SlideShare a Scribd company logo
DATA IN BUSINESS 
ALBERT SOLANA 
CONSULTANT ROCASALVATELLA
BIG DATA 
THE POTENTIAL FOR DATA 
TO IMPROVE SERVICE AND BUSINESS MANAGEMENT 
Big Data Hispano 
November 17th, 2014 
Albert Solana 
albert@rocasalvatella.com 
@iamtxena
This 
is 
me 
3 
years 
ago
BIG 
DATA 
New digital data flows and a real-time 
processing capacity to provide a better 
service and oriented to business goals!
DATA MANAGEMENT: 4-stage digital transformation process 
1 Back Office digitalization!
DATA MANAGEMENT: 4-stage digital transformation process 
Back Office digitalization! 
Product only! 
Internal data: logistics, financial & 
production department! 
ERP! 
1 
2 
3 
4
DATA MANAGEMENT: 4-stage digital transformation process
DATA MANAGEMENT: 4-stage digital transformation process 
2 Customer Digital TouchPoints! 
Source: 
h/p://www.garymagnone.com/blog/content-­‐marke;ng-­‐digital-­‐touchpoints/
Customer Digital TouchPoints! 
Product + Added-value Service! 
Internal & External data: sales, 
marketing & post-serv. depts! 
ERP+CRM+SM! 
1 
2 
3 
4 
DATA MANAGEMENT: 4-stage digital transformation process
DATA MANAGEMENT: 4-stage digital transformation process
DATA MANAGEMENT: 4-stage digital transformation process 
3 Products & Services!
Products & Services! 
Product + Sensor + Platform 
+ Service + Data:! 
R+D + Data analytics depts! 
A unique centric database! 
1 
2 
3 
4 
DATA MANAGEMENT: 4-stage digital transformation process
DATA MANAGEMENT: 4-stage digital transformation process
DATA MANAGEMENT: 4-stage digital transformation process
DATA MANAGEMENT: 4-stage digital transformation process
DATA MANAGEMENT: 4-stage business digital transformation process 
data 
channel 
management 
Social Media 
Web 
Blogs, forums 
Product Data 
WEB 
Customer 
Segment 1 
Customer 
Segment 2 
Customer 
Segment n 
MASS AUDIENCE & MASS 
RAW DATA 
DATA CRUSH & AUDIENCE 
SEGMENTATION 
TAILORED PRODUCTS & 
SOLUTIONS 
DDBB 
External 
Databases 
Mobile 
Webs & SM 
Products 
SOURCES OF DATA OWN DATA MANAGEMENT 
Mobile 
Webs & SM 
Products 
… 
… 
NEW SOURCES OF DATA FROM 
INDIVIDUALS
h1p://bit.ly/RSBigDataTourism 
(web 
document) 
h1p://bit.ly/RSBigDataTourismPDF 
(PDF 
version)
Big Data and Tourism 
Study Main Goals 
Present a new methodology for improved analysis and knowledge of the Spanish 
tourism industry with real data gathered from cellphones and credit card 
transactions. 
State differences between: 
“to have the data” (Telefónica Móviles España or BBVA) 
“to analyze the data” (Telefónica I+D), 
“to set the specific questions to be answered with the 
data” (RocaSalvatella). 
Increase hotel industry business with real tourists data
Big Data and Tourism 
Definition 
Objectives: 
To leverage the data opportunities for the sector, in particular the hotel 
industry 
To incorporate big data collected from the real electronic activity of 
anonymous foreign tourists into their market research. 
Key Challenge: Gather and cross two different datasets (from Telefónica & BBVA) 
2-week data collection (From October 7th to October 21st 2012) 
Barcelona and Madrid (No special holidays or notorious events) 
21 countries studied, 680.928 cellphones and 168.921 credit cards analyzed 
Anonymised, aggregate data (meeting requirements of LOPD 15/1999 and its 
developing regulations, RD 1720/2007, and Ley General de Telecomunicaciones 
Ley 32/2003)
Big Data and Tourism 
3 Key Players 
Telefónica Móviles de España 
Telefónica I+D
Big Data and Tourism 
Main Conclusions 
Where 
from? 
For how 
long? 
Where? 
How 
much?
Big Data and Tourism 
Main Conclusions 
Where 
from? 
For how 
long? 
Where? 
How 
much? 
