Data is disruptive, powerful and dangerous. Data is the opportunity and, as a resource, it is growing exponentially. This is disruption. Linear solutions don't apply.
Be prepared. Data will make the industrial revolution look like a blip. Liken it to an ice-age or a tsunami of information. It will change everything.
The majority of early open data companies responding to the survey were located in Ontario and Quebec, with emerging hubs in the Greater Toronto Area, Waterloo Region, and Montreal. Most companies were young, between 1-5 years old, but 15% had been in business over 21 years, showing established companies' willingness to adopt open data. Many companies provide data analysis and interpretation services for clients since they have expertise in finding and using open data, while others use open data in industries like geospatial/mapping where government data is frequently released. Respondents consistently used data from all levels of Canadian government but noted a need for common data sets and standards to allow companies to scale operations or data use across jurisdictions. Most companies rely heavily on
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
Achieve Federal Open Data Policy Compliance - SlidesSocrata
The November 1, 2013 deadline for compliance with Executive Order 13642 and OMB Memorandum M-13-13 is fast approaching.
Get your questions answered and accelerate your implementation efforts. Attend a free webinar entitled: How to Achieve Open Data Policy Compliance with Socrata.
http://www.socrata.com/webinars/how-to-comply-with-the-federal-open-data-policy/
This document discusses big data and why organizations should care about it. It defines big data as large volumes of diverse data that present challenges to analyze and extract value from. The world is generating much more data from sources like sensors, devices and digital content. Organizations that can analyze big data in real-time will have competitive advantages over those that cannot. The document provides examples of big data sources and opportunities it provides for different industries. Early adopters of big data technologies will be organizations already dealing with large data or those in industries experiencing rapid changes.
Data Transparency 2013 - OrgPedia by 3 Round Stones3 Round Stones
The US government spends billions collecting data that is locked away in proprietary systems, not providing value to taxpayers. However, new policies and initiatives are transforming federal information into open data. The DATA Act calls for publishing federal spending data in open standards to increase innovation, lower costs by eliminating duplication and fraud, and reduce burdens on regulated industries. An open data ecosystem is being created through open data policy, community engagement, standards, and persistent identifiers to leverage existing open government data.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
Power from big data - Are Europe's utilities ready for the age of data?Steve Bray
European utilities are facing growing volumes of data from smart meters and grids, but many are not yet maximizing the value of the data. While utilities rate themselves highly in collecting data, nearly half say they do not consistently maximize its value. Strategies for leveraging big data are immature, with over 40% having no strategy or just beginning to develop one. Utilities will need to improve at analyzing large amounts of diverse data and developing new business models to gain competitive advantage from big data insights. Talent shortages, organizational silos, and a lack of standards also pose challenges to utilities effectively capturing value from big data.
Analytics revolution and democratization of dataDerek Gibson
The document discusses the analytics revolution and democratization of data. It argues that just as the American Revolution and Industrial Revolution transformed society, the current data revolution will also transform the world. This revolution is empowering more people and organizations to access and use data. It is occurring very rapidly, within just 5 to 15 years it could eliminate 40% of repetitive jobs. Every business now needs data scientists to harness data to transform themselves into data-driven organizations. Failing to do so would be like trying to compete against modern factories with just basic manual tools.
The majority of early open data companies responding to the survey were located in Ontario and Quebec, with emerging hubs in the Greater Toronto Area, Waterloo Region, and Montreal. Most companies were young, between 1-5 years old, but 15% had been in business over 21 years, showing established companies' willingness to adopt open data. Many companies provide data analysis and interpretation services for clients since they have expertise in finding and using open data, while others use open data in industries like geospatial/mapping where government data is frequently released. Respondents consistently used data from all levels of Canadian government but noted a need for common data sets and standards to allow companies to scale operations or data use across jurisdictions. Most companies rely heavily on
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
Achieve Federal Open Data Policy Compliance - SlidesSocrata
The November 1, 2013 deadline for compliance with Executive Order 13642 and OMB Memorandum M-13-13 is fast approaching.
Get your questions answered and accelerate your implementation efforts. Attend a free webinar entitled: How to Achieve Open Data Policy Compliance with Socrata.
http://www.socrata.com/webinars/how-to-comply-with-the-federal-open-data-policy/
This document discusses big data and why organizations should care about it. It defines big data as large volumes of diverse data that present challenges to analyze and extract value from. The world is generating much more data from sources like sensors, devices and digital content. Organizations that can analyze big data in real-time will have competitive advantages over those that cannot. The document provides examples of big data sources and opportunities it provides for different industries. Early adopters of big data technologies will be organizations already dealing with large data or those in industries experiencing rapid changes.
