This document provides an overview and guided tour of big data. It begins with defining big data as any data collection that is too large or complex to be managed with traditional data approaches. It then discusses the key characteristics of big data, including volume, variety, and velocity. The document outlines different types of big data sources and discusses both the opportunities and risks of working with big data, emphasizing the importance of understanding the data quality, representativeness, and ensuring insights provide value. It concludes by discussing the need for big data analysis to be grounded in business questions and combining appropriate statistical and analytic techniques.
The document discusses the evolution of big data, from its early beginnings in the 1990s to its current prominence. It describes how the rise of the internet and e-commerce created vast amounts of machine-generated data. This helped popularize the concept of big data and fueled growth in the big data market. The document also explains how big data is now characterized by its volume, velocity and variety (the three V's) as defined in 2001, and how some now argue this definition is outdated and does not fully capture big data's potential business value.
What Is Unstructured Data And Why Is It So Important To Businesses?Bernard Marr
Unstructured data is created at an incredible rate each day and with the advent of artificial intelligence and machine learning tools to gather, process, analyse and report insights from unstructured data, it now provides important business value to organizations. It’s essential for all businesses to start making the most of their unstructured data.
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
Big data is like a two-edged sword: It can bring many new opportunities for business, but it can also harm individuals and businesses in unanticipated ways
In the analogue era information was scarce and came from questionnaires and sampling. Since the dawn of the digital age in 2012 far more data than ever before is stored and it is mainly collected passively, i.e. while people go about doing what they normally do, such as run their businesses, use their cell phones and conduct internet searches.
Analysts, policy makers and business people value business tendency surveys (BTS) and consumer opinion surveys (COS) specifically because the survey results are available before the corresponding (official) quantitative data. However, Big Data has begun to make inroads on areas traditionally covered by BTS and COS. It has a competitive edge over BTS and COS, as it is available in real-time, is based on all observations and does not rely on the active participation of respondents. Furthermore, Big Data has little direct production costs, because it is merely a by-product of business processes. In contrast, putting together and maintaining a sample of active respondents and collecting information through questionnaires as in the case of BTS and COS, require the upkeep of a costly infrastructure and the employment of people with scarce, specialised skills.
However, BTS and COS also have a competitive edge over Big Data in certain aspects. These aspects could broadly be put into two groups, namely 1) BTS and COS offer information that Big Data cannot supply and 2) BTS and COS do not suffer from some of the shortcomings of Big Data. The biggest competitive advantage of BTS and COS is that they measure phenomenon that Big Data does not cover. Big Data records only actual outcomes, while BTS and COS also cover unquantifiable expectations and assessments. Although Big Data often claims that it covers the whole population universe (and not only a selection) this does not necessarily prevent bias. For example, twitter feeds could be biased, because certain demographic or less activist groups are under-represented. In contrast, the research design and random sampling of BTS and COS limit their selection bias.
To remain relevant and survive, producers of BTS and COS will have to adapt and publicise their unique competitive advantage vis-à-vis Big Data in the future. The biggest shift will probably require that producers of BTS and COS make users more aware of the value of the unique forward looking information of BTS and COS (i.e. their recording of expectations about the future).
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
Applications of Big Data Analytics in BusinessesT.S. Lim
The document discusses big data and big data analytics. It begins with definitions of big data from various sources that emphasize the large volumes of structured and unstructured data. It then discusses key aspects of big data including the three Vs of volume, variety, and velocity. The document also provides examples of big data applications in various industries. It explains common analytical methods used in big data including linear regression, decision trees, and neural networks. Finally, it discusses popular tools and frameworks for big data analytics.
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
The document discusses the evolution of big data, from its early beginnings in the 1990s to its current prominence. It describes how the rise of the internet and e-commerce created vast amounts of machine-generated data. This helped popularize the concept of big data and fueled growth in the big data market. The document also explains how big data is now characterized by its volume, velocity and variety (the three V's) as defined in 2001, and how some now argue this definition is outdated and does not fully capture big data's potential business value.
What Is Unstructured Data And Why Is It So Important To Businesses?Bernard Marr
Unstructured data is created at an incredible rate each day and with the advent of artificial intelligence and machine learning tools to gather, process, analyse and report insights from unstructured data, it now provides important business value to organizations. It’s essential for all businesses to start making the most of their unstructured data.
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
Big data is like a two-edged sword: It can bring many new opportunities for business, but it can also harm individuals and businesses in unanticipated ways
In the analogue era information was scarce and came from questionnaires and sampling. Since the dawn of the digital age in 2012 far more data than ever before is stored and it is mainly collected passively, i.e. while people go about doing what they normally do, such as run their businesses, use their cell phones and conduct internet searches.
Analysts, policy makers and business people value business tendency surveys (BTS) and consumer opinion surveys (COS) specifically because the survey results are available before the corresponding (official) quantitative data. However, Big Data has begun to make inroads on areas traditionally covered by BTS and COS. It has a competitive edge over BTS and COS, as it is available in real-time, is based on all observations and does not rely on the active participation of respondents. Furthermore, Big Data has little direct production costs, because it is merely a by-product of business processes. In contrast, putting together and maintaining a sample of active respondents and collecting information through questionnaires as in the case of BTS and COS, require the upkeep of a costly infrastructure and the employment of people with scarce, specialised skills.
However, BTS and COS also have a competitive edge over Big Data in certain aspects. These aspects could broadly be put into two groups, namely 1) BTS and COS offer information that Big Data cannot supply and 2) BTS and COS do not suffer from some of the shortcomings of Big Data. The biggest competitive advantage of BTS and COS is that they measure phenomenon that Big Data does not cover. Big Data records only actual outcomes, while BTS and COS also cover unquantifiable expectations and assessments. Although Big Data often claims that it covers the whole population universe (and not only a selection) this does not necessarily prevent bias. For example, twitter feeds could be biased, because certain demographic or less activist groups are under-represented. In contrast, the research design and random sampling of BTS and COS limit their selection bias.
