Patrick Deglon worked at CERN from 1996-2002 where he analyzed particle collision data from experiments. He used large-scale computational analysis and statistics to make discoveries about particle properties and interactions. In 2004, he joined eBay where he now leads analytics to understand customer behavior and measure the impact of initiatives using A/B testing and other techniques. He discusses how the challenges of analyzing large datasets at CERN prepared him for working with eBay's "big data".
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
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
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
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
From the MarTech Conference in London, UK, October 20-21, 2015. SESSION: The Human Side of Analytics. PRESENTATION: The Human Side of Data - Given by Colin Strong - @colinstrong - Managing Director - Verve, Author of Humanizing Big Data. #MarTech DAY2
From the webinar presentation "Data Science: Not Just for Big Data", hosted by Kalido and presented by:
David Smith, Data Scientist at Revolution Analytics, and
Gregory Piatetsky, Editor, KDnuggets
These are the slides for David Smith's portion of the presentation.
Watch the full webinar at:
http://www.kalido.com/data-science.htm
In the age of information overload, having a social media measurement practice is the key to successful execution of your social strategy. In this session, Debra Askanase looked at what data points tell you that your community cares and is willing to take action, a methodology to figuring what data is relevant to your outcomes, where to find the metrics that matter, and why setting up the right metrics can make the difference between knowing that people visited a page on your website, and if your social media actions sent them there.
VERANTWOORDE WAARDECREATIE MET BIG DATA EN ARTIFICIAL INTELLIGENCE: HOE DAN? ...webwinkelvakdag
Toepassing van Big Data en Artificial Intelligence creëert mogelijkheden maar roept ook vragen op. Privacy is een belangrijk aspect, maar ook onderwerpen als bias, transparantie, veiligheid en betrouwbaarheid hebben nog veel aandacht nodig omdat er vooralsnog meer vragen dan antwoorden zijn. In de Nationale Wetenschaps Agenda (NWA) zijn dit soort vragen ook naar boven gekomen: uit de 13700 vragen die de Nederlandse samenleving indiende is (onder andere) een 'big data route' gedestilleerd. Binnen die route werkt een groot consortium (VWData) van Nederlandse universiteiten, andere onderzoeksinstellingen en stakeholders uit de toepassingsdomeinen aan antwoorden. Hierbij zijn de concepten FAIR en FACT onze leidraad: FAIR betekent Findable, Acessible, Interoperable en Reusable. FACT betekent fair, accurate, confidential en transparent. Doel van het programma is te laten zien hoe deze schijnbaar abstracte concepten zowel concreet als wetenschappelijk kunnen worden ingevuld.
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.
Lecture given at the University of Catania on December 2nd, 2014.
Start from Big Data definitions, continue with real life examples of successful Big Data Projects, go a little bit deeper with Sentiment Analysis, and conclude with a brief overview of Big Data tools and Big Data with Microsoft.
Summary:
1. What is Big Data? (includes the 5Vs of Big Data)
2. Big Data Examples (includes 6 Real Life Examples and comments on Privacy concerns)
3. How to Tackle a Big Data Problem (my 4 Universal Steps to follow)
4. Sentiment Analysis (what is sentiment analysis? Why do we care? A Technique and a plan)
5. Big Data tools (Hadoop, Hadoop Ecosystem, Hive, Pig, Sqoop, Oozie; Azure HDInsight, Excel Power Query, Power Pivot, Power View, Power Map)
Tijdens de vierde sessie van de vierdelige reeks Master Minds on Data Science hield Eric van Tol een presentatie over businesscases en verdienmodellen.
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.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
The concept of Big data has existed for several decades now, but during the past couple of years it has become so trendy that even big data professionals can't figure out what it is and what it isn't. So am I. One part of me thinks it's nothing but a Bubble and the other thinks it's the next Big Bang. In this presentation I have chosen to put forth my views from both sides of the bridge. What do you think?
Hope you enjoy the presentation.
The Black Box: Interpretability, Reproducibility, and Data Managementmark madsen
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust.
Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models.
Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.
This talk will treat the black boxed of ML the way management perceives them, as black boxes.
There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
From the MarTech Conference in London, UK, October 20-21, 2015. SESSION: The Human Side of Analytics. PRESENTATION: The Human Side of Data - Given by Colin Strong - @colinstrong - Managing Director - Verve, Author of Humanizing Big Data. #MarTech DAY2
From the webinar presentation "Data Science: Not Just for Big Data", hosted by Kalido and presented by:
David Smith, Data Scientist at Revolution Analytics, and
Gregory Piatetsky, Editor, KDnuggets
These are the slides for David Smith's portion of the presentation.