Data Transparency 2013 - OrgPedia by 3 Round Stones3 Round Stones
The US government spends billions collecting data that is locked away in proprietary systems, not providing value to taxpayers. However, new policies and initiatives are transforming federal information into open data. The DATA Act calls for publishing federal spending data in open standards to increase innovation, lower costs by eliminating duplication and fraud, and reduce burdens on regulated industries. An open data ecosystem is being created through open data policy, community engagement, standards, and persistent identifiers to leverage existing open government data.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
Power from big data - Are Europe's utilities ready for the age of data?Steve Bray
European utilities are facing growing volumes of data from smart meters and grids, but many are not yet maximizing the value of the data. While utilities rate themselves highly in collecting data, nearly half say they do not consistently maximize its value. Strategies for leveraging big data are immature, with over 40% having no strategy or just beginning to develop one. Utilities will need to improve at analyzing large amounts of diverse data and developing new business models to gain competitive advantage from big data insights. Talent shortages, organizational silos, and a lack of standards also pose challenges to utilities effectively capturing value from big data.
Analytics revolution and democratization of dataDerek Gibson
The document discusses the analytics revolution and democratization of data. It argues that just as the American Revolution and Industrial Revolution transformed society, the current data revolution will also transform the world. This revolution is empowering more people and organizations to access and use data. It is occurring very rapidly, within just 5 to 15 years it could eliminate 40% of repetitive jobs. Every business now needs data scientists to harness data to transform themselves into data-driven organizations. Failing to do so would be like trying to compete against modern factories with just basic manual tools.
This document provides an overview and introduction to big data implementation strategies using Hadoop and beyond. It discusses how big data has evolved from technologies pioneered by companies like Google to analyze vast amounts of diverse data cheaper and more effectively than traditional methods. It also outlines some of the key challenges organizations face as data volumes, varieties, and velocities outgrow existing systems, and how new big data technologies like Hadoop provide more cost-effective solutions to process and analyze data at scale. The document notes that big data represents a shift in computing paradigms rather than just data size alone.
The first of Future Agenda’s ‘World in 2030’ foresights addresses the emerging shift in how multinational digital companies may be taxed in the future so that they make a more balanced contribution to society. In a world increasingly aware of the asymmetric power and influence of organisations that don’t comply to norms and regulations created in the 19th and 20th centuries, it explores three different avenues that could have global impact: the adoption of digital revenue taxes such as those being introduced in Europe; a more sophisticated ‘wealth’ tax on the value of the data an organisation owns, manages, stores or uses; and the idea of a data dividend where all citizens receive a payment for the use of their data as part of a company’s social licence to operate.
Each are being proposed and gaining support with multiple governments globally - and so should be on the radar of any data-rich organisation.
Drawn from multiple expert discussions around the world, this foresight is one of 50 looking at the key issues for the next decade that are being shared throughout 2020.
For more details see https://www.futureagenda.org/the-world-in-2030/
This document discusses the challenges of building a network infrastructure to support big data applications. Large amounts of data are being generated every day from a variety of sources and need to be aggregated and processed in powerful data centers. However, networks must be optimized to efficiently gather data from distributed sources, transport it to data centers over the Internet backbone, and distribute results. The unique demands of big data in terms of volume, variety and velocity are testing whether current networks can keep up. The document examines each segment of the required network from access networks to inter-data center networks and the challenges in supporting big data applications.
This report examines the rise of big data and analytics used to analyze large volumes of data. It is based on a survey of 302 BI professionals and interviews. Most organizations have implemented analytical platforms to help analyze growing amounts of structured data. New technologies also analyze semi-structured data like web logs and machine data. While reports and dashboards serve casual users, more advanced analytics are needed for power users to fully leverage big data.
Smart Data Module 1 introduction to big and smart datacaniceconsulting
This document provides an overview of big and smart data. It defines big data as large volumes of structured, unstructured, and semi-structured data that is difficult to manage and process using traditional databases. It discusses how big data becomes smart data through analysis and insights. Examples of smart data applications are also provided across various industries like retail, healthcare, transportation and more. The document emphasizes that in order to start smart with data, companies need to review their existing data, ask the right questions, and form actionable insights rather than just conclusions.
The document summarizes the global eDiscovery market. Key points include:
- eDiscovery solutions help organizations facilitate business processes by allowing them to exchange, review, collect, and preserve electronically stored information.
- Increasing mobile device usage, regulatory compliance needs, and a focus on reducing legal costs are driving demand for eDiscovery solutions.
- Vendors are focusing on partnerships, mergers, and acquisitions to expand their footprints and integrate new technologies into their solutions.
- North America currently dominates the market but Asia Pacific is expected to see strong growth in the coming years.
This document discusses the promise of open data for business, government, consumers, and technology. It defines open data as publicly available data that can be used and shared by anyone. The document outlines 9 trends in open data, including how open data is driving business growth in sectors like health, education, and energy. It also discusses how open data helps consumers make smarter choices, improves government transparency, and fuels innovation. Overall, the document argues that open data has the potential to generate trillions in economic value globally and transform many industries by making more data available in accessible formats.
The document discusses 25 predictions about the future of big data:
1) Data volumes and ways to analyze data will continue growing exponentially with improvements in machine learning and real-time analytics.
2) More companies will appoint chief data officers and use data as a competitive advantage.
3) Data governance, visualization, and delivery through data fabrics and marketplaces will be key to extracting insights from diverse data sources and empowering partners.