Tourists who visit Barcelona and Madrid are mainly French, Italian and British 
(50% of the total number of visitors during the analyzed period). 
First non-european country ranked in 8th position (USA; 4% of total visitors). 
Preferences: 
Argentinians, Brazilians and Portuguese prioritize Madrid 
Nordic countries choose Barcelona.
Big Data and Tourism 
Main Conclusions 
Where 
from? 
For how 
long? 
Where? 
How 
much? 
The average stay is 2.24 days long. 
Same country visitors may present a different behavior patterns depending on the 
city. For example, India is one of the countries with the longest stays in Madrid 
but the shortest stays in Barcelona.
Big Data and Tourism 
Main Conclusions 
Where 
from? 
For how 
long? 
Where? 
How 
much? 
The further, the more city-centered. 
As a general rule, furthest visitors (Japan, China and Brazil…) tend to stay in city 
centric hotels. 
On the other hand, visitors from nearby countries such as Portugal, France and 
Belgium choose accommodation further from the center.
Big Data and Tourism 
Main Conclusions 
Where 
from? 
For how 
long? 
Where? 
How 
much? 
Global average card spending per visitor during their stay was €161.5 
Average card spending per day was €58.5. 
Average spending on accommodation for the entire stay was around €300 
Average accommodation daily expenditure or price per night was €129.
Big Data and Tourism 
Main Recommendations for the hotel industry 
Capturing more customers and highlighting the countries on which it is 
recommended to focus marketing. 
Detecting areas of the city in which commercial transactions are carried out, 
Specially, those referring to accommodation. 
Ensuring the hotel manager provides an attractive product suited to customers’ 
true needs (ideal length of package offers, information about complementary 
services demanded by nationalities, etc.)
Most 
visited 
areas 
in 
Barcelona 
by 
Russian 
tourists
Big Data the potential for data to improve service and business management by ALBERT SOLANA at Big Data Spain 2014
Business Model! 
Customers: Historical Data! 
Product Providers: HW, SW, Security, Product 
materials! 
Data Management Alliances ! 
1 
2 
3 
4 
DATA MANAGEMENT: 4-stage digital transformation process
DATA MANAGEMENT: 4-stage business digital transformation process 
data 
ecosystem 
Running 
Shoes 
Industry 
Nutri;onist 
Industry 
Medical 
Industry 
Running 
accessories 
Industry 
Other 
Sports 
Industries
DATA MANAGEMENT: 4-stage business digital transformation process 
data 
ecosystem 
Running 
Shoes 
Industry 
Nutri;onist 
Industry 
Medical 
Industry 
Data 
“Productless” 
Industry 
Running 
accessories 
Industry 
Other 
Sports 
Industries
DATA MANAGEMENT: 4-stage digital transformation process 
Source: 
h/p://nuviun.com/content/ 
blog/healthcares-­‐big-­‐data-­‐ 
scramble-­‐and-­‐interoperabilitys-­‐ 
i-­‐told-­‐you-­‐so
DATA MANAGEMENT: 4-stage digital transformation process 
Source: 
h/p://www.3scale.net/2013/05/the-­‐connected-­‐home-­‐app-­‐ecosystem-­‐panel/
1 
New Business Model! 
2 
3 
4 
DATA MANAGEMENT: 4-stage digital transformation process 
From Product to Service! 
Customer Digital TouchPoints! 
Back Office Digitalization!
What’s 
next? 
• Define 
your 
posi;on 
• Define 
your 
data 
strategy 
& 
roadmap 
• Meet 
your 
data 
partners 
• 3, 
2, 
1… 
GO!
Thanks! 
BARCELONA! 
Av. Corts Catalanes 9-11,! 
08173 St Cugat del Vallès! 
(+34) 93 544 24 02! 
! 
MADRID! 
Gran Via 6, ! 
28013 Madrid ! 
(+34) 91 523 73 51! 
! 
BOGOTÁ! 
Calle 73 No. 7 -31 Of. 303! 
Bogotá, Colombia! 
(571) 3473612! 
! 
! 
! 
www.rocasalvatella.com 
Albert Solana 
albert@rocasalvatella.com 
@iamtxena
17TH ~ 18th NOV 2014 
MADRID (SPAIN)

