The Emergence of Alt-Data and its ApplicationsPromptCloud
Alternative data is information gathered from non-traditional information sources. Analysis of this data can provide insights beyond that which an industry’s regular data sources are capable of providing. Here is all you should know about alt-data.
The document provides information about the career path and typical work activities of Christopher Teixeira, a Lead Data Scientist at MITRE Corporation. It describes his educational background in mathematics and statistics, previous roles applying data science in industries like NASA, DHS, and banking, and the types of problems he helps solve in his current role such as improving veterans benefits, nuclear waste cleanup, and increasing child welfare. It also provides summaries of typical daily activities for a data scientist and lead data scientist, focusing on data analysis, cleaning, modeling, visualization, and presentation.
Using Big Data in Finance by Jonah EnglerJonah Engler
How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns.
Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler
Check out how big data is proving invaluable to finance. Here is the top 10 big data trends in finance. Big data place a vital role in analysing the feeds, Predictive models, forecasts & assess trading impacts
Big data provides opportunities for financial institutions to gain competitive advantages. It allows them to analyze vast amounts of structured and unstructured data from various sources to better understand customers, identify risks, predict behaviors, and improve financial products and services. While big data implementations face challenges like integrating diverse data sources and developing analytics talent, companies that execute big data strategies are seeing significant benefits like more personalized customer experiences and better risk management. TD Bank is an example of a company revolutionizing IT and banking through big data analytics that can build comprehensive customer profiles and segment their entire customer base within minutes.
Synthetic data is generic and artificial data used to mimic real-world data sets in order to protect privacy and enable testing of algorithms. It can be created by observing statistic distributions from real data and drawing random numbers, or through agent-based modeling to simulate behaviors. Companies or specialized firms can create synthetic data either through in-house development or outsourcing depending on the complexity of the original data. Synthetic data acts as a substitute for real data without compromising privacy.
Big data landscape v 3.0 - Matt Turck (FirstMark) Matt Turck
This document provides an overview of the big data landscape, covering infrastructure, databases, analytics platforms, applications, industries utilizing big data, and areas of the big data field like machine learning, data visualization, and artificial intelligence. It was created by Matt Turck, Sutian Dong, and FirstMark Capital to map the current state of big data in version 3.0.
The Emergence of Alt-Data and its ApplicationsPromptCloud
Alternative data is information gathered from non-traditional information sources. Analysis of this data can provide insights beyond that which an industry’s regular data sources are capable of providing. Here is all you should know about alt-data.
The document provides information about the career path and typical work activities of Christopher Teixeira, a Lead Data Scientist at MITRE Corporation. It describes his educational background in mathematics and statistics, previous roles applying data science in industries like NASA, DHS, and banking, and the types of problems he helps solve in his current role such as improving veterans benefits, nuclear waste cleanup, and increasing child welfare. It also provides summaries of typical daily activities for a data scientist and lead data scientist, focusing on data analysis, cleaning, modeling, visualization, and presentation.
Using Big Data in Finance by Jonah EnglerJonah Engler
How can you utilize Big Data in the Financial Industry? To leverage Big Data - entrepreneur and finance expert Jonah Engler, has put together this presentation to help the slideshare community understand the value - and HOW TO - use big data in the financial campaigns.
Jonah Engler is a financial expert and stock broker based in NYC. Leveraging his experience in finance, Engler has gone on to have success in the franchise, coffee, startup industries and more. To connect with Jonah - checkout his profile on LinkedIn: https://www.linkedin.com/in/jonahengler
Check out how big data is proving invaluable to finance. Here is the top 10 big data trends in finance. Big data place a vital role in analysing the feeds, Predictive models, forecasts & assess trading impacts
Big data provides opportunities for financial institutions to gain competitive advantages. It allows them to analyze vast amounts of structured and unstructured data from various sources to better understand customers, identify risks, predict behaviors, and improve financial products and services. While big data implementations face challenges like integrating diverse data sources and developing analytics talent, companies that execute big data strategies are seeing significant benefits like more personalized customer experiences and better risk management. TD Bank is an example of a company revolutionizing IT and banking through big data analytics that can build comprehensive customer profiles and segment their entire customer base within minutes.
