This document discusses the growth of data and analytics capabilities. It notes that data storage capacity is growing at 23% annually while computing capacity is growing at 54% annually. Lower barriers to connectivity are integrating different sources of data. The document discusses how Right Brain Systems uses analytics to build smarter organizations by focusing on data foundation, information design, analytics capabilities, operational framework, and business ownership. It provides examples of how different types of analytics can be applied to key areas like customers, operations, finance, and workforce.
To be updated is not enough for companies today. Organizations must be constantly watching also to the trends in order to predict and forecast the next steps for their business. The following document is a Executive Summary of the current situation but also of the more notable trends that will help to understand the basics of the Analytics Market
The document summarizes the top 10 trends in analytics according to Tiger Analytics. It discusses how data enrichment is allowing for better forecasts using internal and external data. It also covers how unstructured data like text, images, audio and video are opening new opportunities. Other trends discussed include the growth of speech/voice analytics, data lakes, big data and IoT analytics, the proliferation of analytic tools including open source tools, new visualization tools, full-pipeline analytics automation, and new decision systems using artificial intelligence.
The document discusses several key trends in analytics for 2015:
1. Data security is a major concern as data volumes grow exponentially, requiring companies to quadruple down on security efforts through innovation, analytics, and tighter integration.
2. The rise of the Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating these systems.
3. Some argue that data should be monetized as an asset, but this brings risks around privacy, ethics, and real costs that companies need to consider carefully.
4. Cognitive analytics is enhancing decision-making by providing users with vast new sources of knowledge, though questions remain about how these systems will impact human roles over time
Guide to Data Analytics: The Trend That's Reshaping the Insurance IndustryApplied Systems
Information you need is in your management system –- you just have to understand how to use it. Read this guide to learn what data analytics is, how it's impacting the insurance industry, why it's important for independent agencies and brokerages, and how to create your own data analytics strategy.
NUS-ISS Learning Day 2018- Start with Data GovernanceNUS-ISS
The document discusses starting data governance from where an organization currently is. It highlights common questions around data that indicate a need for governance. The document then discusses using data to improve operations, customer experience, and more. It also outlines how data is changing different industries. The rest of the document discusses data governance in more detail, including defining it, ensuring data quality, and addressing compliance and security challenges. It provides examples of legislation and offers a framework for establishing an effective data governance program.
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.
Self-service data analytics enables business users to access and analyze corporate data without needing expertise in data analysis, business intelligence, or data mining. It provides an easy-to-use platform for users to prepare, blend, and analyze data using a repeatable workflow and then deploy and share analytics. The benefits of self-service data analytics include faster time to insights, no need for upfront data modeling, a user interface designed for non-technical users, and the ability to connect to more data sources.
Embedded business intelligence involves integrating self-service BI tools directly into commonly used business applications. This allows for enhanced user experience with visualization, real-time analytics and interactive reporting directly within applications. Embedded BI aims to make business
This document discusses the growth of data and analytics capabilities. It notes that data storage capacity is growing at 23% annually while computing capacity is growing at 54% annually. Lower barriers to connectivity are integrating different sources of data. The document discusses how Right Brain Systems uses analytics to build smarter organizations by focusing on data foundation, information design, analytics capabilities, operational framework, and business ownership. It provides examples of how different types of analytics can be applied to key areas like customers, operations, finance, and workforce.
To be updated is not enough for companies today. Organizations must be constantly watching also to the trends in order to predict and forecast the next steps for their business. The following document is a Executive Summary of the current situation but also of the more notable trends that will help to understand the basics of the Analytics Market
The document summarizes the top 10 trends in analytics according to Tiger Analytics. It discusses how data enrichment is allowing for better forecasts using internal and external data. It also covers how unstructured data like text, images, audio and video are opening new opportunities. Other trends discussed include the growth of speech/voice analytics, data lakes, big data and IoT analytics, the proliferation of analytic tools including open source tools, new visualization tools, full-pipeline analytics automation, and new decision systems using artificial intelligence.
The document discusses several key trends in analytics for 2015:
1. Data security is a major concern as data volumes grow exponentially, requiring companies to quadruple down on security efforts through innovation, analytics, and tighter integration.
2. The rise of the Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating these systems.
3. Some argue that data should be monetized as an asset, but this brings risks around privacy, ethics, and real costs that companies need to consider carefully.
4. Cognitive analytics is enhancing decision-making by providing users with vast new sources of knowledge, though questions remain about how these systems will impact human roles over time
Guide to Data Analytics: The Trend That's Reshaping the Insurance IndustryApplied Systems
Information you need is in your management system –- you just have to understand how to use it. Read this guide to learn what data analytics is, how it's impacting the insurance industry, why it's important for independent agencies and brokerages, and how to create your own data analytics strategy.
NUS-ISS Learning Day 2018- Start with Data GovernanceNUS-ISS
The document discusses starting data governance from where an organization currently is. It highlights common questions around data that indicate a need for governance. The document then discusses using data to improve operations, customer experience, and more. It also outlines how data is changing different industries. The rest of the document discusses data governance in more detail, including defining it, ensuring data quality, and addressing compliance and security challenges. It provides examples of legislation and offers a framework for establishing an effective data governance program.
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.
Self-service data analytics enables business users to access and analyze corporate data without needing expertise in data analysis, business intelligence, or data mining. It provides an easy-to-use platform for users to prepare, blend, and analyze data using a repeatable workflow and then deploy and share analytics. The benefits of self-service data analytics include faster time to insights, no need for upfront data modeling, a user interface designed for non-technical users, and the ability to connect to more data sources.
Embedded business intelligence involves integrating self-service BI tools directly into commonly used business applications. This allows for enhanced user experience with visualization, real-time analytics and interactive reporting directly within applications. Embedded BI aims to make business
How CIOs are thinking about big data and the major opportunities, challenges and threats they face in managing the analytics unstructured information. Based on a survey of Canadian IT leaders
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.
This document summarizes the results of a survey of 298 data management professionals about big data challenges and opportunities. It finds that big data is now present in all organizations, with 11% managing over a petabyte and another 20% having hundreds of terabytes. While bigger companies are dealing with more data currently, most companies will soon have petabyte-sized stores. However, the business does not fully understand big data's potential value yet. Capitalizing on big data does not require replacing existing infrastructure but integrating it. The survey also finds that relational databases and Hadoop are both important technologies for managing big data now and in the future.
Data-Driven is Passé: Transform Into An Insights-Driven EnterpriseDenodo
This document summarizes a presentation on transforming companies into insights-driven enterprises. It discusses how most companies are currently data-driven but struggle to consistently turn data into effective actions. An insights-driven approach involves building multidisciplinary insights teams, establishing good data governance foundations, and combining the right tools and processes into systems of insight. Data virtualization is highlighted as a key technology enabler for systems of insight by providing agile data access and logical abstraction across structured and unstructured data sources. Examples are provided of how data virtualization has helped customers achieve single customer views and build logical data warehouses.
