La presentazione del Prof. Furio Camillo dell'Università di Bologna e di DataScienceLAB alla XVI Edizione del Convegno Osservatorio Fedeltà "Loyalty Disruption: Emotion, Big Data & New Players", tenutosi venerdì 21 ottobre 2016.
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
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
my talk to 2/12/09 O'Reilly IgniteBoston, emphasizing that passage of economic stimulus package, combined with current economy, is perfect time to introduce data-centric "democratizing data" approach, giving workers, regulators, public, watchdogs real-time access to critical information! Video version: http://tinyurl.com/c9vkjy
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
O'Reilly eBook: Creating a Data-Driven Enterprise in Media | eubolrVasu S
An O'Reilly eBook about Creating a Data-Driven Enterprise in Media DataOps Insights from Comcast, Sling TV, and Turner Broadcasting.
https://www.qubole.com/resources/ebooks/ebook-creating-a-data-driven-enterprise-in-media
Cutting Edge Predictive Analytics with Eric Siegel Databricks
Apache Spark empowers predictive analytics and machine learning by increasing the reach and potential. But, before jumping to new deployments, it’s critical we 1) get the analytics right and 2) not overlook less conspicuous business opportunities. In this keynote, Predictive Analytics World founder and “Predictive Analytics” author Eric Siegel ramps you up on a dangerous pitfall and a critical value proposition:
– PITFALL: Avoiding BS predictive insights, i.e., “bad science,” spurious discoveries
– OPPORTUNITY: Optimizing marketing persuasion by predicting the *influence* of marketing treatments, i.e., uplift modeling
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
“ Artificial intelligence and Big Data are two burgeoning technologies, full of promise for businesses in all industries. However, the real revolutionary potential of these two technologies is probably their convergence. Discover the possibilities offered by the alliance between Big Data and AI. “
source : www.lebigdata.fr
In business, we need to make sure we are ahead of the
curve as this data revolution takes shape. With connected
technology putting our customers firmly in the driving
seat, there are clear challenges which will need to be
overcome.
Vanson Bourne Research Report: Big DataVanson Bourne
For most organisations, big data is now the reality of doing business. Technological and social innovations are resulting in huge flows of new data every day. As we enter this undeniable era of big data where more information will be captured in ever-finer detail from more sources than ever before does that mean our decision-making is bound to improve?
Challenges are consistent in Big Data environments; resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. Big Data has grown so large that traditional data analysis and management solutions are too slow, too small and too expensive to handle it. Many companies are in the discovery stage of evaluating the best means of extracting value from it. This Enterprise Tech Journal interview with Kevin Goulet, VP Product Management, CA Technologies, explores the challenges of Big Data, the approach to resolving them. With Big Data environments, the challenges are consistent – resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. For more information visit http://www.ca.com/us/products/detail/business-intelligence-and-big-data-management.aspx?mrm=425887
Comarch
La presentazione di Vincenzo Sinibaldi di Comarch alla XVI Edizione del Convegno Osservatorio Fedeltà "Loyalty Disruption: Emotion, Big Data & New Players", tenutosi venerdì 21 ottobre 2016.
MetraTech is hosting a webinar on Oct. 8th, 2013 with Saugatuck VP & Analyst Mike West. Register here: http://goo.gl/q1uRTJ
Subscription billing came into vogue as a simple model to capture subscribers. Increased financial pressure and service commoditization means current subscription pricing models need to be re-evaluated and enhanced to support customer requirements for uniquely tailored offers on top of common service components.
my talk to 2/12/09 O'Reilly IgniteBoston, emphasizing that passage of economic stimulus package, combined with current economy, is perfect time to introduce data-centric "democratizing data" approach, giving workers, regulators, public, watchdogs real-time access to critical information! Video version: http://tinyurl.com/c9vkjy
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
O'Reilly eBook: Creating a Data-Driven Enterprise in Media | eubolrVasu S
An O'Reilly eBook about Creating a Data-Driven Enterprise in Media DataOps Insights from Comcast, Sling TV, and Turner Broadcasting.
https://www.qubole.com/resources/ebooks/ebook-creating-a-data-driven-enterprise-in-media
Cutting Edge Predictive Analytics with Eric Siegel Databricks
Apache Spark empowers predictive analytics and machine learning by increasing the reach and potential. But, before jumping to new deployments, it’s critical we 1) get the analytics right and 2) not overlook less conspicuous business opportunities. In this keynote, Predictive Analytics World founder and “Predictive Analytics” author Eric Siegel ramps you up on a dangerous pitfall and a critical value proposition:
– PITFALL: Avoiding BS predictive insights, i.e., “bad science,” spurious discoveries
– OPPORTUNITY: Optimizing marketing persuasion by predicting the *influence* of marketing treatments, i.e., uplift modeling
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
“ Artificial intelligence and Big Data are two burgeoning technologies, full of promise for businesses in all industries. However, the real revolutionary potential of these two technologies is probably their convergence. Discover the possibilities offered by the alliance between Big Data and AI. “
source : www.lebigdata.fr
In business, we need to make sure we are ahead of the
curve as this data revolution takes shape. With connected
technology putting our customers firmly in the driving
seat, there are clear challenges which will need to be
overcome.