Synthetic data is generic and artificial data used to mimic real-world data sets in order to protect privacy and enable testing of algorithms. It can be created by observing statistic distributions from real data and drawing random numbers, or through agent-based modeling to simulate behaviors. Companies or specialized firms can create synthetic data either through in-house development or outsourcing depending on the complexity of the original data. Synthetic data acts as a substitute for real data without compromising privacy.
Big data landscape v 3.0 - Matt Turck (FirstMark) Matt Turck
This document provides an overview of the big data landscape, covering infrastructure, databases, analytics platforms, applications, industries utilizing big data, and areas of the big data field like machine learning, data visualization, and artificial intelligence. It was created by Matt Turck, Sutian Dong, and FirstMark Capital to map the current state of big data in version 3.0.
This document discusses using big data analytics to enhance the real estate market in Oman. It begins with an abstract on artificial intelligence and its applications. It then provides background on how data has evolved through three stages with business intelligence. The problem statement notes that data typically comes from single structured sources. The study aims to develop an integrated real estate system combining multi-source data. The objectives are to produce reliable statistics, help investors/realtors/buyers, and understand skills required for big data professionals. An artifact was created to collect real estate data from sources for visualization. The conclusion discusses how big data has become important for extracting useful information from unstructured data.
Dark Data Revelation and its Potential BenefitsPromptCloud
Dark data refers to the large amounts of unused data organizations collect during regular business activities. While organizations invest heavily in collecting data, much of it remains unused. There are three main types of dark data: existing unstructured internal data, non-traditional unstructured external data, and data available on the deep web. Analyzing dark data can provide valuable insights but also risks such as privacy issues. Some companies are already leveraging dark data for applications like fraud detection and personalization in retail. Approaching dark data requires getting the right data, augmenting with external sources, building data talent, and using advanced visualization tools.
Big Data is defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
Bigdata Landscape and Competitive IntelligenceJithin S L
The big data market is expected to grow from $28.65 billion in 2016 to $66.79 billion by 2021, attaining a CAGR of 18.45%. Several leading consulting firms offer big data services including data management, analytics, infrastructure setup, and case studies in industries like financial services, healthcare, and telecommunications. Success stories demonstrate improved insights, fraud detection, and optimization through big data transformations.
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Driving the future of big data | PromptCloudPromptCloud
The Big data & Machine Learning emerge as crucial technological assets of the future. Scare over data-driven artificial intelligence replacing human creativity.
The document discusses trends and challenges in big data. It notes that big data is data that exceeds the processing capacity of conventional database systems due to its large size, speed, or structure. The big data market is expected to grow annually by 5-10% over the next 10 years. While the big data industry is growing rapidly, it faces shortages of big data specialists. Modern big data specialists need a combination of mathematical, programming, business, and communication skills.
The most profitable insurance organizations will outperform competitors in key areas as personalized customer service, claims processing, subrogation recovery, fraud detection and product innovation. This requires thinking beyond the traditional data warehouse to the data fabric - an emerging data management architecture.
In this webinar Andy Sohn, Senior Advisor at NewVantage Partners, and Bob Parker, Senior Director for Insurance at Cambridge Semantics, explore the role of the data discovery and integration layer in an enterprise data fabric for the Insurance industry. These are their slides.
Graph technology has truly burst onto the scene with diverse new products and services, proving that graph is relevant and that not all graph use cases are equal. Previously relegated to niche implementations and science projects, graph now finds itself deployed as the foundational technology for enterprise analytics solutions and enterprise Data Fabric strategies. It is no surprise that many are calling 2018 “The Year of the Graph”.
Fighting financial crime with graph analysis at BIWA Summit 2017Linkurious
Additional details on our blog: https://linkurio.us/visualize-oracle-graph-data-ogma-library/
Discover how to use graph analysis to identify suspicious connections and unmask criminals. In this session, Jean will share his experience working on the Panama Papers or with banks and insurance companies (first-party fraud, anti-money laundering, insurance fraud). He will explain how to combine the kind of graph analytics enabled by Oracle Spatial and Graph with powerful graph visualization to help analysts detect, investigate and stop financial crime.
TechConnex Big Data Series - Big Data in BankingAndre Langevin
Big Data in Banking focuses on the use of big data and Hadoop in the Canadian banking sector. The key points are:
1) The RDARR regulatory project is driving major investments in data management by the big six Canadian banks, totaling around $800 million over three years. This has led banks to implement Hadoop data hubs to centralize data.