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
GGV Capital: Venture Investing and the Cloud (2012)GGV Capital
This document discusses venture investing in cloud computing. It provides an overview of why VCs continue to see opportunities in the cloud sector. The presentation agenda covers trends disrupting the cloud like mobile and big data, as well as opportunities in serving small and medium businesses. The document concludes with advice for cloud startups on effectively approaching VCs for funding, emphasizing differentiation, market size, scalability, financial model, and chemistry over legal terms.
Big Data has moved beyond being just a buzzword. Organizations are operationalizing various Big Data technologies to answer critical business questions and power sophisticated workloads.
Building on the success of their 2012 “Big Data Comes of Age” research report, EMA VP of Research, Shawn Rogers, EMA Senior Analyst, John Myers, and 9sight Consulting Founder and Principal, Dr. Barry Devlin, will reveal their latest big data research findings during this informative Webinar.
Attendees will learn not just the what's of Big Data technologies but also the why’s of use cases, implementation strategies and technology choices, as well as discover:
>>Most popular use cases for big data based on nearly 600 projects reviewed in this research
>>Which Hadoop distributions are gaining traction
>>The technical and business-driven-challenges for Big Data
>>Most popular data sources for Big Data
>>How organizations are continuing the trend of implementing the EMA Hybrid Data Ecosystem (HDE) in association with their Big Data initiatives
This document discusses data governance challenges in the era of big data and proposes solutions. It begins by outlining the rise of data-driven businesses and the challenges they face with data quality, access, and trust issues. This has led to the rise of the Chief Data Officer role. The document then discusses how data governance approaches need to shift from hierarchical systems of record to more networked systems of engagement to manage expanding data volumes and types from sources like IoT and big data analytics. Key challenges discussed include digitalizing trust in data and addressing risks from opaque big data models. The document proposes taking a hybrid governance approach and implementing a system of record for data assets to provide findability, understandability and trust for all organizational data. Example use
The Myth of Health Data Integration ComplexityShahid Shah
At Health:Refactored (San Francisco) I presented a practical and technical look at why current health IT systems integrate poorly and how we can fix it.
Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.
MIT ICIQ 2017 Keynote: Data Governance and Data Capitalization in the Big Dat...Pieter De Leenheer
We live in the age of abundant data. Through technology, more data is available, and the processing of that data easier and cheaper than ever before. But to realize the true value of this wealth of data, data leaders must rethink our assumptions, processes, and approaches to managing, governing, and stewarding that data. And to succeed, they must deliver credible, coherent, and trustworthy data into the hands of everyone who can use it.
The document discusses the ethics of big data and how it relates to robotics. It defines big data as data that is large enough to raise practical concerns about its ethical use rather than just theoretical concerns. It also discusses how big data and the internet of things can provide the foundation of knowledge for robots to act upon. Finally, it discusses some of the ethical issues around big data, such as privacy, ownership of data, transparency, and ensuring fair and non-discriminatory use of people's data.
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.
Webinar: The Hybrid Data Ecosystem: Are You Battling an Illogical Data Wareho...SnapLogic
In this webinar, we hear from John Myers of Enterprise Management Associates about the drivers associated with big data implementations, evolving technical requirements for big data environments such as the Hybrid Data Ecosystem and how a robust information management layer is important to big data projects.
We also discuss how big data is evolving as a practice and we are quickly approaching a point at which data will be treated as a single source rather than divided between analytic and operational, big data and traditional enterprise data types, and multi-structured data stores and relational databases.
To learn more, visit: www.snaplogic.com/big-data
NUS-ISS Learning Day 2018- Business agility for business leadersNUS-ISS
VisionLed Consulting provides end-to-end solutions for products and services through agile teams. Their presentation discusses the need for business agility and a hybrid centralized-decentralized model. Their approach emphasizes 7 principles: continuous improvement, empowering people, autonomous teams, simplifying processes, being data-driven, quick delivery through iteration, and work-in-progress management. Techniques include value stream mapping, minimum viable processes, customer metrics, experiments, and kanban boards. The goal is to shift from top-down governance to empowered agile teams through a structured set of rules and roadmap for organizational transformation.
TiE DC GovCon Panel on Emerging Technologies: AI/ML/Blockchain/Data Managemen...Pieter De Leenheer
Pieter De Leenheer presented on the future of data and policy. Technologies have created new ways to use and value data. Data collection, processing, and sharing responsibilities have diffused and control of data has become less institutional. The cycles of data technology and policy have compressed in time. Public health crises will likely drive new data regulations, as the 2008 financial crisis and COVID-19 pandemic have previously done. There are opportunities to create value from AI but also increasing ethical and regulatory challenges to address regarding data use and privacy concerns. Multi-platform innovation, data sharing protocols, smart contracts, and education can help balance data innovation and policy requirements going forward.
The document discusses trends in big data and analytics. It notes that continuous transformation is the new normal due to converging technology disruptors that create opportunities but also threaten business models. IBM's response is focused on its four key plays of cloud, big data, social and mobile. Harnessing all data requires shifting thinking and evolving approaches to leverage all information from all perspectives for all decisions across all departments. Initial big data efforts often focus on gaining insights from existing internal data sources. The document outlines five patterns resulting from high value big data initiatives such as exploring all big data to improve business knowledge or achieving a complete unified view of the customers.
This document discusses the need for combining big data and ethnography. It summarizes that big data alone is not sufficient and can lead to issues like bias, incomplete representations, and misinterpretations without context. Ethnography provides thick contextual data through open-ended research methods like observation and interviews. The document advocates a mixed methods approach, using ethnography to understand problems and qualitative insights to complement and validate quantitative big data findings. This leads to more human and reflective understandings that benefit both innovation and optimization.
Arkuda Digital is a high-end software company, a global provider of wireless Media Network solutions to Consumer Electronics ODM/OEMs, Cable and Telecom Operators, IPTV and STB solution providers and consumers. We provide products and solutions for manufacturers of consumer electronics: PCs, TVs, mobile devices, media storages, digital set-top boxes, game consoles, automotive media, digital photo frames, etc.
Our standards-based software enables connectivity with UPnP, DLNA, Allshare compliant devices, allowing consumers to easily discover, manage and enjoy their digital content on devices with rich functionality and seamless integration capabilities as fast as possible.
How CIOs are thinking about big data and the major opportunities, challenges and threats they face in managing the analytics unstructured information. Based on a survey of Canadian IT leaders
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.