Watch the full webinar at:
http://www.kalido.com/data-science.htm
In the age of information overload, having a social media measurement practice is the key to successful execution of your social strategy. In this session, Debra Askanase looked at what data points tell you that your community cares and is willing to take action, a methodology to figuring what data is relevant to your outcomes, where to find the metrics that matter, and why setting up the right metrics can make the difference between knowing that people visited a page on your website, and if your social media actions sent them there.
VERANTWOORDE WAARDECREATIE MET BIG DATA EN ARTIFICIAL INTELLIGENCE: HOE DAN? ...webwinkelvakdag
Toepassing van Big Data en Artificial Intelligence creëert mogelijkheden maar roept ook vragen op. Privacy is een belangrijk aspect, maar ook onderwerpen als bias, transparantie, veiligheid en betrouwbaarheid hebben nog veel aandacht nodig omdat er vooralsnog meer vragen dan antwoorden zijn. In de Nationale Wetenschaps Agenda (NWA) zijn dit soort vragen ook naar boven gekomen: uit de 13700 vragen die de Nederlandse samenleving indiende is (onder andere) een 'big data route' gedestilleerd. Binnen die route werkt een groot consortium (VWData) van Nederlandse universiteiten, andere onderzoeksinstellingen en stakeholders uit de toepassingsdomeinen aan antwoorden. Hierbij zijn de concepten FAIR en FACT onze leidraad: FAIR betekent Findable, Acessible, Interoperable en Reusable. FACT betekent fair, accurate, confidential en transparent. Doel van het programma is te laten zien hoe deze schijnbaar abstracte concepten zowel concreet als wetenschappelijk kunnen worden ingevuld.
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.
Lecture given at the University of Catania on December 2nd, 2014.
Start from Big Data definitions, continue with real life examples of successful Big Data Projects, go a little bit deeper with Sentiment Analysis, and conclude with a brief overview of Big Data tools and Big Data with Microsoft.
Summary:
1. What is Big Data? (includes the 5Vs of Big Data)
2. Big Data Examples (includes 6 Real Life Examples and comments on Privacy concerns)
3. How to Tackle a Big Data Problem (my 4 Universal Steps to follow)
4. Sentiment Analysis (what is sentiment analysis? Why do we care? A Technique and a plan)
5. Big Data tools (Hadoop, Hadoop Ecosystem, Hive, Pig, Sqoop, Oozie; Azure HDInsight, Excel Power Query, Power Pivot, Power View, Power Map)
Tijdens de vierde sessie van de vierdelige reeks Master Minds on Data Science hield Eric van Tol een presentatie over businesscases en verdienmodellen.
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.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
The concept of Big data has existed for several decades now, but during the past couple of years it has become so trendy that even big data professionals can't figure out what it is and what it isn't. So am I. One part of me thinks it's nothing but a Bubble and the other thinks it's the next Big Bang. In this presentation I have chosen to put forth my views from both sides of the bridge. What do you think?
Hope you enjoy the presentation.
The Black Box: Interpretability, Reproducibility, and Data Managementmark madsen
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust.
Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models.
Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.
This talk will treat the black boxed of ML the way management perceives them, as black boxes.
There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
El Tratado de Neuilly-sur-Seine fue firmado el 27 de noviembre de 1919 en Neuilly-sur-Seine (Francia) entre el Reino de Bulgaria y las potencias vencedoras en la Primera Guerra Mundial.
De acuerdo con lo estipulado en el tratado, Bulgaria reconocía el nuevo Reino de Yugoslavia, se comprometía a pagar 450 millones de dólares en concepto de indemnización2 y reducía su Ejército a 20 000 efectivos. Además, perdía cuatro enclaves de terreno occidental en favor de Yugoslavia2 y cedía Tracia occidental al Reino de Grecia, por lo que quedaba sin acceso al Mar Egeo.2
Scikit-Learn para MLLib: Machine Learning em Larga EscalaGuilherme Peixoto
Imagine que você possua conhecimento básico em ferramentas como Scikit-Learn. Mas quando você se depara com vários terabytes de dados por mês, como você irá lidar com esse conjunto de dados? Você pode aprender a fazer o mesmo com Apache Spark MLlib.
