The document discusses big data and foresight. It covers the following topic areas: foresight and transdisciplinarity, social media and predictive capabilities, new and innovative information sources, the internet of things, big data scenarios, and how to use foresight as part of daily business operations. It provides examples of different types of analyses that can be used for foresight, including trends analysis, scenarios, forecasting, and historical analysis. It also discusses several case studies and examples, such as predicting the success of movies based on social media data, using search engine queries to detect flu trends, and the large amount of data that will be generated by the Square Kilometer Array radio telescope. Overall, the document outlines how big data and
Transforming instagram data into location intelligencesuresh sood
This document discusses using data from Instagram to conduct location intelligence and internet of things research. It motivates an Instagram project using data like user trajectories to enable predictive capabilities, location-based services, and tourism recommendations. It outlines workflows for data science and discovery analytics on Instagram data, stored using MongoDB due to its support for geospatial data and JSON format. Tools are presented for Instagram analytics and push notification providers.
The document discusses a study conducted by the advertising agency The Works on Twitter usage in Australia, which found that Australians send an average of 234 million tweets per month and most retweets occur on Mondays; it also discusses a software created by Dr. Suresh Sood at UTS that analyzed Twitter data to identify archetypes like lovers, carers and jesters and insights into what Australians are doing on Twitter. The study was the first national Twitter study in Australia and aimed to help marketers communicate more effectively with consumers.
Analytic innovation transforming instagram data into predicitive analytics wi...suresh sood
This document discusses plans to transform Instagram data into predictive analytics. It provides references on predicting human mobility and regularity in activities. Topic areas covered include pattern mining trajectories from Instagram data, NoSQL databases like MongoDB, and a datafication backend. The motivations cited for the Instagram project include using trajectory data for authentication, predictive services for phones/wearables, and location-based applications. Analytics tools for Instagram data and storing Instagram posts efficiently in MongoDB are also mentioned. The roadmap outlines collecting Instagram data, analyzing individual/group patterns, and developing recommendations based on preferences, behavior and trajectory patterns.
Living in Tech City: 50+ Technology Trends and Innovations Transforming Workp...cjbonk
Abstract: This session is geared toward trainers, managers, instructional designers, educators, learners, practitioners, and government officials who share an interest in contemporary advances in learning technologies that are shaping education for today’s and tomorrow’s learner. In this session, Professor Curt Bonk of Indiana University will discuss dozens of technologies and Web resources that have emerged over the past few years to transform corporate training as well as higher education and most other learning settings. Among these technologies tools are smartphones and smart watches, digital course resources, social books, social media, online talking dictionaries, video walls, virtual assistants, and Web conferencing. Also exploding at this time is enrollment in online or virtual learning, blended learning, massive open online courses (MOOCs), and the use of collaborative tools in such e-learning courses. While these 50+ technology trends and innovation are exciting and highly transformative, each has pros and cons in how they are used in different training and education spaces. To make it more personal, this session will, in part, be a presentation, and, in part, a conversation about learning technology trends and innovations. As such, there will be much opportunity for question and answer as well as personal reflection.
AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.
Talk given at Delft University speaker series on "Crowd Computing & Human-Centered AI" (https://www.academicfringe.org/). November 23, 2020. Covers two 2020 works:
(1) Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.
Alexander Braylan and Matthew Lease. Modeling and Aggregation of Complex Annotations via Annotation Distances. In Proceedings of the Web Conference, pages 1807--1818, 2020.
Transforming instagram data into location intelligencesuresh sood
This document discusses using data from Instagram to conduct location intelligence and internet of things research. It motivates an Instagram project using data like user trajectories to enable predictive capabilities, location-based services, and tourism recommendations. It outlines workflows for data science and discovery analytics on Instagram data, stored using MongoDB due to its support for geospatial data and JSON format. Tools are presented for Instagram analytics and push notification providers.
The document discusses a study conducted by the advertising agency The Works on Twitter usage in Australia, which found that Australians send an average of 234 million tweets per month and most retweets occur on Mondays; it also discusses a software created by Dr. Suresh Sood at UTS that analyzed Twitter data to identify archetypes like lovers, carers and jesters and insights into what Australians are doing on Twitter. The study was the first national Twitter study in Australia and aimed to help marketers communicate more effectively with consumers.
Analytic innovation transforming instagram data into predicitive analytics wi...suresh sood
This document discusses plans to transform Instagram data into predictive analytics. It provides references on predicting human mobility and regularity in activities. Topic areas covered include pattern mining trajectories from Instagram data, NoSQL databases like MongoDB, and a datafication backend. The motivations cited for the Instagram project include using trajectory data for authentication, predictive services for phones/wearables, and location-based applications. Analytics tools for Instagram data and storing Instagram posts efficiently in MongoDB are also mentioned. The roadmap outlines collecting Instagram data, analyzing individual/group patterns, and developing recommendations based on preferences, behavior and trajectory patterns.
Living in Tech City: 50+ Technology Trends and Innovations Transforming Workp...cjbonk
Abstract: This session is geared toward trainers, managers, instructional designers, educators, learners, practitioners, and government officials who share an interest in contemporary advances in learning technologies that are shaping education for today’s and tomorrow’s learner. In this session, Professor Curt Bonk of Indiana University will discuss dozens of technologies and Web resources that have emerged over the past few years to transform corporate training as well as higher education and most other learning settings. Among these technologies tools are smartphones and smart watches, digital course resources, social books, social media, online talking dictionaries, video walls, virtual assistants, and Web conferencing. Also exploding at this time is enrollment in online or virtual learning, blended learning, massive open online courses (MOOCs), and the use of collaborative tools in such e-learning courses. While these 50+ technology trends and innovation are exciting and highly transformative, each has pros and cons in how they are used in different training and education spaces. To make it more personal, this session will, in part, be a presentation, and, in part, a conversation about learning technology trends and innovations. As such, there will be much opportunity for question and answer as well as personal reflection.
AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.
Talk given at Delft University speaker series on "Crowd Computing & Human-Centered AI" (https://www.academicfringe.org/). November 23, 2020. Covers two 2020 works:
(1) Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.
Alexander Braylan and Matthew Lease. Modeling and Aggregation of Complex Annotations via Annotation Distances. In Proceedings of the Web Conference, pages 1807--1818, 2020.