To remain relevant and survive, producers of BTS and COS will have to adapt and publicise their unique competitive advantage vis-à-vis Big Data in the future. The biggest shift will probably require that producers of BTS and COS make users more aware of the value of the unique forward looking information of BTS and COS (i.e. their recording of expectations about the future).
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
Applications of Big Data Analytics in BusinessesT.S. Lim
The document discusses big data and big data analytics. It begins with definitions of big data from various sources that emphasize the large volumes of structured and unstructured data. It then discusses key aspects of big data including the three Vs of volume, variety, and velocity. The document also provides examples of big data applications in various industries. It explains common analytical methods used in big data including linear regression, decision trees, and neural networks. Finally, it discusses popular tools and frameworks for big data analytics.
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
Tools and techniques adopted for big data analyticsJOSEPH FRANCIS
This document discusses tools and techniques for big data analytics. It begins by defining big data and explaining why big data analysis is important for businesses. It then outlines the characteristics and history of big data, as well as the challenges and phases of big data analysis. The document proceeds to describe several tools and techniques used for big data analytics, including machine learning, natural language processing, and visualization. It provides examples of how these tools and techniques have been applied through case studies of Indian elections, AirBnB, and Shoppers Stop.
Big Data is creating large amounts of metadata from users' smartphones and online activities. While this data is now being collected, enterprises still struggle to effectively analyze it and develop useful algorithms from the poor mining of Big Data. As more resources are devoted to analyzing metadata, automated tasks will be able to make better use of Big Data. However, the rapid growth of Big Data outpaces what most enterprises can currently handle from a technology and personnel standpoint.
The Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center asked digital stakeholders to weigh two scenarios for 2020, select the one most likely to evolve, and elaborate on the choice. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence. The other expressed the view that Big Data could cause more problems than it solves between now and 2020
1. The document provides an overview of Hadoop and big data technologies, use cases, common components, challenges, and considerations for implementing a big data initiative.
2. Financial and IT analytics are currently the top planned use cases for big data technologies according to Forrester Research. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers.
3. Organizations face challenges in implementing big data initiatives including skills gaps, data management issues, and high costs of hardware, personnel, and supporting new technologies. Careful planning is required to realize value from big data.
Semantic Web Investigation within Big Data ContextMurad Daryousse
This document discusses how the semantic web can help address challenges associated with big data. It describes the 5 V's of big data: volume, variety, velocity, veracity, and value. For each V, it outlines related challenges in data acquisition, integration, and analysis. The document argues that semantic web concepts like ontologies, linked data, and reasoning can help solve problems of data heterogeneity, scale, and timeliness across different phases of the big data analysis pipeline, in order to ultimately extract value from data.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
Smart Data Module 1 introduction to big and smart datacaniceconsulting
This document provides an overview of big and smart data. It defines big data as large volumes of structured, unstructured, and semi-structured data that is difficult to manage and process using traditional databases. It discusses how big data becomes smart data through analysis and insights. Examples of smart data applications are also provided across various industries like retail, healthcare, transportation and more. The document emphasizes that in order to start smart with data, companies need to review their existing data, ask the right questions, and form actionable insights rather than just conclusions.
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.
The document discusses 25 predictions about the future of big data:
1) Data volumes and ways to analyze data will continue growing exponentially with improvements in machine learning and real-time analytics.
2) More companies will appoint chief data officers and use data as a competitive advantage.
3) Data governance, visualization, and delivery through data fabrics and marketplaces will be key to extracting insights from diverse data sources and empowering partners.
4) Data is becoming a new global currency and companies are monetizing their data through algorithms, services, and by becoming "data businesses."
Enabling Big Data with Data-Level Security:The Cloud Analytics Reference Arch...Booz Allen Hamilton
; Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
BIG Data & Hadoop Applications in Social MediaSkillspeed
This document discusses how major social media networks like Facebook, Twitter, LinkedIn, Pinterest, and Instagram utilize big data and Hadoop technologies. It provides examples of how each network uses Hadoop for tasks like storing user data, performing analytics, and generating personalized recommendations at massive scales as their user bases and data volumes grow enormously. The document also briefly outlines SkillSpeed's Hadoop training course, which covers topics like HDFS, MapReduce, Pig, Hive, HBase and more to prepare students for jobs working with big data.
Hvilke teknologier forventer IBM får størst betydning fremover?
Få indblik i hvordan det er gået med IBM's tidligere forudsigelser og få et bud på, hvad fremtiden bringer fra IBM Research.
Anders Quitzau, Chief Technologist, IBM
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
Cutting Edge Predictive Analytics with Eric Siegel Databricks
Apache Spark empowers predictive analytics and machine learning by increasing the reach and potential. But, before jumping to new deployments, it’s critical we 1) get the analytics right and 2) not overlook less conspicuous business opportunities. In this keynote, Predictive Analytics World founder and “Predictive Analytics” author Eric Siegel ramps you up on a dangerous pitfall and a critical value proposition:
– PITFALL: Avoiding BS predictive insights, i.e., “bad science,” spurious discoveries
– OPPORTUNITY: Optimizing marketing persuasion by predicting the *influence* of marketing treatments, i.e., uplift modeling
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
This document provides an overview of big data by discussing its background and definitions. It describes how data has grown exponentially in recent years due to factors like the internet, cloud computing, and internet of things. Big data is defined as data that cannot be processed by traditional technologies due to its huge size, speed of growth, and variety of data types. The document outlines several common definitions of big data, including the 3Vs (volume, velocity, variety) and 4Vs (volume, variety, velocity, value) models. It aims to provide readers with a comprehensive understanding of the emerging field of big data.
Big data for the next generation of event companiesRaj Anand
Only on rare occasions do we consider the amount of data that our every action produces. It’s pretty overwhelming just to think about every interaction on every app on every device in our bag or pocket, in every environment and every location.
But then there’s more. We also use access cards, transportation passes and gym memberships. We have hobbies, we travel, buy groceries, books and maybe warm beverages on rainy days. We are part of multiple communities. Looking around billions of people are doing the same. Our every action produces data about us. This is big.
