Big data is a hot topic across all industries, Josh Marks, CEO, masFlight talks to the decision makers of the air service development community at World Routes 2014 about how large corporations can work agilely and harness the power of data.
From FAA Forecast Conference, March 2007. Reviews the future of long-haul LCC (Low-cost carrier) business models in aviation. Presents economic analysis of all-economy cabin services and differentiated premium business models.
Airline and Airport Big Data: Impact and EfficienciesJoshua Marks
Keynote presentation at Routes 2014 in Chicago - how big data changes aviation efficiencies, and what airlines and airports need to know about cloud data warehouses, real-time integration and predictive analytics.
This document provides an overview of Aviation Analytics Group, a company that provides data analytics and consultancy services to the aviation industry. It describes Aviation Analytics Group's structure and key personnel. It then outlines the company's data services, software products, and consultancy products. These include products for route analysis, network analysis, and custom reports and studies. Finally, it lists the company's flexible service options and highlights why Aviation Analytics Group is experienced, has extensive data sets, provides high-quality independent analysis, and offers competitive pricing.
Business intelligence and airline operational improvementJoshua Marks
The document discusses how airlines can use big data analytics and cloud-based data integration to gain operational visibility and improve profitability. It provides examples of how analyzing integrated data on schedules, weather, aircraft fleets, and airport operations can help airlines more accurately plan schedules, identify infrastructure improvements, and reduce disruptions for high-value passengers. The CEO argues that cloud-hosted big data solutions can help airlines lower IT costs while providing more flexibility and new insights to support planning, real-time monitoring, and predictive analytics.
Big Data Analytics for Commercial aviation and AerospaceSeda Eskiler
globalaviationaerospace.com
An opportunity for insight in the changing commercial aerospace business
Vision for New Applications of Analytic Insight in Commercial Aerospace
Benefit of Big Data Analytics for the Airline Operator
Modern, Mobile Experience
Big Data Analytics In Action
Predictive Analytics To Prevent Engine Events
Predictive Analytics Improves Safety and Quality
Predictive Analytics Keeps More Planes in the Air
Business Discovery @ Delhi International Airport - GMR GroupQlikView-India
Learn how QlikView Business Discovery is enabling Delhi International Airport Ltd. (DIAL) with strategic decision making. The speaker shares his views on how Qlik is helping DIAL in their Business Intelligence & Analytics approach to fact-based decision making
A presentation by Neil Frost (Chief Executive Officer: iSAHA), at the Transport Forum SIG: "Cost Effective Public Transport Management Systems" on 12 May 2016 hosted by University of Johannesburg. The theme of the presentation was: "Big Data and Public Transport."
From FAA Forecast Conference, March 2007. Reviews the future of long-haul LCC (Low-cost carrier) business models in aviation. Presents economic analysis of all-economy cabin services and differentiated premium business models.
Airline and Airport Big Data: Impact and EfficienciesJoshua Marks
Keynote presentation at Routes 2014 in Chicago - how big data changes aviation efficiencies, and what airlines and airports need to know about cloud data warehouses, real-time integration and predictive analytics.
This document provides an overview of Aviation Analytics Group, a company that provides data analytics and consultancy services to the aviation industry. It describes Aviation Analytics Group's structure and key personnel. It then outlines the company's data services, software products, and consultancy products. These include products for route analysis, network analysis, and custom reports and studies. Finally, it lists the company's flexible service options and highlights why Aviation Analytics Group is experienced, has extensive data sets, provides high-quality independent analysis, and offers competitive pricing.
Business intelligence and airline operational improvementJoshua Marks
The document discusses how airlines can use big data analytics and cloud-based data integration to gain operational visibility and improve profitability. It provides examples of how analyzing integrated data on schedules, weather, aircraft fleets, and airport operations can help airlines more accurately plan schedules, identify infrastructure improvements, and reduce disruptions for high-value passengers. The CEO argues that cloud-hosted big data solutions can help airlines lower IT costs while providing more flexibility and new insights to support planning, real-time monitoring, and predictive analytics.
Big Data Analytics for Commercial aviation and AerospaceSeda Eskiler
globalaviationaerospace.com
An opportunity for insight in the changing commercial aerospace business
Vision for New Applications of Analytic Insight in Commercial Aerospace
Benefit of Big Data Analytics for the Airline Operator
Modern, Mobile Experience
Big Data Analytics In Action
Predictive Analytics To Prevent Engine Events
Predictive Analytics Improves Safety and Quality
Predictive Analytics Keeps More Planes in the Air
Business Discovery @ Delhi International Airport - GMR GroupQlikView-India
Learn how QlikView Business Discovery is enabling Delhi International Airport Ltd. (DIAL) with strategic decision making. The speaker shares his views on how Qlik is helping DIAL in their Business Intelligence & Analytics approach to fact-based decision making
A presentation by Neil Frost (Chief Executive Officer: iSAHA), at the Transport Forum SIG: "Cost Effective Public Transport Management Systems" on 12 May 2016 hosted by University of Johannesburg. The theme of the presentation was: "Big Data and Public Transport."
The document provides information about the SmartLab research group at the University of Genoa in Italy. It discusses SmartLab's work in areas like real-time analytics for fuel prediction and skid prediction in racing cars. It also mentions past projects involving traffic forecasting and bus arrival time prediction. The document outlines SmartLab's computing resources and plans to expand its IBM cluster. It discusses potential future work in areas like process mining, condition-based maintenance using NoSQL databases, and advanced data analytics.
SC4 Workshop 1: Evangelos Mitsakis: Big data Sources for/from Intelligent Roa...BigData_Europe
This document discusses big data sources for intelligent road transport. It explains that even small datasets from individual vehicles can grow very large in aggregate when many vehicles are transmitting GPS data. Transportation agencies are now collecting petabytes of data on traffic patterns, public transit use, and vehicle locations using sensors, vehicle fleets, and smartphones. This big data is helping to optimize traffic management, asset maintenance, and traveler information services. Researchers are also able to conduct more accurate studies without relying on samples by analyzing vast amounts of real-world transportation data. The document provides examples of big data collection and use for traffic, public transit, and smart cities in Greece.