2) Adoption of Hadoop for risk applications is still in early stages, with a focus on regulatory reporting. Capital markets has led adoption so far.
3) Lessons learned include choosing flexible Hadoop distributions, using native Hadoop tools for best performance, and designing hubs for data engineers rather than casual users. Infrastructure must have
The document summarizes an academic seminar presentation on applications of statistics in big data. It defines big data as very large quantities of digital information that cannot be analyzed with traditional techniques. It discusses characteristics of big data like volume, velocity and variety. Statistical techniques like regression analysis, probability distributions, and time series analysis can be used to analyze big data and find useful patterns. Examples are given of how big data is used in areas like healthcare, traffic control, and e-commerce. Risks and benefits of big data are also outlined. The future of big data is presented as a growing field with increasing commercial opportunities.
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
This document discusses Generali Group, an international insurance company, and its efforts to detect insurance fraud. It provides an overview of Generali Group, describing it as one of the leading insurers in the world with operations in over 60 countries. It then discusses the problem of insurance fraud and some common types. It outlines Generali's solution to fraud detection, which involves using a graph database and applying techniques like rule-based analysis, social network analysis, and machine learning to processed insurance claims data.
Predictive analytics in uae government organizationsSaeed Al Dhaheri
This presentation is to create awareness of the use the use of predicative analytics in public sector organizations with emphasis on UAE government organizations.
This document discusses data science innovations and systems of insight. It provides examples of new data sources like social media language and drone/mobile sensor data that can generate novel insights. Systems of insight use machine learning and natural language generation to automatically analyze data, detect patterns, and present findings and narratives to users without extensive data preparation. This approach reduces the time spent on data wrangling and moves organizations from crisis-level talent shortages to faster decision making. The document advocates starting to use innovative data sources and systems of insight to generate customer insights, optimize processes, and gain a competitive advantage.
This document discusses using big data analytics to enhance the real estate market in Oman. It begins with an abstract on artificial intelligence and its applications. It then provides background on how data has evolved through three stages with business intelligence. The problem statement notes that data typically comes from single structured sources. The study aims to develop an integrated real estate system combining multi-source data. The objectives are to produce reliable statistics, help investors/realtors/buyers, and understand skills required for big data professionals. An artifact was created to collect real estate data from sources for visualization. The conclusion discusses how big data has become important for extracting useful information from unstructured data.
Dark Data Revelation and its Potential BenefitsPromptCloud
Dark data refers to the large amounts of unused data organizations collect during regular business activities. While organizations invest heavily in collecting data, much of it remains unused. There are three main types of dark data: existing unstructured internal data, non-traditional unstructured external data, and data available on the deep web. Analyzing dark data can provide valuable insights but also risks such as privacy issues. Some companies are already leveraging dark data for applications like fraud detection and personalization in retail. Approaching dark data requires getting the right data, augmenting with external sources, building data talent, and using advanced visualization tools.
Big Data is defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.
Bigdata Landscape and Competitive IntelligenceJithin S L
The big data market is expected to grow from $28.65 billion in 2016 to $66.79 billion by 2021, attaining a CAGR of 18.45%. Several leading consulting firms offer big data services including data management, analytics, infrastructure setup, and case studies in industries like financial services, healthcare, and telecommunications. Success stories demonstrate improved insights, fraud detection, and optimization through big data transformations.
BIG Data & Hadoop Applications in FinanceSkillspeed
Explore the applications of BIG Data & Hadoop in Finance via Skillspeed.
BIG Data & Hadoop in Finance is a key differentiator, especially in terms of generating greater investment insights. They are used by companies & professionals for risk assessment, fraud detection & forecasting trends in financial markets.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Driving the future of big data | PromptCloudPromptCloud
The Big data & Machine Learning emerge as crucial technological assets of the future. Scare over data-driven artificial intelligence replacing human creativity.
The document discusses trends and challenges in big data. It notes that big data is data that exceeds the processing capacity of conventional database systems due to its large size, speed, or structure. The big data market is expected to grow annually by 5-10% over the next 10 years. While the big data industry is growing rapidly, it faces shortages of big data specialists. Modern big data specialists need a combination of mathematical, programming, business, and communication skills.