We believe taking an interest in this wealth of data will be the key to success for next generation Event Companies.
We are living in a fast changing world, where it’s ever more important to foresee trends and seize opportunities. A global perspective is not a strategic advantage anymore it is a necessity.
Event companies are facilitators , they create common grounds for brands and audiences, by thoughtfully connecting goals and means. Having a deep understanding of customer behaviour, group psychology, digital habits, brand interaction, communication, and awareness through unlocking the power of big data will ensure next generation event companies thrive on strategy.
Two hedge funds are gaining access to university supercomputers through cloud networks to power their trading strategies. Stony Brook University has created a "research cloud" allowing funds to use its IBM Blue Gene supercomputers. One fund will use the machines to design trading algorithms, while another aims to spot market anomalies. Separately, a company is renting time on Rensselaer Polytechnic Institute's Blue Gene supercomputer to adapt gene analysis software for analyzing financial markets.
Big data refers to large data sets that are too large or complex for traditional data processing systems. It is characterized by high volume, velocity, and variety. Common challenges with big data include analysis, storage, search, sharing, transfer, and privacy. Traditional systems are inadequate for big data, which requires massively parallel software running on many servers. Architectures for handling big data include distributed file systems, MapReduce frameworks like Hadoop, and data lake systems.
The document introduces 17 customer segments for the London Borough of Barnet that were developed using various data and engagement methods. Each segment represents a unique group of households that share demographic, socioeconomic, and behavioral characteristics. The segments allow resources to be targeted to effectively meet customer needs. An example digital segmentation from Lambeth shows how residents are grouped into categories like "Online Trendsetters" based on their technology usage. The segments can be used to inform policies, services, and justifications by identifying relevant groups and their characteristics.
Tools and techniques adopted for big data analyticsJOSEPH FRANCIS
This document discusses tools and techniques for big data analytics. It begins by defining big data and explaining why big data analysis is important for businesses. It then outlines the characteristics and history of big data, as well as the challenges and phases of big data analysis. The document proceeds to describe several tools and techniques used for big data analytics, including machine learning, natural language processing, and visualization. It provides examples of how these tools and techniques have been applied through case studies of Indian elections, AirBnB, and Shoppers Stop.
Big Data is creating large amounts of metadata from users' smartphones and online activities. While this data is now being collected, enterprises still struggle to effectively analyze it and develop useful algorithms from the poor mining of Big Data. As more resources are devoted to analyzing metadata, automated tasks will be able to make better use of Big Data. However, the rapid growth of Big Data outpaces what most enterprises can currently handle from a technology and personnel standpoint.
The Pew Research Center’s Internet & American Life Project and Elon University’s Imagining the Internet Center asked digital stakeholders to weigh two scenarios for 2020, select the one most likely to evolve, and elaborate on the choice. One sketched out a relatively positive future where Big Data are drawn together in ways that will improve social, political, and economic intelligence. The other expressed the view that Big Data could cause more problems than it solves between now and 2020
1. The document provides an overview of Hadoop and big data technologies, use cases, common components, challenges, and considerations for implementing a big data initiative.
2. Financial and IT analytics are currently the top planned use cases for big data technologies according to Forrester Research. Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of computers.
3. Organizations face challenges in implementing big data initiatives including skills gaps, data management issues, and high costs of hardware, personnel, and supporting new technologies. Careful planning is required to realize value from big data.
Semantic Web Investigation within Big Data ContextMurad Daryousse
This document discusses how the semantic web can help address challenges associated with big data. It describes the 5 V's of big data: volume, variety, velocity, veracity, and value. For each V, it outlines related challenges in data acquisition, integration, and analysis. The document argues that semantic web concepts like ontologies, linked data, and reasoning can help solve problems of data heterogeneity, scale, and timeliness across different phases of the big data analysis pipeline, in order to ultimately extract value from data.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
Smart Data Module 1 introduction to big and smart datacaniceconsulting
This document provides an overview of big and smart data. It defines big data as large volumes of structured, unstructured, and semi-structured data that is difficult to manage and process using traditional databases. It discusses how big data becomes smart data through analysis and insights. Examples of smart data applications are also provided across various industries like retail, healthcare, transportation and more. The document emphasizes that in order to start smart with data, companies need to review their existing data, ask the right questions, and form actionable insights rather than just conclusions.
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.
The document discusses 25 predictions about the future of big data:
1) Data volumes and ways to analyze data will continue growing exponentially with improvements in machine learning and real-time analytics.
2) More companies will appoint chief data officers and use data as a competitive advantage.
3) Data governance, visualization, and delivery through data fabrics and marketplaces will be key to extracting insights from diverse data sources and empowering partners.
4) Data is becoming a new global currency and companies are monetizing their data through algorithms, services, and by becoming "data businesses."
Enabling Big Data with Data-Level Security:The Cloud Analytics Reference Arch...Booz Allen Hamilton
; Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
BIG Data & Hadoop Applications in Social MediaSkillspeed
This document discusses how major social media networks like Facebook, Twitter, LinkedIn, Pinterest, and Instagram utilize big data and Hadoop technologies. It provides examples of how each network uses Hadoop for tasks like storing user data, performing analytics, and generating personalized recommendations at massive scales as their user bases and data volumes grow enormously. The document also briefly outlines SkillSpeed's Hadoop training course, which covers topics like HDFS, MapReduce, Pig, Hive, HBase and more to prepare students for jobs working with big data.
Hvilke teknologier forventer IBM får størst betydning fremover?
Få indblik i hvordan det er gået med IBM's tidligere forudsigelser og få et bud på, hvad fremtiden bringer fra IBM Research.