You're traveling through another dimension, a dimension not only of sight and sound but of data; a journey into a wondrous land whose boundaries are that of the imagination. In this talk we will learn the relationship between Big Data, Artificial Intelligence, and Augmented Reality. We'll discuss the past, present and futures of these technologies to determine if we are heading towards paradise or into the twilight zone.
Talk given August 29, 2018 at the 1st Biannual Conference on Design of Experimental Search & Information Retrieval Systems (DESIRES 2018). Paper: https://www.ischool.utexas.edu/~ml/papers/lease-desires18.pdf
Social Machines: The coming collision of Artificial Intelligence, Social Netw...James Hendler
Jim Hendler discusses social machines, which he defines as networks of machines supporting networks of people working together in ways that impact the real world. He argues that social networking consumes huge amounts of human time and that this time could be harnessed through social machines to solve problems like curing disease and feeding the hungry. Examples of early social machines include games with a purpose that harness human computation and citizen science projects like Galaxy Zoo. Moving forward, social machines may blend more with artificial intelligence, and their study requires multidisciplinary perspectives from computing, social science, and other fields. Realizing their potential faces both social challenges around online communities and technical challenges in platform design.
Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact...Matthew Lease
Presented at the 31st ACM User Interface Software and Technology Symposium (UIST), 2018. Paper: https://www.ischool.utexas.edu/~ml/papers/nguyen-uist18.pdf
Social Machines - 2017 Update (University of Iowa)James Hendler
This is an update to the talk entitled "Social Machines: the coming collision of artificial intelligence, social networks and humanity." It was presented as an ACM Distinguished Speaker lecture at the "University of Iowa Computing Conference" 2017-02-24
This document discusses the opportunities and challenges presented by big data for social sciences research. New forms of data from social media, tracking, and internet-connected devices allow researchers to study social processes as they unfold in real-time at large scales. However, analyzing this data requires new computational skills and infrastructure. Researchers must also consider new methods and address issues like reproducibility, ethics, and access to ensure quality results. Overall, big data has the potential to transform social sciences by enabling the study of phenomena in new ways, but significant challenges around data, skills, and research practices must be overcome.
Designing Human-AI Partnerships to Combat Misinfomation Matthew Lease
The document discusses designing human-AI partnerships to combat misinformation. It describes a prototype partnership where a human and AI work together to fact-check claims. The partnership aims to make the AI more transparent and address user bias by allowing the user to adjust the perceived reliability of news sources, which then changes the AI's political leaning analysis and fact checking results. The discussion wraps up by noting challenges like avoiding echo chambers and assessing potential harms, as well as opportunities for AI to reduce bias and increase trust through explainable, interactive systems.
Behavioral Analytics with Smartphone Data. Talk at Strata + Hadoop World 2014...Joerg Blumtritt
Joerg Blumtritt, Datarella, at Strata+Hadoop World 2014, Barcelona
Smartphones are the most common wearable devices. In every single smartphone, more than 20 sensors continuously track all kind of behavioral and environmental data, from geo-location to even remote influences like magnetic field and local gravitation.
To develop meaningful models of users’ behavior, we need to put the technical measurements into context. This is done via interactions with an app. We at Datarella trigger the interactions on the phone by geo-fencing (coming near a pre-defined location, e.g. a retailer’s outlet), and other “complex events” using an Event Processing Language (EPL) and a Complex Event Processing Engine (CEPE).
We want also show best practices how to deal with privacy and informational self-determination, and how to give users a fair deal and transparancy to the data, that we collect by their cooperation in our projects.
Get the Strata + Hadoop World in Barcelona 2014 Complete Video Compilation here: http://strataconf.com/strataeu2014/public/sv/q/608
Guest presentation: SASUF Symposium: Digital Technologies, Big Data, and Cybersecurity, Vaal University of Technology, Vanderbijlpark, South Africa, 15 May 2018
A smartphone is a mighty array of sensors. How to access the data, and get meaningful information from the various readings, like geo-location, gyroscope, accelerometer, or even the magnetic flux.
We also discuss the ehtical implication of mobile tracking: informational self-determination, "other-tracking" vs. self-tracking, and how to do spooky things with apparently innocent measurements.
What Actor-Network Theory (ANT) and digital methods can do for data journalis...Liliana Bounegru
Slides from a talk I gave at the University of Ghent on 21 October 2014 about how Actor-Network Theory (ANT) and digital methods can be used to study and inform data journalism.
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013Gigaom
Big data has limitations in solving complex problems. While data science can improve predictions by 10%, human intelligence can improve understanding by 10 times when dealing with small, messy datasets. To effectively use data for strategic problems requires a human-centered approach that recognizes the limits of human processing, acknowledges data biases and gaps, combines data with theory to build understanding, and integrates quantitative findings with qualitative stories. Progress requires collaborative partnerships between humans and machines.
Facilitating Web Science Collaboration through Semantic MarkupJames Hendler
These are the slides that accompanied the paper "Dominic DiFranzo, John S. Erickson, Marie Joan Kristine T. Gloria, Joanne S. Luciano, Deborah McGuinness, & James Hendler, The Web Observatory Extension: Facilitating Web Science Collaboration through Semantic Markup, Proc. WWW 2014 (Web Science Track), Seoul, Korea, 2014." They describe an extension to schema.org that can be used for sharing Web-related datasets and projects.
This document discusses opportunities for the Australian travel and tourism industry in 2015 and beyond based on emerging technology trends. Key points discussed include:
1. The growth of online travel markets and how search engines and social media will influence travel research and planning.
2. Emerging technologies like augmented reality, electronic publishing, and remote robotics that could impact the travel experience.
3. How search engines like Google and Bing are analyzing traveler behavior and customizing travel search results and tasks.
4. The increasing importance of social networks like Facebook and review sites in influencing travel decisions and sharing travel experiences.
5. Possible future scenarios for inbound international tourism in Australia in 2015 based on key technology and social
This document provides an agenda for a presentation on social media marketing practice (SMMP). It discusses topics like marketing transitions, tools and tactics for SMMP, Facebook fan marketing, Twitter implications for real-time marketing, best practices in social media, social graphs, and future implications for jobs. It also includes summaries of research reports on social media usage in Australia and case studies of companies measuring social media referral traffic.
The document discusses using social media for marketing purposes and outlines strategies for various platforms like Facebook, Twitter, LinkedIn and blogs. It provides statistics on usage of these networks by non-profit organizations and discusses how social networks can be used to engage supporters, share content and help create online communities. The document also presents examples of how social media data can provide insights into consumer behavior and trends.