More Related Content

PDF
GesTools ASP overview
PDF
Marketing Plan
PDF
Getting the best insights from your data using Apache Metamodel by Alberto Ro...
PDF
Intro to the Big Data Spain 2014 conference
PDF
Dataflows: The abstraction that powers Big Data by Raul Castro Fernandez at ...
PDF
ToroDB: Scaling PostgreSQL like MongoDB by Álvaro Hernández at Big Data Spain...
PDF
Data warehouse modernization programme by TOBY WOOLFE at Big Data Spain 2014
PDF
Location analytics by Marc Planaguma at Big Data Spain 2014
GesTools ASP overview
Marketing Plan
Getting the best insights from your data using Apache Metamodel by Alberto Ro...
Intro to the Big Data Spain 2014 conference
Dataflows: The abstraction that powers Big Data by Raul Castro Fernandez at ...
ToroDB: Scaling PostgreSQL like MongoDB by Álvaro Hernández at Big Data Spain...
Data warehouse modernization programme by TOBY WOOLFE at Big Data Spain 2014
Location analytics by Marc Planaguma at Big Data Spain 2014

Viewers also liked (19)

PDF
The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012
PDF
State of Play. Data Science on Hadoop in 2015 by SEAN OWEN at Big Data Spain ...
PDF
Big Data Web applications for Interactive Hadoop by ENRICO BERTI at Big Data...
PPTX
Convergent Replicated Data Types in Riak 2.0
PPTX
CloudMC: A cloud computing map-reduce implementation for radiotherapy. RUBEN ...
PDF
Data Science in 2016: Moving up by Paco Nathan at Big Data Spain 2015
PDF
How to integrate Big Data onto an analytical portal, Big Data benchmarking fo...
PDF
IAd-learning: A new e-learning platform by José Antonio Omedes at Big Data Sp...
PDF
Big Data, analytics and 4th generation data warehousing by Martyn Jones at Bi...
PDF
Geospatial and bitemporal search in C* with pluggable Lucene index by Andrés ...
PDF
Essential ingredients for real time stream processing @Scale by Kartik pParam...
PDF
Analyzing organization e-mails in near real time using hadoop ecosystem tools...
PDF
A new streaming computation engine for real-time analytics by Michael Barton ...
PDF
Processing large-scale graphs with Google(TM) Pregel by MICHAEL HACKSTEIN at...
PDF
BigQuery JavaScript User-Defined Functions by THOMAS PARK and FELIPE HOFFA at...
PDF
Begin at the beginning: Feature selection for Big Data by Amparo Alonso at Bi...
PDF
Securing Big Data at rest with encryption for Hadoop, Cassandra and MongoDB o...
PDF
Apache flink: data streaming as a basis for all analytics by Kostas Tzoumas a...
PDF
New usage model for real-time analytics by Dr. WILLIAM L. BAIN at Big Data S...
The top five questions to ask about NoSQL. JONATHAN ELLIS at Big Data Spain 2012
State of Play. Data Science on Hadoop in 2015 by SEAN OWEN at Big Data Spain ...
Big Data Web applications for Interactive Hadoop by ENRICO BERTI at Big Data...
Convergent Replicated Data Types in Riak 2.0
CloudMC: A cloud computing map-reduce implementation for radiotherapy. RUBEN ...
Data Science in 2016: Moving up by Paco Nathan at Big Data Spain 2015
How to integrate Big Data onto an analytical portal, Big Data benchmarking fo...
IAd-learning: A new e-learning platform by José Antonio Omedes at Big Data Sp...
Big Data, analytics and 4th generation data warehousing by Martyn Jones at Bi...
Geospatial and bitemporal search in C* with pluggable Lucene index by Andrés ...
Essential ingredients for real time stream processing @Scale by Kartik pParam...
Analyzing organization e-mails in near real time using hadoop ecosystem tools...
A new streaming computation engine for real-time analytics by Michael Barton ...
Processing large-scale graphs with Google(TM) Pregel by MICHAEL HACKSTEIN at...
BigQuery JavaScript User-Defined Functions by THOMAS PARK and FELIPE HOFFA at...
Begin at the beginning: Feature selection for Big Data by Amparo Alonso at Bi...
Securing Big Data at rest with encryption for Hadoop, Cassandra and MongoDB o...
Apache flink: data streaming as a basis for all analytics by Kostas Tzoumas a...
New usage model for real-time analytics by Dr. WILLIAM L. BAIN at Big Data S...
Ad