Anders Quitzau, Chief Technologist, IBM
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
Cutting Edge Predictive Analytics with Eric Siegel Databricks
Apache Spark empowers predictive analytics and machine learning by increasing the reach and potential. But, before jumping to new deployments, it’s critical we 1) get the analytics right and 2) not overlook less conspicuous business opportunities. In this keynote, Predictive Analytics World founder and “Predictive Analytics” author Eric Siegel ramps you up on a dangerous pitfall and a critical value proposition:
– PITFALL: Avoiding BS predictive insights, i.e., “bad science,” spurious discoveries
– OPPORTUNITY: Optimizing marketing persuasion by predicting the *influence* of marketing treatments, i.e., uplift modeling
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
This document provides an overview of big data by discussing its background and definitions. It describes how data has grown exponentially in recent years due to factors like the internet, cloud computing, and internet of things. Big data is defined as data that cannot be processed by traditional technologies due to its huge size, speed of growth, and variety of data types. The document outlines several common definitions of big data, including the 3Vs (volume, velocity, variety) and 4Vs (volume, variety, velocity, value) models. It aims to provide readers with a comprehensive understanding of the emerging field of big data.
Big data for the next generation of event companiesRaj Anand
Only on rare occasions do we consider the amount of data that our every action produces. It’s pretty overwhelming just to think about every interaction on every app on every device in our bag or pocket, in every environment and every location.
But then there’s more. We also use access cards, transportation passes and gym memberships. We have hobbies, we travel, buy groceries, books and maybe warm beverages on rainy days. We are part of multiple communities. Looking around billions of people are doing the same. Our every action produces data about us. This is big.
We believe taking an interest in this wealth of data will be the key to success for next generation Event Companies.
We are living in a fast changing world, where it’s ever more important to foresee trends and seize opportunities. A global perspective is not a strategic advantage anymore it is a necessity.
Event companies are facilitators , they create common grounds for brands and audiences, by thoughtfully connecting goals and means. Having a deep understanding of customer behaviour, group psychology, digital habits, brand interaction, communication, and awareness through unlocking the power of big data will ensure next generation event companies thrive on strategy.
Two hedge funds are gaining access to university supercomputers through cloud networks to power their trading strategies. Stony Brook University has created a "research cloud" allowing funds to use its IBM Blue Gene supercomputers. One fund will use the machines to design trading algorithms, while another aims to spot market anomalies. Separately, a company is renting time on Rensselaer Polytechnic Institute's Blue Gene supercomputer to adapt gene analysis software for analyzing financial markets.
Big data refers to large data sets that are too large or complex for traditional data processing systems. It is characterized by high volume, velocity, and variety. Common challenges with big data include analysis, storage, search, sharing, transfer, and privacy. Traditional systems are inadequate for big data, which requires massively parallel software running on many servers. Architectures for handling big data include distributed file systems, MapReduce frameworks like Hadoop, and data lake systems.
The document introduces 17 customer segments for the London Borough of Barnet that were developed using various data and engagement methods. Each segment represents a unique group of households that share demographic, socioeconomic, and behavioral characteristics. The segments allow resources to be targeted to effectively meet customer needs. An example digital segmentation from Lambeth shows how residents are grouped into categories like "Online Trendsetters" based on their technology usage. The segments can be used to inform policies, services, and justifications by identifying relevant groups and their characteristics.
Students in 6th grade classes participated in a 2 hour recycling patrol program. The program involved a 1 hour PowerPoint presentation and educational games to teach concepts about packaging waste. It also included a school patrol where students spent 1 hour symbolically fining unsustainable actions and informing people about good environmental practices while surveying the streets near their school. At the end, the students reported their findings from the workshop and patrols to the local City Council.
This report provides a summary of performance in the third quarter of 2015/16 against outcome indicators and key performance measures. Overall progress against community outcomes is reasonable, with 6 outcomes rated green, 6 amber, and 1 red. For key performance measures, progress has marginally worsened compared to last quarter, with fewer achieving targets but also fewer red ratings. Specific issues are highlighted around financial security, crime rates, and support for vulnerable groups. Recommendations focus on scrutinizing and improving performance on indicators that are off target.
This document discusses factoring as an alternative source of financing for small businesses when banks deny loans. It explains that factoring involves selling accounts receivable (invoices) to a factoring company for immediate payment, rather than waiting for payment from customers. This provides working capital to pay expenses like payroll. Factoring is appealing for small businesses because it focuses on the creditworthiness of customers rather than the business itself. The document promotes factoring as a simple way to access cash and continue pursuing new opportunities without being constrained by lack of working capital.
El documento discute los usos educativos del video. Señala que los medios de comunicación han desempeñado un papel fundamental en la sociedad y la cultura, y que a través de la tecnología se han notado efectos en el aprendizaje de los estudiantes. También indica que el uso de multimedia en las clases hará que estas sean más interesantes y participativas, enriqueciendo la experiencia sensorial de los estudiantes. Finalmente, concluye que el video promueve la comunicación y el aprendizaje, y que los maestros pued
The document outlines the objectives and skills that students of community health worker placements should develop. This includes carrying out basic medical tasks like dressings and vital signs; advising on family planning, reproductive health, and nutrition; identifying and referring high-risk patients; providing health education to communities; and monitoring programs like immunizations and managing common health issues. The objectives are organized into several sections covering maternal and child health, public health, family planning, antenatal care, postnatal care, nutrition, immunizations, disability care, school health, disease control and mental health.
This article profiles Cheryl Christmas, a Christian woman from Michigan who has developed a strong connection to Judaism and the Jewish people. She feels called to educate others about the Holocaust and has traveled to Israel numerous times. Her most recent trip involved participating in a Holocaust seminar at Yad Vashem, which had a profound impact on her. Since returning, she has volunteered weekly at the Holocaust Memorial Center, where she shares what she learned with visitors. Her goal is to educate the Christian world about the horrors of the Holocaust.
The document discusses the Energy Industry Collaboration Group (EICG) which aims to build a world-leading oil and gas industry in Australia through collaboration between member organizations. The EICG identifies opportunities for members to work together to improve industry competitiveness. It has three main objectives: developing common industry standards and practices; promoting new technologies; and supporting long-term industry sustainability and growth. The EICG has established three work streams focused on skills and competency development, standardization, and sharing of resources. It provides the structure and timeline for working groups in these areas over 2016.