In AI, it's all about the data. But it's hard to get the data, and to get *good* data with provenance. This talk shows how blockchains can help, with real-world examples including:
-a data exchange for self-driving car data (with Toyota Research and others)
-pooling designs for 3d printing fraud detection (with Innogy and others)
-and AI DAOs - AIs that can accumulate wealth
This was given as an invited talk at Consensus 2017, May 22 in NYC.
Data Science for Business Managers - Trends and EvolutionsAkin Osman Kazakci
This first module is an overview of the current data science panorama. Why this is happening now? Who are various actors? Where will it impact next? A special attention is paid to how predictive technologies will transform legacy industries.
Summary
There has never been a time in the history of our species that has seen such innovation and rapid progress; and we have never been so confounded by the world we have realized! For sure, we have crossed the Rubicon from a linear past to a non-linear future and find ourselves lacking many of the basic tools we need to fully address the major problems confronting us.
In such an environment we have to prepared to be ‘unreasonable’, to challenge established wisdom, conventions and practices. So in this session, I present three challenging cases that do just that:
1) Wireless Spectrum: It is infinite and there is no bandwidth crisis!
2) Cyber Security: We need auto-immune systems aka biology
3) Information War: The biggest threat to the survival of our species
Zezan Tam's slides at Mobile Monday. Zezan Tam is a Melbourne based entrepreneur. After leaving his job at Boston Consulting Group, Zezan attended Singularity University in Silicon Valley, which kickstarted his thinking and excitement towards technology and entrepreneurship. He is currently working on a number of businesses in Australia, as well as being Entrepreneur in Residence at the University of Melbourne Accelerator Program. He travelled to Yangon to see the Myanmar entrepreneurship scene, and is interested in investing into talented entrepreneurs operating in a vibrant country poised for an exciting growth period.
Neural Networks and Deep Learning for PhysicistsHéloïse Nonne
Introduction to neural networks and deep learning. Seminar given by Héloïse Nonne on February 19th, 2015 at CINaM (Centre Interdisciplinaire de Nanosciences de Marseille) at Aix-Marseille University
Hadoop, streaming, terabytes, machine learning, batch, etc. Ces problématiques sous-tendent le déploiement d’une architecture Big Data en production. Mais si fondamentales soient-elles, qu’en est-il de l’utilisation de ces données ? Du point de vue de l’utilisateur, il s’agit de répondre à des questions comme : qu’y-a-t-il dans mes données ? Quel est le modèle numérique pertinent pour adresser mes questions métiers ? Mon modèle délivre-t-il la valeur attendue ? Comment partager cette valeur ?
Ces questions partagent une même racine, à savoir, comment passer de Hadoop ou du data lake à un environnement de travail utile et utilisable sans se noyer ?
C’est ce que nous vous proposons de découvrir dans cette session
Nous naviguerons au travers de différents cas d’utilisation (exploration, interprétation, et communication des résultats) et découvrirons les architectures et les outils à notre disposition à même de nous ouvrir des horizons presque infinis : Superset, Tableau, PowerBI pour naviguer, des notebooks (Jupyter, Zeppelin, R) pour analyser, D3.js pour créer des visualisation personnalisées dans le browser.
Pour cela, nulle raison de plonger dans un océan de données. Un sous ensemble modeste, un échantillon qui peut tenir dans la mémoire d’un laptop suffit dans la plupart des cas. On parle alors de data science, de data lab, mais surtout de data visualization.
En se basant sur des cas d’utilisation, nous illustrerons ces différentes étapes et découvrirons ensemble comment faire sortir l’information de votre data lake pour l'amener sur votre écran.
Nature is the ultimate complex system. Nature 1.0 is seeds & soil. *Evolving.* Nature 2.0 adds silicon & steel. *Evolving.*
Presented to Complex Systems Group, Stanford University, on May 4, 2018.
As a Presidio Fellow in Sustainability and Sports, at the Presidio Graduate School, San Francisco, CA, [http://www.presidio.edu/academics/presidiopro/certificates/sports- sustainability] I presented a class on energy efficiency and solar in sports stadiums and arenas. It covers related issues of advanced BIM (Building Information Modeling or Building Intelligence Management), Internet of Everything (IoT), continuous commissioning over building lifecycle, LED lighting systems, and more.