The most profitable insurance organizations will outperform competitors in key areas as personalized customer service, claims processing, subrogation recovery, fraud detection and product innovation. This requires thinking beyond the traditional data warehouse to the data fabric - an emerging data management architecture.
In this webinar Andy Sohn, Senior Advisor at NewVantage Partners, and Bob Parker, Senior Director for Insurance at Cambridge Semantics, explore the role of the data discovery and integration layer in an enterprise data fabric for the Insurance industry. These are their slides.
Graph technology has truly burst onto the scene with diverse new products and services, proving that graph is relevant and that not all graph use cases are equal. Previously relegated to niche implementations and science projects, graph now finds itself deployed as the foundational technology for enterprise analytics solutions and enterprise Data Fabric strategies. It is no surprise that many are calling 2018 “The Year of the Graph”.
Fighting financial crime with graph analysis at BIWA Summit 2017Linkurious
Additional details on our blog: https://linkurio.us/visualize-oracle-graph-data-ogma-library/
Discover how to use graph analysis to identify suspicious connections and unmask criminals. In this session, Jean will share his experience working on the Panama Papers or with banks and insurance companies (first-party fraud, anti-money laundering, insurance fraud). He will explain how to combine the kind of graph analytics enabled by Oracle Spatial and Graph with powerful graph visualization to help analysts detect, investigate and stop financial crime.
TechConnex Big Data Series - Big Data in BankingAndre Langevin
Big Data in Banking focuses on the use of big data and Hadoop in the Canadian banking sector. The key points are:
1) The RDARR regulatory project is driving major investments in data management by the big six Canadian banks, totaling around $800 million over three years. This has led banks to implement Hadoop data hubs to centralize data.
2) Adoption of Hadoop for risk applications is still in early stages, with a focus on regulatory reporting. Capital markets has led adoption so far.
3) Lessons learned include choosing flexible Hadoop distributions, using native Hadoop tools for best performance, and designing hubs for data engineers rather than casual users. Infrastructure must have
The document summarizes an academic seminar presentation on applications of statistics in big data. It defines big data as very large quantities of digital information that cannot be analyzed with traditional techniques. It discusses characteristics of big data like volume, velocity and variety. Statistical techniques like regression analysis, probability distributions, and time series analysis can be used to analyze big data and find useful patterns. Examples are given of how big data is used in areas like healthcare, traffic control, and e-commerce. Risks and benefits of big data are also outlined. The future of big data is presented as a growing field with increasing commercial opportunities.
Synthetic data generation for machine learningQuantUniversity
As machine learning becomes more pervasive in the industry, data scientists and quants are realizing the challenges and limitations of machine learning models. One of the primary reasons machine learning applications fail is due to the lack of rich, diverse and clean datasets needed to build models. Datasets may have missing values, may not incorporate enough samples for all use cases (for example: availability of fraudulent transaction records to train a model) and may not be easily sharable due to privacy concerns. While there are many data cleansing techniques to fix data-related issues and we can always try and get new and rich datasets, the cost is at times prohibitive and at times impractical leading many institutions to abandon machine learning and go back to rule-based methods.
Synthetic data sets and simulations are used to enrich and augment existing datasets to provide comprehensive samples while training machine learning problems. In addition, synthetic datasets can be used for comprehensive scenario analysis, missing value filling and privacy protection of the datasets when building models. The advent of novel techniques like Deep Learning has rekindled interest in using techniques like GANs and Encoder-Decoder architectures in financial synthetic data generation.
In this workshop, we will discuss the state of the art in Synthetic data generation and will illustrate the various techniques and methods that can be used in practice. Through examples using QuSynthesize & QuSandbox, we will demonstrate how these techniques can be realized in practice.
This document discusses Generali Group, an international insurance company, and its efforts to detect insurance fraud. It provides an overview of Generali Group, describing it as one of the leading insurers in the world with operations in over 60 countries. It then discusses the problem of insurance fraud and some common types. It outlines Generali's solution to fraud detection, which involves using a graph database and applying techniques like rule-based analysis, social network analysis, and machine learning to processed insurance claims data.
Predictive analytics in uae government organizationsSaeed Al Dhaheri
This presentation is to create awareness of the use the use of predicative analytics in public sector organizations with emphasis on UAE government organizations.