The document discusses using geographic information systems (GIS) in telecommunications. It notes that GIS is vital for performing information analysis, automated mapping, and data integration to increase access to information and facilitate planning. The document provides examples of how GIS can be used to map telephone line density and pay phone locations in Egypt. It identifies the key components of GIS as people, hardware, software, data and maps, and procedures. The document concludes by proposing a GIS-based system for ICT indicators that would integrate data from the National Telecom Regulatory Authority database with GIS mapping capabilities.
SC4 Workshop 1: Roberto Baldessari: The use of big data for public transport ...BigData_Europe
This document discusses how public transportation agencies can use big data to improve performance. It provides examples of how transportation agencies can use automated vehicle location (AVL) data from buses to identify excess waiting times, busy routes, and bottlenecks. This data can then be used to optimize schedules and reduce excess waiting times. The document also discusses how AVL data combined with automatic passenger counter (APC) data can be used to analyze bus load profiles and driving patterns to find opportunities for fuel savings and safety improvements. Finally, the document presents a vision of integrating various data sources like video, sensors and customer apps to further optimize public transportation systems.
Big data has the potential to significantly impact freight transport. The document discusses three "mountaintops" of big data in freight transport: (1) collecting data in real-time from vehicles, drivers, cargo and more, (2) processing this large and complex data in real-time, and (3) exploiting the data in real-time for applications like optimized routing, forecasting, and human resource management. Big data applications could improve operational efficiency, enhance the customer experience, and enable new business models in freight transport. However, big data also presents challenges that are cross-disciplinary, cross-industrial and cross-border in nature.
Innovations in London's Transport: Big Data for a Better Customer ServiceGovnet Events
Presentation on Innovations in London's Transport: Big Data for a Better Customer Service by Andrew Hyman, TFL at HPC and Big Data 2016 in Central London
This document discusses using data from multiple domains in smart cities to improve energy demand prediction. It proposes using data from mobility networks and patterns, in combination with energy and weather data, through a data market platform. This could allow more accurate predictions of energy demand, including for areas without direct energy monitoring. The data market approach aims to overcome barriers to data sharing by establishing standards for data curation, security and brokerage that respect privacy.
SC4 Workshop 1: Simon Scerri (Fraunhofer) - What can big data do for transport?BigData_Europe
The document describes a series of workshops on empowering communities with data technologies for transport. It provides context on big data, including the large volume of data being created and its dimensions of volume, velocity, variety and veracity. It outlines the motivation and objectives of the Big Data Europe project, including establishing data value chains across domains and lowering barriers to using big data. Current activities for Year 1 include a series of societal workshops and setting up interest groups in health, food, energy, transport, climate, societies and security.
SC6 Workshop 1: Official Statistics in the Age of Big DataBigData_Europe
Presentation by Michail Skaliotis, Head of the Eurostat Big Data Task Force, EC, at the first workshop of Societal Challlenge 6 in the BigDataEurope project, taking place in Luxembourg on 18 November 2015.
http://www.big-data-europe.eu/social-sciences/
SC4 Workshop 1: Nick Cohn: Traffic managementBigData_Europe
This document discusses how big data from real-time traffic sources can be used for active traffic management to reduce delays and improve safety. Data is merged from fixed detectors and used to monitor traffic networks, manage incidents, inform the public, prevent accidents, and trigger management scenarios. Archived traffic data can also be used to improve individual route planning, measure bottlenecks and delays, analyze system reliability, and determine infrastructure improvement priorities. Next steps include developing cooperative traffic management systems, improving real-time data timeliness and precision, and incorporating multi-modal transportation data.
This document discusses big data and how it relates to cities. It provides examples of how big data is being used in various industries like transportation, utilities, and communications infrastructure. Challenges of working with big data are mentioned. Stakeholders who work with big data are discussed, as well as examples of startups using big data to provide services. Opportunities for using big data to address social and environmental issues are presented.
Big data in transport an international transport forum overview oct 2013OpenSkyData
Comprehensive Guide on the use of Big Data in Transportation Services from the International Transport Forum. OpenSky loves making big data work for organisations large and small.
http://www.openskydata.com/our-sectors/transport.html
Contour Business Intelligence is a platform for building corporate reporting systems and information delivery solutions for all kinds of business, governments, and statistical and state agencies.Contour Business Intelligence (Contour BI) Includes Contour Reporter and Contour Publisher.
The highly productive graphical environment of Contour BI lets IT specialists and users create corporate reports quickly and easily.
Reports in Contour Reporter are created without programming, making a deep technical knowledge unnecessary.
Contour BI's various modes get all the information to the user quickly and clearly.
Oman SCTP is a federal government organization which oversees planning activities. Due to constant development, it was difficult for the authorities to draw down a consistent plan or map. Rolta was contacted to assist Oman SCTP with the challenge. Rolta with their OnPoint and Web based GIS technology displayed a successful solution to the problem.
How will a data center spur economic development in our area?Ann Treacy
How will a data center spur economic development in our area? as pressnted by Chris Shroyer at the Northern Regional Broadband Networks Forum in DUluth October 2011.
This document discusses smart cities in Romania. It provides definitions of smart cities from the European Commission and British Standards Institute. It then outlines some of the key challenges facing cities, such as increasing populations, economic growth, and environmental issues. It discusses Romania's Authority for Digitalization and some of its current projects to improve digital identification and public sector interoperability. Finally, it notes barriers to digitalization in Romania's public and private sectors, and provides an overview of the Romanian Association for Smart Cities and some statistics about Romania's digital performance based on the DESI index.
SC4 Workshop 1: Logistics and big data German herreroBigData_Europe
This document discusses the potential of big data in logistics. It notes that big data in logistics is characterized by large volumes of diverse data from both structured and unstructured sources. Applying advanced analytics to big data can provide greater insights across supply chains, enabling more efficient routing, inventory control, and issue resolution. Key challenges to realizing big data's potential in logistics include identifying appropriate business cases, gaining data sharing between stakeholders, and developing data science expertise in the logistics field.