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
BigData and Machine Learning: Usage and Opportunities for your IT department
Talk presented at The Developer Conference in São Paulo - 12/0713
Mathieu DESPRIEE
Arthur C. Nielsen, the founder of ACNielsen said, “The price of light is less than the cost of darkness.” This is becoming even more important in the day and age of IoT devices and ubiquitous internet connectivity. The amount of data that is at the fingertips of our companies’ decision makers is colossal. Yet very few business leaders and their direct teams can analyze their data by themselves to uncover insights that will improve our products and services to delight their customers and grow their business.
With the rise of low-code/no-code tools, cloud infrastructure, and the convergence of AI and BI, the democratization of analytics can accelerate the time to answer a question while improving its relevancy.
In this presentation, we will cover the 12 critical capabilities to succeed in enabling self-service analytics and augmenting data literacy across the enterprise.
Reporting at Motorola - Predictive analytics & business insights 2014Patrick Deglon
In this presentation, Patrick Deglon will share his learnings and provide best practices when using open Google tools & API. He will present his daily email report that hundreds of key Motorola stakeholders are receiving to drive the business, as well as a mobile solution based on the latest web technologies, including Google Visualization, Bootstrap CSS and many of the Google APIs (Gmail, BigQuery, Analytics, Drive, App Engine, Users authentication, etc.).
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
Remote sensing and monitoring are changing the mining industry for the better. These are providing innovative solutions to long-standing challenges. Those related to exploration, extraction, and overall environmental management by mining technology companies Odisha. These technologies make use of satellite imaging, aerial photography and sensors to collect data that might be inaccessible or from hazardous locations. With the use of this technology, mining operations are becoming increasingly efficient. Let us gain more insight into the key aspects associated with remote sensing and monitoring when it comes to mining.
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India Orthopedic Devices Market: Unlocking Growth Secrets, Trends and Develop...Kumar Satyam
According to TechSci Research report, “India Orthopedic Devices Market -Industry Size, Share, Trends, Competition Forecast & Opportunities, 2030”, the India Orthopedic Devices Market stood at USD 1,280.54 Million in 2024 and is anticipated to grow with a CAGR of 7.84% in the forecast period, 2026-2030F. The India Orthopedic Devices Market is being driven by several factors. The most prominent ones include an increase in the elderly population, who are more prone to orthopedic conditions such as osteoporosis and arthritis. Moreover, the rise in sports injuries and road accidents are also contributing to the demand for orthopedic devices. Advances in technology and the introduction of innovative implants and prosthetics have further propelled the market growth. Additionally, government initiatives aimed at improving healthcare infrastructure and the increasing prevalence of lifestyle diseases have led to an upward trend in orthopedic surgeries, thereby fueling the market demand for these devices.
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Business Valuation Principles for EntrepreneursBen Wann
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Taurus Zodiac Sign_ Personality Traits and Sign Dates.pptxmy Pandit
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Accpac to QuickBooks Conversion Navigating the Transition with Online Account...PaulBryant58
This article provides a comprehensive guide on how to
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Attending a job Interview for B1 and B2 Englsih learnersErika906060
It is a sample of an interview for a business english class for pre-intermediate and intermediate english students with emphasis on the speking ability.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
3. FROM THE BIG BANG
TO ECOMMERCE, A
JOURNEY IN MAKING
SENSE OF BIG DATA
Patrick Deglon
Director of Global Traffic Analytics
pdeglon@ebay.com
linkd.in/pdeglon
6. During 1996-2002, worked at CERN
(the European Laboratory for Particle Physics)
for my MS and PhD at the University of Geneva
Mont
Blanc
Geneva
Switzerland
17 miles underground tunnel
for the LEP & LHC accelerator
Source: CERN 6
Image: CERN
8. Example of a particle collision
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
8
9. Solving the puzzle… which particles go together?
1. AB + CD?
2. AC + BD?
3. AD + BC?
A
B
?