This document discusses data science innovations and systems of insight. It provides examples of new data sources like social media language and drone/mobile sensor data that can generate novel insights. Systems of insight use machine learning and natural language generation to automatically analyze data, detect patterns, and present findings and narratives to users without extensive data preparation. This approach reduces the time spent on data wrangling and moves organizations from crisis-level talent shortages to faster decision making. The document advocates starting to use innovative data sources and systems of insight to generate customer insights, optimize processes, and gain a competitive advantage.
Data science is the sheer skill to gain competence in making sense of all the data pools that are being generated by organizations worldwide. From being the most promising and the hottest jobs in the world in 2023 global rankings by the World Economic Forum, you are sure to gain as a certified data scientist in the years to follow as well.
Hedge Fund case study solution - Credit default swaps execution system and Gr...Naveen Kumar
I designed the entire end-to-end trading architecture of a hedge fund.
The execution system for integrating a fund with Credit default swap capabilities and also solved Hedge fund's liquidity constraint in moving funds across the countries.
This document discusses the future of big data, including predictions such as machine learning becoming prominent and data scientists being in high demand. It outlines trends like the growth of open source technologies, in-memory computing, machine learning, predictive analytics, intelligent applications, integrating big data with security and the internet of things. Challenges mentioned include dealing with large amounts of data from IoT and high salaries for data professionals.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
Why is Data Science a Popular Career Choice.pdfUSDSI
Do you want to become the backbone of big corporates and giant business groups around the world? Beginning your career trajectory by grabbing the perfect spot in the data science certification courses provided around the world. The US Bureau of Labor Statistics projects 35.8% employment growth for data scientists till 2031, over a decade period beginning 2021. The growing use of machine learning and artificial intelligence technologies is another factor driving the demand for professionals skilled in data science.
Brimming with humungous career opportunities, the data science industry is set in motion to yield multitudinous growth opportunities across diversified industries worldwide. By automating procedures, increasing effectiveness, and allowing predictive capabilities, Artificial intelligence and machine learning algorithms hold the ability to change the entire landscape.
Data Science has become a fascinating career choice that calls for working closely with cutting-edge technology and addressing challenges. If you are someone who wishes to work with humungous data, has a passion for numbers, and has a clear vision of setting their career on a thriving path; data science is the right pick for you!
A diversified array of organizations is actively looking for data-hungry professionals who are coarsely skilled at data science to analyze data and churn out business decisions for the greater good of the company. Today is the ripe time to get started with a data science career, that promises an elevated trajectory and nothing else.
With the rise of technological innovations and industrial evolution, massive datasets become unmanageable. The future of such a massive explosion of data calls for an urgent appointment of qualified data scientists; enabling bigger business moves. This is where getting certified in the field makes sense.
Without wasting any further time, it is an advisable move to get certified in key data science skills that are sure to rage in the industry worldwide. Begin with the most trusted names in the data science certifications providers industry today!
https://www.usdsi.org/data-science-insights/why-is-data-science-a-popular-career-choice
La base para optimizar y potenciar la toma de decisiones en cualqueir empresa es la información. Pero no la información en bruto, sino aquella de la que podemos obtener valor tras su análisis.
What exactly is big data? What exactly is big data? .pptxTusharSengar6
big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.” Put simply, big data is larger, more complex data sets, especially from new data sources.
Come diventare data scientist - Si ringrazie per le slide Paolo Pellegrini, Senior Consultant presso P4I (Partners4Innovation) e referente di tutte le progettualità relative alle tematiche Data Science e Big Data Analytics. Owner del primo gruppo in Italia dedicato dai Data Scientist.
ABOUT DATA SCIENCE big data analytics ppt.pptxVASANTHIG10
This document provides an overview of data science, including its definition, tools, hardware/software used, applications, advantages, disadvantages, and architecture. Data science is defined as extracting meaningful insights from large amounts of data using principles from mathematics, statistics, artificial intelligence, and computer engineering. It uses tools like SAS, Hadoop, TensorFlow, and Python. Data science is applied in many fields including banking, manufacturing, e-commerce, and education to improve business decisions, performance, products, efficiency, and customer experiences.