This document discusses future-focused inquiry and collaboration. It notes that knowledge is no longer thought of as static facts but as dynamic networks and flows. This represents a major shift with implications for education. The document encourages groups to discuss how they take a future orientation in their practice and enable collaborative leadership and intelligence. It provides characteristics of effective school collaboration, including commitment to common goals, use of inquiry cycles, and presence of challenge and critique. Overall, the document promotes collaborative and future-focused approaches to education.
VPLDFFI14 pecha kucha on effective collaborationRebbecca Sweeney
Improve your collaborative practice as a cluster or network of schools - this was a five minute presentation for the Te Toi Tupu VPLD, Future Focused Inquiries Hui in May 2014. I will be presenting workshops soon in more depth on this same subject and will share here on Slideshare
The document provides information about the SmartLab research group at the University of Genoa in Italy. It discusses SmartLab's work in areas like real-time analytics for fuel prediction and skid prediction in racing cars. It also mentions past projects involving traffic forecasting and bus arrival time prediction. The document outlines SmartLab's computing resources and plans to expand its IBM cluster. It discusses potential future work in areas like process mining, condition-based maintenance using NoSQL databases, and advanced data analytics.
SC4 Workshop 1: Evangelos Mitsakis: Big data Sources for/from Intelligent Roa...BigData_Europe
This document discusses big data sources for intelligent road transport. It explains that even small datasets from individual vehicles can grow very large in aggregate when many vehicles are transmitting GPS data. Transportation agencies are now collecting petabytes of data on traffic patterns, public transit use, and vehicle locations using sensors, vehicle fleets, and smartphones. This big data is helping to optimize traffic management, asset maintenance, and traveler information services. Researchers are also able to conduct more accurate studies without relying on samples by analyzing vast amounts of real-world transportation data. The document provides examples of big data collection and use for traffic, public transit, and smart cities in Greece.
The document discusses using geographic information systems (GIS) in telecommunications. It notes that GIS is vital for performing information analysis, automated mapping, and data integration to increase access to information and facilitate planning. The document provides examples of how GIS can be used to map telephone line density and pay phone locations in Egypt. It identifies the key components of GIS as people, hardware, software, data and maps, and procedures. The document concludes by proposing a GIS-based system for ICT indicators that would integrate data from the National Telecom Regulatory Authority database with GIS mapping capabilities.
SC4 Workshop 1: Roberto Baldessari: The use of big data for public transport ...BigData_Europe
This document discusses how public transportation agencies can use big data to improve performance. It provides examples of how transportation agencies can use automated vehicle location (AVL) data from buses to identify excess waiting times, busy routes, and bottlenecks. This data can then be used to optimize schedules and reduce excess waiting times. The document also discusses how AVL data combined with automatic passenger counter (APC) data can be used to analyze bus load profiles and driving patterns to find opportunities for fuel savings and safety improvements. Finally, the document presents a vision of integrating various data sources like video, sensors and customer apps to further optimize public transportation systems.
Big data has the potential to significantly impact freight transport. The document discusses three "mountaintops" of big data in freight transport: (1) collecting data in real-time from vehicles, drivers, cargo and more, (2) processing this large and complex data in real-time, and (3) exploiting the data in real-time for applications like optimized routing, forecasting, and human resource management. Big data applications could improve operational efficiency, enhance the customer experience, and enable new business models in freight transport. However, big data also presents challenges that are cross-disciplinary, cross-industrial and cross-border in nature.
Innovations in London's Transport: Big Data for a Better Customer ServiceGovnet Events
Presentation on Innovations in London's Transport: Big Data for a Better Customer Service by Andrew Hyman, TFL at HPC and Big Data 2016 in Central London
This document discusses using data from multiple domains in smart cities to improve energy demand prediction. It proposes using data from mobility networks and patterns, in combination with energy and weather data, through a data market platform. This could allow more accurate predictions of energy demand, including for areas without direct energy monitoring. The data market approach aims to overcome barriers to data sharing by establishing standards for data curation, security and brokerage that respect privacy.
SC4 Workshop 1: Simon Scerri (Fraunhofer) - What can big data do for transport?BigData_Europe
The document describes a series of workshops on empowering communities with data technologies for transport. It provides context on big data, including the large volume of data being created and its dimensions of volume, velocity, variety and veracity. It outlines the motivation and objectives of the Big Data Europe project, including establishing data value chains across domains and lowering barriers to using big data. Current activities for Year 1 include a series of societal workshops and setting up interest groups in health, food, energy, transport, climate, societies and security.
SC6 Workshop 1: Official Statistics in the Age of Big DataBigData_Europe
Presentation by Michail Skaliotis, Head of the Eurostat Big Data Task Force, EC, at the first workshop of Societal Challlenge 6 in the BigDataEurope project, taking place in Luxembourg on 18 November 2015.
http://www.big-data-europe.eu/social-sciences/
SC4 Workshop 1: Nick Cohn: Traffic managementBigData_Europe
This document discusses how big data from real-time traffic sources can be used for active traffic management to reduce delays and improve safety. Data is merged from fixed detectors and used to monitor traffic networks, manage incidents, inform the public, prevent accidents, and trigger management scenarios. Archived traffic data can also be used to improve individual route planning, measure bottlenecks and delays, analyze system reliability, and determine infrastructure improvement priorities. Next steps include developing cooperative traffic management systems, improving real-time data timeliness and precision, and incorporating multi-modal transportation data.
This document discusses big data and how it relates to cities. It provides examples of how big data is being used in various industries like transportation, utilities, and communications infrastructure. Challenges of working with big data are mentioned. Stakeholders who work with big data are discussed, as well as examples of startups using big data to provide services. Opportunities for using big data to address social and environmental issues are presented.