Similar to Big Data the potential for data to improve service and business management by ALBERT SOLANA at Big Data Spain 2014 (20)

PDF
IQNOMY converting big data into highter occupancy rates
PDF
Panel: Powering Business Decision Making
 
PDF
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...
PDF
Personalizing in Travel and Hospitality
PDF
360i Report: Big Data
PDF
BBDO Connect Big Data
PPTX
Identifying the new frontier of big data as an enabler for T&T industries: Re...
PPTX
Neodata roundtable final com
PDF
Subasta de Ocio: Behavioral Targeting (Marc Zinck)
PPTX
Improving the Customer Experience by Capturing and Using the Right Data - by ...
PDF
Build Smarter Travel Sites Using Data & BI
PDF
Big Data for Marketers
PDF
How to get started in extracting business value from big data 1 of 2 oct 2013
PPTX
Amadeus big data
PPTX
Creds 030409
PDF
7 Experts on Transforming Customer Experience with Data Insights (1)
PDF
Big Data why Now and where to?
PDF
big data: to smart data
PDF
5 ways marketing will change in the next 5 years
PPT
Bootstrap Big Data Webinar
IQNOMY converting big data into highter occupancy rates
Panel: Powering Business Decision Making
 
Big Data & Analytics 101: How Customer Lifetime Value Enhances Predictive Mar...
Personalizing in Travel and Hospitality
360i Report: Big Data
BBDO Connect Big Data
Identifying the new frontier of big data as an enabler for T&T industries: Re...
Neodata roundtable final com
Subasta de Ocio: Behavioral Targeting (Marc Zinck)
Improving the Customer Experience by Capturing and Using the Right Data - by ...
Build Smarter Travel Sites Using Data & BI
Big Data for Marketers
How to get started in extracting business value from big data 1 of 2 oct 2013
Amadeus big data
Creds 030409
7 Experts on Transforming Customer Experience with Data Insights (1)
Big Data why Now and where to?
big data: to smart data
5 ways marketing will change in the next 5 years
Bootstrap Big Data Webinar
Ad

More from Big Data Spain (20)

