, and strategy considerations to making it work. This presentation given at UCSD in May 2017 discusses the journey companies take towards becoming "data-driven", including where they most often get stuck. Also discussed are various roles required (i.e. data scientist, data engineer, data analysts and more) and the skills needed to succeed now and in the future. This presentation will show you how to stay relevant in an age of disruption by leveraging data to make the best decisions possible.
This talk reviews probabilistic models including frequentism and Bayesian logic before discussing business scenarios where statistics will fail to provide answers.
Everyone knows there's too much big data. But what's the best way to harness the power of big data? This presentation discusses three analytic engines that companies big and small are using to capture, store, transform and use big data. Also included are case studies of big data in action.
Lecture three skills to thrive in new economy slidesharePaul Barsch
Want to know three critical skills that could significantly differentiate you from the rest of the competition? This presentation was given to students at California University, San Marcos, CA in April 2013.
Surviving The Corporate World - 4 Lessons LearnedPaul Barsch
Covering more than fifteen years in high technology companies, this twenty minute presentation given to students at California State University, covers four things I’ve learned along the way in my travels through small, medium, large and multi-national companies. I hope you can learn from my mistakes and/or best practices, and maybe you can also contribute some lessons learned not covered here? If so, please add your comments.
MBA Lecture: Supply Chain Risk ManagementPaul Barsch
The world is getting more complex and interdependent, leaving the old supply chain management assumptions out in the cold. Executives need a new way of thinking when it comes to supply chain risk and better options on dealing with volatility. This presentation discusses problems with predictions, a new framework for risk management, and potential solutions for more effectively countering the effects of globalization.
More than the 4Ps or simply social media, Boundaryless Marketing refers to what marketing should always be about: connecting with customers, understanding finance, risk management and operations, harnessing the power of technology, and providing strategic guidance towards helping companies adapt to rapid and systemic change in markets.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
3. 3
Data Growth
Source: IDC - sponsored by EMC. As the Economy Contracts, the Digital Universe Expands. May 2009
Transactions
Interactions
1024
1021
1018
1015
1012
109
Yottabyte
Zettabyte
Exabyte
Petabyte
Terabyte
Gigabyte
4. 4
IoT, Sensors, and Tiny Computers
Internet of Things
SensorsEmbedded
computers
Industry Specific
(eg CatScans, etc.)
Data Center
systems
5. 5
• Events generating data
– Vibration
– Temperature, humidity
– Wind speed, direction
– Air/liquid flow or pressure
– Location, navigation
– Tilt level, rotation
– Light, sound
– Radiation, chemicals
– Biological
- Heart rate, blood pressure
- Brain activity, chemicals
– Inventory, sales (RFID)
• Data format: JSON or proprietary
The Data Sensors Collect
6. 6
• A new wave of devices will help you track health statistics but security and
privacy concerns loom for this health “Big Data”
Google Glass Jawbone UP Ingestible
Sensors
The Quantified Self
7. 7
Gartner: Growth of the Internet of Things
Source: Gartner, Forecast: The Internet of Things, Worldwide, 2013, Nov 2013
Billions of Things in Use
Connected PC, smartphone, tablet IoT
0
5
10
15
20
25
30
2009
2020
13. 13
New Tech for Vehicles Focused on Safety & Autonomy
Carmakers are facing seismic change. Suppliers which
were largely kept under the hood are set to grow in
influence as the industry adds more and autonomous
features to vehicles
Detects close range
objects to aid parking and
avoid collision by using
radio waves
Integrates driver
assistance functions:
algorithms for every
scenario
Semiconductors underpin
advanced electronic
functionality
Seeks longer range objects
for use in Adaptive Cruise
Control systems
Used in front and rear
parking sensors in modern
cars. Will be adapted for
assisted parking and short
range pedestrian/obstacle
detection
Enables in-car night vision
systems that can detect
objects further away than
traditional headlights to
avoid collisions at night
Integrates both directional and
distance information in lane
departure systems and traffic sign
recognition
For precise navigation
Allows vehicles to
communicate with
each other
Advanced driver assistance systems
Ultrasound
Front/rear
short radar
Infrared
Vehicle to
vehicle
communication
Advance
mapping
Semi
conductors
Long range
radar
Stereo
cameras
Software
14. 14
• Customer centric experience –
connected cars and hazards ahead
• Dealer Analytics –Scorecards and
Action Plan
• Warranty/ Repair/ Failure
Predictions
• Add New Services
• Featured Used/Not Used- learn for
the future
• Buy and customize online – most of
sales activity is done prior to walk-
in’s… “Gamechanging”
Connected car Driver Model
Case Study: IoT at Volvo
Not all sensors or micro computers are part of the internet of things. IoT mainly points to machines and devices that communicate over the internet via Wifi, 3G, 4G, or Ethernet expecting some receiving computer to use the data and provide assistance. But let’s start with the keyword here: THINGS. IoT does not include all the computer servers in the world. We already have that – its called a data center or a Local Area Network, or a home office network. The IoT name describes the trillions of THINGS that are not full computers but are emitting data.