Big data in transport an international transport forum overview oct 2013OpenSkyData
Comprehensive Guide on the use of Big Data in Transportation Services from the International Transport Forum. OpenSky loves making big data work for organisations large and small.
http://www.openskydata.com/our-sectors/transport.html
Contour Business Intelligence is a platform for building corporate reporting systems and information delivery solutions for all kinds of business, governments, and statistical and state agencies.Contour Business Intelligence (Contour BI) Includes Contour Reporter and Contour Publisher.
The highly productive graphical environment of Contour BI lets IT specialists and users create corporate reports quickly and easily.
Reports in Contour Reporter are created without programming, making a deep technical knowledge unnecessary.
Contour BI's various modes get all the information to the user quickly and clearly.
Oman SCTP is a federal government organization which oversees planning activities. Due to constant development, it was difficult for the authorities to draw down a consistent plan or map. Rolta was contacted to assist Oman SCTP with the challenge. Rolta with their OnPoint and Web based GIS technology displayed a successful solution to the problem.
How will a data center spur economic development in our area?Ann Treacy
How will a data center spur economic development in our area? as pressnted by Chris Shroyer at the Northern Regional Broadband Networks Forum in DUluth October 2011.
This document discusses smart cities in Romania. It provides definitions of smart cities from the European Commission and British Standards Institute. It then outlines some of the key challenges facing cities, such as increasing populations, economic growth, and environmental issues. It discusses Romania's Authority for Digitalization and some of its current projects to improve digital identification and public sector interoperability. Finally, it notes barriers to digitalization in Romania's public and private sectors, and provides an overview of the Romanian Association for Smart Cities and some statistics about Romania's digital performance based on the DESI index.
SC4 Workshop 1: Logistics and big data German herreroBigData_Europe
This document discusses the potential of big data in logistics. It notes that big data in logistics is characterized by large volumes of diverse data from both structured and unstructured sources. Applying advanced analytics to big data can provide greater insights across supply chains, enabling more efficient routing, inventory control, and issue resolution. Key challenges to realizing big data's potential in logistics include identifying appropriate business cases, gaining data sharing between stakeholders, and developing data science expertise in the logistics field.
This document discusses future-focused inquiry and collaboration. It notes that knowledge is no longer thought of as static facts but as dynamic networks and flows. This represents a major shift with implications for education. The document encourages groups to discuss how they take a future orientation in their practice and enable collaborative leadership and intelligence. It provides characteristics of effective school collaboration, including commitment to common goals, use of inquiry cycles, and presence of challenge and critique. Overall, the document promotes collaborative and future-focused approaches to education.
VPLDFFI14 pecha kucha on effective collaborationRebbecca Sweeney
Improve your collaborative practice as a cluster or network of schools - this was a five minute presentation for the Te Toi Tupu VPLD, Future Focused Inquiries Hui in May 2014. I will be presenting workshops soon in more depth on this same subject and will share here on Slideshare
1) The world chess championship match between American champion Frederick Trumper and Soviet challenger Anatoly Sergievsky is set to begin in Bangkok, Thailand amidst great excitement and media attention.
2) Both sides make objections and accusations against each other during the opening ceremony, with Trumper's second Florence Vassy and Sergievsky's advisor Molokov playing key roles.
3) The arbiter oversees the proceedings and ensures a fair match will occur, overruling all objections while maintaining control over the event.
This document contains a collection of random sentences, phrases, and short passages in both English and Chinese on various topics including ducks, Google's CEO, a new Android symbol, the sound of a fox, a discussion about slides, introductions of several individuals, researching for an assignment, looking like an actor, wanting to be friends, and advertising in Japan. The document lacks a clear overall topic or purpose and simply provides a disorganized assortment of short snippets without much context or connection between them.
This document discusses future focused education and the need to transform education systems to prepare students for an uncertain future. It argues that education must shift from an industrial, compliance-based model to focus on developing skills like critical thinking, problem solving, creativity and collaboration. Schools need more flexible structures that allow for innovation, collaboration between educators, and input from students and communities. The focus should be on designing the future rather than looking back, and allowing new practices to emerge from the bottom up through an open, adaptive culture of innovation.
This document provides tips and tricks for using Google Search. It discusses how Google Search works, and provides tips for using it for research, learning new things, news, and voice search. It also outlines how Google Search is smart in handling typos, languages, and punctuation. Specific tips covered include searching by file type, finding related pages, specifying number ranges, getting conversions and calculations, searching by image, including or excluding words, and searching for weather, time, and local information. The document encourages trying fun searches using the "I'm Feeling Lucky" button.
Web 2.0 szerepe a szervezeti kommunikációban és a tudásmegosztásbanNora Dr. Obermayer
"A kis- és középvállalatok kommunikációja" c. konferencia
MTA, GTB, Kommunikációmenedzsment Munkabizottság és Pannon Egyetem, Gazdaságtudományi Kar, Veszprém
2014. november 14.
ULearn14 Improve your collaborative practice sharing is not enoughRebbecca Sweeney
This document discusses effective practices for collaboration among schools. It emphasizes that simply sharing ideas is not enough, and that effective collaboration requires commitment to a common goal based on needs, use of inquiry and evidence to facilitate continuous improvement, practices that challenge assumptions and provide critique, clearly defined roles and trust among members. It provides examples of next steps a collaborative group can take to strengthen their practices.
Effective collaboration in education | CPPA 30 april 2015Rebbecca Sweeney
Presentation for the Christchurch Primary Principals' Association in 2015 about the drivers of future focused education and the importance of networked organisations in relation to that.
Global warming adalah peningkatan suhu bumi secara bertahap akibat peningkatan gas rumah kaca di atmosfir. Suhu rata-rata global telah naik 0.74°C dalam seratus tahun terakhir.
The power of social media in fostering knowledge sharingNora Dr. Obermayer
The document discusses how social media can foster knowledge sharing in organizations. It presents research on knowledge management strategies and technology usage in Hungarian companies. Many companies recognize knowledge as a strategic asset but few have formal strategies. While external technologies like social networking, blogs and video sharing exist in many companies, usage is lower. The document recommends that companies implement social media technologies for knowledge sharing, establish clear usage policies, communicate benefits to encourage participation, and provide training and rewards to promote sharing.