This document summarizes the results of a survey of 298 data management professionals about big data challenges and opportunities. It finds that big data is now present in all organizations, with 11% managing over a petabyte and another 20% having hundreds of terabytes. While bigger companies are dealing with more data currently, most companies will soon have petabyte-sized stores. However, the business does not fully understand big data's potential value yet. Capitalizing on big data does not require replacing existing infrastructure but integrating it. The survey also finds that relational databases and Hadoop are both important technologies for managing big data now and in the future.
Data-Driven is Passé: Transform Into An Insights-Driven EnterpriseDenodo
This document summarizes a presentation on transforming companies into insights-driven enterprises. It discusses how most companies are currently data-driven but struggle to consistently turn data into effective actions. An insights-driven approach involves building multidisciplinary insights teams, establishing good data governance foundations, and combining the right tools and processes into systems of insight. Data virtualization is highlighted as a key technology enabler for systems of insight by providing agile data access and logical abstraction across structured and unstructured data sources. Examples are provided of how data virtualization has helped customers achieve single customer views and build logical data warehouses.
Analytics is all about course correcting the future. While this starts with accurate predictions of the future, without resultant actions steering the future toward company goals, knowing that future is academic. Successful companies must be grounded in successful data-based prescription. In this webinar, William will present a data maturity model with a focus on how analytic competitors outdo the competition by looking forward to a data-influenced future.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
GGV Capital: Venture Investing and the Cloud (2012)GGV Capital
This document discusses venture investing in cloud computing. It provides an overview of why VCs continue to see opportunities in the cloud sector. The presentation agenda covers trends disrupting the cloud like mobile and big data, as well as opportunities in serving small and medium businesses. The document concludes with advice for cloud startups on effectively approaching VCs for funding, emphasizing differentiation, market size, scalability, financial model, and chemistry over legal terms.
Big Data has moved beyond being just a buzzword. Organizations are operationalizing various Big Data technologies to answer critical business questions and power sophisticated workloads.
Building on the success of their 2012 “Big Data Comes of Age” research report, EMA VP of Research, Shawn Rogers, EMA Senior Analyst, John Myers, and 9sight Consulting Founder and Principal, Dr. Barry Devlin, will reveal their latest big data research findings during this informative Webinar.
Attendees will learn not just the what's of Big Data technologies but also the why’s of use cases, implementation strategies and technology choices, as well as discover:
>>Most popular use cases for big data based on nearly 600 projects reviewed in this research
>>Which Hadoop distributions are gaining traction
>>The technical and business-driven-challenges for Big Data
>>Most popular data sources for Big Data
>>How organizations are continuing the trend of implementing the EMA Hybrid Data Ecosystem (HDE) in association with their Big Data initiatives
This document discusses data governance challenges in the era of big data and proposes solutions. It begins by outlining the rise of data-driven businesses and the challenges they face with data quality, access, and trust issues. This has led to the rise of the Chief Data Officer role. The document then discusses how data governance approaches need to shift from hierarchical systems of record to more networked systems of engagement to manage expanding data volumes and types from sources like IoT and big data analytics. Key challenges discussed include digitalizing trust in data and addressing risks from opaque big data models. The document proposes taking a hybrid governance approach and implementing a system of record for data assets to provide findability, understandability and trust for all organizational data. Example use
The Myth of Health Data Integration ComplexityShahid Shah
At Health:Refactored (San Francisco) I presented a practical and technical look at why current health IT systems integrate poorly and how we can fix it.
Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.
MIT ICIQ 2017 Keynote: Data Governance and Data Capitalization in the Big Dat...Pieter De Leenheer
We live in the age of abundant data. Through technology, more data is available, and the processing of that data easier and cheaper than ever before. But to realize the true value of this wealth of data, data leaders must rethink our assumptions, processes, and approaches to managing, governing, and stewarding that data. And to succeed, they must deliver credible, coherent, and trustworthy data into the hands of everyone who can use it.
The document discusses the ethics of big data and how it relates to robotics. It defines big data as data that is large enough to raise practical concerns about its ethical use rather than just theoretical concerns. It also discusses how big data and the internet of things can provide the foundation of knowledge for robots to act upon. Finally, it discusses some of the ethical issues around big data, such as privacy, ownership of data, transparency, and ensuring fair and non-discriminatory use of people's data.
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.
Webinar: The Hybrid Data Ecosystem: Are You Battling an Illogical Data Wareho...SnapLogic
In this webinar, we hear from John Myers of Enterprise Management Associates about the drivers associated with big data implementations, evolving technical requirements for big data environments such as the Hybrid Data Ecosystem and how a robust information management layer is important to big data projects.
We also discuss how big data is evolving as a practice and we are quickly approaching a point at which data will be treated as a single source rather than divided between analytic and operational, big data and traditional enterprise data types, and multi-structured data stores and relational databases.
To learn more, visit: www.snaplogic.com/big-data
NUS-ISS Learning Day 2018- Business agility for business leadersNUS-ISS
VisionLed Consulting provides end-to-end solutions for products and services through agile teams. Their presentation discusses the need for business agility and a hybrid centralized-decentralized model. Their approach emphasizes 7 principles: continuous improvement, empowering people, autonomous teams, simplifying processes, being data-driven, quick delivery through iteration, and work-in-progress management. Techniques include value stream mapping, minimum viable processes, customer metrics, experiments, and kanban boards. The goal is to shift from top-down governance to empowered agile teams through a structured set of rules and roadmap for organizational transformation.
TiE DC GovCon Panel on Emerging Technologies: AI/ML/Blockchain/Data Managemen...Pieter De Leenheer
Pieter De Leenheer presented on the future of data and policy. Technologies have created new ways to use and value data. Data collection, processing, and sharing responsibilities have diffused and control of data has become less institutional. The cycles of data technology and policy have compressed in time. Public health crises will likely drive new data regulations, as the 2008 financial crisis and COVID-19 pandemic have previously done. There are opportunities to create value from AI but also increasing ethical and regulatory challenges to address regarding data use and privacy concerns. Multi-platform innovation, data sharing protocols, smart contracts, and education can help balance data innovation and policy requirements going forward.
The document discusses trends in big data and analytics. It notes that continuous transformation is the new normal due to converging technology disruptors that create opportunities but also threaten business models. IBM's response is focused on its four key plays of cloud, big data, social and mobile. Harnessing all data requires shifting thinking and evolving approaches to leverage all information from all perspectives for all decisions across all departments. Initial big data efforts often focus on gaining insights from existing internal data sources. The document outlines five patterns resulting from high value big data initiatives such as exploring all big data to improve business knowledge or achieving a complete unified view of the customers.
This document discusses the need for combining big data and ethnography. It summarizes that big data alone is not sufficient and can lead to issues like bias, incomplete representations, and misinterpretations without context. Ethnography provides thick contextual data through open-ended research methods like observation and interviews. The document advocates a mixed methods approach, using ethnography to understand problems and qualitative insights to complement and validate quantitative big data findings. This leads to more human and reflective understandings that benefit both innovation and optimization.