PDF
Big Data, Big Quality? by Irene Gonzálvez at Big Data Spain 2017
PDF
Scaling a backend for a big data and blockchain environment by Rafael Ríos at...
PDF
AI: The next frontier by Amparo Alonso at Big Data Spain 2017
PDF
Disaster Recovery for Big Data by Carlos Izquierdo at Big Data Spain 2017
PDF
Presentation: Boost Hadoop and Spark with in-memory technologies by Akmal Cha...
PDF
Data science for lazy people, Automated Machine Learning by Diego Hueltes at ...
PDF
Training Deep Learning Models on Multiple GPUs in the Cloud by Enrique Otero ...
PDF
Unbalanced data: Same algorithms different techniques by Eric Martín at Big D...
PDF
State of the art time-series analysis with deep learning by Javier Ordóñez at...
PDF
Trading at market speed with the latest Kafka features by Iñigo González at B...
PDF
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...
PDF
The Analytic Platform behind IBM’s Watson Data Platform by Luciano Resende a...
PDF
Artificial Intelligence and Data-centric businesses by Óscar Méndez at Big Da...
PDF
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017
PDF
Meme Index. Analyzing fads and sensations on the Internet by Miguel Romero at...
PDF
Vehicle Big Data that Drives Smart City Advancement by Mike Branch at Big Dat...
PDF
End of the Myth: Ultra-Scalable Transactional Management by Ricardo Jiménez-P...
PDF
Attacking Machine Learning used in AntiVirus with Reinforcement by Rubén Mart...
PDF
More people, less banking: Blockchain by Salvador Casquero at Big Data Spain ...
PDF
Make the elephant fly, once again by Sourygna Luangsay at Big Data Spain 2017
Big Data, Big Quality? by Irene Gonzálvez at Big Data Spain 2017
Scaling a backend for a big data and blockchain environment by Rafael Ríos at...
AI: The next frontier by Amparo Alonso at Big Data Spain 2017
Disaster Recovery for Big Data by Carlos Izquierdo at Big Data Spain 2017
Presentation: Boost Hadoop and Spark with in-memory technologies by Akmal Cha...
Data science for lazy people, Automated Machine Learning by Diego Hueltes at ...
Training Deep Learning Models on Multiple GPUs in the Cloud by Enrique Otero ...
Unbalanced data: Same algorithms different techniques by Eric Martín at Big D...
State of the art time-series analysis with deep learning by Javier Ordóñez at...
Trading at market speed with the latest Kafka features by Iñigo González at B...
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...
The Analytic Platform behind IBM’s Watson Data Platform by Luciano Resende a...
Artificial Intelligence and Data-centric businesses by Óscar Méndez at Big Da...
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017
Meme Index. Analyzing fads and sensations on the Internet by Miguel Romero at...
Vehicle Big Data that Drives Smart City Advancement by Mike Branch at Big Dat...
End of the Myth: Ultra-Scalable Transactional Management by Ricardo Jiménez-P...
Attacking Machine Learning used in AntiVirus with Reinforcement by Rubén Mart...
More people, less banking: Blockchain by Salvador Casquero at Big Data Spain ...
Make the elephant fly, once again by Sourygna Luangsay at Big Data Spain 2017

Recently uploaded (20)

PDF
Electronic commerce courselecture one. Pdf
PPTX
Effective Security Operations Center (SOC) A Modern, Strategic, and Threat-In...
PDF
Dropbox Q2 2025 Financial Results & Investor Presentation
PDF
Peak of Data & AI Encore- AI for Metadata and Smarter Workflows
PDF
NewMind AI Weekly Chronicles - August'25 Week I
PDF
Diabetes mellitus diagnosis method based random forest with bat algorithm
PDF
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
PPTX
VMware vSphere Foundation How to Sell Presentation-Ver1.4-2-14-2024.pptx
PDF
cuic standard and advanced reporting.pdf
PDF
CIFDAQ's Market Insight: SEC Turns Pro Crypto
PPTX
Understanding_Digital_Forensics_Presentation.pptx
PDF
Transforming Manufacturing operations through Intelligent Integrations
PDF
The Rise and Fall of 3GPP – Time for a Sabbatical?
PPTX
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
PDF
madgavkar20181017ppt McKinsey Presentation.pdf
PDF
Reach Out and Touch Someone: Haptics and Empathic Computing
PDF
TokAI - TikTok AI Agent : The First AI Application That Analyzes 10,000+ Vira...
PDF
Network Security Unit 5.pdf for BCA BBA.
PDF
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
PPTX
Spectroscopy.pptx food analysis technology
Electronic commerce courselecture one. Pdf
Effective Security Operations Center (SOC) A Modern, Strategic, and Threat-In...
Dropbox Q2 2025 Financial Results & Investor Presentation
Peak of Data & AI Encore- AI for Metadata and Smarter Workflows
NewMind AI Weekly Chronicles - August'25 Week I
Diabetes mellitus diagnosis method based random forest with bat algorithm
Architecting across the Boundaries of two Complex Domains - Healthcare & Tech...
VMware vSphere Foundation How to Sell Presentation-Ver1.4-2-14-2024.pptx
cuic standard and advanced reporting.pdf
CIFDAQ's Market Insight: SEC Turns Pro Crypto
Understanding_Digital_Forensics_Presentation.pptx
Transforming Manufacturing operations through Intelligent Integrations
The Rise and Fall of 3GPP – Time for a Sabbatical?
PA Analog/Digital System: The Backbone of Modern Surveillance and Communication
madgavkar20181017ppt McKinsey Presentation.pdf
Reach Out and Touch Someone: Haptics and Empathic Computing
TokAI - TikTok AI Agent : The First AI Application That Analyzes 10,000+ Vira...
Network Security Unit 5.pdf for BCA BBA.
Bridging biosciences and deep learning for revolutionary discoveries: a compr...
Spectroscopy.pptx food analysis technology