Also notice that sensors range from simple electronic devices to small embedded computers. The embedded computers are constantly growing in capacity due to Moore’s law and economics. So the sensor that detects the refrigerator door opening someday evolves to a micro computer keeping track of inventory inside the refrigerator. Similarly, the on board computer in a Ford Focus evolves to collecting data on driving habits, brake wear, and other maintenance or performance objectives. This can then be used within customer service, engineering and design or warranty to improve insights in these areas.
The business question is how valuable is what specific data? There is a lot of redundant data - is all of it needed? If not, then how to determine what is valuable. Additionally, as sensors increase in computational power and calculations are pushed to the sensors, then derived data may become the most valuable.
Sensors are most frequently used for MEASUREMENT. Types of measurements include: acceleration, tilt, shock, rotation, temperature / humidity / atmospheric conditions, pressure, vibration, strain, force, location, time (traffic movement, usage (minutes/hours of run, dwell, idle time)), engine data (e.g. speed/RPM), miles per gallon, component performance, diagnostic codes, location and status tracking, and more.
Data transmission timing varies by sensor and network availability. It may be in real time, or it may be in batch. For example, many of the early onboard diagnostic sensors in automotive are uploaded in batch once they reach a dealership vs streaming.
Sensors collect all kinds of data. JSON is one of the more common data formats because its small and easy to implement. However, many manufacturers of sensors emit proprietary data formats along with an API programming manual. Its not hard to work with the proprietary data but its also not as easy as selecting an icon in Informatica’s PowerCenter or in DataStage.
If the data format is JSON, use Teradata 15.0 or an ETL tool to easily consume the data.
The growth in IoT will far exceed that of other connected devices. By 2020, the emergence of mass market smartphones and tablets, combined with the mature PC market, will result in an installed base of about 7.3 billion units, which compares with the expected human population of 7.7 billion in that year (based on information from the United Nations Population Division). In contrast, the IoT will have expanded at a much faster rate, resulting in an installed base of about 26 billion units at that time. Installed base is important because it drives the value of service revenue, aggregate communications bandwidth and data center activity. Due to the low cost of adding IoT capability to consumer products, we expect that ghost devices with unused connectivity will be common.
In addition, enterprises will make extensive use of IoT technology, and there will be a wide range of products sold into various markets, such as:
Advanced medical devices, including surgical tools, instrumentation and wearable medical sensors/monitors, and ingestible devices, such as smart pills
Factory automation sensors and applications in industrial robotics
Sensor motes for increased agricultural yield
Automotive sensors
Infrastructure integrity monitoring systems for diverse areas, such as road and railway transportation, water distribution, and electrical transmission
The most popular IoT sensors initially installed are those on items that provide remote monitoring (eg. wind turbines, ATMs/ kiosks, heavy equipment, drilling equipment, etc.) or are mobile (autos, aircraft & engines, people, packages, rail, medical equipment, etc.). This will evolve quickly.
I want you to take away a VERY SIMPLE view of what Internet of Things is. So here is a very simple diagram that will clarify it.
The Internet of things is an expansion of our notion of the internet, to include things….large and small.
And the things are able to, through sensors and tiny chips embedded in them, communicate their state.
And they do this through a series of networking devices called Gateways, which aggregate and package up the data for sending onward, networks themselves, that convey the sensor data either to Public Clouds, or Private Data Centers.
So that companies can do analytics on them.
That’s the simple view.
What is terrifying companies – our customers, is the prospect that this volume of data is yet again another couple of orders of magnitude bigger then when it was just the internet of people and their devices.
The data produced by the IoT is doubling every two years. This creates a "fear" in our customers (budgeting, complexity, executiion, opportunity loss)
What kind of analytics willl be done in the edge? That’s the $64k question.
Event Message (DEMN): DENMs are event-triggered messages broadcasted to alert road users of a hazardous event. Both CAM and DENM messages are delivered to vehicles in a particular geographic region: to the immediate neighborhood in case of CAMs (single hop), and to the area affected by the event for DENMs (multi-hop).
Safety Messages – sent from Vehicle
Probe Data: Data collected from sensors on the car or on the road.. Summarized vs detailed.. There are several papers on how to “summarize” the data through algorithms and modeling
ADAS: Advanced Driver Assist
DMA: Smart phones communicating with vehicle in support of safety, transportation data, smarter travel etc..
Slef-Service – schedule service apporintments, user reset alerts
Usage based services –used car valuation..cross brand usage if own other brands
Telematics tailord services – insurance, parking
In addition to delivering an enhanced level of in-vehicle consumer services, Teradata also has the ability to capture, analyze and report on vehicle performance sensor data to determine potential mechanical failures BEFORE they happen.
This level of predictive maintenance allows Teradata to help car makers improve vehicle safety, maintenance and customer satisfaction.
Today new vehicles are equipped with hundreds iof diagnostic sensors that are equipped to capture data and alert drivers of pending or actual parts/system failures.
These sensors monitor everything from engine performance to breaking, steering, blind spot detection, inclement weather and driver alertness in the vehicle. As these sensors transmit large volumes of vehicle performance data, its important to have an analytics system in place to detect potential failures and determine the size and scope of a potential problem across multiple vehicles or an entire model fleet.
Often a vehicle part failure across multiple vehicles or an entire fleet can trigger a root cause investigation. The ability to quickly and easily capture, analyze and triage a maintenance problem is often the difference in both improving driver/passenger safety and mitigating large scale vehicle outage interruptions.