10 POPULAR DATA SCIENCE TOOLS TO CONSIDER EXPLORINGUSDSI
Get maximum competence in top data science tools to power creative data visualization. Explore the variety of data science tools that can enable high-end data optimization for business amplification.
Big Data, analytics and 4th generation data warehousing by Martyn Jones at Bi...Big Data Spain
Martyn Jones presented on Big Data, analytics and 4th generation data warehousing. He discussed the importance of a comprehensive data supply framework to obtain, integrate and analyze data from various sources to provide strategic, tactical and operational decision making support. He described moving beyond traditional data warehousing to a 4th generation approach that leverages big data and analytics to gain insights and measure outcomes.
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
Analytics Trends 2015: A below-the-surface lookDeloitte Canada
Big Data is a big deal for everyone these days and only growing in importance, especially when it comes to analytics generating actionable insights. Deloitte has identified eight significant analytics trends to watch in 2015 – including one supertrend that will impact everything else.
The world around us is changing. Data is embedded in everything, and users from all lines of business want to leverage this data to influence decisions. The trick is to create a culture for pervasive analytics and empower the business to use data everywhere.
The core enabling technology to make this happen is Apache Hadoop. By leveraging Hadoop, organizations of all sizes and across all industries are making business models more predictable, and creating significant competitive advantages using big data.
Join Cloudera and Forrester to learn:
- What we mean by pervasive analytics, how it impacts your organization, and how to get started
- How leading organizations are using pervasive analytics for competitive advantage
- How Cloudera’s extensive partner ecosystem complements your strategy, helping deliver results faster
Module 6 The Future of Big and Smart Data- Online caniceconsulting
This document provides an overview of the future predictions and trends related to big data. Some of the key predictions discussed include machine learning becoming prominent in big data analysis, privacy emerging as a major challenge, and the creation of chief data officer positions. Emerging trends covered include the growth of open source solutions like Hadoop, the use of in-memory technologies to speed processing, and the incorporation of machine learning and predictive analytics. The document also discusses opportunities that big data presents for industries like increased productivity and sales.
Big Data & Analytics Trends 2016 Vin MalhotraVin Malhotra
This document discusses several trends in analytics for 2016:
1. Data security is a major concern as data volumes grow exponentially and security risks increase. Analytics can help secure data but requires integration across innovation, analytics, connectivity and technology.
2. The Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating sensor and structured data in real time.
3. Open source analytics solutions like Hadoop are increasingly used by enterprises but also require careful risk management and a clear strategy to ensure they align with technology needs.
Applying AI & Search in Europe - featuring 451 ResearchLucidworks
In the current climate, it’s now more important than ever to digitally enable your workforce and customers.
Hear from Simon Taylor, VP Global Partners & Alliances, Lucidworks and Matt Aslett, Research Vice President, 451 Research to get the inside scoop on how industry leaders in Europe are developing and executing their digital transformation strategies.
In this webinar, we’ll discuss:
The top challenges and aspirations European business and technology leaders are solving using AI and search technology
Which search and AI use cases are making the biggest impact in industries such as finance, healthcare, retail and energy in Europe
What technology buyers should look for when evaluating AI and search solutions
This document provides an overview of big data, including definitions of key terms like data, big data, and examples of big data. It describes why big data is important, how big data analytics works, and the benefits it provides. It outlines different types of big data like structured, unstructured, and semi-structured data. It also discusses characteristics of big data like volume, velocity, variety, and veracity. Additionally, it identifies primary sources of big data and examples of big data tools and software. Finally, it briefly discusses how big data and machine learning are related and how AI can be used to enhance big data analytics.
Contributi dei parlamentari del PD - Contributi L. 3/2019Partito democratico
DI SEGUITO SONO PUBBLICATI, AI SENSI DELL'ART. 11 DELLA LEGGE N. 3/2019, GLI IMPORTI RICEVUTI DALL'ENTRATA IN VIGORE DELLA SUDDETTA NORMA (31/01/2019) E FINO AL MESE SOLARE ANTECEDENTE QUELLO DELLA PUBBLICAZIONE SUL PRESENTE SITO
Combined Illegal, Unregulated and Unreported (IUU) Vessel List.Christina Parmionova
The best available, up-to-date information on all fishing and related vessels that appear on the illegal, unregulated, and unreported (IUU) fishing vessel lists published by Regional Fisheries Management Organisations (RFMOs) and related organisations. The aim of the site is to improve the effectiveness of the original IUU lists as a tool for a wide variety of stakeholders to better understand and combat illegal fishing and broader fisheries crime.