Big Data the potential for data to improve service and business management by ALBERT SOLANA at Big Data Spain 2014

  • 1. DATA IN BUSINESS ALBERT SOLANA CONSULTANT ROCASALVATELLA
  • 2. BIG DATA THE POTENTIAL FOR DATA TO IMPROVE SERVICE AND BUSINESS MANAGEMENT Big Data Hispano November 17th, 2014 Albert Solana [email protected] @iamtxena
  • 3. This is me 3 years ago
  • 4. BIG DATA New digital data flows and a real-time processing capacity to provide a better service and oriented to business goals!
  • 5. DATA MANAGEMENT: 4-stage digital transformation process 1 Back Office digitalization!
  • 6. DATA MANAGEMENT: 4-stage digital transformation process Back Office digitalization! Product only! Internal data: logistics, financial & production department! ERP! 1 2 3 4
  • 7. DATA MANAGEMENT: 4-stage digital transformation process
  • 8. DATA MANAGEMENT: 4-stage digital transformation process 2 Customer Digital TouchPoints! Source: h/p://www.garymagnone.com/blog/content-­‐marke;ng-­‐digital-­‐touchpoints/
  • 9. Customer Digital TouchPoints! Product + Added-value Service! Internal & External data: sales, marketing & post-serv. depts! ERP+CRM+SM! 1 2 3 4 DATA MANAGEMENT: 4-stage digital transformation process
  • 10. DATA MANAGEMENT: 4-stage digital transformation process
  • 11. DATA MANAGEMENT: 4-stage digital transformation process 3 Products & Services!
  • 12. Products & Services! Product + Sensor + Platform + Service + Data:! R+D + Data analytics depts! A unique centric database! 1 2 3 4 DATA MANAGEMENT: 4-stage digital transformation process
  • 13. DATA MANAGEMENT: 4-stage digital transformation process
  • 14. DATA MANAGEMENT: 4-stage digital transformation process
  • 15. DATA MANAGEMENT: 4-stage digital transformation process
  • 16. DATA MANAGEMENT: 4-stage business digital transformation process data channel management Social Media Web Blogs, forums Product Data WEB Customer Segment 1 Customer Segment 2 Customer Segment n MASS AUDIENCE & MASS RAW DATA DATA CRUSH & AUDIENCE SEGMENTATION TAILORED PRODUCTS & SOLUTIONS DDBB External Databases Mobile Webs & SM Products SOURCES OF DATA OWN DATA MANAGEMENT Mobile Webs & SM Products … … NEW SOURCES OF DATA FROM INDIVIDUALS
  • 17. h1p://bit.ly/RSBigDataTourism (web document) h1p://bit.ly/RSBigDataTourismPDF (PDF version)
  • 18. Big Data and Tourism Study Main Goals Present a new methodology for improved analysis and knowledge of the Spanish tourism industry with real data gathered from cellphones and credit card transactions. State differences between: “to have the data” (Telefónica Móviles España or BBVA) “to analyze the data” (Telefónica I+D), “to set the specific questions to be answered with the data” (RocaSalvatella). Increase hotel industry business with real tourists data
  • 19. Big Data and Tourism Definition Objectives: To leverage the data opportunities for the sector, in particular the hotel industry To incorporate big data collected from the real electronic activity of anonymous foreign tourists into their market research. Key Challenge: Gather and cross two different datasets (from Telefónica & BBVA) 2-week data collection (From October 7th to October 21st 2012) Barcelona and Madrid (No special holidays or notorious events) 21 countries studied, 680.928 cellphones and 168.921 credit cards analyzed Anonymised, aggregate data (meeting requirements of LOPD 15/1999 and its developing regulations, RD 1720/2007, and Ley General de Telecomunicaciones Ley 32/2003)
  • 20. Big Data and Tourism 3 Key Players Telefónica Móviles de España Telefónica I+D
  • 21. Big Data and Tourism Main Conclusions Where from? For how long? Where? How much?