D
C
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
9
10. PAW – Physics Analysis Workstation
Source: Wikipedia
Tape robot
Data collection & analysis was
done in Fortran. Advance
analysis/statistics was done
through PAW. [1996-2002]
Source: CERN
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
10
11. Solution: Big Data infrastructure enables large scale
computational such as combine all possibilities (cross-product)
Schematic View
CERN Example
(discovery of a new particle bb)
Signal
(particle resonance)
Statistical Noise
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
Source: http://www.atlas.ch/news/2011/ATLAS-discovers-its-first-new-particle.html
11
12. Size of the electron?
R < 5.1 x 10-19 m ***
*** Patrick Deglon, Etude de la diffusion Bhabha avec le détecteur L3
au LEP, Th. phys. Genève, 2002; Sc. 3332
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
12
13. Extra dimension?
MS > 1.1 TeV ***
graviton
extra
dimension
e+
e+
ee-
our universe in 4 dimensions
*** Patrick Deglon, Etude de la diffusion Bhabha avec le détecteur L3
au LEP, Th. phys. Genève, 2002; Sc. 3332
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
13
14. 2004, joined
eBay European HQ
in Bern, Switzerland
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
14
15. $68 billion
in merchandise traded in 2011 ... or
$1.3 million every
10 minutes
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
15
16. eBay: The World's Online Marketplace®
every
every
every
26
2
4
min. min. sec.
a Ford Mustang is sold
a major appliance is sold
a pair of shoes is sold
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
16
17. CERN vs EBAY
CERN
EBAY
• Write kilometers long Fortran code
• Analysis can run for many hours… before a
batch robot error
• Write miles long SQL code
• Queries can run for many hours… before a
spool space error
• Study billions of collision data
• Study billions of transactional data
• Great depth of data structure & complexity
• Great depth of data structure & complexity
• Know your local expert for question – but try
to find the solution by yourself… much
quicker
• Know your local expert for question – but try
to find the solution by yourself… much
quicker
• Remove “bad runs” (unclean data batch)
• Remove “wackos” (non material
transactions)
• Transform a complex system into insights
• Transform a complex system into insights
• Communicate findings to conferences
• Communicate recommendation to business
review
• Strong competitive landscape (4 distinct
experiments competing to the first to
publish, or publish better results)
• Strong competitive landscape
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
17
18. Analytics at eBay
“CIO”
“CDO”
“CAO”
“CMO”
Analytics Platforms & Delivery (APD)
Analytics
Marketing
Technology
Finance
Business
Units
End Users
of Big Data
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
18
19. What my friends
think I do
What my mum
thinks I do
What the BU
thinks I do
What I think I do
What the BU
wants me to do
What I really do
Source: Pierre Donzier
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
19
21. Core Analytics
Data Access
Business Centric
DataHub
MS Excel
Tableau
Data Platform
Technology Centric
SAS/R
OBIEE
MicroStrategy
Analyze & Report
SOA/DAL
Purpose
Built Aps
SQL
Discover & Explore
EDW
“SINGULARITY”
HADOOP CLUSTERS
ENTERPRISE-CLASS SYSTEM
LOW END ENTERPRISE-CLASS SYSTEM
COMMODITY HARDWARE SYSTEM
Teradata 55xx and 66xx Series
Relational Data
Dual System
10+ PB
Semi Structured &
Relational Data
Deep Storage
Unstructured Data
Pattern Detection
Deep Storage
40+ PB
40+ PB
Data Integration
Ab Initio
Informatica
Golden Gate
UC4
BES
MapReduce
21
22. DW Sandbox enables agile analytics
Analytics teams have access
to sandboxes within eBay
Teradata data warehouses
(~ 100 GB per sandbox):
• Enable to keep the “Single
analyst’s
sandbox
Teradata Data Warehouse
Point of Truth” philosophy
• Improved Time To Market – Days / Weeks vs Months
• Enable the business to do agile prototyping
• Enable the users to “Fail
Fast” – Make it easy to try out new ideas
• Eliminate isolated Data Marts
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
22
23. SO… WHERE DO WE GO
FROM HERE?
1
Intro:
CERN & eBay
2
eBay
Infrastructure
3
Examples
of Analysis
4
Partnership
& Trust
24. Measuring impact of initiatives
A/B test
Pre/Post analysis
illustrative example (Simulation)
illustrative example (Simulation)
Number
of purchases
Number
of listings
35,000
Initiative
launched
450
400
Impact of the
initiative
350
300
test group
200
150
50
0
Aug 1st
pre
2012
post
D
25,000
20,000
250
100
30,000
Impact of the
initiative
Initiative
launched
B
15,000
2011
C
10,000
control
group
Sep 1st
5,000
Oct 1st
• Randomized Test/Control group
methodology is a golden standard in
research
A
0
Aug 1st
Sep 1st
Oct 1st
• Used to measure the impact of an
initiative in a full market or a market
segment
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
25. Marketing 101
Cost
Direct Return
Purchase
L C
L
Incr Return
?