To date, the following regional organisations maintain or share lists of vessels that have been found to carry out or support IUU fishing within their own or adjacent convention areas and/or species of competence:
Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR)
Commission for the Conservation of Southern Bluefin Tuna (CCSBT)
General Fisheries Commission for the Mediterranean (GFCM)
Inter-American Tropical Tuna Commission (IATTC)
International Commission for the Conservation of Atlantic Tunas (ICCAT)
Indian Ocean Tuna Commission (IOTC)
Northwest Atlantic Fisheries Organisation (NAFO)
North East Atlantic Fisheries Commission (NEAFC)
North Pacific Fisheries Commission (NPFC)
South East Atlantic Fisheries Organisation (SEAFO)
South Pacific Regional Fisheries Management Organisation (SPRFMO)
Southern Indian Ocean Fisheries Agreement (SIOFA)
Western and Central Pacific Fisheries Commission (WCPFC)
The Combined IUU Fishing Vessel List merges all these sources into one list that provides a single reference point to identify whether a vessel is currently IUU listed. Vessels that have been IUU listed in the past and subsequently delisted (for example because of a change in ownership, or because the vessel is no longer in service) are also retained on the site, so that the site contains a full historic record of IUU listed fishing vessels.
Unlike the IUU lists published on individual RFMO websites, which may update vessel details infrequently or not at all, the Combined IUU Fishing Vessel List is kept up to date with the best available information regarding changes to vessel identity, flag state, ownership, location, and operations.
karnataka housing board schemes . all schemesnarinav14
The Karnataka government, along with the central government’s Pradhan Mantri Awas Yojana (PMAY), offers various housing schemes to cater to the diverse needs of citizens across the state. This article provides a comprehensive overview of the major housing schemes available in the Karnataka housing board for both urban and rural areas in 2024.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
This report explores the significance of border towns and spaces for strengthening responses to young people on the move. In particular it explores the linkages of young people to local service centres with the aim of further developing service, protection, and support strategies for migrant children in border areas across the region. The report is based on a small-scale fieldwork study in the border towns of Chipata and Katete in Zambia conducted in July 2023. Border towns and spaces provide a rich source of information about issues related to the informal or irregular movement of young people across borders, including smuggling and trafficking. They can help build a picture of the nature and scope of the type of movement young migrants undertake and also the forms of protection available to them. Border towns and spaces also provide a lens through which we can better understand the vulnerabilities of young people on the move and, critically, the strategies they use to navigate challenges and access support.
The findings in this report highlight some of the key factors shaping the experiences and vulnerabilities of young people on the move – particularly their proximity to border spaces and how this affects the risks that they face. The report describes strategies that young people on the move employ to remain below the radar of visibility to state and non-state actors due to fear of arrest, detention, and deportation while also trying to keep themselves safe and access support in border towns. These strategies of (in)visibility provide a way to protect themselves yet at the same time also heighten some of the risks young people face as their vulnerabilities are not always recognised by those who could offer support.
In this report we show that the realities and challenges of life and migration in this region and in Zambia need to be better understood for support to be strengthened and tuned to meet the specific needs of young people on the move. This includes understanding the role of state and non-state stakeholders, the impact of laws and policies and, critically, the experiences of the young people themselves. We provide recommendations for immediate action, recommendations for programming to support young people on the move in the two towns that would reduce risk for young people in this area, and recommendations for longer term policy advocacy.
RFP for Reno's Community Assistance CenterThis Is Reno
Property appraisals completed in May for downtown Reno’s Community Assistance and Triage Centers (CAC) reveal that repairing the buildings to bring them back into service would cost an estimated $10.1 million—nearly four times the amount previously reported by city staff.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
How To Cultivate Community Affinity Throughout The Generosity JourneyAggregage
This session will dive into how to create rich generosity experiences that foster long-lasting relationships. You’ll walk away with actionable insights to redefine how you engage with your supporters — emphasizing trust, engagement, and community!