Arkuda Digital is a high-end software company, a global provider of wireless Media Network solutions to Consumer Electronics ODM/OEMs, Cable and Telecom Operators, IPTV and STB solution providers and consumers. We provide products and solutions for manufacturers of consumer electronics: PCs, TVs, mobile devices, media storages, digital set-top boxes, game consoles, automotive media, digital photo frames, etc.
Our standards-based software enables connectivity with UPnP, DLNA, Allshare compliant devices, allowing consumers to easily discover, manage and enjoy their digital content on devices with rich functionality and seamless integration capabilities as fast as possible.
Dall’assessment tecnologico alla business intelligence: approccio integrato e...AREA Science Park
Dall’assessment tecnologico alla business intelligence: approccio integrato e nuovi strumenti per tracciare il percorso d’innovazione
Stephen Taylor
Direttore Servizio Trasferimento Tecnologico AREA Science Park, Trieste
Presentazione Tesi di Laurea "Il Ruolo dei Social Media nelle organizzazioni moderne: profili comportamentali e social organization".
Professore Garraffo Francesco
Marzo 2015
The document covers the all the aspect related to IPTV set top boxes. In this article we would look at the following
aspects:-
• CUSTOMER TRENDS & GROWTH TRAJECTORY
• CONDUCIVE MARKET FOR GROWTH
• MARKET ANALYSIS
• ARCHITECTURE
• STANDARDS
• BUNDLING STRATEGIES
• STB VENDOR CAPABILITIES
• STB VENDOR SELECTION CRITERIA
• RECOMMENDATIONS FOR OPERATORS AND VENDORS
Este documento presenta el planteamiento de un proyecto sobre la crisis eléctrica en Venezuela. Describe el contexto general y específico del sistema eléctrico venezolano, identifica los problemas como los constantes apagones y la insuficiencia de generación. Justifica la investigación para encontrar soluciones a corto y largo plazo. Detalla las causas como falta de mantenimiento e inversión, y las consecuencias como impactos en la producción y vida cotidiana. Presenta la metodología que incluye actualizar los planes de desarrollo elé
DLP (Data Loss Protection) is NOT dead, but needs to be revisited in the context of new methodologies and threats. Here are some practical steps to improve your cybersecurity awareness and response to data loss.
This document discusses different sampling techniques that can be used in a thesis. It defines key terms like population, sample, parameter, and statistic. It explains that sampling is necessary when it is impossible or too costly to study the entire population. The document outlines probability sampling methods like simple random sampling, systematic sampling, stratified sampling, multistage sampling, and cluster sampling. It also discusses non-probability sampling techniques such as convenience sampling, purposive sampling, and quota sampling. Probability samples aim for randomness while non-probability samples rely on availability or purpose.
This presentation deals with the recent advancement in the field of ground water sampling and analysis technique and water born survey as well as Indian scenario to interpret.
10 Enterprise Analytics Trends to Watch in 2020MicroStrategy
As businesses face a 2020 reality check and use this year to hone their strategy for the next decade, MicroStrategy has compiled insights on the top enterprise analytics trends to watch from leading BI, analytics and digital transformation influencers including analysts from Forrester, IDC, Constellation Research, Ventana Research and more.
From artificial intelligence and mobile intelligence, to the explosion of data and data sources, to some very human factors, we hope you’ll find this gathering of insights (plus the patterns and themes that have emerged here) a valuable resource for taking action now, but also looking and planning ahead to become an Intelligent Enterprise.
The document discusses six emerging trends in business analytics:
1. Humans and machines will increasingly work together in complementary roles, with machines handling tasks like data processing and humans focusing on creativity, empathy, and oversight of machine performance.
2. Analytics capabilities are expanding across entire organizations, moving from isolated initiatives to enterprise-wide strategies aimed at creating "insight-driven organizations."
3. Cybersecurity is becoming more important and proactive, utilizing predictive analytics to anticipate threats rather than just reacting to attacks.
4. The Internet of Things is expanding to include people and generating new business models by aggregating and analyzing behavioral data.
5. Companies are getting creative in addressing talent shortages, collaborating more closely
Analytics trends 2016 the next evolutionYann Lecourt
The document discusses six emerging trends in business analytics:
1. Humans and machines will increasingly work together in complementary roles, with machines handling tasks like data processing and humans focusing on creativity, empathy, and oversight of machine performance.
2. Analytics capabilities are expanding across entire organizations to create "insight-driven organizations" and scale initiatives from targeted areas to the enterprise level.
3. Cybersecurity is becoming more important as threats evolve, requiring proactive approaches like predictive modeling rather than just reactive defenses.
4. The Internet of Things is expanding to include people and generating new business models by aggregating and analyzing behavioral data.
5. Companies are addressing talent shortages by cultivating external talent providers and collaboration with
Big data refers to large and complex data sets that are difficult to manage and analyze using traditional data management tools. It is generated from various sources like social media, scientific instruments, mobile devices, and sensor technology. Big data provides opportunities for insights and smart solutions but also poses challenges in processing, analyzing, and gaining insights from such large volumes of data. For managers in India, big data is highly relevant as the Indian analytics industry is growing rapidly and is expected to reach $16 billion by 2025, with big data being a major driver of growth in industries. Digitalization is also expanding the big data market in India as internet and smartphone usage increases across more regions of the country.
This document discusses new ways of handling old data and unlocking value from unstructured content through cognitive systems. It provides predictions for big data and analytics spending and adoption through 2020. Key points include:
- 90% of digital information is unstructured content stored in separate repositories that don't communicate.
- By 2020, 50% of business analytics software will incorporate prescriptive analytics using cognitive computing.
- Organizations that can analyze all relevant data and provide actionable insights will gain $430 billion in productivity over less analytical peers.
- Cognitive software can support better decision-making by applying broader evidence without bias to situations.
- The cognitive software market is expected to grow rapidly over the next five
Data science and its potential to change business as we know it. The Roadmap ...InnoTech
The document summarizes a presentation on data science and its potential to change business. It discusses how organizations can increase their data science maturity and capabilities to gain more value from data. As data volumes continue growing exponentially, data science can help organizations move from simple reporting to predictive analytics in order to make real-time decisions. The presentation examines how data science is an emerging field that incorporates techniques from many areas and how organizations can assess their analytics maturity.
This document discusses big data analytics and its use in digital marketing. It begins by introducing big data and how early adopters like Google, eBay, and Facebook were built around big data. It then discusses how both individuals and companies now generate and consume large amounts of data. Examples are given of how much data companies like Google and Facebook process daily. The characteristics of big data are described. Traditional analytics are compared to big data analytics. Applications of big data analytics are discussed for various sectors like retail, healthcare, and government. Specific examples are provided of how analytics can provide insights from website visitors. The challenges and power of big data are also summarized before concluding with references.