This document provides an agenda for a social media marketing presentation. It includes topics such as the transition to social media marketing, tools and tactics for social media, the birth of video analytics, Facebook fan marketing, social networking analysis, social dashboards, industry adoption and ROI of social media, and emerging trends in social networking. The presentation will also feature a guest speaker on creating and growing communities using Elgg.
You're traveling through another dimension, a dimension not only of sight and sound but of data; a journey into a wondrous land whose boundaries are that of the imagination. In this talk we will learn the relationship between Big Data, Artificial Intelligence, and Augmented Reality. We'll discuss the past, present and futures of these technologies to determine if we are heading towards paradise or into the twilight zone.
Talk given August 29, 2018 at the 1st Biannual Conference on Design of Experimental Search & Information Retrieval Systems (DESIRES 2018). Paper: https://www.ischool.utexas.edu/~ml/papers/lease-desires18.pdf
Social Machines: The coming collision of Artificial Intelligence, Social Netw...James Hendler
Jim Hendler discusses social machines, which he defines as networks of machines supporting networks of people working together in ways that impact the real world. He argues that social networking consumes huge amounts of human time and that this time could be harnessed through social machines to solve problems like curing disease and feeding the hungry. Examples of early social machines include games with a purpose that harness human computation and citizen science projects like Galaxy Zoo. Moving forward, social machines may blend more with artificial intelligence, and their study requires multidisciplinary perspectives from computing, social science, and other fields. Realizing their potential faces both social challenges around online communities and technical challenges in platform design.
Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact...Matthew Lease
Presented at the 31st ACM User Interface Software and Technology Symposium (UIST), 2018. Paper: https://www.ischool.utexas.edu/~ml/papers/nguyen-uist18.pdf
Social Machines - 2017 Update (University of Iowa)James Hendler
This is an update to the talk entitled "Social Machines: the coming collision of artificial intelligence, social networks and humanity." It was presented as an ACM Distinguished Speaker lecture at the "University of Iowa Computing Conference" 2017-02-24
This document discusses the opportunities and challenges presented by big data for social sciences research. New forms of data from social media, tracking, and internet-connected devices allow researchers to study social processes as they unfold in real-time at large scales. However, analyzing this data requires new computational skills and infrastructure. Researchers must also consider new methods and address issues like reproducibility, ethics, and access to ensure quality results. Overall, big data has the potential to transform social sciences by enabling the study of phenomena in new ways, but significant challenges around data, skills, and research practices must be overcome.
Designing Human-AI Partnerships to Combat Misinfomation Matthew Lease
The document discusses designing human-AI partnerships to combat misinformation. It describes a prototype partnership where a human and AI work together to fact-check claims. The partnership aims to make the AI more transparent and address user bias by allowing the user to adjust the perceived reliability of news sources, which then changes the AI's political leaning analysis and fact checking results. The discussion wraps up by noting challenges like avoiding echo chambers and assessing potential harms, as well as opportunities for AI to reduce bias and increase trust through explainable, interactive systems.
Behavioral Analytics with Smartphone Data. Talk at Strata + Hadoop World 2014...Joerg Blumtritt
Joerg Blumtritt, Datarella, at Strata+Hadoop World 2014, Barcelona
Smartphones are the most common wearable devices. In every single smartphone, more than 20 sensors continuously track all kind of behavioral and environmental data, from geo-location to even remote influences like magnetic field and local gravitation.
To develop meaningful models of users’ behavior, we need to put the technical measurements into context. This is done via interactions with an app. We at Datarella trigger the interactions on the phone by geo-fencing (coming near a pre-defined location, e.g. a retailer’s outlet), and other “complex events” using an Event Processing Language (EPL) and a Complex Event Processing Engine (CEPE).
We want also show best practices how to deal with privacy and informational self-determination, and how to give users a fair deal and transparancy to the data, that we collect by their cooperation in our projects.
Get the Strata + Hadoop World in Barcelona 2014 Complete Video Compilation here: http://strataconf.com/strataeu2014/public/sv/q/608
Guest presentation: SASUF Symposium: Digital Technologies, Big Data, and Cybersecurity, Vaal University of Technology, Vanderbijlpark, South Africa, 15 May 2018
A smartphone is a mighty array of sensors. How to access the data, and get meaningful information from the various readings, like geo-location, gyroscope, accelerometer, or even the magnetic flux.
We also discuss the ehtical implication of mobile tracking: informational self-determination, "other-tracking" vs. self-tracking, and how to do spooky things with apparently innocent measurements.
What Actor-Network Theory (ANT) and digital methods can do for data journalis...Liliana Bounegru
Slides from a talk I gave at the University of Ghent on 21 October 2014 about how Actor-Network Theory (ANT) and digital methods can be used to study and inform data journalism.
WHERE IS THE BIG DATA INDUSTRY GOING? from Structure:Data 2013Gigaom
Big data has limitations in solving complex problems. While data science can improve predictions by 10%, human intelligence can improve understanding by 10 times when dealing with small, messy datasets. To effectively use data for strategic problems requires a human-centered approach that recognizes the limits of human processing, acknowledges data biases and gaps, combines data with theory to build understanding, and integrates quantitative findings with qualitative stories. Progress requires collaborative partnerships between humans and machines.
Facilitating Web Science Collaboration through Semantic MarkupJames Hendler
These are the slides that accompanied the paper "Dominic DiFranzo, John S. Erickson, Marie Joan Kristine T. Gloria, Joanne S. Luciano, Deborah McGuinness, & James Hendler, The Web Observatory Extension: Facilitating Web Science Collaboration through Semantic Markup, Proc. WWW 2014 (Web Science Track), Seoul, Korea, 2014." They describe an extension to schema.org that can be used for sharing Web-related datasets and projects.
This document discusses opportunities for the Australian travel and tourism industry in 2015 and beyond based on emerging technology trends. Key points discussed include:
1. The growth of online travel markets and how search engines and social media will influence travel research and planning.
2. Emerging technologies like augmented reality, electronic publishing, and remote robotics that could impact the travel experience.
3. How search engines like Google and Bing are analyzing traveler behavior and customizing travel search results and tasks.
4. The increasing importance of social networks like Facebook and review sites in influencing travel decisions and sharing travel experiences.