Big Data refers to large, complex datasets that traditional data processing applications are unable to handle efficiently. Spark is a fast, general engine for large-scale data processing that supports multiple languages and data sources. Spark uses resilient distributed datasets (RDDs) that operate on data stored in cluster memory for faster performance compared to the disk-based MapReduce model. DataFrames provide a distributed collection of data organized into named columns similar to a relational database, enabling SQL-like queries and optimizations.
Hadoop is an open-source software framework used for storing and processing large datasets in a distributed manner across commodity hardware. It was created in 2005 by Doug Cutting and Mike Cafarella to address the issue of processing big data at a reasonable cost and time. Hadoop uses HDFS for storage and MapReduce for processing data distributed over a network of nodes in parallel. It allows organizations to gain insights from vast amounts of structured and unstructured data faster and at lower costs than traditional approaches.
This document discusses big data analytics and how it is transforming business intelligence. It defines big data analytics as combining large datasets ("big data") with advanced analytic techniques. It describes big data using the three V's: volume, variety, and velocity. Volume refers to the large size of datasets. Variety means data comes from many different sources and formats. Velocity means data streams in continuously and in real-time. The document provides examples of how companies are using big data analytics to discover new insights and track changing customer behavior.
Big data analytics involves capturing, storing, processing, analyzing, and visualizing huge quantities of information from a variety of sources. This data is characterized by its volume, variety, velocity, veracity, variability, and complexity. Traditional analytics are not suited to handle big data due to its size and constantly changing nature. By analyzing patterns in big data, businesses can gain insights to improve processes and campaigns. However, specialized software is needed to make sense of big data's different types and formats from numerous sources. The right big data solution depends on an organization's specific data, budgets, skills, and future needs.
Big data consumer analytics and the transformation of marketingNicha Tatsaneeyapan
This document discusses how big data consumer analytics is transforming marketing through the extraction of consumer insights from large volumes of diverse consumer data. It proposes a conceptual framework based on resource-based theory to understand how firms can gain sustainable competitive advantages from big data. Specifically, it argues that physical (e.g. data storage and analytics platforms), human (e.g. data scientists), and organizational resources moderate how firms collect consumer data, extract insights from it, and utilize those insights. It also discusses how big data can enhance dynamic capabilities to respond to changes and adaptive capabilities to predict trends. Finally, it notes that an "ignorance-based view" focusing on unknowns may be important for profiting from big data alongside traditional knowledge
Bda assignment can also be used for BDA notes and concept understanding.Aditya205306
Big data refers to large and complex datasets that are difficult to analyze using traditional methods. It is characterized by high volume, velocity, and variety of data from numerous sources. Big data analytics uses tools like Hadoop and Spark to extract meaningful insights from large, unstructured datasets in real-time. This allows companies to gain valuable business insights, reduce costs, enhance customer experience, innovate products, and make faster decisions.
Big Data and Artificial Intelligence in IndonesiaHeru Sutadi
The document discusses big data and artificial intelligence. It defines big data as massive volumes of both structured and unstructured data that is difficult to process using traditional techniques due to its size. It notes big data is characterized by high volume, variety, and velocity of data. It also discusses artificial intelligence as creating intelligent machines that work like humans through activities like speech recognition, learning, planning and problem solving. Finally, it emphasizes that big data and artificial intelligence are just tools, and that change management is key when adopting new technologies to address business problems rather than technological issues alone.
This document provides information about big data analytics. It defines what data and big data are, explaining that big data refers to extremely large data sets that are difficult to process using traditional data management tools. It discusses the volume, variety, velocity, and veracity characteristics of big data. Examples of big data sources and sizes are provided, such as the terabytes of data generated each day by the New York Stock Exchange and Facebook. The document also covers structured, unstructured, and semi-structured data types; advantages of big data processing; and types of digital advertising.
Big data refers to extremely large data sets that are too large to be processed using traditional data processing applications. It is characterized by high volume, variety, and velocity. Examples of big data sources include social media, jet engines, stock exchanges, and more. Big data can be structured, unstructured, or semi-structured. Key characteristics include volume, variety, velocity, and variability. Analyzing big data can provide benefits like improved customer service, better operational efficiency, and more informed decision making for organizations in various industries.
This document discusses uncertainty in big data analytics. It begins by providing background on big data, defining the common "5 V's" characteristics of big data - volume, variety, velocity, veracity, and value. It then discusses uncertainty, which exists in big data due to noise, incompleteness, and inconsistency in data. The document surveys techniques for big data analytics and how uncertainty impacts machine learning, natural language processing, and other artificial intelligence approaches. It identifies challenges that uncertainty presents and strategies for mitigating uncertainty in big data analytics.
Big data presents opportunities for communications service providers (CSPs) to capture new revenue streams by optimizing large amounts of structured and unstructured customer data. To take advantage, CSPs must develop a strategic plan and roadmap to transform how they use customer data, identifying specific business values. Success stories show how CSPs have improved operational efficiency, provided targeted marketing offers, and created new business models through partnerships. The document recommends CSPs formulate a big data strategy and business case with measurable outcomes to guide strategic transformation and monetization of big data opportunities.
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
The document provides an overview of data science. It defines data science as a field that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, and deep learning. It explains that data science uses both traditional structured data stored in databases as well as big data from various sources. The document also describes how data scientists preprocess and analyze data to gain insights into past behaviors using business intelligence and then make predictions about future behaviors.
The document discusses big data, including its definition, types, benefits, and challenges. It describes how big data is generated from a variety of sources and is characterized by its volume, velocity, and variety (the 3Vs). Big data provides benefits like improved customer insights and business optimization. However, it also poses challenges to deal with its huge volume, high velocity, varied types (structured and unstructured), and issues of data veracity (uncertainty). Techniques to address these challenges include using distributed file systems, parallel processing frameworks like Hadoop, and data fusion or advanced mathematics to manage uncertainty.
Introduction to big data – convergences.saranya270513
Big data is high-volume, high-velocity, and high-variety data that is too large for traditional databases to handle. The volume of data is growing exponentially due to more data sources like social media, sensors, and customer transactions. Data now streams in continuously in real-time rather than in batches. Data also comes in more varieties of structured and unstructured formats. Companies use big data to gain deeper insights into customers and optimize business processes like supply chains through predictive analytics.