No Purchase
?
C
D
Don‟t
Do Marketing
D
Do Marketing
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
25
26. Medici Effect
• New ideas proliferate when professional or cultural fields collide.
That‟s the “Medici Effect.“
• During the Renaissance, the Medici family enabled such collisions
by funding various fields and facilitating interdisciplinary creativity.
House of Medici
Michelangelo
Source:
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
26
27. Remember this physics problem?
1. AB + CD?
2. AC + BD?
3. AD + BC?
A
B
?
D
C
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
27
28. Solution: Big Data infrastructure enables large scale
computational such as combine all possibilities (cross-product)
Schematic View
CERN Example
(discovery of a new particle bb)
Signal
(particle resonance)
Statistical Noise
Combine correlated events and uncorrelated events produce a system with a
statistical noise (which is simple enough to extract) and the researched signal
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
Source: http://www.atlas.ch/news/2011/ATLAS-discovers-its-first-new-particle.html
28
29. Big Data technologies enable the full Cartesian product of
Marketing action & Revenue generating events
Clicks – Conversion
Playground
Marketing Events
(Clicks or Impressions)
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
29
30. Alternative way to understand customer behavior &
incrementally: geographic experimentation
Revenues / Cost
3 per. Mov. Avg. (Group 1)
Baseline
3 per. Mov. Avg. (Group 2)
3 per. Mov. Avg. (Group 3)
Phase 1
3 per. Mov. Avg. (Group 4)
Phase 2
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
30
32. Analytics as a function?
Embedded Model
Functional Model
“I‟m following my BU leader,
but can‟t get promoted”
“I‟m a partner of
business execution”
Need to track
satisfaction/loyalty/trust
of our partnership
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
32
33. Net Promoter Score
NPS: How likely is that you will recommend [Brand Name] to a friend or a colleague?
0
1
2
3
4
5
6
7
8
very unlikely
9
10
very likely
Detractors
Passives
Promoters
NPS = % Promoters - % Detractors
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
33
34. The logic behind NPS
• To improve NPS, a company need to work on 2 fronts:
– Move Detractors into Passives
(i.e. fix the holes, i.e. no more unacceptable bad experiences)
– Move Passives into Promoters
(i.e. improve the whole experience, best-in-class buyer experience)
0
1
2
3
Detractors
4
5
6
7
8
Passives
9
10
Promoters
NPS = % Promoters - % Detractors
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
34
35. Side note: Error on NPS measurement
• NPS is a multinomial distribution with
– p the probability to answer 0 to 6
– q the probability to answer 7 or 8
– r the probability to answer 9 or 10
– N the number of answers
• The Expected value for the Net Promoter Score is then
E(NPS) = r – p
• The Variance is then
V(NPS) = V(r-p) = V(r) + V(p) – 2 Cov(r,p) =
r (1-r) / N + p (1-p) / N + 2 r p / N
• Hence the error on NPS, i.e. the Standard Deviation, is then
(NPS) = SQRT [ r (1-r) / N + p (1-p) / N + 2 r p / N ]
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
35
36. NPS is a measurement of Loyalty in a free environment. In a
paid environment, it‟s more a measurement of Trust between
co-workers/partners
Net Promoter Score
How likely is it that you would recommend working
with Analyst XXX to a friend or colleague?
0
1
2
3
4
5
6
7
8
9
10
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
36
37. eNPS Survey
Team eNPS
Survey
Partner eNPS
Survey
• Identify opportunity to better partner with the business
• Identify to better work together as a team
• Enable directional assessment of eNPS; keeping in mind
biases: low N, subjective question, unlikely to promote an
unknown entity, partner <> client (i.e. Finance vs Agency)
Now that we have a
measurement,
how to improve it?
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
37
38. What is Trust? How to improve it?
Trust =
Credibility
Reliability
Intimacy
Unselfishness
http://www.collieassociates.com/common/Trust_Equation.pdf
Words: Convincing & believable
Actions: Consistently good in
quality & performance
Emotions: Feel comfortable talking to you
about the sensitive, personal issues connected
to the surface issue
Motives: Know that you care about serving
higher interests
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
38
39. Build Trust: Trust Equation
Trust
=
R
×
C
×
I
×
Trust
Component
Reliability
(Actions = Consistently
good in quality &
performance)
Credibility
(Words = Convincing &
believable)
Insights
Discovery ®
Colors
Hartman
Personality
Profiles
Lead
completely
Fiery RED
“Do it now!”