5. Possible future scenarios for inbound international tourism in Australia in 2015 based on key technology and social
This document provides an agenda for a presentation on social media marketing practice (SMMP). It discusses topics like marketing transitions, tools and tactics for SMMP, Facebook fan marketing, Twitter implications for real-time marketing, best practices in social media, social graphs, and future implications for jobs. It also includes summaries of research reports on social media usage in Australia and case studies of companies measuring social media referral traffic.
The document discusses using social media for marketing purposes and outlines strategies for various platforms like Facebook, Twitter, LinkedIn and blogs. It provides statistics on usage of these networks by non-profit organizations and discusses how social networks can be used to engage supporters, share content and help create online communities. The document also presents examples of how social media data can provide insights into consumer behavior and trends.
This document provides an agenda for a social media marketing presentation. It includes topics such as the transition to social media marketing, tools and tactics for social media, the birth of video analytics, Facebook fan marketing, social networking analysis, social dashboards, industry adoption and ROI of social media, and emerging trends in social networking. The presentation will also feature a guest speaker on creating and growing communities using Elgg.
The document discusses data science innovation and the future of professions in light of new technologies. It describes how accounting work may be automated or replaced by computer-assisted techniques and predictive analytics software. This would allow accountants to shift from reactive to proactive work by leveraging accounting data and insights to predict client scenarios and advise clients. Key areas discussed include systems of insight using big data, machine engineering to create applications from insights, and the role of data science.
This document summarizes an agenda for an online briefing about non-profit organizations' use of social media. The agenda includes discussions of topics like the benefits of blogging, social networking analysis, building online communities, social objects for marketing, benchmark studies of NFPs' use of Facebook and Twitter, launching a social network service, engaging in online conversations, and how social media helps disabled youth.
This document provides an agenda for a social media marketing practice discussion. It begins with an overview of marketing transition and tools/tactics of social media. Subsequent topics include the birth of video analytics, Facebook fan marketing, B2B social networking, social network analysis, social dashboards for brand monitoring and management, creating and growing communities using Ning, industry adoption and ROI of social media, emerging directions for social networking, and potential new jobs from social media. The document also includes summaries of relevant research papers and examples of social media content practices from various companies.
Session: Advancing Your SMMP - Globally
Presented by: Kevin Iwamoto , Vice President, Industry Strategy, ACTIVE Network Business Solutions and Susan Dupart , Senior Director, Global Travel, Meetings, Corporate Card & Mobility, McAfee Corporate Event Planners
Date and time: Tuesday, June 25, 2013, 3pm
pcma.org/educon
Big data and predictive analytics will transform accounting work and require accountants to develop new skills. By 2018, there will be a shortage of 30,000 data-savvy managers in Australia who can make effective decisions based on big data analysis. Accountants will need to shift from reactive to proactive roles by leveraging accounting data and predictive tools to find patterns, gain insights, and predict client scenarios in order to maximize opportunities and minimize risks for their clients. The "predictive accountant" who adopts these new data-focused skills will be well-positioned for the future of the profession.
This document discusses several topics related to data and data-driven businesses. It begins by outlining trends in big data and machine learning. It then discusses how to build data-centric businesses by identifying data opportunities and sources, understanding the data lifecycle, and extracting value from data. Examples are provided of Netflix as a data-driven company. The future of professions in a data-driven world is also examined, as well as talent scarcity issues and the need for data-savvy managers. The document provides an overview of many relevant topics at the intersection of data and business.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
Data Science Innovations is a guest lecture for the Advanced Data Analytics (an Introduction) course at the Advanced Analytics Institute at University of Technology Sydney
This document discusses various topics related to data science innovations including natural language generation, systems of insight, and deep learning. It provides an overview of these areas and references additional resources. It also discusses data science algorithms and how companies are using them to reimagine business processes. Finally, it considers the roles of statistics, data mining, and data science and how they differ in terms of the type of data and analysis used.
This document discusses several topics related to data science innovations including:
1. The democratization of data science and big data through more accessible tools and platforms.
2. How companies are using algorithms to reimagine business processes and gain customer insights.
3. Emerging trends in natural language processing, systems of insight, and using alternative data sources.
4. The potential for natural language generation to automatically present narratives and insights from data.
5. How these techniques can help organizations move from traditional analytics to more cost effective systems of insight.
Netnography online course part 1 of 3 17 november 2016suresh sood
The document discusses findings from two studies on social media usage in Australia. The first study found that Australians send an average of 234 million tweets per month, with females more likely to retweet than males. The second study was the first analysis of Instagram usage in Australia. It also discusses a social media research project called "Datafication" that analyzed Twitter data to understand user motivations and behaviors. Software created by Dr. Suresh Sood then analyzed the data to produce insights into what people do on Twitter.
Researchers, Discovery and the Internet: What Next?David Smith
A web2.0 issues and implications overview I put together for the Research Information Network as part of their workshop on researchers and discovery services.
http://www.rin.ac.uk/discovery-services-workshop
This is a citizen science overview particularly aimed at graduate students enrolled in a new course at Arizona State University, aptly titled "Citizen Science." The author of this presentation, and course instructor, Darlene Cavalier, will talk students through its nuances and intersections with science, technology, and society.
Digital research: Collections, data, tools and methods Stella Wisdom
Presentation for the Economic and Social Research Council North West Social Sciences Doctoral Training Partnership event on 26th November 2021, by Stella Wisdom, Digital Curator, British Library
1. The document outlines three goals - to inspire interest in science through Science Cheerleaders, engage more citizen scientists through SciStarter, and empower citizen involvement in science policy through ECAST.
2. SciStarter is a website that connects regular people to real science projects they can participate in, such as monitoring birds, water quality, and analyzing space images and data. It has millions of citizen scientist volunteers.
3. ECAST brings citizen science and citizen input to science policy matters by interfacing citizen scientists with policymakers and facilitating participatory technology assessments on issues like analyzing space microbes and forecasting solar activity risks.
This document discusses leveraging social big data and the evolution from existing rigid operations to predictive analytics using social media. It begins with an overview of handouts and reference materials on big data, Hadoop, Spark, and data science projects. It then discusses areas for conversation around social content, structure and analytics, data science primers and resources, and data science innovation. It presents a roadmap showing the evolution from rigid and siloed operations to being more flexible, connected, adaptive and predictive using social media. Finally, it discusses types of intentionality and how social CRM can integrate social data.