This document discusses the Spirals of Inquiry framework for transforming learning in schools through collaborative teaching and inquiry. It provides an overview of the phases of scanning, focusing, developing hunches, learning, taking action, and checking, and emphasizes the importance of involving learners, whānau, and communities. Various tools and methods are presented for each phase, such as learner maps for scanning, developing top ideas through brainstorming, and using data to identify themes and develop hunches. Collective professional learning and the development of agency through collaborative inquiry are positioned as key.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
This document discusses enterprise asset management for aviation. It provides an overview of IBM's vision, which includes leveraging condition monitoring, visualization, mobility, analytics and intelligence to optimize asset management. Some key goals are improving reliability, reducing costs, recovering lost revenue, and assuring safety. The aviation industry faces challenges from factors like economic growth, passenger growth and globalization, which are driving new technology solutions to better predict demand, improve operations and customer experience, and increase efficiency and security.
ADV Slides: The Data Needed to Evolve an Enterprise Artificial Intelligence S...DATAVERSITY
This webinar will focus on the promise AI holds for organizations in every industry and every size, and how to overcome some of the challenges today of how to prepare for AI in the organization and how to plan AI applications.
The foundation for AI is data. You must have enough data to analyze to build models. Your data determines the depth of AI you can achieve – for example, statistical modeling, machine learning, or deep learning – and its accuracy. The increased availability of data is the single biggest contributor to the uptake in AI where it is thriving. Indeed, data’s highest use in the organization soon will be training algorithms. AI is providing a powerful foundation for impending competitive advantage and business disruption.
Frontiers in Alternative Data : Techniques and Use CasesQuantUniversity
QuantUniversity Summer School 2020 (https://qusummerschool.splashthat.com/)
https://quspeakerseries10.splashthat.com/
Lecture 1: Alexander Denev
In this talk, Alexander will introduce Alternative Data and discuss it's uses from his book, The Book of Alternative Data
- What is alternative data?
- Adoption of alternative data
- Information value chain
- Risks associated with alternative data
- Processes required to develop signals
- Valuation of alternative data
Lecture 2: Saeed Amen
In this talk, Saeed will discuss use cases in Alternative Data
-Deciphering Federal Reserve communications
- Using CLS flow data to trade FX
- Geospatial Insight satellite data to estimate retailers' EPS
- Saving "alpha" with transaction cost analysis
- Using Bloomberg News data to trade FX
Application of Big Data Systems to Airline ManagementIJLT EMAS
The business world is in the midst of the next
revolution following the IT revolution – the Big Data revolution.
The sheer volume of data produced is a major reason for the big
data revolution. Aviation and aerospace are typical areas that
can apply big data systems due to the scale of data produced, not
only by the plane sensors and passengers, but also by the
prospective passengers. Data that need to be considered include,
but are not limited to, aircraft sensor data, passenger data,
weather data, aircraft maintenance data and air traffic data.
This paper aims at identifying areas in aviation where big data
systems can be utilized to enhance operational performances
improve customer relations and thereby aiding the ultimate goal
of increased profits at reduced costs. An improved management
model built on a strong big data infrastructure will reduce
operation costs, improve safety, bring down the cost and time
spent on maintenance and drastically improve customer
relations.
Putting Data at the Heart of Energy TradingCTRM Center
In a digital world, data is King, and while energy and commodities have been going through a period of digitalizing for the last several years, there can be no doubt that coming up with data management strategies has also played an important, if not critical, role in those strategies as well. Indeed, this was strongly emphasized in Commodity Technology Advisory’s disruptive technologies research over the last few years, where more than half of all of those questioned in the industry.
Pipeline and Gas Tech April 09 - SCADA Evolutionsmrobb
The document discusses the evolution of SCADA systems from early systems that collected small amounts of data from remote field devices to modern enterprise operations platforms that integrate field data across business systems. It describes how one large oil and gas company overhauled its SCADA system, replacing 14 separate data silos with a single system to collect daily updates from across operations and transform data into useful information for various business user groups. The new platform improved productivity by reducing time spent on tasks like production monitoring and regulatory reporting and allowing the company to handle more work with existing staff.
Analyze to Optimize - Connect airport data to refine intertwined operations.InteractiveNEC
Explore how KMD – an NEC company – enables better planning with business intelligence software designed for the airline industry. Quick to implement and easy to use and maintain, the solution works by capturing, integrating and connecting relevant data sources for better informed decisions that ease employee stress, improve customer satisfaction and identify areas for increase profitability. NEC Aviation Analytics is already assisting airports
in Europe with data-drive transformation.
For this and other free similar documents, visit https://www.necam.com/idelight/aviation/
Data Strategies for Managing the Cycles in Oil and GasDenodo
This document summarizes a webinar about data strategies for managing commodity cycles in the oil and gas industry. It discusses how commodity prices experience long-term supercycles of price increases and decreases. It then covers how different oil and gas companies approach business strategy and data strategy to both reduce costs in downturns and capture growth in upturns. Finally, it discusses how adopting logical and virtualized data architectures can provide oil and gas companies with more flexible, secure, and real-time access to diverse operational data sources to inform business decisions.
Developments in IT and business strategy a humble attemptRajesh Kumar Iyer.A
Inspired by new concepts and the changing business environment, this is a humble attempt at putting together the pieces of the complex (or simpler) world of IT and its effect on modern business
Synegys' mobile Field Data Capture (mFDC) module enables upstream oil & gas operators achieve operational excellence in oil and reservoir management. mFDC is a cost effective strategy for monitoring remote, non-instrumented assets using a mobile app to automate the collection of field data.