4) Data is becoming a new global currency and companies are monetizing their data through algorithms, services, and by becoming "data businesses."
1) State and local governments are increasingly relying on network-delivered solutions like cloud services, big data analytics, and e-government applications to improve services while reducing costs. However, managing increasing bandwidth demands and ensuring network performance are top challenges for IT leaders.
2) The top five priorities for government IT leaders are improving cloud services, cybersecurity, business intelligence and analytics capabilities, modernizing legacy systems, and upgrading networking and communications infrastructure. Ensuring high network performance is critical to support these priorities and deliver benefits to organizations and citizens.
3) Upgrading to fiber-rich WANs that provide high bandwidth, low latency and high availability allows governments to securely access applications anywhere and transform service delivery through e-government
Big data refers to large and complex datasets that require new techniques and technologies to capture, manage, and analyze the data. Common characteristics of big data include large volumes of data generated from sources like social media, sensors, and mobile devices with high velocity and variety of structured and unstructured data types. Managing and analyzing big data allows organizations to extract hidden patterns and insights to improve decision making.
EUBrasilCloudFORUM actively participated at Beyon2020 event. Professor Sergio Takeo Kofuji from the University of São Paulo (USP) discussed the importance of Open Data for cities.
The Beyond 2020 event took place at "Centro de Convenções de Pernambuco" from the 27th to the 29th of July, focusing on how urban innovation ecosystems can support citizens and what the future will look like for Cities, Politics, Citizens, Local Development, and Tourism, based on the use of Open data Platforms.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
This document summarizes an algorithm for efficiently filtering big data in telecommunications networks. It begins by introducing the challenges of unprecedented rises in data volume, variety, and velocity. It then describes an algorithm developed comprising stages like artificial neural networks and graph search methods. The algorithm is represented as a flowchart to filter data for preventative purposes like detecting criminal activity. Overall, the algorithm aims to effectively uncover patterns in large, complex datasets to help telecommunications providers address big data challenges.
Big data is used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. The term big data is believed to have originated with Web search companies who had to query very large distributed aggregations of loosely-structured data.
Societal Impact of Applied Data Science on the Big Data StackStealth Project
This document discusses big data and data science projects at a Center for Data Science. It provides an overview of various research areas like healthcare informatics, intelligent systems, social computing, and big data security. It also describes technologies used for big data like machine learning, distributed databases, and data integration. Specific projects are summarized, such as predicting hospital readmissions for congestive heart failure patients and detecting malware activity based on domain names. The document outlines the steps involved in building predictive models, from data understanding to predictive modeling. Performance of initial models is discussed, with areas for improvement noted.
Data monopolists like google are threatening the economy hbrankiteny
Big data holds risks beyond threats to consumer privacy, including threats to free market competition from data monopolies. As companies build proprietary data sets and use them to create new products and markets, data becomes an unfair barrier to entry that prevents new competitors. This hurts competition and the economy. The search market provides an example where Google's vast historical search data gives it an insurmountable advantage over competitors. Regulators should consider whether a company's exclusive ownership of certain data that blocks market entry constitutes a monopoly problem requiring antitrust intervention, similar to Standard Oil in oil or Northern Securities in railroads.
Cyber break-ins are affecting the private and public sectors at an alarming rate. In fact, intrusions in the federal systems alone saw a 1,121% increase from 2006 to 2014. To address this issue, we partnered with the Partnership for Public Service to publish “Cyber In-Security: Closing the Federal Gap.” This new report outlines the challenges faced by the federal government in building an enterprise-wide, first-class cybersecurity workforce and offers recommendations for a total workforce solution.
151111 BASE ELN 151112 CIO Big Data CollaborationDr. Bill Limond
The document discusses the role of CIOs and big data collaboration. It notes that big data is growing exponentially, with 2.5 quintillion bytes of data created every day from a variety of sources. Big data offers significant value if organizations can analyze it, with potential savings in healthcare, retail, and other sectors. However, big data requires collaboration both internally within organizations and externally with partners. The document provides examples of successful big data collaborations and argues that CIOs will continue playing an important role in facilitating information management and digital transformation through big data initiatives.
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
This document provides an overview and introduction to big data implementation strategies using Hadoop and beyond. It discusses how big data has evolved from technologies pioneered by companies like Google to analyze vast amounts of diverse data cheaper and more effectively than traditional methods. It also outlines some of the key challenges organizations face as data volumes, varieties, and velocities outgrow existing systems, and how new big data technologies like Hadoop provide more cost-effective solutions to process and analyze data at scale. The document notes that big data represents a shift in computing paradigms rather than just data size alone.
The first of Future Agenda’s ‘World in 2030’ foresights addresses the emerging shift in how multinational digital companies may be taxed in the future so that they make a more balanced contribution to society. In a world increasingly aware of the asymmetric power and influence of organisations that don’t comply to norms and regulations created in the 19th and 20th centuries, it explores three different avenues that could have global impact: the adoption of digital revenue taxes such as those being introduced in Europe; a more sophisticated ‘wealth’ tax on the value of the data an organisation owns, manages, stores or uses; and the idea of a data dividend where all citizens receive a payment for the use of their data as part of a company’s social licence to operate.