NYC Data Science Meetup: Computational Social Sciencejakehofman
An emerging field called computational social science leverages the ability to collect and analyze large datasets to study questions in social sciences. This field is occurring in technology companies and government agencies but could also be established in open academic environments. Computational social science intersects social sciences, statistics, and computer science to address long-standing questions through large-scale data analysis and modeling.
Slides from keynote lecture by Andrew Prescott to the 7th Herrenhausen conference of the Volkswagen Foundation, 'Big Data in a Transdisciplinary Perspective'
Introduction to privacy feedback research @ DesRes2016Alessandro Carelli
This document provides an introduction to research being conducted on feedback methods for privacy-sensitive runtime data leaked by smartphone applications. It discusses the personal information commonly leaked by apps, including name, email, location, and other details. The document outlines the research aim to find novel ways to visualize leaked data and improve user awareness and control. It reviews related work on eco-feedback displays and discusses technological and economic challenges to privacy. The goal is to extend understanding of user expectations and conduct empirical studies to design and evaluate new privacy feedback solutions.
Information Literacy, Privacy, & Risk: What Are the Implications of Mass Surv...g8briel
In light of new revelations about government warrantless wiretapping and electronic surveillance what role do librarians have in educating our patrons about digital privacy and security issues? Given that digital privacy is further complicated by for-profit Internet companies services, such as those provided by Facebook and Google, are our users savvy enough to understand threats to their information in this increasingly complex digital landscape? This presentation will explore issues related to current events and information security with an eye towards the implications for information literacy standards; brief examination of tools used to enhance information privacy; and discuss how librarians might play a role in helping users become more information aware.
This document summarizes a presentation by Little SDO and Camilla SDO about using social media to educate the public about science. It discusses the NASA Solar Dynamics Observatory mission and its goals of understanding the Sun's influence. It introduces Camilla Corona SDO, the NASA mission mascot who uses social media platforms like Facebook, Twitter, and YouTube to get people excited about science and space weather. Examples are given of how Camilla and Little SDO use educational and fun content on social media to start dialogues and inspire people about NASA's work.
Will We Command Our Data? From the Petascale to the PersonalRichard Akerman
The document discusses the increasing amount of data being generated from various sources and the drivers pushing for greater access and sharing of data. It notes that petabytes of data are being created from research, government, libraries, and personal sources. Specific drivers include open data policies from organizations like the OECD and memorandums calling for increased transparency of government data. Libraries are seen as advocates and experts who can help ensure access to and reuse of different types of data. The talk examines issues around accessing raw data versus APIs and examples of platforms and projects dealing with research, government, and personal data.
Getting to the Edge of the Future - Tools & Trends of Foresight to Nowcastingsuresh sood
The document discusses tools and trends in foresight and nowcasting. It covers topics such as a quick history of foresight, nowcasting using social media and predictive capabilities, the Recorded Future architecture for collecting time-tagged facts from over 70,000 real-time sources, and new and innovative information sources like social media and the Internet of Things. Examples of using these tools and data sources to predict events, trends, and innovations are also provided.
This document discusses various topics related to data science innovations including:
- The democratization of data science and big data through tools and technologies.
- How companies are reimagining business processes using algorithms and data science.
- Natural language processing, natural language generation, and systems of insight being important trends and technologies.
- Various resources mentioned like courses, reports, libraries, platforms, and hardware related to data science.
- The importance of using data science to generate insights from data and tell stories through natural language generation to drive business value.
This document discusses data science innovations and systems of insight. It provides examples of new data sources like social media language and drone/mobile sensor data that can generate novel insights. Systems of insight use machine learning and natural language generation to automatically analyze data, detect patterns, and present findings and narratives to users without extensive data preparation. This approach reduces the time spent on data wrangling and moves organizations from crisis-level talent shortages to faster decision making. The document advocates starting to use innovative data sources and systems of insight to generate customer insights, optimize processes, and gain a competitive advantage.
This document discusses big data, the Internet of Things (IoT), analytics, and foresight. It focuses on natural language generation, systems of insight, and deep learning. Key points include that by 2020-2022, natural language generation will be used in smart data discovery platforms to automatically present narratives summarizing findings from data. Systems of insight will enable automated pattern extraction, outlier detection, correlation analysis, and integration of analytics with processes, applications, and IoT systems. The document provides references and examples relating to big data, data science, machine learning, and the use of algorithms.
The document discusses the future of accounting and the rise of "big data accounting" and the "predictive accountant" by 2020-2022. It notes that algorithms will positively alter the behavior of over 1 billion people and blockchain business will be worth $10 billion. It highlights that analytics accounting professionals using tools like predictive analytics, data mining, and data science will become more common. Overall, the accounting profession will need to adapt to incorporating big data and new data-driven technologies to provide insights to clients.
Insights-driven businesses that utilize systems of insight rather than just dashboards will generate $1.2 trillion in revenue by 2020, growing at a faster rate than other companies. Systems of insight automate the extraction of patterns from diverse data sources like social media and IoT to provide actionable insights. They reduce the time spent analyzing data and increase the time spent making decisions, reimagining business processes. Adopting systems of insight helps move beyond traditional analytics and crisis-level talent issues while minimizing data preparation efforts.
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The document discusses tools for analyzing dark data and dark matter, including DeepDive and Apache Spark. DeepDive is highlighted as a system that helps extract value from dark data by creating structured data from unstructured sources and integrating it into existing databases. It allows for sophisticated relationships and inferences about entities. Apache Spark is also summarized as providing high-level abstractions for stream processing, graph analytics, and machine learning on big data.
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This document summarizes an interactive master class on putting the human context into business using big data perspectives. The class covered various topics:
1. Datafication and analyzing social media data like tweets, Instagram posts, and blogs to understand human behaviors and motivations.
2. Tools for linguistic analysis of text like LIWC, RID, and Twitter analysis to study personality, deception, and predict marketing based on word usage.
3. Developing a predictive, empathetic organization using social listening and recognizing distress signals to improve customer experience.
4. An ongoing study of baby feeding experiences analyzing video signals of joy to understand communication and develop recommender systems.
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1. Indigenous Australians and acknowledging traditional owners of the land.
2. Important dates and public holidays in Australia as well as profiles of the Australian and Young Australian of the Year for 2014.
3. Key aspects of Australian history from British colonization to modern icons and innovations like WiFi and the Square Kilometer Array radio telescope.
4. Australians as predominantly urban dwellers and statistics about major cities.
5. Elements of Australian business culture like introductions, negotiating, and entertaining.
6. The multicultural nature of Australian society today with over a quarter of residents born overseas.
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- Historical examples show how prizes like the Longitude Prize and the Orteig Prize spurred innovation through competitions.