RED
Power Wielders
Practice
judgment
Cool BLUE
“Do it right!”
BLUE
The Do-gooders
Keep it
human
Earth GREEN
“Do it
harmoniously!”
WHITE
The Peacekeepers
Trust each
other
Sunshine
YELLOW
“Do it together!”
YELLOW
The Fun Lovers
Intimacy
(Emotions = Feel
comfortable talking to
you about the
sensitive/personal
issues connected to
the surface issue)
Unselfishness
U
eBay
Success
Factor
(Motives = Know that
you care about serving
our higher interests)
Carl Jung,
Swiss psychologist
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
39
40. Example of an internal partners survey on
the Trust foundation
Translates ideas and concepts into action.
4.9
Turnaround requests effectively.
5.0
Is comfortable with change.
5.0
Is adept at prioritizing tasks.
Does what one says one will do.
Tell the truth.
Is genuine in saying „Thank you‟ or „I don‟t
know‟.
Is comfortable saying 'no' at the beginning
rather than being unable to deliver in the end.
Creates an environment to address potential
conflicts openly.
Reliability (4.9)
4.9
5.2
5.6
5.5
Credibility (5.3)
5.0
5.0
Seeks help when facing difficulties.
5.3
Has an appropriate sense of humor.
5.3
Responds to and understand the
feelings/needs of others.
5.4
Uses „we‟ rather than „they‟ or „I‟.
Makes time for others.
Intimacy (5.2)
5.2
5.4
Supports ideas for innovation from others.
5.3
Trusts others to make decisions and get things
done for them.
Unselfishness (5.3)
5.2
Please complete each of the following statements using the rating guide. Try to provide a rating for every statement
and be honest with your feedback.
Weak in this area=1, Some concerns=2, A minor shortfall=3, Competent=4, Better than competent=5, Outstanding=6
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
40
41. Trust Equation assessment by the team and our partners
Partner average answer
90
85
under confidence
zone
over confidence
zone
Intimacy,
Keep It Human
Credibility,
Meets Quality
80
Non Political,
Unselfishness
75
Reliability,
Meets Deadline
70
65
60
60
65
70
75
80
85
90
Team average answer
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
41
42. Reliability: Value of an Analysis
Keep It Simple & Stupid
Individual Limit
Total Cost
Direct Return
Preferred
analyst‟s
level of
complexity
Optimal
level of
complexity
Complexity of Analytics
Net Return (Profit)
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
42
43. Credibility: Principle Of Least Surprise (POLS)
Don‟t surprise executives & partners
with new metrics, new definition,
new format or anything new…
without a proper business reason.
Setup Insights & Recommendation
in a natural, logical, global &
agreed-upon framework.
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
43
44. Credibility: Fixed Standard… or Flexible Chaos?
Standardized
Global
Metrics
Store any thing to
enable measuring any
metrics to answer any
questions
Chaos enable
flexibility, but require a
strong process to
maintain credibility
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
44
45. (Business) Intimacy
• Keep It Human – meet people, talk to people, walk to desk, pick-up the phone
• Seek help when needed
• Have a good sense of humor – “It‟s just a website…”
• Create an enviroment where people can open-up and discuss underlying issue
• Respond to the need/feeling of others
• CONNECT with people (Avatar‟s “I see you”)
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
45
46. Unselfishness
• Don‟t work in silo
• Consider “we” rather than “I” or “they”
• Support ideas for innovation from other (improv‟s “yes, and…”)
• Trust other to make the right decision – and live with it
• Be AVAILABLE – make time for other
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
46
47. Wrapping Up
How complexity can spark innovation, but also kill effectiveness
• Medici principle
• KISS
• Managing chaos
Why an embedded or client-centric Analytics organization is not
necessarily a great idea
• Enable career path with an Analytics organization
• Partner vs Client
• eNPS - Maintain the pulse on the internal-client/partner satisfaction
Why analyst creativity is antagonistic to executive reporting
• Trust pillars: Reliability, Credibility, Intimacy, Unselfishness
• POLS
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
47
49. FROM THE BIG BANG
TO ECOMMERCE, A
JOURNEY IN MAKING
SENSE OF BIG DATA
Patrick Deglon
Director of Global Traffic Analytics
pdeglon@ebay.com
linkd.in/pdeglon
50. Credibility: Key Phases of an Analytics Project
Move the
Business
Follow-up /
Implementation
Readout
Executive
Summary
Scoping
Hypothesis
to be verified
Scoping the
question
Measurement
set up
Measuring
Query
Data check
Guiding the
Business
Story Line /
Deck
Driving
Insights
Facts / Slides
Review
hypothesis
Data
manipulation
Interpretation
Statistics
Graphs
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
50
51. James, 32, live in Pittsburgh,
married, 1 child, Electronics Enthusiast
Site Visit
Site Visit
YouTube
Display Click
Site Visit
Offline
Store
Visit
Google Search on
“Digital Camera”,
click on eBay PS Ad
Google Search on
“eBay Digital Camera”
Click on NS link
Purchase
Loyalty Level
i.e. Likelihood to purchase on eBay
Woa.. They
really have
nice deals
on eBay
Ah…yes, e
Bay was a
good idea
– what do
they have?