Vanson Bourne Research Report: Big DataVanson Bourne
For most organisations, big data is now the reality of doing business. Technological and social innovations are resulting in huge flows of new data every day. As we enter this undeniable era of big data where more information will be captured in ever-finer detail from more sources than ever before does that mean our decision-making is bound to improve?
Challenges are consistent in Big Data environments; resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. Big Data has grown so large that traditional data analysis and management solutions are too slow, too small and too expensive to handle it. Many companies are in the discovery stage of evaluating the best means of extracting value from it. This Enterprise Tech Journal interview with Kevin Goulet, VP Product Management, CA Technologies, explores the challenges of Big Data, the approach to resolving them. With Big Data environments, the challenges are consistent – resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. For more information visit http://www.ca.com/us/products/detail/business-intelligence-and-big-data-management.aspx?mrm=425887
Comarch
La presentazione di Vincenzo Sinibaldi di Comarch alla XVI Edizione del Convegno Osservatorio Fedeltà "Loyalty Disruption: Emotion, Big Data & New Players", tenutosi venerdì 21 ottobre 2016.
MetraTech is hosting a webinar on Oct. 8th, 2013 with Saugatuck VP & Analyst Mike West. Register here: http://goo.gl/q1uRTJ
Subscription billing came into vogue as a simple model to capture subscribers. Increased financial pressure and service commoditization means current subscription pricing models need to be re-evaluated and enhanced to support customer requirements for uniquely tailored offers on top of common service components.
La presentazione di Enzo Grassi, Direttore Generale di Catalina Marketing, nell'ambito della XIV Edizione del Convegno Annuale dell'Osservatorio Fedeltà UniPR dal titolo "Dalla carta al digitale: cosa cambia per la loyalty"
Refurbish and restore suspended commercial ceilings to new condition 70% less than replacement. No VOC\'s, No landfill, all Green products. Reduce energy bills. Reduce carbon footprint.
Journey toward Learning Society: Possibilities and challenges of Viet Nam Li...Zakir Hossain/ICS, Zurich
The government of Viet Nam has made a commitment to build a Learning Society by 2020 as a part of ASEAN countries’ lifelong learning ambition. A range of related initiatives have been launched by Viet Nam government to encourage reading and raising awareness of its importance for sustainable development of knowledge and skills. Viet Nam aims to implement and develop lifelong learning activities in out-of-school education institutions such as libraries, museums, Community Learning Centres (CLCs) and cultural center clubs to achieve this national and regional goal. The government of Viet Nam currently operates more than 23,000 state funded libraries and 11,900 CLCs throughout the country and is in the process of both renovating and innovating public libraries, CLCs and museums. In addition to the work undertaken by the Viet Nam government, a number of enterprises have also been initiated by non-government and non-profit organisations to promote literacy and lifelong learning using libraries. As the concern authority believes that libraries can be a potential catalyst for Learning Society project, it is also essential to measure the possibilities and challenges of Viet Nam libraries. This analytical study, therefore, investigates some government and non-governmental initiatives focused on libraries, their impact on reading promotion and lifelong learning, and challenges they face. Proposing a way forward, the paper confirms that Viet Nam’s libraries play an essential role in promoting reading and building a Learning Society in Viet Nam.
Jasa Pembuatan Peredam Suara Studio Musik Murah, Tukang Bikin Peredam Suara Studio Musik Bergaransi, Jasa Tukang Peredam Suara Studio Musik, Bikin Peredam Suara Studio Musik, Kontraktor Pembuatan Peredam Suara Studio Musik
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Dirigida a directivos y analistas de mediana y gran empresa, Big Data Spain celebró una charla previa a la conferencia de la segunda edición del 7y 8 de noviembre del 2013.
Vídeo youtube: https://www.youtube.com/watch?v=6HbWErRCD1g
¿Quieres saber más?
http://www.paradigmatecnologico.com/
Oscar Méndez, co-fundador de www.paradigmatecnologico.com y www.stratio.com, habló de Big Data desde un punto de vista de negocio, y despejó dudas acerca del coste y recursos necesarios para aprovechar esta tecnología.