- Modern examples include the Ansari X Prize for private spaceflight and the Netflix Prize for improving movie recommendations.
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This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
2. Topic Areas
1. Foresight and Transdisciplinarity
2. Social media and predictive capabilities
3. New and innovative information sources
4. Internet of Things
5. Big Data Scenarios
6. How to use foresight as part of daily business operations
3. Concept of Transdisciplinarity
Tress, B. Tress, G. & Fry, G. (2006). Defining concepts and the process of knowledge production in integrative research. In B. Tress, G. Tress, G. Fry
& P. Opdam (Eds.), From landscape research to landscape planning: Aspects of integration, education and application (pp. 13-26). Dordrecht:
Springer. https://library.wur.nl/ojs/index.php/frontis/article/view/1096/667
6. Future of Higher Education in Australia, S. Welsman, Australian, 11 Jan, 2006
Collapse
of
Higher
Education
2008 -
Regulatory
Change
Technology
Student
needsGlobal
Competition
Demo-
graphics
2008 - 2016
Variety of :
Providers
Alliances
Course options
Content
Delivery modes
7. Global Strategic Trends out to 2045
UK Ministry of Defence, 15 July 2014
https://www.gov.uk/government/publications/global-strategic-trends-out-to-2045
10. Datafication 2 : First National Study of Twitter Usage in Australia
Australians send an average of 234 million tweets per month and 5,000 tweets per minute, a new Twitter
study by advertising agency The Works has found. Aussie females are more likely to retweet than males
and most retweets occur on Mondays, according to the agency's 'datafication' research project. Douglas
Nicol, creative partner and director at The Works, said the study was designed to help marketers talk to
consumers more effectively. “There’s a lot of hype around social media. Using research from datafication,
we are able to equip Australian marketers with no nonsense practical advice,” Nicol said.“This in turn will
help marketers appeal directly to an audience. We believe that in turn, this will boost the way people view
and talk about a brand or product online.”
Lovers, carers and jesters were identified as the top three archetypical personalities on Twitter.
According to the study marketers can talk most effectively to lovers by being passionate, carers by being
gentle and jesters by being mischievous.“If you understand what drives the motivations behind Australians
you will be in a better position to connect with them,” Nicol said. Almost 11% of the Australian population
is on Twitter and of those users 46% are male and 54% are females.
The study also found that Sydney hosted the largest population of Twitter users while Hobart is
responsible for the most tweets per capita.
'Datafication', which was supported by the University of Technology Sydney (UTS), analysed the most
popular words used in Twitter over an eight week period to rank motivations and behaviours on the
social site.
Software created by Dr Suresh Sood, a social media expert at UTS, then analysed the data to produce
the insights into what individuals are doing on Twitter.
'Datafication' is set to launch as a real-time service for the agency’s clients early next year.
12. Analytic Insights from Millions of Images
• Sunday at 5pm is the peak usage for Instagram in Australia while on
weekdays 8pm is the most popular posting time
• The average Aussie Instagram user posts 2.3 times a week with around 10
posts being made a month
• Sydney, Brisbane and the Gold Coast are the ‘selfie’ capitals of Australia,
with more pictures of people taking photos of themselves posted than any
other category
• In Melbourne images of food are the most popular Instagram subject, while
in Perth its portrait piccies and in Adelaide it’s more artistic shots.
• Brand recognition on Instagram is low. The most popular hashtag is
#instagood with more than 1.6 million references, however brands such as
McDonald’s, Nike and Holden have been hashtagged less than 15,000 times.
14. The Newman Model of Deception (Pennebaker et al)
Key word categories for deception mapping:
1. Self words e.g. “I” and “me” – decrease when someone distances
themselves from content
1. Exclusive words e.g. “but” and “or” decrease with fabricated
content owing to complexity of maintaining deception
1. Negative emotion words e.g. “hate” increase in word usage owing
to shame or guilty feeling
1. Motion verbs e.g. “go” or “move” increase as exclusive words go
down to keep the story on track
15. Ideas concerning thinking between Australia and Chinese citizens (1st
May 2014)
Chinese
social networks
Australian
social networks
• Sina weibo (132,555,895)
• QQ weibo (3,721,300)
• Taisha BBS (1,228,967)
In total: 137,506,162
• Tigtag (21,755,909)
• Oursteps (14,568,879)
• Yeeyi(6,635,153)
• FreeOZ (4,718,210)
In total: 47,678,151
The numbers are the Australia-related posts found in each site.
16.
17.
18. Twitter and Marketing Predictions
• Tweets is “found data” without asking questions
• More meaning than typical search engine query
•
• Large numbers of passive participants in natural settings
• Twitter can predict the stock market (Lisa Grossman, Wired, Oct 19 2010)
• Predict movie success in first few weekends of release
– “…it also raises an interesting new question for advertisers and marketing
executives. Can they change the demand for their film, product or service buy
directly influencing the rate at which people tweet about it? In other words,
can they change the future that tweeters predict?”
Tech Review, http://www.technologyreview.com/blog/arxiv/25000/
18
20. Data Types
• Astronomical
• Documents
• Earthquake
• Email
• Environmental sensors
• Fingerprints
• Health (personal) Images
• Location
• Marine
• Particle accelerator Satellite
• Scanned survey data Social media
• Sound
• Text
• Transactions
• Video
21. Square
Kilometer Array
(SKA)
• The data collected by SKA in a single day take nearly two million years to playback on an MP3 player The
SKA central computer has processing power of about one hundred million PCs.
• The SKA will use enough optical fiber linking up all the radio telescopes to wrap twice around the Earth.
• The dishes of the SKA when fully operational will produce 10 times the global internet traffic as of 2013.
• The aperture arrays in the SKA could produce more than 100 times the global internet traffic as of 2013.
• The SKA will generate enough raw data to fill 15 million 64 GB MP3 players every day.
• The SKA supercomputer will perform 1018 operations per second - equivalent to the number of stars in
three million Milky Way galaxies - in order to process all the data that the SKA will produce.
• The SKA will be so sensitive that it will be able to detect an airport radar on a planet 50 light years away.
• The SKA will contain thousands of antennas with a combined collecting area of about one square
kilometer (that's 1,000,000 square meters).
• Previous mapping of Centaurus A galaxy took a team 12,000 hours of observations and several years.