That‟s really
expensive in
a store
Let‟s get
that
camera
now
Time
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
51
52. Marketing Attribution Logic
$
YouTube
Display Impression
Google Search on
“Digital Camera”,
click on eBay PS Ad
Google Search on
“eBay Digital Camera”
Click on NS link
Purchase
How does the purchase correlate to the customer touch points?
How “close”/”distant” are the clicks & the purchase?
Which one is the most important?
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
52
53. What is more important:
the front wheel or the back wheel?
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
53
54. Marketing Attribution Management
YouTube
Display Impression
Google Search on
“Digital Camera”,
click on eBay PS Ad
Google Search on
“eBay Digital Camera”
Click on NS link
Purchase
Define correlation (“distance”) between
customer touch points and purchase and
the likelihood that it happens
distance in time
distance in KW space
distance in Mindset
• Latency: time between click and ROI event (2 minutes? 2 hours? 2 days?)
• Relevancy: difference between Search keyword and Item purchased (KW-Title
relevancy, KW-Vertical relevancy)
• Loyalty: mindset of customer, i.e. RFM segment (Reactivation or Top Buyer)
• …
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
54
55. Marketing Attribution Management
Last Click
First Click
All Clicks
Model
YouTube
Display Impression
Google Search on
“Digital Camera”,
click on eBay PS Ad
Google Search on
“eBay Digital Camera”
Click on NS link
100%
YouTube
Display Impression
100%
Google Search on
“Digital Camera”,
click on eBay PS Ad
Google Search on
“eBay Digital Camera”
Click on NS link
YouTube
Display Impression
33%
Google Search on
“Digital Camera”,
click on eBay PS Ad
33%
Google Search on
“eBay Digital Camera”
Click on NS link
33%
YouTube
Display Impression
60%
Google Search on
“Digital Camera”,
click on eBay PS Ad
35%
Google Search on
“eBay Digital Camera”
Click on NS link
5%
Purchase
Purchase
Purchase
Purchase
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
55
56. … So what?
Last Click
Channel A
Channel B
Channel C
GMB
8%
5%
1%
ROI
+20%
-10%
+10%
• Reduce spend on channel B
• Invest in channel A
• When prioritizing, ignore
channel C
<>
All Clicks Model
Channel A
Channel B
Channel C
GMB
7%
6%
12%
ROI
-20%
+30%
+60%
• Reduce spend on channel A
• Invest heavily on channel C
• Marketing counts actually for
25% of the site
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
56
57. Example of the International Weekly Variance
Infrastructure (2007)
Automated SQL
Core DW
database
Excel
inputs
PDF
print-out
PET*
Modular
Back-end
single
pivot table
PPT &
Excel
report
Flexible
Front-end
* PET is a small database inside the Teradata Data Warehouse for building prototypes.
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
57
58. Example of Automated Quarterly Market Review deck (2007)
PowerPoint chart object with a
“SQL” field containing a EXEC
MACRO to refresh data content
of the chart
Linked to an Excel file that can
we refresh when needed
PowerPoint table object with a
“SQL” field containing a EXEC
MACRO to refresh the table
content
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59. PowerPoint Reporting Tool (2012)
Update the content of the selected objects (table or chart)
Update the content of all objects in the PowerPoint
Login to DW
Add a “SQL” tag to
objects (table of chart)
and edit the SQL
Create a dummy chart
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
59
60. Example of BI report using Tableau
FROM THE BIG BANG TO ECOMMERCE,
A JOURNEY IN MAKING SENSE OF BIG DATA
60