The document discusses alternative data and its importance. It defines alternative data as data derived from non-traditional sources like mobile devices, websites, and sensors. This data can provide insights that complement traditional sources and help with decision-making. The document outlines 8 types of alternative data and 3 ways to access it, including hiring a data scientist, partnering with a third party, or using web scraping software. It provides examples of alternative data's applications in advertising, tracking corporate revenues, risk assessment, and more. Overall, the document promotes alternative data as a valuable new resource for businesses seeking a competitive edge.
This document summarizes a presentation by PwC on artificial intelligence and its applications and risks in the legal services industry. The presentation covers how AI can be used for tasks like legal research, e-discovery, contracts management, and compliance. It also discusses challenges of AI adoption like data and tool issues. Risks of AI like bias, lack of explainability, and job disruption are examined. The document concludes with a proposed breakout session for the event attendees to analyze which legal tasks could be automated or augmented with AI.
This document provides an introduction to data science. It discusses what data science is, its importance and career opportunities. Data science is an interdisciplinary field that uses algorithms and processes to examine large amounts of data to uncover patterns and insights. The rise of data science is creating many new job openings and it will impact many industries. Data science helps brands understand customers and communicate engagingly. It also discusses applications of data science in healthcare, transportation, sports, government and other industries. Various techniques used in data science are also mentioned.
Big Data & Business Analytics: Understanding the MarketspaceBala Iyer
This document provides an overview of big data and business analytics. It discusses the growth of data and importance of analytics to businesses. The key topics covered include defining big data and data science, analyzing the analytics ecosystem and key players, examining use cases of analytics at companies like Target and Whirlpool, and providing recommendations for building an analytics capability and working with analytics vendors. The presentation emphasizes how data-driven decisions can improve business performance but also notes challenges to overcome like skills shortages and changing organizational culture.
“People analytics” is a frequently used buzzword. But questions remain as to why this is becoming such a prominent challenge for HR. What are leading organizations doing to develop their understanding of how data analytics can drive better people decisions? In this session, learn what you can start doing tomorrow to accelerate and mobilize your people analytics efforts.
Learning Objectives
• Learn the research and trends in data & analytics.
• Learn what is driving the people analytics movement.
• Learn the barriers to entry for companies.
• Learn how to mobilize your efforts in building out your people & analytics capabilities.
Speaker: Diego Gomez, Vice President of Human Capital Management Transformation, Oracle
Big Data Analytics in light of Financial Industry Capgemini
Big data and analytics have the potential to transform economies and competition by delivering new productivity growth. Effective use of big data can increase operating margins over 60% for retailers and save $300 billion in US healthcare and $250 billion in European public sector. Companies that improve decision making through big data have seen a 26% performance improvement over 3 years on average. Emerging technologies like self-driving cars will rely heavily on analyzing vast amounts of real-time sensor data.
The document discusses six analytics trends that are likely to influence business in coming years:
1. Analytics is expanding across enterprises as organizations move towards becoming insight-driven.
2. Cognitive technologies and machines are evolving to work alongside humans in complementing roles.
3. Cybersecurity is becoming more predictive and proactive to anticipate threats.
4. The Internet of Things is enabling new innovations through aggregating and analyzing sensor data.
5. Companies are taking creative steps to address the shortage of analytics talent.
6. Analytics success requires a mix of both new and familiar topics as analytics becomes embedded in decision making.
BI, AI/ML, Use Cases, Business Impact and how to get startedKarthick S
This deck contains insights on Impact of Business Intelligence, Visualization, Artificial Intelligence, Machine Learning, Deep Learning, Use Cases and how to get started...
Organizations in a Future with Generative AIKye Andersson
The document discusses how organizations can prepare for a future with generative AI. It begins by acknowledging people's mixed feelings about such a future and lack of clarity on how jobs may change. It then defines AI and generative AI, providing examples of how the latter can automate tasks like content generation, customer support, and sales. The document also notes challenges of rapid change and two potential AI futures - continuing existing approaches or finding new solutions beyond human limitations. It emphasizes that constant change requires AI to help supercharge organizations, people, and adaptability, but getting there requires ownership and understanding of AI at the top of organizations and shifts in culture, goals, and autonomy.
- Data has been seen as a competitive advantage for some early adopter companies like Tesco, but many organizations have struggled to realize benefits from their AI investments.
- Developing a well-defined data and AI strategy is important to generate value and competitive advantages, but large organizations face challenges with change management, talent, data issues, and integrating models.
- High performing AI companies focus on vision, talent, governance processes, standardized platforms, understanding how to move from pilots to production, and measuring comprehensive metrics.
Presentation that I delivered at "Accelerate AI, Europe 2018" in London on Sept 19, 2018. My focus is on socio-cultural perspective as well as proving information about various tools, vendors and partners available to help companies get started using AI.
The document discusses big data, including what it is, why it is exciting, key concerns around security and privacy, and recommended measures to address these concerns. It was written by Keith Prabhu, who is the Executive Director of Confidis Advisory Services and Founder & Director of the Cloud Security Alliance Mumbai Chapter. The document provides an overview of big data and security issues and recommendations related to big data.
This document discusses the topics of implementation and maintenance, reliability, and quality for Week 13 of an Introduction to Computer Software Engineering course. It covers the implementation process, including equipment installation, training, conversion procedures, and post-implementation evaluation. It also discusses maintenance types, reliability, and software quality, defining them and listing characteristics of quality. Readings are assigned from the textbook on these topics.
This document provides an overview of topics to be covered in a software engineering course, including coding and debugging, software testing. The weekly schedule lists topics like the software development life cycle, requirements analysis, object-oriented analysis and design, coding and debugging, and software testing to be covered over 14 weeks. The sections on coding and debugging discuss writing clear and efficient code, testing code, programming language characteristics. The software testing section describes objectives of testing like identifying defects, and testing phases from analysis to implementation. Readings from the textbook on related topics are also assigned.
This document discusses the topics of interfaces, dialogs, and databases (IID) covered in Week 11 of the course. It covers key concepts like user interface design, logical data modeling, and the relational data model. The basics of Entity-Relationship diagrams are explained including entities, entity types, and relationship types. User interface design considerations like forms, controlling input, and providing feedback and help are outlined. Database design models like hierarchical, network, and relational models are also summarized.
This document discusses object-oriented analysis and design (OOAD). It introduces key OOAD concepts like classes, objects, abstraction, encapsulation, inheritance, polymorphism, and associations. It explains that OOAD implements object-oriented analysis to develop an object model of the problem, object-oriented design to develop a model of the solution, and object-oriented programming to develop the model using an OO language. The document also discusses using the Unified Modeling Language (UML) for OOAD, with examples of use case diagrams and class diagrams. It provides external readings on OOAD and gives homework questions related to analyzing a class diagram for an elevator system.