Las plataformas v2.0 post-Hadoop permiten el despligue rápido y simple de herramientas integradas de data mining, data processing, data analysis y data visualization. Los avances de los últimos 12 meses dejan atrás las limitaciones de sistemas de Business Intelligence tradicionales.
Identify and analyze the greatest insights from big dataTheInnovantes
Identify and analyze the greatest insights from big data by leveraging technologies that can effectively handle the growing volume, velocity, and variety of data.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
The simplest definition of Big Data is large and complex unstructured data (images posted on Facebook, email, text messages, GPS signals from mobile phones, tweets, and other social media updates…etc.) that cannot be processed by traditional database tools.
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In the world of content creation, many AI bloggers have drifted away from their original vision, resulting in low-quality articles that search engines overlook. Don't let that happen to you! Join us to discover how to leverage AI tools effectively to craft high-quality content that not only captures your audience's attention but also ranks well on search engines.
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• Uncover the power of a foundational marketing system that dynamically communicates with prospects and customers on autopilot.
• Harness neuroscience and Tribal Alignment to transform your communication strategies, turning potential clients into fans and those fans into loyal customers.
• Discover the art of automated segmentation, pinpointing your most lucrative customers and identifying the optimal moments for successful conversions.
• Streamline your business with a content production plan that eliminates guesswork, wasted time, and money.
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• Streamline your business with a content production plan that eliminates guesswork, wasted time, and money.
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1. furio.camillo@unibo.it
Making Loyalty with Big Data: What challenges?
(Fare Loyalty con i Big Data: quali sfide?)
Concepts cloud
furio camillo
Department of Statistical Sciences
Alma Mater Studiorum
University di Bologna - Italy
furio.camillo@unibo.it
2. furio.camillo@unibo.it
Big-data, self-selection, predictions in real-time, intangible
variables, opinion and sentiment manipulation: the
landing in the future of business statisticians
The basic theme is to share and to
discuss some recent experiences
relating to the application of
statistical analysis in companies
and organizations, taking into
account the scenario of context
in order to clarify what are the
frontiers of the future and the
challenges of the present
Concepts cloud
Points of view:
Business
Data Analyst
Statistician
Context:
loyalty
New problems vs classical problems
New solutions vs classical solutions
3. furio.camillo@unibo.it
• Big data is a term for data sets that are so large or complex that
traditional data processing applications are inadequate to deal with
them. Challenges include analysis, capture, data curation,
search, sharing, storage, transfer, visualization, querying, updating
and information privacy
• The term "big data" often refers simply to the use of predictive
analytics, user behavior analytics, or certain other advanced data
analytics methods that extract value from data, and seldom to a
particular size of data set
• Accuracy in big data may lead to more confident decision-making, and
better decisions can result in greater operational efficiency, cost
reduction and reduced risk
4. furio.camillo@unibo.it
• Volume: big data doesn't sample; it just observes and tracks what happens
• Velocity: big data is often available in real-time
• Variety: big data draws from text, images, audio, video; plus it completes
missing pieces through data fusion
• Machine Learning: big data often doesn't ask why and simply detects
patterns
• Digital footprint: big data is often a cost-free by-product of digital interaction
• The growing maturity of the concept more starkly delineates the difference
between big data and Business Intelligence
5. furio.camillo@unibo.it
• Business Intelligence uses descriptive statistics with data with high
information density to measure things, detect trends, etc..
• Big data uses inductive statistics and concepts from nonlinear system
identification to infer laws (regressions, nonlinear relationships, and causal
effects) from large sets of data with low information density to reveal
relationships and dependencies, or to perform predictions of outcomes and
behaviors.
8. furio.camillo@unibo.it
How do you respond when you hear the phrase
‘big data’?
Un sospiro rassegnato. 'Big Data' è proclamata come la risposta ai problemi dell'umanità.
Mentre è vero che, grazie alle tecnologie moderne di data collection, grandi insieme di dati hanno un
grande potenziale per progressi interessanti, dobbiamo riconoscere che nello stesso tempo si presentano
con notevole sfide tecniche. Le più semplici sono legate agli aspetti di data manipulation (l’ordinamento, il
search ed il merge di grandi insiemi dei dati) mentre gli aspetti più difficili sono quelli inferenziali.
L'idea che oggi si disponga di 'tutti' i dati per ogni contesto particolare è raramente vera o rilevante. Oltre
ai problemi di qualità di ‘Small data’, i ‘Big data’ presentano quindi delle sfide proprie e peculiari.