This document discusses big data and its applications in various industries. It begins by defining big data and its key characteristics of volume, velocity, variety and veracity. It then discusses how big data can be used for log analytics, fraud detection, social media analysis, risk modeling and other applications. The document also outlines some of the major challenges faced in the banking and financial services industry, including increasing competition, regulatory pressures, security issues, and adapting to digital shifts. It concludes by noting how big data analytics can help eCommerce businesses make fact-based, quantitative decisions to gain competitive advantages and optimize goals.
Big data 1 4 vint-sogeti-on-big-data-1-of-4-creating clarity with big dataRick Bouter
This document discusses the rise of Big Data and its importance for organizations. It notes that digital data is fueling a new industrial revolution. Big Data represents the combination of transactions, interactions, and observations. The growth of digital data from various sources is expanding exponentially. To gain competitive advantages, organizations must implement total data management and analyze all available data, not just samples. This will allow them to better understand customer behavior, detect fraud, and make improved business decisions. The document outlines several Big Data challenges that organizations face and questions for the reader regarding their Big Data profile and management.
Since 2005, when the term “Big Data” was launched, Big Data has become an increasingly topical theme. In terms of technological development and business adoption, the domain of Big Data has made powerful advances; and that is putting it mildly.
In this initial report on Big Data, the first of four, we give answers to questions concerning what exactly Big Data is, where it differs from existing data classification, how the transformative potential of Big Data can be estimated, and what the current situation (2012) is with regard to adoption and planning.
VINT attempts to create clarity in these developments by presenting experiences and visions in perspective: objectively and laced with examples. But not all answers, not by a long way, are readily available. Indeed, more questions will arise – about the roadmap, for example, that you wish to use for Big Data. Or about governance. Or about the way you may have to revamp your organization. About the privacy issues that Big Data raises, such as those involving social analytics. And about the structures that new algorithms and systems will probably bring us.
http://www.ict-books.com/books/inspiration-trends
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
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.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
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
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
3. IPSOS
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FOREWORD
Look at its power. Imagine the potential. It often feels like
Big Data is omnipresent. Not least in the workplace, where
it has become a feature of so many current discussions about
strategies and business plans. As the US President’s
office points out, it can be a force for good: saving lives
and making the economy work better. But Big Data also
brings with it risks and responsibilities – as witnessed by
growing consumer concerns about data protection and
information security.
Big Data may now be famous. But it can be hard to pin
down – a little mysterious, even. In this Ipsos Views
paper, Rich Timpone sets us off on a guided tour of the
subject matter – taking us from the all-important
definitions through to questions around how it should
(and should not) be used.
For those of us involved in the research industry, there
are some clear rules we need to follow if we are to make
sense of it all. And that’s before the role of “traditional”
market research techniques come in.
And, in turn, this framework helps us to understand:
• Opportunities for leveraging new
sources of data
• The risks that co-exist with the
opportunities
• Where there are connections to more
traditional market research approaches
Thisdefinitionprovidesaframeworkforcategorising:
• The types of data that exist
• The business problems the data can be
applied to
• The methods used for capturing data
and extracting insight from it
WHAT IS BIG DATA?
“Big Data” is a very broad term, used often - and for a
variety of different purposes. But it can be simply defined as
any collection of data or datasets so complex or large
that traditional data management approaches
become unsuitable (Cielen and Meysman 2016).
Big Data: A Guided Tour
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Big Data: A Guided Tour
THE CHARACTERISTICS OF
BIG DATA
The term “Big Data” is often applied to datasets simply
because of their size and/or their complexity. Some
definitions are so broad that they lack clarity - for example,
in 2014, the Executive Office of the President of the United
States issued a report on Big Data that defined it as: “Large,
diverse, complex, longitudinal, and/or distributed datasets
generated from instruments, sensors, internet transactions,
email, video, click streams, and/or all other digital sources
available today and in the future”.
The typical industry description of Big Data rests on the “Three
Vs”, which has its origins in META Group (now Gartner). The
three Vs are the elements that fundamentally define Big Data:
While some commentators say all three elements should be
present before data can be considered to be “Big Data”, other
researchers and clients believe that only one of these
elements may be enough.
• Volume
• Variety
• Velocity
Some definitions of Big Data are so broad
that they lack clarity.
“ “
Passive Data
Active Data
Interactive Data
MONITOR
ASK
INTERACT
Directly observe and
track consumer behaviour,
what they say, where they
go, what they watch/listen
to, and ultimately what
they buy. Leverage user
generated and other data
sources directly.
Measuring what matters,
when it matters.
Traditional qual and
quant data that directly
engages individuals to
provide their attitudes
and behaviours. Includes
tracking and deep dive
studies.
Highly involving interactive
activities which leverage
social sharing, dynamic
experienceandgamification
mechanics to engage
consumers in ideation,
co-creation and more.
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Volume
As one might imagine from the use of the word “Big”, the
size of the data source is a core characteristic, though
there are few numerical measures of how large a dataset
must be before it can be considered “Big”. What matters
is that the data is so large that traditional processing
tools and techniques become ineffective.
Variety
By some estimates, more than 2.5 exabytes
(2,500,000,000,000,000,000 bytes) of data are being
produced each day. This creates challenges beyond
scale and this data comes in many varied forms, including:
• Simple datasets
• Natural text
• Online communication (discussions and
blogs in social listening as well as web
page content itself)
• Pictures
• Video
• Sensor data (from road cameras, satellites
and other recording devices)
• Digital exhaust (the information leftover
from transactions and other activities online)
Not only are these things complex on their own, the challenges
grow further as we combine them. The different forms of
data have different units of analysis (such as individual,
household, neighborhood, etc.), and traditional data processing
techniques often cannot help us to gain insight from
them.
Velocity
Modern data is fast, not just because it is being produced
quickly, but because it moves quickly and is consumed
quickly too.