This document discusses how Hewlett-Packard Enterprise (HPE) can help airlines transform their maintenance, repair, and overhaul (MRO) processes through new technologies. It describes how HPE can leverage big data platforms, cloud computing, analytics, mobility, 3D printing, and security to help airlines increase efficiency, reduce costs and risk, and improve customer experiences. The document provides examples of how HPE has helped clients in industries like mining use real-time data analytics to enable predictive maintenance and optimize operations.
An insightful and visionary speech about the future of smart city by Mr. Ronald RAFFENSPERGER, Chief Technology Officer, Data Center Solution Sales, Huawei Technologies Company Limited
Kaushal Amin & Big 5 IT trends in the worldQuang PM
Kaushal Amin, CTO of KMS Technology, presented the top 6 technology trends for 2013:
1. Mobile apps will continue to dominate, with businesses shifting to complex mobile business applications. Native apps will remain preferred but HTML5/hybrid will gain ground.
2. Big data will continue growing exponentially from various sources such as social media and sensors. NOSQL databases are better suited than SQL for massive scalability and unstructured data.
3. Cloud computing adoption will increase, with more organizations using cloud-based disaster recovery and personal cloud storage.
4. In-memory computing allows orders of magnitude faster analytics on large datasets and will see further adoption through cloud services.
5. Action
British Airways was struggling with flight disruptions at Heathrow airport. They hired Atos to develop a real-time dashboard to improve disruption management. Atos worked collaboratively to understand BA's needs. They designed a browser-based dashboard accessible from any device that aggregated data from 10 systems into easy-to-understand visuals. Atos then rapidly developed the dashboard in just 8 weeks using agile methods and open source technologies. The new dashboard reduced the time it takes operations staff to be alerted of issues from 20 minutes to 20 seconds, improving decision making and reducing costs for BA.
Bridging Data Gaps with a Solid Data Foundation - A Key Imperative for Today’...Denodo
Watch full webinar here: https://bit.ly/3CjoaxS
In this session, the panel will discuss the importance of laying out a solid data foundation for everything digital for any financial institution. The panelists from UFCU and DevFacto will share their journey and agile approach toward data management in a hybrid data environment.
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Keynote Presentation – How Big Date Changes Aviation Efficiency (Josh Marks, CEO, masFlight)
1. Keynote Presentation
How big data changes aviation efficiency
Josh Marks
Chief Executive Officer
masFlight
2. Efficiency and optimization
Airlines and airports generate tremendous amounts of data
Legacy technology limits what we can log, merge and use
Big Data unlocks the value of ambient data
The cloud and “Big Data” tools transform how we collect,
merge and analyze data, opening new frontiers of capability
• Material change in operations and commercial capability
• Highly disruptive to global aviation – winners and losers
• Changes the industry’s profit horizon and long-term
Slide 2
3. Operations
Flight plan
Fuel loaded
Weight/balance
Taxi times
Flight path
Resources used
Slide 3
Lots of useful information
Bookings &
Transactions
Loyalty
Programs
Seats sold
Prices paid
Elasticity
Route demand
Points of sale
Ancillaries
Customer name
Demographics
Location
Travel history
Preferences
Offline activities
Airport
Operations
Flight
Facilities used
Time on gate
Checked bags
Carry on bags
Above-wing
Below-wing
Supporting Information – Weather, Fleet, Revenue, Social Media, Etc.
4. Slide 4
What’s the problem?
Critical info is trapped in silos, crippling big data
Needs structure, standardization and validation to be useful
5. Slide 5
Unified platforms are essential
Blended
Data
Sets
Single
Data
Slice
Retrospective Predictive
MySQL
Oracle
Excel
Access
Core
Value for
Aviation
Today’s
Modeling
Tools
• There are great visualization tools to
improve planning and analysis, but
what data do you feed them?
• How do you ensure the integrity and
reliability of data collected
if you fully automate analytics?
• How can you access large enough
volumes of historical data to gamble
on predictive analytics?
6. Slide 6
Big data feedback loop
Cloud infrastructure
Virtualized, on-demand
resources with infinitely
extensible processing,
bandwidth and storage
Data pooling & query platforms
Connect data & create
structure by merging,
conditioning streams
and archived data
Predictive analytics
Automated analytics
integrated into workflow
that unlock data value
and improve profitability
Business intelligence
Data mining and
visualization software
that reveals trends and
useful information
DRIVING EFFICIENCY GAINS
7. Slide 7
Changing attitudes
Limited by usable data
and computational power
Use past transactions and
isolated data slices to guess
what the future looks like
Today
Tomorrow Robust data foundation
with computational power
Real-time analytics observe
and compare to historical trends
automating/improving decisions
Commercial example:
Real-time demand monitoring
Current systems:
Past transactions reflect
when supply matched
demand, but don’t track
abandoned purchases
New approach:
Track search and
profile info on public
websites to identify both
completed transactions
and abandoning users
8. Airports: comparative metrics
Major U.S. Airline: Daily Departures per Gate
Slide 8
Big Data illustrates each airport’s
operational, commercial advantages
• Demographics – wealth, demand,
drive times from local communities
• Commercial – flight connectivity,
checkpoint crowds & vendor traffic
• Operations – delays and congestion
• Gates – availability and utilization
Unlock differentiators that attract
airlines, customers on multiple axes
AVERAGE TAXI-OUT TIME (MINUTES)
BW
I
CLT
DC
A
EW
R
IAD
PH
L
American 16.8 19.2 16.4 23.2 16.6 19.6
Delta 19.3 23.1 19.4 21.8 18.5 20.8
United 14.4 19.3 17.3 22.1 17.2 18.3
US
Airways
17.1 19.4 22.1 19.6 19.7 19.4
Southwes
t
14.0 15.7 20.1 12.4 15.2
10.3
9.5
8.5 8.5 8.4 8.3 7.9 7.5 7.5 7.2
BWI LAS OAK DEN DAL LAX MCO HOU MDW PHX
9. Airports: operational variability
Outer Domestic Pier
(Gates 76-77 and 80, 82, 84, 88)
18.6 min taxi-out
Slide 9
East International
(Even gates 90-100)
21.3 min taxi-out
West International
(Odd gates 91-99)
23.5 min taxi-out
East Base Domestic
(Gates 68-71)
18.1 min taxi-out
Inner Domestic Pier
(Gates 81, 83, 85, 87, 89)
20.7 min taxi-out
masFlight Data - All UA SFO Operations
West Base Domestic
(Gates 72-75)
21.0 min taxi-out
10. Connected aircraft
Real-time connectivity and
tracking – commercial and
operational implications
High fidelity visibility into
aircraft health, location
and customers on board
Slide 10
The data flood is coming
Infinite storage
Inexpensive cloud options,
no bandwidth restrictions
and an ecosystem of apps
Freedom from legacy IT
constraints – collect as