Each are being proposed and gaining support with multiple governments globally - and so should be on the radar of any data-rich organisation.
Drawn from multiple expert discussions around the world, this foresight is one of 50 looking at the key issues for the next decade that are being shared throughout 2020.
For more details see https://www.futureagenda.org/the-world-in-2030/
This document discusses the challenges of building a network infrastructure to support big data applications. Large amounts of data are being generated every day from a variety of sources and need to be aggregated and processed in powerful data centers. However, networks must be optimized to efficiently gather data from distributed sources, transport it to data centers over the Internet backbone, and distribute results. The unique demands of big data in terms of volume, variety and velocity are testing whether current networks can keep up. The document examines each segment of the required network from access networks to inter-data center networks and the challenges in supporting big data applications.
This report examines the rise of big data and analytics used to analyze large volumes of data. It is based on a survey of 302 BI professionals and interviews. Most organizations have implemented analytical platforms to help analyze growing amounts of structured data. New technologies also analyze semi-structured data like web logs and machine data. While reports and dashboards serve casual users, more advanced analytics are needed for power users to fully leverage big data.
Smart Data Module 1 introduction to big and smart datacaniceconsulting
This document provides an overview of big and smart data. It defines big data as large volumes of structured, unstructured, and semi-structured data that is difficult to manage and process using traditional databases. It discusses how big data becomes smart data through analysis and insights. Examples of smart data applications are also provided across various industries like retail, healthcare, transportation and more. The document emphasizes that in order to start smart with data, companies need to review their existing data, ask the right questions, and form actionable insights rather than just conclusions.
The document summarizes the global eDiscovery market. Key points include:
- eDiscovery solutions help organizations facilitate business processes by allowing them to exchange, review, collect, and preserve electronically stored information.
- Increasing mobile device usage, regulatory compliance needs, and a focus on reducing legal costs are driving demand for eDiscovery solutions.
- Vendors are focusing on partnerships, mergers, and acquisitions to expand their footprints and integrate new technologies into their solutions.
- North America currently dominates the market but Asia Pacific is expected to see strong growth in the coming years.
This document discusses the promise of open data for business, government, consumers, and technology. It defines open data as publicly available data that can be used and shared by anyone. The document outlines 9 trends in open data, including how open data is driving business growth in sectors like health, education, and energy. It also discusses how open data helps consumers make smarter choices, improves government transparency, and fuels innovation. Overall, the document argues that open data has the potential to generate trillions in economic value globally and transform many industries by making more data available in accessible formats.
The document discusses 25 predictions about the future of big data:
1) Data volumes and ways to analyze data will continue growing exponentially with improvements in machine learning and real-time analytics.
2) More companies will appoint chief data officers and use data as a competitive advantage.
3) Data governance, visualization, and delivery through data fabrics and marketplaces will be key to extracting insights from diverse data sources and empowering partners.
4) Data is becoming a new global currency and companies are monetizing their data through algorithms, services, and by becoming "data businesses."
1) State and local governments are increasingly relying on network-delivered solutions like cloud services, big data analytics, and e-government applications to improve services while reducing costs. However, managing increasing bandwidth demands and ensuring network performance are top challenges for IT leaders.
2) The top five priorities for government IT leaders are improving cloud services, cybersecurity, business intelligence and analytics capabilities, modernizing legacy systems, and upgrading networking and communications infrastructure. Ensuring high network performance is critical to support these priorities and deliver benefits to organizations and citizens.
3) Upgrading to fiber-rich WANs that provide high bandwidth, low latency and high availability allows governments to securely access applications anywhere and transform service delivery through e-government
Big data refers to large and complex datasets that require new techniques and technologies to capture, manage, and analyze the data. Common characteristics of big data include large volumes of data generated from sources like social media, sensors, and mobile devices with high velocity and variety of structured and unstructured data types. Managing and analyzing big data allows organizations to extract hidden patterns and insights to improve decision making.
EUBrasilCloudFORUM actively participated at Beyon2020 event. Professor Sergio Takeo Kofuji from the University of São Paulo (USP) discussed the importance of Open Data for cities.
The Beyond 2020 event took place at "Centro de Convenções de Pernambuco" from the 27th to the 29th of July, focusing on how urban innovation ecosystems can support citizens and what the future will look like for Cities, Politics, Citizens, Local Development, and Tourism, based on the use of Open data Platforms.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
This document summarizes an algorithm for efficiently filtering big data in telecommunications networks. It begins by introducing the challenges of unprecedented rises in data volume, variety, and velocity. It then describes an algorithm developed comprising stages like artificial neural networks and graph search methods. The algorithm is represented as a flowchart to filter data for preventative purposes like detecting criminal activity. Overall, the algorithm aims to effectively uncover patterns in large, complex datasets to help telecommunications providers address big data challenges.