There are more than 500 million tweets every day on
Twitter, and Walmart handles over a million customer
transactions every hour. So, there is an opportunity to
think about delivering insight in near real-time.
While this speed creates new challenges and opportunities,
it alsogivesrisetoquestionsaroundthedurationofmeasurement
for specific examinations – such as how long is long
enough in order to discover what we want to know?
There are more than 500 million tweets every
day on Twitter, and Walmart handles over a
million customer transactions every hour. So,
there is an opportunity to think about delivering
insight in near real-time.
“
“
Big Data: A Guided Tour
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TYPES OF BIG DATA
Ipsos generally classifies data as:
• ACTIVE - where respondents are explicitly
asked their views in surveys, focus
groups etc
• PASSIVE - any observational or extracted
information where the individual not
queried directly
• INTERACTIVE - such as from our social
communities where dialogue is the source
of the insights
Typically, most Big Data has its origins
in the passive domain.
“ “
Common sources of data are:
Behavioural Data
Purchase Data
Point of sales transactions, as well as credit card and online
purchases are being captured and mined for broad insight.
Many companies and clients collect data that track the
actual behaviors of customers and individuals from online
transactions and company activities, web patterns, mobile
activity, software use, to the Internet of Things (IoT) with the
modern capturing of sensors from TVs, refrigerators, cars etc.
Social Listening
Digital discourse, including tweets, Facebook updates,
blogs, vlogs, and online discussions, allow unprecedented
ability to ‘listen in’ on actual exchanges.
Geo-location
A special form of behavioral data - the capturing of GPS data
(often as part of the ‘digital exhaust’ of mobile and other services)
allows for geographic analysis and movement patterns.
Unstructured Data
While text data, such as the social listening above, falls in
this category, it also includes sources as diverse as photos
and videos that are created by individuals. Note that the term
unstructured is not meant to be pejorative but emphasises
the fact that the sources are not captured in standard data
layouts that allow straightforward summary and analysis.
Logistical
The bulk of the data we have been discussing focuses on
individuals. Aggregate and logistical data may be analysed
on its own or as context for individual behavioural understanding.
In this way, we are often examining questions at different levels
of analysis or integrating multiple levels. Machine generated
as well as explicitly captured sources such as power
consumption in national grids or air traffic in metropolitan
areas may be valuable to analyse.
Big Data: A Guided Tour
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THE RISKS OF BIG DATA
The same concerns of traditional research also apply to
the study of Big Data. Some of these are highlighted below,
not because they are unique to Big Data, but because they
are often overlooked due to assumptions about the data or in
the excitement that surrounds this new and developing field.
Bad Data
At Ipsos, the quality of the data we use is fundamental to
all of our projects, and we do not take it for granted when
analysing Big Data. What we collect and how it is classified
is crucial – and care must be taken when integrating and fusing
data, as this can introduce additional measurement and
prediction errors which may result in noise or bias.
We understand that data quality is not simply a question of
good or bad but a range from one to the other, and understanding
biases and the models to deal with them is a critical element
that takes us beyond the simple collection and analysis of data.
What we collect and how it is classified is
crucial - and care must be taken when
integrating and fusing data.
“
“
False Positives
Nassim Taleb (Beware the Big Errors of Big Data) argued that
the number of spurious correlations grows disproportionately
to the number of variables in the data (over 100,000 from
analysing a few thousand variables). Big Data studies aren’t
big just in terms of the number of observations, but also in
the number of variables.
Although there are statistical adjustments that can be made
to lessen the risk (e.g. the Bonferroni correction, holdout
validation, and cross validation), we must take care to
identify relationships that are meaningful both statistically
and substantively. Just as with traditional research, we must
balance the approaches to deal with the increased risk of
false positives with approaches to guard against creating
false negatives.
Generalisability
Issues around the representativeness of Big Data are
sometimes acknowledged, but the full implications are often
underplayed. For instance, while it is known that the content of
Twitter is unrepresentative, these and other online discourses
are often treated as generalisable. While the data they produce
is very valuable, we know that those passionate enough to
discuss toilet paper or nappies online do not represent a
random cross-section of potential purchasers.
This extends to situations where companies look at all the
current users of their products and services. We are mindful
that this information overlooks previous users and potential
purchasers but not current subscribers. This is where
blending more traditional active data sources with Big (and
other passive) Data is helpful.
In the historical parallel, the Literary Digest poll for the 1936 US
Presidential election had an unprecedented sample of 2 million
respondents. In spite of this, their prediction was abysmally
wrong due to the lack of generalisability of the sample. This
lesson that larger does not always mean more representative
extends to Big Data today.
Non-representative data is common in market research, so
understanding and modelling the biases is crucial if we are to
make generalisations. We must be careful not to mistake size
for representativeness.
Big Data: A Guided Tour
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Stability over Time
With so many variables, the possibility for shifts over time
should be considered. For example - the Google Flu Trends
project initially found that looking at people’s search engine
patterns could reveal where outbreaks were happening much
more quickly than traditional approaches. But when the exercise
was repeated, the model led to dramatic over-predictions,
because people’s behaviour had changed in the meantime.
Underlying Causes
Much of the early value of Big Data is in uncovering surprising
relations, like the sale of Strawberry Pop-Tart sales at
Wal-Mart in advance of hurricanes. Deciding what to do with
these insights can still be difficult and understanding what is
behind the patterns is usually important to make the most of
the opportunity they present.
Privacy
Even blinded Big Data can be used to identify individuals, as
seen in the competition Netflix ran to create an improved
recommendation engine. So it is important to apply the same
standard of care here as we do when protecting the rights of
research participants in ‘active data’ studies.
TWO MORE Vs
We think two qualities of Big Data are important, its veracity
and its value. Unlike the three original Vs (volume, variety,
and velocity), they do not describe what Big Data is, but
they are important to consider when moving a discussion
about Big Data from theory to actionable insight.
Veracity
The accuracy of Big Data is important, and this is true
whether examining a single data source or integrating
or fusing different sources. Traditional research issues
of bias and quality are just as important when studying
Big Data.