much data as you can
Mobile engagement
Pervasive, connected,
and location-aware through
GPS, WiFi and Beacons
Personalized interaction
employees & customers
… and profile data too
Future applications will require robust histories & perspectives
Imperative to invest in data platforms that create the foundation
11. Slide 11
Conclusions
• We already live in a sea of data – collect it and leverage it
– Commercial, operational, and social sources
– 3 billion passengers, 35 million flights, trillions of data points annually
– Critical to store every aspect of customer interaction
• Applications are moving to the cloud – they need data
– Full transition in coming years to cloud-based apps and data sets
– IT systems must be open architecture with easy data input/output
– Link and pool data to create valuable structured information
• Prioritize data collection as foundation for future efficiency gains
12. 4833 Rugby Avenue, Suite 301, Bethesda, MD 20814
www.masflight.com +1 (888) 809-2750
@joshmarks linkedin.com/in/joshuabmarks
In partnership with
Editor's Notes
Let’s start with the basics. Airlines and airports generate tremendous data. Whether we’re talking about the 3 billion enplanements or the 35 million flights each year, or the dozens of times we “touch” a customer, we amass literally trillions of data points from different sources.
So what’s stopping us from creating a mosaic of data that illuminates our business decisions? Legacy technologies limit what we can log, merge and use. We all know we have a problem with modernization. But the silos created by our operations, commercial and financial systems limits what we can merge and contribute to business problems.
Industries further ahead in the e-commerce curve have discovered the value of ambient data. The data that we treat as disposable. Amazon collects unbelievable detail about you to personalize your experience - and make their operation more intelligent. As an industry, we’re big enough that simple data management really is a big data problem. But as an industry we haven’t embraced the potential of Big Data yet.
Big Data simply means the ability to make sense of very large, different and inconsistent data sets. Big Data tools exist today that open frontiers of knowledge, and are going to drive changes in both operational and commercial capability. Most importantly, big data can change our industry’s profit horizon – and differentiate those airlines and airports with new revenue and profitability sources.
Let’s think about what information is really useful, and the scale of data involved. The first thing most people think about is booking and transactional data. That’s what people actually bought, how much they paid, and where they travelled. Soon, it may also include what ancillaries people buy.
There’s also loyalty program information that connects a passenger identity with actual flight data – while airlines starting to connect loyalty and transactional data to customize offers, customer relationship management is still new territory for airports.
There are additional dimensions of data around airport and flight operations. Every flight information display contains a wealth of statistically important information – what times flights arrived and departed, and what gates they used. And of course baggage tracking is relevant too. From the airline perspective, there is a wealth of data in flight planning systems that is far richer than just flight logs.
And of course, underlying all this are explanatory factors that provide context to what we collect – weather information and even social media logs that illustrate through tweets and posts.
Every day we see these data sets flow past our eyes. But we haven’t built the platforms as an industry that pool and connect those information sources. The data may be collected today, sitting in log files or on local hard drives. But it’s trapped in silos, and it needs structure and validation to be useful.
That’s the real potential of big data platforms, and in particular in different data so we can use it to monitor the past and to provide a robust foundation for finding new efficiencies.
Everything starts with a business case, and it’s important to define our objectives in more detail than just “efficiencies” and “optimization”.
Our industry is impossibly complex and seemingly random. The butterfly in China today that flaps its wings really does impact how many flights are going to cancel next month in New York. As an industry, we look to a future where complex pricing and operational decisions can be automated. That will require data sets built at the most granular level, taking into account information about specific passengers and flight operations,
instead of blocky decisions today that are limited by our human capabilities and limited tools.
Predictive analytics involves modeling, learning and mining. It’s about exploiting patterns in historical data sets to track and capture relationships among factors. Predictive analytics goes far beyond our world of today, where we look retrospectively at single data slices or use expensive databases to mix limited pools of data. That allows assessment of risk and efficiency.
A great analogy is credit scoring. Every factor about you, as an individual, is mixed together. Historical behavior is the foundation to predict your likelihood of future default.
That’s essentially what we need to build in aviation to unlock efficiencies at the airport and airline level. Except instead of personal information, we’re mixing commercial, operational and contextual data.
We’re shooting for a future where we can use composite information to predict the future, based on relevant pools of historical data that drive our predictions. At core, predictive analytics is about identifying when history is repeating and optimizing our response.
So we get to the fundamental question: how do we take diverse grains of data and combine them into a reliable platform? How can we automate while maintaining integrity? While processing the volumes of historical data needed for predictive analytics?
We can evolve and build the business case for new investment. At masFlight, we see an ongoing cycle of client cloud investment, driving systems that pool data and ultimately make predictive analytics a reality. There’s a positive feedback loop and real efficiency gains.
The first step is an organizational commitment to cloud resources so constraints of storage, bandwidth and engineering skills go away. Renting infrastructure from Google or Amazon can be cost effective, and you don’t need to worry about maintenance and reliability.
The second step is to leverage cloud systems to store lots of data, to connect them and create streams of integrated information. Doing that over time builds rich archives of what happened.
The third step is make those archives relevant for business decisions. There are great third-party tools that do data mining and visualization. They are relatively inexpensive to deploy. But they are only as good as the data sources you feed them.