This document outlines the weekly schedule and topics for an introduction to computer software engineering course. Week 9 covers software project analysis and design (SAD). SAD involves mapping requirements from a software requirements specification document to a software design. The design shows how requirements may be met through different abstraction levels from architectural to high-level to detailed. Software design relies on concepts like abstraction, modularity, and information hiding. Considerations like maintainability, testability, and usability also factor into the design. Common SAD methodologies are outlined as well as tools like data flow diagrams and data dictionaries. Readings and examples of SAD are referenced. Homework involves developing a data flow diagram for a project management example.
This document provides an overview of the weekly schedule and topics for an introductory computer software engineering course. The course covers topics like the software development life cycle, requirements analysis, software project management, object-oriented analysis and design, coding and debugging, and software testing. It also lists readings and project management tools. Students are assigned homework to develop a basic Gantt chart for a sample project using project management software.
The document discusses the software development life cycle (SDLC), including its key phases and methodologies. It provides an overview of the SDLC, describing the typical phases as requirement gathering and analysis, software design, coding and implementation, testing, and maintenance. Various SDLC methodologies are mentioned, such as the waterfall model, iterative model, and spiral model. Terminology related to the SDLC and software engineering is also defined.
This document provides an overview of engineering, software engineering, and ethics. It defines engineering as applying science and math to solve problems, and software engineering as a systematic approach to designing, developing, and maintaining software systems. The document outlines the objectives of software engineering like maintainability, correctness, reusability, and reliability. It also discusses ethics in software engineering, highlighting principles like being impartial, respecting others, and maintaining integrity.
This document discusses requirements analysis and software specification. It covers eliciting requirements through interviews, focus groups, and questionnaires. Requirements are then analyzed, recorded, and documented in a Software Requirements Specification (SRS). The SRS is a complete description of the target system's functional and non-functional requirements. Joint Application Development, Group Support Systems, and Computer-Aided Software Engineering are modern methods used for requirements analysis. Requirements documentation serves many purposes for all roles involved in software production.
The document provides information about the 8th International Conference on Security of Information and Networks (SIN 2015) to be held September 8-10, 2015 in Sochi, Russia. It calls for submissions of papers, special sessions, tutorials, and workshops on topics related to security of information and networks. Important dates include a May 11, 2015 deadline for paper submissions and a July 1, 2015 deadline for author registrations. The conference is organized by universities and research institutions from Russia, the UK, Turkey, India, and Australia and will bring together international experts on security theory, technology and applications.
Sunu unikop2014-elçi tolunakgünsarıuzunAtilla Elçi
Elektrik, Elektronik ve Bilgisayar Alanlarında Üniversite-Sanayi Etkileşimi için Öneriler: -Aksaray Bağlamı
Haberleşme ve Bilişim Alanı: Çağrılı Konuşma
UNIKOP 2014- II. KOP Bölgesel Kalkınma Sempozyumu
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
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.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
1. Big Data & Analytics:
What is Ahead?
Panel, WEDA Symposium
@ 40th COMPSAC
June 14th, 2016,
Atlanta, GA, USA
AtillaElçi,AksarayUniv.
1
2. Agenda
• Big Data Analytics is in Your Future!
• Don’t Give Me Data!
• BDA in Your Health
• Workplace 2026
• 100 BDA Predictions Through 2020
• BDA Futures in APEJ through 2020
• Big Data Digs Big Holes:
• Of Security & Privacy
• Privacy Preserving Analytics
• Lapses/Cases
• How to Approach the Issues?
• So what?
• BDA: tool or outcome?
• How-to Guide
• Advanced Analytics
• A Little Help from AI
• Edge BDA?
• BDA Goes Better with Semantics
AtillaElçi,AksarayUniv.
2
3. BDA is in Your Future!
• “Never give me data. Only provide me with information.”
Anon. (http://www.clevity.com/it-is-not-analytics/)
AtillaElçi,AksarayUniv.
3
4. BDA in Your Health
• "Medicine in the near future will be predictive, preventive, and
personalized thanks to big data-driven analysis. " SI in Omics-Based
Medicine
• Healthcare technology in 2026 will facilitate access to GP and
hospital records online by patients routinely- just as online
banking today.
• Individuals’ health will be linked to:
• Environmental data obtained through monitors of public transport,
airports, hospitals, rural location and other places of interest for the
appearance and evolution of viruses.
• Compared to continuously collected vital data from millions of
patients around the world.
• Medical conditions will be diagnosed in that perspective.
IDG Connect: What will health tech mean…
AtillaElçi,AksarayUniv.
4
5. Workplace 2026?
• “The workplace of the future will be 360 degrees and 24/7…”
• “In 2026 the work place will be smart.”
• “The biggest transformation will be change in mindset. ”
• “Data analytics and visual analytics tools will be as ubiquitous
as word processors are today, and there will be a seismic shift
in working culture whereby it will be unacceptable for
decisions to be made based simply on assumption or ‘gut
instinct’.”
AtillaElçi,AksarayUniv.
5
«What will the workplace of 2026 look like?»
6. 100 BDA Predictions Through 2020ByGartner
• Of Core Analytics Predictions:
• Advanced Analytics and Data Science: Advanced Analytics Are at
the Beating Heart of Algorithmic Business:
• «Advanced analytics solutions are becoming increasingly popular in
driving business innovation and experimentation, and creating
competitive advantage. Analytics leaders must now exploit new
business models and ecosystems that will drive the operation of
algorithmic business.»
• Business Intelligence: Changes Coming in How We Buy Business
Analytics Technology:
• «Changes to the business intelligence and analytics platform market
will include further bundling of next-generation capabilities along
with a major emphasis on product trials in the vendor selection
process.»
AtillaElçi,AksarayUniv.
6
7. BDA Futures in APEJ through 2020ByIDC
1. Cloud BDA
2. Cognitive
3. Labor Shortage
4. In-Memory Computing
5. Distributed Micro Analytics
6. Self-Service
7. Data Monetization
8. Analyzable Data
9. Actionable Information
10. BDA Value
AtillaElçi,AksarayUniv.
7Li, Zhang, & Chua, Dec. 2015
8. 1. Cloud BD&A. Spending on cloud-based BDA technology will grow 3x faster
than that for on-premises solutions; open source technology will be core.
2. Cognitive Computing. 40% of all business analytics software will
incorporate prescriptive analytics built on cognitive computing functionality.
3. Labor shortage of data scientists to architects and experts in data
management; Big Data–related professional services will have a 29% CAGR.
4. In-Memory Computing. 75% of databases will be based on memory-
optimized technology.