  • 22. Big Data and Tourism Main Conclusions Where from? For how long? Where? How much? Tourists who visit Barcelona and Madrid are mainly French, Italian and British (50% of the total number of visitors during the analyzed period). First non-european country ranked in 8th position (USA; 4% of total visitors). Preferences: Argentinians, Brazilians and Portuguese prioritize Madrid Nordic countries choose Barcelona.
  • 23. Big Data and Tourism Main Conclusions Where from? For how long? Where? How much? The average stay is 2.24 days long. Same country visitors may present a different behavior patterns depending on the city. For example, India is one of the countries with the longest stays in Madrid but the shortest stays in Barcelona.
  • 24. Big Data and Tourism Main Conclusions Where from? For how long? Where? How much? The further, the more city-centered. As a general rule, furthest visitors (Japan, China and Brazil…) tend to stay in city centric hotels. On the other hand, visitors from nearby countries such as Portugal, France and Belgium choose accommodation further from the center.
  • 25. Big Data and Tourism Main Conclusions Where from? For how long? Where? How much? Global average card spending per visitor during their stay was €161.5 Average card spending per day was €58.5. Average spending on accommodation for the entire stay was around €300 Average accommodation daily expenditure or price per night was €129.
  • 26. Big Data and Tourism Main Recommendations for the hotel industry Capturing more customers and highlighting the countries on which it is recommended to focus marketing. Detecting areas of the city in which commercial transactions are carried out, Specially, those referring to accommodation. Ensuring the hotel manager provides an attractive product suited to customers’ true needs (ideal length of package offers, information about complementary services demanded by nationalities, etc.)
  • 27. Most visited areas in Barcelona by Russian tourists
  • 29. Business Model! Customers: Historical Data! Product Providers: HW, SW, Security, Product materials! Data Management Alliances ! 1 2 3 4 DATA MANAGEMENT: 4-stage digital transformation process
  • 30. DATA MANAGEMENT: 4-stage business digital transformation process data ecosystem Running Shoes Industry Nutri;onist Industry Medical Industry Running accessories Industry Other Sports Industries
  • 31. DATA MANAGEMENT: 4-stage business digital transformation process data ecosystem Running Shoes Industry Nutri;onist Industry Medical Industry Data “Productless” Industry Running accessories Industry Other Sports Industries
  • 32. DATA MANAGEMENT: 4-stage digital transformation process Source: h/p://nuviun.com/content/ blog/healthcares-­‐big-­‐data-­‐ scramble-­‐and-­‐interoperabilitys-­‐ i-­‐told-­‐you-­‐so
  • 33. DATA MANAGEMENT: 4-stage digital transformation process Source: h/p://www.3scale.net/2013/05/the-­‐connected-­‐home-­‐app-­‐ecosystem-­‐panel/
  • 34. 1 New Business Model! 2 3 4 DATA MANAGEMENT: 4-stage digital transformation process From Product to Service! Customer Digital TouchPoints! Back Office Digitalization!
  • 35. What’s next? • Define your posi;on • Define your data strategy & roadmap • Meet your data partners • 3, 2, 1… GO!
  • 36. Thanks! BARCELONA! Av. Corts Catalanes 9-11,! 08173 St Cugat del Vallès! (+34) 93 544 24 02! ! MADRID! Gran Via 6, ! 28013 Madrid ! (+34) 91 523 73 51! ! BOGOTÁ! Calle 73 No. 7 -31 Of. 303! Bogotá, Colombia! (571) 3473612! ! ! ! www.rocasalvatella.com Albert Solana [email protected] @iamtxena
  • 37. 17TH ~ 18th NOV 2014 MADRID (SPAIN)