SKA ETA 5 minutes !
To the scientists involved, however, the SKA is no testbed, it’s a transformative instrument
which, according to Luijten, will lead to “fundamental discoveries of how life and planets and
matter all came into existence. As a scientist, this is a once in a lifetime opportunity.”
Sources: http://bit.ly/amazin-facts & http://bit.ly/astro-ska
Galileo
22. Centaurus A
Image credit: Ilana Feain, Tim
Cornwell & Ron Ekers
(CSIRO/ATNF). ATCA northern
middle lobe pointing courtesy R.
Morganti (ASTRON), Parkes data
courtesy N. Junkes (MPIfR).
Centaurus A
The image has been created by Dr Ilana Feain and
her team using CSIRO’s Australia Telescope
Compact Array telescope near Narrabri in New
South Wales to observe the galaxy over several
years.
With ASKAP these same observations will take just
five minutes. In first six hours of operation, ASKAP
will generate more information than all previous
radio telescopes in the world combined.
23. Number of journeys made
Distances travelled
Types of roads used
Speed
Time of travel
Levels of acceleration and braking
Any accidents which may occur
(http://bit.ly/Black_box)
http://tacocopter.com/
New Sources of Information (Big data) : Social Media + Internet of Things
Data Driven Innovations
24. The ANZ Heavy Traffic Index comprises flows
of vehicles weighing more than 3.5 tonnes
(primarily trucks) on 11 selected roads around
NZ. It is contemporaneous with GDP growth.
The ANZ Light Traffic Index is made up of light
or total traffic flows (primarily cars and
vans) on 10 selected roads around the
country. It gives a six month lead on GDP
growth
http://www.anz.co.nz/commercial-institutional/economic-markets-research/truckometer/
25. Smartphone, Google Glass or Apple Watch will
Know What you Want before you do
“…from 2014 your phone [glasses or watch] will
anticipate your needs, do the research, tell you
what what you want to know – sometimes
before the question even occurs to you…”
Chapman, Jake (2013), The Wired World in 2014
26. Useful References Informing our Thinking
on Mobility and Movement
(Silva et al (2013) A comparison of Foursquare and Instagram to the study of city
dynamics and urban social behavior, Proceedings of the 2nd ACM SIGKDD
International Workshop on Urban Computing
Instagram and Foursquare datasets might be compatible in finding popular regions of
city
Chaoming Song, et al. (2010), Limits of Predictability in Human Mobility, Science
There is a potential 93% average predictability in user mobility, an exceptionally high
value rooted in the inherent regularity of human behavior. Yet it is not the 93%
predictability that we find the most surprising. Rather, it is the lack of variability in
predictability across the population.
Scellato et al. (2011), NextPlace: A Spatio-temporal Prediction Framework for
Pervasive Systems. Proceedings of the 9th International Conference on Pervasive
Computing (Pervasive'11)
Daily and weekly routines => Few significant places every day => Regularity in human
activities => Regularity leads to predictability
27. Domenico, A. Lima, Musolesi.M. (2012) Interdependence and Predictability of Human
Mobility and Social Interactions. Proceedings of the Nokia Mobile Data Challenge
Workshop.
we have shown that it is possible to exploit the correlation between movement data and
social interactions in order to improve the accuracy of forecasting of the future geographic
position of a user. In particular, mobility correlation, measured by means of mutual
information, and the presence of social ties can be used to improve movement forecasting
by exploiting mobility data of friends. Moreover, this correlation can be used as indicator of
potential existence of physical or distant social interactions and vice versa.
Sadilek, A and Krumm, J. (2012) Far Out: Predicting Long-Term Human Mobility
Where are you going to be 285 days from now at 2pm …we show that it is possible to
predict location of a wide variety of hundreds of subjects even years into the future and
with high accuracy.
Useful References Informing our Thinking
on Mobility and Movement
28. Roadmap to Recommender Tool
Data
collection
Individual(Group) Analysis
Find Preference and Behavior
pattern(including Trajectory pattern)
Recommendation
Recommend right product (or
service) to right person ( or
group) at right time and place
Manual Automatic Recommndation
36. Big Data Scenarios
• Broadcasting
• Education
• Insurance
• Brands - Luxury goods
• Retailing
37. Big Data and Broadcasting in Australia 2014
As an advertising data analyst working with broadcasters and content providers, I recognise online
advertising alone is worth over $4 billion in 2014. (http://www.iabaustralia.com.au/). To help clients and my
own employer benefit from the change occurring as well as maintain increases in salary, I undertook the
MDSI. This degree helped to provide me with a brush up of core analytical skills (mathematics and
statistics), technical (programming in the context of big data), soft skills of storytelling to help fellow team
members understand actions stemming from the data analysis and innovation to explore new data sources
to help find new solutions.
Just looking at TV advertising alone, a major paradigm shift is underway. Since the 1950s, TV advertising is
literally hardwired into the show. Hence, the entire audience of one million watching “Home and Away” got
to see the same advertisement. This is no longer the case, TV is becoming available everywhere. The
television has broken free from the box in the living room with many Australians watching programs on
mobile and tablets. TV programs are even available on demand at the whim of a viewer. This second screen
or multi-platform phenomena creates new opportunities including an entirely new revenue stream from the
viewings happening outside of the TV. For example, the sports fans in Australia receive real time statistics
and scores for the Australian Open generating via mobile creating more data for analysis (IBM 2014).
Predictions are possible being able to determine a hit even before filming (House of cards; Carr 2013). Other
data sources available are from the actual digital players on the second screen providing users and times of
pausing, forward, rewinding and replaying or stopping the TV show. Complimenting this viewer data with
the comments on TV shows and films in social media provides an understanding of viewer behavior during
and outside of the program schedule.
Using the variety of data, I am expected to help provide reporting and dashboards to support:
An increase in revenue through monetizing new views across devices
The development of new programs or movies
Web or mobile products around popular shows including recommender systems
38. Big Data and Insurance in Australia 2014
Since the launch of insurance box (Collett 2013; insurancebox.com.au) in Australia, a rethink of our existing
approach to insuring drivers is driving changes to our existing business structure and processes. Previously,
the actuaries provided the necessary quantitative skills. Today, the transforming business requires data
science for insurance capabilities across our entire enterprise. Some of this capability includes our existing
actuaries with additional training in machine learning. The necessity to move to a data driven organization is
fostered by our data science graduates. The organization now uses data and analysis over the previous gut
feel and intuition in all facets of business including the growth of our new revenues via the website and
mobile from younger drivers adopting the “black box” recorder. Our data science team focuses on data
collection from the black boxes, analysis and reporting as well as ongoing deployment of new sources of
information enriching the business.