5. Distributed Micro Analytics. Distributed micro analytics and data
manipulation will be part of 80% of Big Data and analytics deployments.
6. Self-Service. Spending on self-service visual discovery and data preparation
market will grow 2.5x faster than traditional IT-controlled tools for similar
functionality.
7. Data Monetization. Enterprises will pursue digital transformation
initiatives, increasing the marketplace's consumption of their own data by
100-fold or more.
8. Analyzable Data. The high-value data that is worth analyzing to achieve
actionable intelligence will double.
9. Actionable Information. 40% of information delivered to decision makers
will be considered by them as always actionable, doubling the rate from the
current (2015) level.
10. BDA Value. Organizations using BDA will achieve an extra US$65 billion in
productivity benefits over their less analytically-oriented peers.
AtillaElçi,AksarayUniv.
8
9. Big Data Digs Big Holes
• Of Security & Privacy
• Privacy Preserving Analytics
• Lapses/Cases
• How to Approach the Issues?
AtillaElçi,AksarayUniv.
9
10. Of Security/Privacy
• «These days, when people over 80 in Beijing take a bus, see a
doctor or spend money, their activities are digitally tracked by
the government, as part of an effort to improve services for
the country's rapidly growing elderly population.» Wat, 2016.
• Today’s initiative, tomorrows standard: ‘Smart homes’:
appliances, utility consumption, security systems, all media
sources are all connected and monitored via our smartphones,
tablets and smartwatches not to mention remote
management service sites. How to maintain a privacy-
preserved safe environment? (Vickery, 2016)
• «A service like IBM's Personality Insights can build a detailed
profile of you, moving well beyond basic demographics or
location information.» Ryoo, 1016.
AtillaElçi,AksarayUniv.
10
11. Of Security/Privacy
• The President’s Council of Advisors on Science and Technology
(PCAST):
• Indicated that «the privacy challenges big data poses in a world
where technologies for re-identification often outpace privacy-
preserving de-identification capabilities»
• Recommended «adopting policies that stimulate the use of
practical privacy-protecting technologies» PCAST 2014.
AtillaElçi,AksarayUniv.
11
12. Lapses/Cases
• «Big data for categorizing people should be used with caution»
• «… big data could result in patterns that distracted from core issues and
could be open to politically-influenced interpretation.» Gillingham et al, 2016.
• «For data analytics to be useful, it needs to be theory- or problem-
driven, not simply driven by data that is easily available.»
• ‘Street light phenomena’: Twitter users are atypical compared with the
rest of humanity:
• "WEIRDO" problem of data analytics: most people are not Western,
Educated, Industrialized, Rich, Democratic and Online. Moritz, 2016.
• Data breach cases: too many to list here but a few examples follow:
• eBay: 145 M users
• LinkedIn confirms 2012 hack exposed 117M user passwords
• Report: Three of five Californians may have had data stolen in 2015
• And, …
AtillaElçi,AksarayUniv.
12
13. Big Data - Big Numbers
AtillaElçi,AksarayUniv.
13
It’s in the news: The Wall Street Journal, Sect. D-
Technology, June 10,2016:
33 M Twitter account PWs are announced on
LeakedSource!
15. How to Approach the Issues?
• According to CompTIA's 2016 report titled "The International Trends
in Cybersecurity", about three fourths of organizations have
experienced at least one security breach or incident in the past year,
with about 60 percent of breaches categorized as serious.
Cybersecurity 2016.
• What can then companies do to protect information assets?
«Countermeasures such as encryption, access control, intrusion
detection, backups, auditing and corporate procedures can prevent
data from being breached and falling into the wrong hands.»
Security should promote privacy.
• «Banning large-scale data collection is unlikely to be a realistic
option to solve the problem. Whether we like it or not, the age of
big data has already arrived. We should find the best way of
protecting our privacy while allowing legitimate uses of big data,
which can make our lives much safer, richer and more productive.»
Ryoo,2016.
AtillaElçi,AksarayUniv.
15
16. How to …
• «For example, when used legitimately and securely, big data
technology can drastically improve the effectiveness of fraud
detection, which, in turn, frees us from worrying about stolen
identities and potential monetary loss.
• «Transparency is the key to letting us harness the power of big
data while addressing its security and privacy challenges.
Handlers of big data should disclose information on what they
gather and for what purposes.
• «In addition, consumers must know how the data is stored,
who has access to it and how that access is granted. Finally,
big data companies can earn public trust by giving specific
explanations about the security controls they use to protect
the data they manage.» Ryoo,2016.
AtillaElçi,AksarayUniv.
16
17. So What?
• BDA: tool or outcome?
• How-to Guide
• Advanced Analytics
• A Little Help from AI
• Edge BDA?
• BDA Goes Better with Semantics
AtillaElçi,AksarayUniv.
17
18. BDA: tool or outcome?
• BDA may be in need of a How-to Guide.
• Here are two examples
• Apparently they are not generic nor universal
AtillaElçi,AksarayUniv.
18
19. BDA: How to Go About It? (Parsons,2015)
• Many confuse data collection and data utilization as the same thing, or at
least being very similar.
• What is the impact of spending too much time trying to utilize the new
pile of information?
• Time suck
• Dwelling on details that do not impact the business
• The anchoring effect
• “That is what the numbers say ….”
• Does the tool save your time or steal your eyeballs?
• Hire a dedicated analysis person
• Ask discrete questions
• Plan your logs in advance for utilization, not collection
• Focusing
• FIND THE RIGHT TOOL!
• Discover what is happening, what is not happening, and what is out of
normal.
AtillaElçi,AksarayUniv.
19
20. Advanced Analytics:
A Use Case
• Gartner advises(Customer Engagement, 2015):
• Use Analytics to Measure the Present State of Affairs
• Determine Improvements , Where and How
• Select the Technologies to Drive Advanced, Predictive Capabilities
• Select the Technologies to Drive Prescriptive Capabilities
• Find the Business Analysts With the Advanced Analytics Skills
Required
AtillaElçi,AksarayUniv.
20
21. Case: Customer Service Benefits From
Advanced Analytics (CustomerEngagement,2015)
AtillaElçi,AksarayUniv.
21
23. Advance Analytics
Advanced analytics may mean several approaches in different
cases:
• Predictions/Forecasting/Deep Learning/Scoring –
Predicting/projecting to future values:
• Through statistics,
• By AI / machine learning models
• Experiment Design & Testing –
• Understanding the cause/variance, the drivers of variability,
• In order to improve a process or a task
• Optimization – Finding the optimal solution.
(Hariharan 2016)
AtillaElçi,AksarayUniv.
23
24. Edge BDA?
• The Internet of Things (IoT) promises to change everything by
enabling “smart” environments which is destined to generate
huge data.