The Antyodaya Saral Haryana Portal is a pioneering initiative by the Government of Haryana aimed at providing citizens with seamless access to a wide range of government services
AHMR is an interdisciplinary peer-reviewed online journal created to encourage and facilitate the study of all aspects (socio-economic, political, legislative and developmental) of Human Mobility in Africa. Through the publication of original research, policy discussions and evidence research papers AHMR provides a comprehensive forum devoted exclusively to the analysis of contemporaneous trends, migration patterns and some of the most important migration-related issues.
1. E r i c A n v a r, H e a d o f S m a r t D a t a P r a c t i c e s a n d S o l u t i o n s
O E C D , J u n e 2 0 1 9
Insurance & Private Pensions Committee
Towards Smart Data Strategies
Link to related paper is here
3. C O N T E X T – W H Y S M A R T D ATA ?
https://trends.google.fr/trends/explore?date=today%205-y&q=big%20data,smart%20data,data%20science,machine%20learning,artificial%20intelligence
4. T H E R E G U L A R D ATA C Y C L E …
OneSurveyOneTeamOneProduct
5. T H E R E G U L A R D ATA C Y C L E …
OneSurveyOneTeamOneProduct
6. XSourcesYPlatformsZProducts
… I S C H A L L E N G E D T O ‘ E M B R A C E S M A R T D ATA’
Demand for new/ehanced evidence for policy…
• Real-time – Time-to-Market
• Granular – Localised, Targetted
• Trusted – Quality Assurance
…feeding into new policy models & simulations
7. … I S C H A L L E N G E D T O ‘ E M B R A C E S M A R T D ATA’
8. Data Sourcing Gap
D E M A N D T O M E E T, G A P S T O O V E R C O M E
DataSkillsGap
EfficiencyGap
Data Platforms Gap
Demand
for New
Evidence
Demand
for Trusted
Quality
9. S M A R T D ATA S T R AT E G Y
Integrating
The Data
Cycle
Embracing
Smart
Data
10. S M A R T D ATA P L AT F O R M
ALGORITHMS
BANK &
SANDBOX
DATA
CORE(S)
DATA
LAKE(S)
Data
Science
Environment
Data
Management
Environment
Open
Data
Policy
Simulators
Open
Algorithms
Augment
Data
Combine
Data
Data
Dissemination(s)
11. S M A R T D ATA S O U R C I N G
Combine
Established
Sources…
Countries
PolicyDomains
Policy
Reporting
Statistical
Reporting
…with Alternative
Sources of
Data
Admin. &
Survey
Microdata
…
OECD Data Collection Programme
Microdata from commercial sources
Open data (academia, public, private)
Web scraped & text mined data
Geospatial data
Data from GAFAs & platforms
Transaction data
IoT data
Crowdsourced, web survey data
Partner IOs
Which business model
for new data sources
Regulation, Commercial, Open, Partnership,
Web scraping, Crowdsourcing, Distributed Protocoles
12. PRIVATE PARTNERS
IT vendors, Start-ups, DPO..
DATA
PROVIDERS
OPEN
ECOSYSTEMS
Open source
Open algorithms
Open knowledge
Open data
PUBLIC PARTNERS
IOs, NSOs, CBs, Agencies
S M A R T D ATA E C O S Y S T E M S
CIVIL
SOCIETY
Academia
NGOs…
13. H Y P O T H E S I S
In order to regulate a market that is transformed
with the adoption of big data, regulators and
policy makers have to embrace smart data.
14. E M B R A C I N G S M A R T D ATA - E X A M P L E
New legal process
for data acquisition
Universities’ student records
Approx 2 million per year
National death registry
Approx 5000 suicides per year
Students = ??
Use of suicide rate as
proxy for the
population of interest
Stakeholder discussions on
priority policy and social
questions
Assessing trends
and needs in mental
health in a
population group
ENSURE
TRUSTED
QUALITY
?
Secure ONS
data linkage
infrastructure
15. W H I C H I S Y O U R U S E C A S E ?
CULTIVATE
ECOSYSTEMS
SOURCE
NEW DATA
CREATE
NEW
EVIDENCE
DEVELOP SKILLS
MEET
NEW POLICY
QUESTIONS
ENSURE
TRUSTED
QUALITY
?
BUILD
DATA SCIENCE
PLATFORM