Once you have the foundation of data to work from, you can deploy business intelligence platforms with ease.
That builds to where the real money is going to be – automation of trend analysis and forward decision making. For airports, it’s about real-time visibility into checkpoint congestion, emergencies and delays. For airlines, it’s about using customer data to make more dynamic pricing decisions. That’s where the ROI for cloud and big data investments gets justified.
And that’s the important quality in achieving big data efficiencies – a new organizational philosophy is needed. Think of it this way. Today we drive our businesses by looking in the rear-view mirror. We use past transactions and data history to guess what the future looks like.
First, that’s a pretty narrow view of the past. But we’re limited by our ability to focus on narrow slices. You can adjust the mirror and see different histories – but you’re inherently limited in what you can project.
The future is looking out the windshield – the windshield is a lot bigger and a lot more relevant to where you’re going. You know where you’ve been, you see where you’re going, and your speed and other vital metrics are in front of you. That’s the future – making course corrections based on the trends.
I have been talking at a high level, and we need to drill down to specifics. Let’s look at some specific use cases. Specifically, what are the implications of big data for the everyday challenges of airlines and airports?
Let’s start with airline pricing and sales. Airlines are huge e-commerce players. This year, U.S. airlines will transact more than $50 billion on their websites alone, and that doesn’t count what they sell through OTAs. They sell tickets online to more than 100 million people each year. Think about the number of searches that occur – if it takes ten flight searches to sell one ticket, then that’s a billion searches this year on airline websites alone.
Every single one of those searches carries information that’s useful. What airports, what times people wanted to fly, and what fares they were offered. How did search criteria change over time. You can track that information down to the IP address, cookie or frequent flyer number.
Critically, you can use that information to understand the real-time intersection of supply and demand. You can track it even if a ticket wasn’t purchased. You get a richer, broader picture of what’s happening long before it translates to a transaction record.
Harnessing that requires big data processing, because the volume of search data is real-time and overwhelming. But think of the implications for pricing and personalization – looking out the windshield, so to speak, instead of the rear-view mirror that just shows what was ticketed. You could spot instant changes in demand and understand when price sensitive customers are ready to purchase. And all of that can be achieved within the context of today’s booking systems.
I just gave an airline example, but the same principles apply to airports. For air service development, access to big data processing will be critical to winning new airline business, because airlines will be processing many more dimensions of data as well.
Big data matters for air service development – tracking demographics, business, network connectivity and airport performance. Big data analytics of population data illustrates not only underserved routes, but also if the airport has a differential advantage. When you combine demographic data with network connectivity and quality of service models, you get more compelling results to win new airline service.
For airport operations, understanding how airlines schedule flights and use infrastructure is important. Airport congestion matters for service development, and big data illustrates factors for your own airport and for competitors. Knowing that United schedules longer turns than American helps in matching gate infrastructure and whether schedule changes will impact airport congestion.
The last slide showed how big data can illuminate air service development. It’s also important for on-time performance and reliability. Airlines can get precise measurements of taxi times by gate, which is essential for understanding where airport changes can drive new performance efficiencies.
For example, here’s a view of United’s operation at San Francisco over a multi-year period. We’re looking at more than 200,000 flights and integrating gate, aircraft, and taxiway information. What we can see is that there’s a substantial difference in taxi time by gate.
Some of that is runway driven, but there are specific congestion issues caused by blocked alleyways and simultaneous pushes that are controllable by the airline and airport. Big data gives us visibility into factors that were hidden, or at least hard to see given the volume of data.
Which brings us to the present. The data flood is coming – what should we be doing today to prepare for that future?
First, infinite storage from cloud architecture is the foundation on which these big data efficiencies reside. IT departments should be deploying in the cloud, or at least investigating how current infrastructure can be transitioned in coming years.
Second, expect an increasing ocean of customer data. For airports, new hardware that allows real-time customer mapping and tracking is illuminating – where are passengers congregating, when do they head to the gate – and relevant for retail optimization. SITA introduced beacon technology that’s worth a hard look. And any airport that isn’t offering free wifi in exchange for basic customer profile data is missing the boat – airports should be collecting whatever customer profile data they can, and the best way to do that is to offer something valuable like Internet access that incentivizes customers to participate.
Finally, for both airlines and airports, you want to be investing in big data today to prepare for the near future when aircraft are fully connected. Real-time aircraft tracking will have profound implications on flight disruptions.
The only certainty is that tomorrow’s applications and software tools will depend on robust, connected data platforms. Organizations that start to adapt now will have a smooth path to tomorrow’s analytics systems.
As you can tell, organizational mindset is key. We’re in the exploratory phase where we recognize big data’s potential and foundational technology is available. As an industry, capturing that potential requires early investment and a change in organizational mindset.
You have the capacity to capture, archive and query everything in the course of business, from your information feeds to the siloed data across your organization. Intelligent archiving is critical, and it should be a priority. There is no such thing as bad data. Every data source contributes when blended, some more than others of course, but the point is you don’t know today what might be useful tomorrow. With trillions data points of data created each year, it’s critical to capture as many dimensions of your customer interaction as possible.
Second, there are many reasons why applications – the tools we use every day – are moving to the cloud. The cloud solves problems like expandability and bandwidth. As applications move to the cloud, so too must our data collection and processing.
Finally, I recognize that leaping into the cloud and embracing big data has a price tag, and that compelling businesses cases are needed to justify investments. You can see the impact in air service development, in on-time performance improvement, in gate and ramp optimization and in terminal retail, and with big data you can achieve higher performance. If you’re not looking at big data platforms as drivers of profit improvement in the future, you’re missing the boat.
I’m going to post and tweet a copy of this presentation with hashtag #worldroutes. My twitter handle is @joshmarks. I’ll also be around this afternoon at the OAG booth. masFlight has had a multi-year partnership with OAG, the leader in airline schedules and flight status, and if you haven’t looked at their flight status solutions to feed your mobile applications and FIDS boards you should. Thank you again to the Routes team and I hope you enjoy your lunch!