Totally new capability is now available within the business not hitherto seen. This includes A/B testing of the
online environment, trend analysis, segmentation, text mining as well as the important black box dashboard
visualisations of driving scores (http://insurancebox.com.au/tour). The business is now able to acquire close
to real time from our drivers, make predictions and start to look forward to creating new products entirely
tailored to the needs of our individual customers with a “pay as you” go business model. True driving
behavior as well as the actual overnight location of the car determines the insurance price not the previous
statistical base of drivers and demographics.
39. Big Data and Education 2014
As a project manager for a local educational institution, I am driving our student dashboard
project. I attended the MSCI course to understand the project management of data science
intensive projects while mindful of privacy and the ability to innovate using new data
sources. The idea behind the project is to predict and student challenges with regard to
academic performance and any behavioral challenges. The dashboard notifies lecturers and
tutors with a recommended course of action. Students receive similar recommendations
directly.
The key aim of the dashboard is ensuring the student is able to perform at the best while
maximizing student retention through providing assistance to help with grades. The system
generates a predictive model from past performance, library usage, wi-fi hot spots around
campus and frequency of access of the learning management system.
As project manager, I am continually on the look out to integrate new information sources
including social media providing a representation of the student behavior as well as
ensuring the system output provides students with directly actionable information. The
system undergoes regular penetration security testing at each new release ensuring the
privacy of student information
40. Big Data and Luxury Goods 2014
Forecasting the sale of our luxury goods has often been an art rather than a science. As a marketing data
analyst, I support the sales and marketing team by honing forecasts to bring just enough stock from Italy to
satisfy demand in Australasia. Furthermore, the provision of accurate forecasting is complicated with
customers not just buying from bricks and mortar stores but online directly from our own Website and other
online properties. Online shopping revenue sales 2013/14 according to IBISWorld is AUD12.4 billion.
In 2016, I attended the MDSI course and enhanced my profile as a marketing data analyst/scientist. The
course was instrumental in providing me with a mix of mathematical and statistical skills as well as coding.
Innovation aspects of the course let me feel comfortable with introducing new approaches in the business.
The course is timely, as the company had been thinking about offshoring our data analysis capabilities work
to India and the company MuSigma. Instead, the expertise gained from MDSI allows me to develop and lead
a balanced team of marketing specialists and general data talent. The communication skills learnt from the
course form the basis of the shared language of data analysis amongst team members and executives.
Amongst the innovation, I have put in place is the big data forecasting system. The MDSI course reinforced
the notion of finding new innovative sources for making marketing discoveries. The idea is simple to use a
variety of information sources not previously considered to help improve our luxury goods forecasting. With
the team, we hit upon using indicators derived from Tasmanian oyster farms (Sense-T), Twitter luxury goods
bloggers and finding correlations with historical sales. This helps us forecast 6 months in advance with far
greater accuracy than previously available. The data is multi-structured and required using Hadoop to
acquire the large volumes of data. The approach learnt from MDSI worked to a tee by starting simple with
the forecasting problem, building the team to including mathematical/statistical specialists, training on not
just sandboxes but the actual cluster of 4 nodes for processing the data and documenting the results.
41. Big Data and Retailing in Australia 2014
Retailing is not only one of the world’s top ten industries and in some countries (e.g. India) the largest but
above all presents great opportunity for data science innovation as “the retail store experience is set to
change more in the next five years than it has over the past century” (McKinsey 2013). In Australia
Woolworths and Wesfarmers are in the top 20 worldwide retailers (Deloitte 2013). Until recently, the
understanding of shopper behaviour in the Australian and global marketplaces is built atop volumes of
scanner data under the control of the major retailers e.g. Wesfarmers and Woolworths working in
conjunction with a handful of large research organisations (Nielsen and Ipsos). Hence, the research insights
on shopper behaviour are often not transparent and not readily available to members of the global retail
ecosystem including brand owners.
As head of the retail analytics team with a major retailer, the importance of extracting value from the big
data available from the supply chain, point of sale, online web site and shoppers is paramount. The data
sources include store cameras to obtain shopper demographics, tracking behavior and heat mapping
around key store positions.
Today, as the head of retail analytics, we can utilise data mining techniques in conjunction with multi-
structured data inclusive of shopper CCTV videos, traffic counters, online click stream data of retailers using
social media to make pre-purchase buying decisions, the opportunity exists to generate predictions built on
highly transparent big data sets combining rich data from a variety of information sources. However, these
rich information sources are real time signals providing very big multi-structured data streams not
traditionally manageable by retailers or research organisations. The retailer interest includes the availability
of predictions to not only help with minimising stock-outs but even help ensure correct staffing levels. The
Mckinsey Global Institute Big Data report (2011) supports this thinking by recognising “…the use of large
datasets will continue to transform the face of retail…”.
42. Roadmap – Evolution from Existing Operations to Predictive
Making Foresight Relevant to Daily Operations !
Rigid Flexible Connected
What if conversations continue?
(Adapted from Solis, 2012 and Davenport 2007)
Themes
Silo, rigid
Hoarding info
Vs. collaboration
Freely share info and
Knowledge on internal basis
acting social with customers
2 –way communications
Connected internal and
External. Listening and
Learning. Internal and
external engagement
Shared via hub and
Spoke. Employees
Connected directly to
Customers.
Adaptive
Agile, integrate customer
Experiences and feedback
Loops. Listening and
Learning now become
analyse and insights
Makes sense of data
And transforms into
Intelligence.
Respond in Real time
Predictive
Shift from reactive to
Proactive and predictive
Business uses social
media heavily and is
flexible, connected,
adaptive and predictive
in terms of customer
experiences,
needs and new
opportunities. Predict
scenarios before they
occur maximise
opportunity and limit risk
How can we lead conversations?
(predictive recommendation)
What conversations are next?
Why are these conversations occurring?
What actions are required?
What are the sentiment of conversations?
When and where are conversations taking place?
What conversations are taking place?
Business
Intelligence
42
43. The future is impossible to predict. However one
thing is certain :
The company that can excite it’s customers
dreams is out ahead in the race to business
success
Selling Dreams, Gian Luigi Longinotti
43