Value
Any study that we do focuses on providing meaningful
and useful insights. So we apply all of our knowledge to
avoiding the pitfalls and risks listed earlier.
Some of the promises made about Big Data have been
dramatic and ambitious, raising huge expectations. In
turn this has created something of a backlash and some
disillusionment.
With quotes such as the US Executive Office of the President
stating “Big Data is saving lives… making the economy work
better… making government work better and saving
taxpayer dollars”, it is not surprising that many are
wondering why they aren’t benefiting from the
remarkable opportunities.
Big Data: A Guided Tour
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Our view remains that the value of Big Data is very real,
and that there are significant insights to be gained from
examining it, many of which are unique.
But the following points are not always clear:
So, while there are technical challenges, we believe the
main ones are actually more about research in a more
general sense. Big Data is not a black box beyond the
understanding of ordinary people or decision makers.
The value from Big Data only comes when expectations
are realistic, and proper objectives are set.
• What data exists
• What questions can be answered with it
• What its limitations are
• How one can access and mine it
KNOWING WHAT VERSUS
KNOWING WHY
A significant area of debate is whether the discovery of
correlations within Big Data is where most future value
will be, and that past attempts to understand causal
relationships in the data will become much less important.
Put more simply, “knowing what, not why, is good
enough” (Mayer-Schönberger & Cukier, Big Data).
Of course, there is a spectrum of views about this. At
one extreme is the view that “Petabytes allow us to say: Correlation
is enough” (Anderson, The End of Theory), but a more
moderate position is provided by Mayer-Schönberger &
Cukier, namely that “Causality won’t be discarded, but it
is being knocked off its pedestal as the primary fountain
of meaning”.
Sometimes, it’s about knowing ‘what’ people do or say
alone is enough for action. The classic case of Wal-Mart
examining its own sales data in advance of hurricanes
and finding spikes in sales of Strawberry Pop-Tarts
allowed it to further increase sales. But even this analysis
was built by starting with a business question.
Often companies need to understand the ‘why’ to
really understand what they should do in response to
what is discovered in the data. Even the Wal-Mart example
above would benefit by understanding why, so that it
could find out whether other products could be promoted
to tap into the same underlying impulses.
Big Data: A Guided Tour
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BIG DATA NEEDS BIG THEORY
Our view is that there are three components needed
to get the maximum value from Big Data:
• Business Questions and Theory
• Statistical Considerations
• Appropriate Analytic Techniques
• Lacking theory may lead to the ‘so
what’ findings that have driven some
companies to question the value of Big Data
• Lacking statistical considerations
may lead to invalid findings and inferences
• Lacking advanced analytics may lead
tomissingthepatternsandinsightsthemselves
Having only two of the three creates risks:
Big Data: A Guided Tour
11. IPSOS
VIEWS
9
With all three components present, we can understand
the data, use increasingly sophisticated tools to identify
patterns, and determine what is meaningful. Without all
three components, results may lack value or, worse, be
misleading.
We agree that there is some merit in just knowing ‘what’
(not ‘why’), but believe that even the basic decision about
which data source(s) to examine and how to capture the
data requires a focus on a business question.
Ipsos has been growing its expertise and capability
across every aspect of Big Data – from capture, through
analysis, to insight. Our approach, earlier set out in an
Ipsos paper from 2014, begins with understanding the
underlying business purpose.
We are committed to innovation and staying at the forefront
of trends in research, including Big Data and Big Data
Analytics.
The tools of Data Science are often discussed interchangeably
with Big Data Analytics (Cielen and Meysman 2016) and
Ipsos has been growing its capability by linking computational
modelling with statistical analysis in traditional research
domains as well as Big Data.
In addition to developing new tools and techniques, the
Ipsos Science Centre has used tools to help with massive
parallel processing of data sets in the hundreds of
gigabytes and extending into terabytes.
Although this is changing the balance of research that
we do, we will continue to leverage our expertise across
all types of data, including traditional forms, and make
sure we use the most appropriate tools and resources to
best position our clients for success.
So, we do not see Big Data as a simple alternative to traditional
methods like surveys. Our view is that the different types of
data possess distinct merits for strategic and tactical
questions. Depending on the question, these can be
employed separately or in coordination.
As discussed earlier, Big Data includes mostly ‘passive’
data where individuals are not explicitly engaged to
answer questions or otherwise interact with researchers.
While this does provide new opportunities to examine
what individuals are doing and saying; often pairing this
with more traditional active and interactive sources
provides the deepest and most actionable insights.
We do not see Big Data as a simple alternative to
traditionalmethodslikesurveys. Ourviewisthat
the different types of data possess distinct
merits for strategic and tactical questions.
Depending on the question, these can be
employed separately or in coordination.
“
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Big Data: A Guided Tour
12. IPSOS
VIEWS
Rich Timpone leads the business team within the Ipsos Science Centre and is a research
professional with 20+ years of experience and leadership. His role bridges the R&D efforts of
the Ipsos Science Centre, supporting analytic innovation work in areas including Data Fusion,
Customer Retention modelling and Bayesian Network analysis, with the delivery of advanced
analytic solutions to meet client business needs.
Rich’s prior role at Ipsos was leading the Marketing Science team dedicated to P&G which
included partnering in the development of their current global brand equity programme.
Rich came to Ipsos with 8 years of client experience at financial service providers VPI and
Nationwide Insurance; and prior to moving to the private sector served on the faculties of The
Ohio State University and SUNY at Stony Brook for an additional 8 years.
Rich has conducted research, taught, and published extensively in the social sciences and
on research methods including advanced statistical analysis and computational modelling.
He received his M.A. and Ph.D. in Political Science from SUNY at Stony Brook, and his B.A.
from Washington University in St. Louis.
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GAME CHANGERS
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At Ipsos we are passionately curious about people,
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world easier and faster to navigate and inspire
clients to make smarter decisions. We deliver with
security, simplicity, speed and substance. We are
Game Changers.
Big Data: A Guided Tour