Big data is used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. The term big data is believed to have originated with Web search companies who had to query very large distributed aggregations of loosely-structured data.
Societal Impact of Applied Data Science on the Big Data StackStealth Project
This document discusses big data and data science projects at a Center for Data Science. It provides an overview of various research areas like healthcare informatics, intelligent systems, social computing, and big data security. It also describes technologies used for big data like machine learning, distributed databases, and data integration. Specific projects are summarized, such as predicting hospital readmissions for congestive heart failure patients and detecting malware activity based on domain names. The document outlines the steps involved in building predictive models, from data understanding to predictive modeling. Performance of initial models is discussed, with areas for improvement noted.
Data monopolists like google are threatening the economy hbrankiteny
Big data holds risks beyond threats to consumer privacy, including threats to free market competition from data monopolies. As companies build proprietary data sets and use them to create new products and markets, data becomes an unfair barrier to entry that prevents new competitors. This hurts competition and the economy. The search market provides an example where Google's vast historical search data gives it an insurmountable advantage over competitors. Regulators should consider whether a company's exclusive ownership of certain data that blocks market entry constitutes a monopoly problem requiring antitrust intervention, similar to Standard Oil in oil or Northern Securities in railroads.
Cyber break-ins are affecting the private and public sectors at an alarming rate. In fact, intrusions in the federal systems alone saw a 1,121% increase from 2006 to 2014. To address this issue, we partnered with the Partnership for Public Service to publish “Cyber In-Security: Closing the Federal Gap.” This new report outlines the challenges faced by the federal government in building an enterprise-wide, first-class cybersecurity workforce and offers recommendations for a total workforce solution.
151111 BASE ELN 151112 CIO Big Data CollaborationDr. Bill Limond
The document discusses the role of CIOs and big data collaboration. It notes that big data is growing exponentially, with 2.5 quintillion bytes of data created every day from a variety of sources. Big data offers significant value if organizations can analyze it, with potential savings in healthcare, retail, and other sectors. However, big data requires collaboration both internally within organizations and externally with partners. The document provides examples of successful big data collaborations and argues that CIOs will continue playing an important role in facilitating information management and digital transformation through big data initiatives.
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
The document discusses how the rise of the Internet of Things (IoT) will change product roadmaps. It notes that IoT will result in more smart, connected devices generating large amounts of fragmented data from multiple sources. This will require applications to become smarter by incorporating predictive and prescriptive capabilities to leverage IoT data. Challenges also need to be overcome, such as handling big data, privacy, and security issues. However, properly leveraging IoT data through smarter applications can provide significant financial benefits and opportunities for innovation across industries.
The Evolution of Data and New Opportunities for AnalyticsSAS Canada
BIG DATA IS EVERYWHERE!
Today we produce around five Exabyte every two days … and this is accelerating.
The intelligent devices, what we call the internet of things, promise to be the next big explosion.
Explore evolution of data and new opportunities for analytics.
www.sas.com
For over two decades, organizations have struggled with enterprise content management (ECM), both as a technology and as an information management strategy. Now, intelligent content services platforms are on the rise—and so is a different way of thinking about information.
In this webinar, we will explore eight common ECM challenges and how modern content services platforms leverage new capabilities, like AI and machine learning, to help organizations overcome content challenges.
Learn real-life examples of how customers across different industries are leveraging intelligent content services to accelerate products to market, improve decision making, enhance their customer experience, and how custom AI models can intelligently enrich content and add meaningful value to your business.
Big data is very large data that is difficult to process using traditional methods. It is characterized by high volume, velocity, and variety. Examples of real-life big data implementations include using social media to understand customer behavior, tracking social media for marketing campaigns, and analyzing medical data to predict readmissions. Challenges include integrating diverse data sources and ensuring ethical access. Common techniques for processing big data are parallel database management systems and MapReduce frameworks like Hadoop.
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
Open government international garry lloydGarry Lloyd
“Our vision is for an open government. For the government and community to be able to leverage a government platform with social media tools, developing a community instinct. This would then enable both government and community to have an inherent inclination toward the same behaviour / goal.”
¿En qué se parece el Gobierno del Dato a un parque de atracciones?Denodo
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
My perspective on the evolution of big data from the perspective of a distributed systems researcher & engineer -- the background of how it get started, the scale-out paradigm, industry use cases, open source development paradigm, and interesting future challenges.
This document summarizes key tech and built environment trends for 2022 as identified by Arcadis experts. The trends include: achieving net zero carbon goals across organizations; adapting workplaces to mixed-mode working; utilizing collected building data more effectively; developing sustainable next-generation data centers; and demonstrating improved ESG metrics to stakeholders. The trends highlight challenges companies face in these areas and recommendations for addressing them through technologies, analytics, partnerships and more.
Using Big Data for Product & Service InnovationGini Lin
The document discusses using big data in product design. It provides examples of Danish companies innovating through big data, including LEGO using social media data to inform product design, Vestas using weather data from turbines to choose wind farm locations, and a startup collecting wind speed data through an app. The document also discusses using big data to create new online services like a real estate portal and e-invoicing system. Finally, it outlines how Danish cities like Aarhus are releasing open data to power smart city applications like traffic monitoring and finding available parking. Overall, the document promotes the benefits of data-driven business while also noting barriers that must be overcome.