• «For example, the current Airbus A350 model has close to
6,000 sensors and generates 2.5 Tb of data per day, while an
even newer model – expected to be available in 2020 – will
capture more than triple that amount!»
• We will need to develop distributed micro analytics and data
manipulation, a.k.a. ‘analytics at the edge’!
AtillaElçi,AksarayUniv.
24
25. A Little Help from AI
• A little help from AI will go a long way! (Q&A: AI & The “Industrial Revolution” in
IT):
• “AI can also help humans manage the immense increase in data
available in order to make better business decisions."
• "In the next five years, a majority of enterprises will adopt – if
they haven’t already – expert systems, robotics and virtual agents
or assistants. Within five to ten years, it is unlikely anyone will not
interact with these technologies on a daily basis at work."
• "Initially, AI adoption will focus on making the business processes
we use today far more efficient and equip us to manage higher
volumes of data, as well as customer interactions. The next five
years will see the development of radically different business
processes as the potential of AI is better explored.”
AtillaElçi,AksarayUniv.
25
26. BDA Goes Better with Semantics
• Big Data is transformed into “Smart” Data when processed and
analyzed properly, thus reveal huge amounts of useful information.
This in turn avails better-founded, more robust predictions and
hugely improved decision-making. New predictive and prescriptive
analytic approaches help realize this outcome.
• Real meaning and relations of the data are still hard-coded to data
formats and applications with concomitant difficulty of repurposing
the data.
• Semantic technologies on the other hand encode meaning of data
explicitly and independent from its consumer application thus
enabling machines and people alike process it.
• Semantic technologies provide a semantics-rich abstraction layer on
top of data and processes which facilitate dealing with high amounts
of heterogeneous data.
AtillaElçi,AksarayUniv.
26
CFP BDSDST 2016
27. BDA Goes … continued
• "… using Big and Smart Data as well as methods and tools
based on semantic technologies will provide more
transparency, enable precise and well-founded decisions and
improve planning processes, which will result in more efficient
and user-centric processes and systems …"
• "Integrating things, data and semantic opens opportunities for
knowledge discovery, and further makes it possible to provide
advanced and intelligent services." CFP SI Big Data Fusion in IoT.
AtillaElçi,AksarayUniv.
27
CFP BDSDST 2016
28. Best Semantics Tool
• PROTÉGÉ :
• «A free, open-source ontology editor and framework for
building intelligent systems»
• Developed by the Stanford Center for Biomedical Informatics
Research (BMIR) at the Stanford University School of Medicine.
• «As healthcare and biomedicine overflow with more data than
we can deal with, and as the knowledge base of medicine and
biology expands exponentially», BMIR focus on developing the
tools and methods needed to translate biomedical data into
actionable insights.
• And, attachable reasoner and visualizer APIs.
• Most commonly and extensively used semantics tool by
ontology engineers for ANY domain of interest.
AtillaElçi,AksarayUniv.
28
http://protege.stanford.edu/about.php
30. References
• SI in Omics-based Medicine (2016). http://www.hindawi.com/journals/bmri/si/503682/cfp/
• Q&A: AI & The “Industrial Revolution” in IT. IDG Connect. Aug. 21, 2014.
http://www.idgconnect.com/abstract/8669/q-a-ai-the-industrial-revolution-it
• What will the workplace of 2026 look like? http://www.idgconnect.com/abstract/13248/what-
workplace-2026-look
• Qiao Li, Chris Zhang, & Chwee Kan Chua (Dec. 2015). IDC FutureScape: Worldwide Big Data and
Analytics 2016 Predictions. APEJ Implications. An IDC Excerpt.
http://thefutureofanalytics.com/idc-futurescape-predictions/
• What will health tech mean for ordinary people in 2026?
http://www.idgconnect.com/abstract/15263/what-health-tech-mean-ordinary-people-2026
• 100 Data and Analytics Predictions Through 2020. Gartner Report preview, 24 March 2016, Doc
#G00301430. https://www.gartner.com/doc/3263218/-data-analytics-predictions
• CFP: 2nd International Workshop on Big Data, Smart Data and Semantic Technologies – BDSDST
2016. http://www.informatik2016.de/1171.html
• CFP Special Issue on Big Data Fusion in Internet of Things.
http://www.journals.elsevier.com/information-fusion/call-for-papers/special-issue-on-big-data-
fusion-in-internet-of-things
• Louise Wat (May 30, 2016). Beijing tracks the elderly as they take buses, go shopping.
http://phys.org/news/2016-05-beijing-tracks-elderly-buses.html
• Jungwoo Ryoo (March 23, 2016). Big data security problems threaten consumers' privacy. The
Conversation. http://phys.org/news/2016-03-big-problems-threaten-consumers-privacy.html
AtillaElçi,AksarayUniv.
30
31. References …
• Philip Gillingham et al. Big Data in Social Welfare: The Development of a Critical Perspective on
Social Work's Latest "Electronic Turn", Australian Social Work (2016). DOI:
10.1080/0312407X.2015.1134606
• Mark Moritz (May 17, 2016). Big data's 'streetlight effect'—where and how we look affects what
we see. The Conversation. http://phys.org/news/2016-05-big-streetlight-effectwhere-
affects.html
• Trevor Parsons (Jan. 12, 2015)· How to Avoid the Big Data Black Hole. Big Data Zone.
https://dzone.com/articles/how-avoid-big-data-black-hole
• Drive Customer Engagement With Advanced Analytics. Gartner Report, 14 May 2015, Doc #
G00277298. https://www.gartner.com/doc/3053417?refval=&pcp=mpe#-1890094435
• PCAST (2014). PCAST Releases Report on Big Data and Privacy. May 1, 2014.
https://www.whitehouse.gov/blog/2014/05/01/pcast-releases-report-big-data-and-privacy
• Ramesh Hariharan (2016). Data Analytics: Past, Present and Future. Blog.
http://www.latentview.com/blog-data-analytics-past-present-and-future/
• Nate Vickery (June 6, 2016). Smarthome Security Concerns: The Question of Privacy.
http://www.iotcentral.io/blog/smarthome-security-concerns
• Bill Schmarzo (June 7, 2016). The Internet of Things (IoT) and Analytics at The Edge.
http://www.gladwinanalytics.com/blog/the-internet-of-things-iot-and-analytics-at-the-edge
• Cybersecurity Breaches Hit Nearly Three in Four Organizations.
http://www.securitymagazine.com/articles/87104-cybersecurity-breaches-hit-nearly-
three-in-four-organizations
AtillaElçi,AksarayUniv.
31
32. Training Sources on BDA
• https://www.coursera.org/
• https://www.udacity.com/
• http://bigdatauniversity.com/
• Udemy: https://www.udemy.com/
• https://www.edx.org/
AtillaElçi,AksarayUniv.
32