Probably a resigned sigh. ‘Big data’ is proclaimed as the answer to humanity’s problems. However, while
it’s true that large data sets, a consequence of modern
data capture technologies, do hold great promise for interesting and valuable advances, we should not fail to
recognise that they also come with considerable
technical challenges. The easiest of these lie in the data manipulation aspects of data science (the searching,
sorting, and matching of large sets) while the toughest
lie in the essentially statistical inferential aspects. The notion that one nowadays has ‘all’ of the data for any
particular context is seldom true or relevant. And big
data come with the data quality challenges of small data along with new challenges of its own.
9. furio.camillo@unibo.it
Dealing with data generated by non-experimental
studies
• Big data
• Algorithms vs. Statistical approach
• Professional skills
• BIG-Propensity score FOR BIG-Data!!!
• Cookies and digital (and real) footprint as data source
• No-structured data use (opinions)
12. furio.camillo@unibo.it
Dealing with data generated by non-
experimental studies
(sometimes with really self-selected samples)
…….. using Propensity Score approach and Counterfactual Frame
(Rubin)
14. furio.camillo@unibo.it
Potential outcome framework
A causal effect is the comparison of the outcome that would be observed with the
interventions ( treatment ) and without intervention, both measured at the same point
in time ( D. B. Rubin, R.P. Waterman, 2006)
Change in blood pressure (mm mercury)
Pill=yes Pill=no
If the selection process
depends on the same
covariates conditioning «the
result», the experiment will be
biased
In BIG-DATA, the use of
experiments, in general, is not
controlled
ATE = ((-10)+(-5)+(-10)+(-15)) / 4 = -40/4 = -10
15. furio.camillo@unibo.it
A cookie is a small piece of data sent from a website and stored in a user's
web browser while the user is browsing that website. Every time the user
loads the website, the browser sends the cookie back to the server to
notify the website of the user's previous activity.
Track of a behaviour
Big-data, complex data, unstructured data:
anyway, behavioural data (in Italy more
than 55million of tracked-cookies)
Main ingredients: Cookie!!!!
Cookies are primarily used to provide services to the e-user (especially in the mobile-apps). In detail, main
services from the use of cookies are:
• fill the cart in commercial websites
• to allow login to a user
• customize the website according to user preferences
• management of a website: understand navigation, adjusting the spacing to web browsing needs and
eliminate dead ends
• track paths of users: advertising companies should use such information to plan well
communication and adjust in real time the messages according to the user's profile
16. furio.camillo@unibo.it
what kind of modeling?
what kind of response?
what kind of prediction?
what kind of variables
profiling?
A possible frame: supervised classification
17. furio.camillo@unibo.it
Predictive discriminant model
SUCCESSFUL EVENT: to complete the purchase of the new
bank account (1=success; 2=no-success)
Well predicted rate
Outcome=no-success: 99.2%
Outcome=success: 74.1%
+ unsctructured infos about
«user agent» string (coordinates
of a Textual Binary
Correspondence analysis)
Saporta scoring
trasformation of
parameters
Day
Time-band
Banner-format
Origin-site
Campaign-name
23. furio.camillo@unibo.it
Resarch project about (with Beintoo)
1. Modelling Spatial Interaction and
natural Gravity areas
2. Indirect loyalty estimation
3. Churn timing
4. Budget time as a proxy for style
profiling based on values or limbic
propensities
24. furio.camillo@unibo.it
Case: digital measurement of loyalty to drug
(self-selection)
• USA, drug evaluation, 1.000.000 geo-tracked citizen (high social class)
• Evaluation: 2014 vs 2016
• Clustering using more than 150 geo-behavioural variables and 25
«hard» variables
• Global Imbalance Index (GI) to select only balanced clusters
• Treatment: year; Outcome: Indirect loyalty index about drugs use
(high cholesterol, high blood pressure)
• Collection: by specific tracked IoS and Android APPs
• Qualitative information: open textual opinions
27. furio.camillo@unibo.it
Perspectives
• Interpretation of data vs. Big Data:
complexity
• Scientific, rational, illuministic approach
• Y = f(X) (Correlation vs Causality)
• French school (1968): «Le modèle est dans
les données, mais il faut le chercher»
• Navigation Hypothesis: Business KPIs
• Small data mixed with Big data
• Leadership in a Big-Data Project
• Technology does not necessarily reduce
the number of people working in business
28. furio.camillo@unibo.it
Making Loyalty with Big Data: What challenges?
(Fare Loyalty con i Big Data: quali sfide?)
furio camillo
Department of Statistical Sciences
Alma Mater Studiorum
University di Bologna - Italy
furio.camillo@unibo.it
www.datasciencelab.it
In cooperation with Valentina Adorno
A «Bologna city-brand» company