9. Beechwood Reverse Pitch FINALISTS 6.14.17 Joel Bennett
The document discusses a proposed platform called Beechwood that would enable communities to share data and applications. It would use high-speed networks and cloud infrastructure to securely replicate and pool existing private and public databases. This would allow for collective innovation and the development of shared and premium applications. Examples shown are apps for education data and crime mapping. The business model involves pricing tiers for data storage, API access, and application hosting for private and public entities. The team believes this platform could address regional data sharing needs while generating over $1 billion in annual revenue from various potential customer types.
This document discusses several topics related to data and data-driven businesses. It begins by outlining trends in big data and machine learning. It then discusses how to build data-centric businesses by identifying data opportunities and sources, understanding the data lifecycle, and extracting value from data. Examples are provided of Netflix as a data-driven company. The future of professions in a data-driven world is also examined, as well as talent scarcity issues and the need for data-savvy managers. The document provides an overview of many relevant topics at the intersection of data and business.
ODI Node Vienna: Best Practise Beispiele für: Open Innovation mittels Open DataMartin Kaltenböck
Vortrag im Rahmen des Data Pioneers Workshop am 10.10.2016 am BMVIT zum Thema Open Innovation und Open Data (Open Innovation mittels Open Data) seitens Elmar Kiesling (TU Wien) und Martin Kaltenböck (SWC) für den ODI (Open Data Institute) Node Vienna.
This document provides a brief history of big data, from the earliest known uses of data storage thousands of years ago to modern applications of big data. It outlines key developments such as the creation of early data storage and analysis methods, the development of computerized data processing, and the growth of data collection and sharing through the internet and mobile technology. The document also discusses the increasing volume of data generated every day through online activities and defines some of the main challenges in working with big data today.
This document discusses the rise of data and data-driven business models. It notes that unprecedented volumes of data are being generated from sources like the internet, smartphones, apps, and IoT devices. This presents both challenges and opportunities for businesses. The author analyzes over 500 UK startups utilizing data and finds that most focus on B2B services using a subscription model. Many are building platforms to connect various stakeholders around data sharing and analytics. Successful data businesses develop scalable and defensible platforms that benefit from network effects. The document examines examples of both large and small data-driven companies and their business models.
Similar to Lessons from Canada’s First Open Data Exchange (20)
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
3. Data is disruptive, powerful and dangerous
Data is the “new oil” and the “asbestos.”
Unlike oil, we are not going to run out of
it. We are amassing more and more and
unlike asbestos, the problems it’ll
introduce, are also endless.
–Tom Jenkins
Google KW
DATA CHANGES EVERYTHING
THE
NEW OIL
4. Can’t apply linear solutions
Electromagnetic radiation lab UW
90% OF THE
WORLDS DATA
LIVING IN AN EXPONENTIAL WORLD
CREATED IN THE
LAST 2 YRS
5. codx.caCanada’s Open Data Exchange 5
Before Its News
DATA EXHAUST
* McKinsey, 2015
30,000 sensors
Less than 1% of data being
used
Better data analytics could
result in:
Boosted production by 6-
8%
Improved uptime upwards
of 10%
6. codx.caCanada’s Open Data Exchange 6
WIRED
TRUMP’S
WIN ISN’T
THE DEATH
OF DATA –
IT WAS
FLAWED
ALL ALONG
Flawed data?
Flawed analytics?
Programmed
biases?
Data poverty?
11. codx.caCanada’s Open Data Exchange
“A piece of data is open if anyone is free
to use, reuse, and redistribute it —
subject only, at most, to the requirement
to attribute and/or share-alike.”
A key element of Open Government
Open Data is often associated with
government data but doesn't have to be
What is Open Data?
14. codx.caCanada’s Open Data Exchange
WHO HAS WHAT OPEN DATA?
Level of Gov’t Responsibilities Types of Data
Federal Agriculture, Financial Services,
Economic Data, Natural
Resources, CRA, Census, Weather
Criminal Code
Weather
GIS and Imagery
Economic Data
Tax Code
Provincial Courts, Education, Environment,
Energy, Health Care
K-12 Curriculum
Court Rulings
Municipal Water, Traffic, Parking, Property
Tax
Property Tax
Traffic Flows
17. codx.caCanada’s Open Data Exchange
McKinsey report released in October 2013, Open data: Unlocking innovation and performance with liquid information
40+
Countries with government
open data platforms
1million+
Data sets made open by
governments worldwide
102
Cities that participated in 2013
International Open Data
Hackathon Day
The GLOBAL Opportunity for Open Data
$3,000,000,000,000
in economic value
18. codx.caCanada’s Open Data Exchange
What $3,000,000,000,000 in economic value means
+50%
Consumer share of
potential value of open data
35
Hours per year, per commuter, could
be saved from schedule changes based
on open data
3 billion
Metric tons of carbon dioxide equivalent
emission reductions from buildings that
could be identified through the use of
open data
100,000+
Medical, health, and fitness apps
for smartphones
21. codx.caCanada’s Open Data Exchange
PiinPoint sources data sets from
municipality open data websites
across North America, providing
retailers with continuous
municipal open data.
Open data at PiinPoint
• Traffic counts
• Pedestrian counts
• Building permits
• Development plans
• Parking spots
• Public transportation
• Zoning
• Census
• …and more
22. codx.caCanada’s Open Data Exchange
ThinkData Works sources open
data from governments around
the world and makes it available
on a consistent API within the
firewall.
Features and data sets
• Search (Namara.io)
• Boundaries &Regions
• Economy
• Education
• Energy
• Environment
• Parking
• Transport
• Trade
• Housing
Used by asset managers and in programmatic trading.
23. codx.caCanada’s Open Data Exchange
Privacy
Prioritization
Resources
Used by asset managers and in programmatic trading.
Discovery & Access
Standards
Quality
Barriers to
mainstream adoption
24. codx.caCanada’s Open Data Exchange
A private-public
partnership The founding partners have committed to
$3 million in cash and in-kind contributions over
three years, which has been matched by the
Government of Canada.
25. codx.caCanada’s Open Data Exchange
Building the open data supply chain
RawData
Public data
• Government
Organization has
a challenge or
opportunity?
Collection &
transmission
• Security
• Storage
• Networks
Analytics,
discovery
& management
• Data Mining
• Data Fusion & Interoperability
• Data Manipulation
• Visualization
Applications
• Business case development
• Systems Integration
• uX/uD
• Commercialization expertise
Your organization
StructuredData
Knowledge
EndUser
26. codx.caCanada’s Open Data Exchange
What have we learned?
• Analysts are more and
more in demand
• Companies cant be
expected to capitalize
on open data unless
they have the
capabilities to properly
analyze and mine their
own data for
knowledge
• Data is a disrupter
• Many companies
don’t identify as being
open data enabled
companies even
though they might be
using open data of
some sort
• Data is the new
currency
• Large enterprises are
starting to realize the
intrinsic value of their
own data and are
looking to develop
strategies to open up
their data to a wider
developer community
to fuel innovation
• Enterprise, SME and
start ups alike are
getting more engaged
in open data
• Many companies aren’t
sure where to start with
open data OR it’s not top of
mind
?
27. codx.caCanada’s Open Data Exchange
Skills Development
• Workshops
• Peer2Peer Groups
Services and Tools
• Mentorship
• Concierge
Awareness
• OD150 Project
• Open Cities Index
• Events
Ecosystem
Development
• Online Community
• Project Funding
• ODX Connect
Soft Landing Program
• ODX Ventures
• ODX Challenge Series
PROGRAMS AND SERVICES
28. codx.caCanada’s Open Data Exchange
The Open Data 150
The OD150 Survey will create the first
comprehensive, internationally comparable
inventory of Canadian companies that use open
data to:
• Launch new products and services,
• Optimize their business processes,
• Make data-driven decisions,
• And solve complex problems.
Why undertake this initiative?
• Raise awareness of open data use in Canada
• More effective advocacy efforts
• Understand what support companies need most
urgently
• Encourage more open data sets
• Identify best practices of how companies can use open
data to enhance their products/servicesFor more information: www.opendata500.com/ca/
31. codx.caCanada’s Open Data Exchange
ODX Open Data Challenge
Problem
Areas
Data
Staff Time
Companies
Tools
Expertise
Applicants Finalists
Sponsoring
Municipality
Objectives:
• Tackle complex municipal problems with new, innovative solutions
• Create applications, products and services that create jobs and wealth
• Demonstrate that open data can catalyze private sector to solve problems in public sector
?
Problem
Identification
Solution
32. codx.caCanada’s Open Data Exchange
Challenges:
Challenge 1: Water Usage Data- How can Guelph Water Services enable
citizens to detect leaks and reduce their water use?
Challenge 2: Parking – How can we maximize the value of parking space
in the downtown?
Challenge 3: Statutory Notices- How can we make it easier for the public
to provide feedback on planning decisions?
33. codx.caCanada’s Open Data Exchange
• City/Municipality must be prepared to:
– Have a champion to lead from within
– Build internal trust/relationships with departments and subject matter experts
– Clearly define the problem to be solved
– Modify their procurement process to allow start-ups and SME’s to participate
– Work with challenge finalists to improve their solutions
– Be prepared to implement solutions should they meet the criteria
– Bring open data to the table to enable problem definition and solution
evaluation
Critical elements
34. codx.caCanada’s Open Data Exchange
Business Ready Badge
Canada’s Open Data Exchange
wants to recognize businesses
and governments who are
maximizing the economic value
from Open Data by providing
them with this badge that they
can proudly display.
Contact info@codx.ca to request
your badge.
35. codx.caCanada’s Open Data Exchange
• LinkedIn: Canada’s Open Data Exchange
• Web: codx.ca
• Twitter: @codx
• Newsletter: for updates on open data news, events and programs within
Canada
Follow our progress
Notion of using data not new. More focus on evidence based decision making is being powered by digital and big data
An American engineer, statistician, professor, author, lecturer, and management consultant
Many in Japan credit Deming as one of the inspiration for what has become known as the Japanese post-war economic miracle of 1950 to 1960, when Japan rose from the ashes of war to start Japan on the road to becoming the second largest economy in the world through processes partially influence by the ideas Deming taught
Be prepared. It will make the industrial revolution look like blip. Liken it to an ice-age or a tsunami of information. it will change everything.
Data is the opportunity and, as a resource, it is growing exponentially.
This is disruption.
Linear solutions don’t apply.
A challenge the entire industry is facing, almost putting cart before the horse
There has been a focus on creating data, less around what to do with the data
As such, we are leaving significant value on the table
Recent US election has illustrated our shortcomings in dealing with data
Points to a systemic breakdown that we need to address before we can unlock the true value
The value flow of data in a perfect world
Arguably, not a shortage of data
We need to do a better job of processing it
Most lucrative part of the value chain
Must be all about the data
Source agnostic
Need to shed the hype and focus on value creation
I want to focus on open data in particular.
What is open data?
How open is the data?
Who has the data?
Readiness: the readiness of states, citizens and entrepreneurs to secure the benefits of open data
Implementation: Are governments putting their commitments into practice?
Impact: Is open government data being used in ways that bring practical benefit?
The release of open data is trending in the upward direction.
Canada is one of many nations that are adopting open data policies.
Open data is not a resource that is unique to Canada. This is a global phenomena
McKinsey report released
in October 2013, Open data: Unlocking innovation
and performance with liquid information.
It can be used to provide value in a wide variety of ways
Companies have already built multi-billion dollar businesses on Open Data, even though we may not know them as such.
Intuit – TurboTax, based on open disclosure of tax calculations
Weather Network – based on one of the oldest forms of government open data, weather forecasts and reports
Garmin – heavy user of open GIS data and GPS location, and
Zillow – heavy user of geospatial, tax rolls, and real-estate data
A sampling of activity in Canada
Retail analytics
Historically, open data was made available with transparency and open government in mind
The Canadian Open Data Exchange, a public-private partnership, has been created to help the private sector tap into open data sources for commercial benefit
Its success will be measured by the success of the private sector partners it serves.
Along the way, ODX will enable the creation of supply chains using an ecosystem approach.
ODX will identify Canadian capabilities in every part of the supply chain.
ODX will create awareness around those that are already using open data and will serve as an advocate to inform the supply chain of the needs of the applications, thereby driving standards and policy.
OD150 for Canada’s 150th birthday. Hoping to have action plans based on the findings from OD150 for Canada’s 150th birthday.
Canada is the 6th member of the OD500 international network, which also includes Australia, Mexico, Italy, Korea, and the United States.
By joining this network, ODX gains knowledge of how companies around the world are using open data. We’ll also get to learn how other countries are improving the business climate for open companies through improved access to data and ongoing advocacy efforts
Respondent location:
The majority of early respondents are located in Ontario & Quebec, with 3 regions demonstrating an emerging yet mature industrial Open Data capability – Greater Toronto Area, Waterloo Region, and Montreal.
Greatest challenge to business success:
Respondents were asked to identify their top business challenges. Unsurprisingly, there are a myriad of challenges facing companies using open data in Canada, and respondent companies were consistent in the areas they found most challenging. As Canada begins to understand the needs of businesses using Open Data, these challenges present important guideposts for creating greater opportunity for businesses.
Data sources:
There is a consistent use of data from all government jurisdictions in Canada – knowing that many of these companies are location specific due to data availability, this demonstrates a significant opportunity for governments to create common data sets and standards to permit companies to scale. It is also interesting that some Canadian companies have been able to integrate US data sets into their business.
Revenue source:
There is a strong preponderance among respondent companies towards revenue streams based on analyzing and interpreting data for clients. As many respondents identified “difficulty finding/accessing data” as a key challenge, it is reasonable to expect that those who are familiar with open data have parlayed this knowledge into analytical services for companies that do not have this capability.
Industries represented:
There is a wide distribution of industries represented, a result of the (1) varying availability of data across jurisdictions and (2) levels of granularity. It is expected that one of the leading industries is Geospatial/Mapping, as at the Federal and Provincial levels the most frequently released open data sets are geospatial (NRCan is the leading Federal department with over 110,000 open data sets on Canada’s open data portal).
Criteria for businesses:
Identify how open data drives economic benefit for your business
Identify and advocate for the release of open data sets
Share standards and other requirements to simplify and improve the use of Open Data
Participate and provide employees with access to Open Data training programs
Criteria for Government:
Release and update Open Data as part of their mandate; data must be machine-readable and allowed for re-use and re-distribution
Provide the means for businesses & individuals to download Open Data
Promote programs that use Open Data
Regularly Interface with businesses to understand commercial uses of open data and additional needs