El 3 de julio de 2014, organizamos en la Fundación Ramón Areces una jornada con el lema 'Big Data: de la investigación científica a la gestión empresarial'. En ella estudiamos los retos y oportunidades del Big data en las ciencias sociales, en la economía y en la gestión empresarial. Entre otros ponentes, acudieron expertos de la London School of Economics, BBVA, Deloite, Universidades de Valencia y Oviedo, el Centro Nacional de Supercomputación...

1. Los datos, la nueva materia prima del
marketing

2. Too many
Vs for Big
Data
Batch of new technologies that allow
us to extract value out of a dataset
which, due to it’s volume, variety or
velocity, was not previously exploited

3. “Set of new technologies, able to extract additional
value of all the available data of a company”

4. Petabytes: Google 300 PB, facebook: 45 PB, Yahoo! 180 PB
Exabytes: U.S. healthcare
Zetabytes: 2011, 1.8 ZB created. World Information 9.57 ZB
YottaByte, Brontobyte, GeopByte to be reached
I don’t have so much data…
A big European company = Terabytes

5. Why does it apply to marketing?

6. Marketing has all that data….
and more
M2M not a trend, your future

7. MARKET TRENDS

11. DATA = VALUE
DATA = COMPANY VALUE

12. Startups
Gran parte del valor de las
empresas se mide por sus datos

13. Grandes empresas
467.142
Mill $
373.608
Mill $
150.211
Mill $
148.210
Mill $
1 3 20 21
Data Loyalty

14. DowJones PER2014 = 13
Facebook PER2014 = 48
Diferencia 35

15. PER = PER + PDR+
PER = 13 + 35 =
48
+

16. UNIVERSAL DATA VALUE
BUSINESS INTELLIGENCE
DATA DRIVEN DECISIONS
BIG DATA INTERACTIVE
BIG DATA REAL TIME DATA
STREAMING
BIG DATA INTELLIGENCE
10
10
20
20
20
BIG DATA STORED 20
100
UDV= = = 0,35
PDR
100
35
100
10 10 20 20 20 20
35
PDR

17. UDV DE TU EMPRESA
BUSINESS INTELLIGENCE
DATA DRIVEN DECISIONS
BIG DATA INTERACTIVE
BIG DATA REAL TIME DATA
STREAMING
BIG DATA INTELLIGENCE
5
5
0
0
0
BIG DATA STORED 0
10
PDR (tú) = UDV 10 = 0,35 10 = 3,5
10 10 20 20 20 20
35
3,5(tú)
PDR
X X

18. Opportunities/possibilities
Threats/risk for marketing

19. The Bubble filter

20. You must enter in the user bubble

22. 83% of the surveyed companies were
able to do things with Big Data that
seemed impossible to achieve before
“The art of possible”
“Impossible is not a fact, it’s an opinion”

23. Visualizations
And Analysis

24. Social networks tracking (Tag Clouds)

25. Social networks tracking and flow of data

26. Social networks tracking and geolocation

27. Marketing with Sentiment analysis and semantic engines

28. Description:
Search the social network comments and
mentions of interest of a particular issue or event
for further evaluation, influencers detection and
graphical display of the conversation to facilitate
analysis.
Advantages:
Show real-time event (symposium, forum,
seminar, etc..) with visual information.
 Get opinions and feelings about a topic in social
networks in real time
Identify the influencers of a hot topic
 Risk detection and prevention
 Emotional mining: Know the term that is most
popular for some people, brand, event, etc.and
this way you can know about the generated
feelings by the most important terms.
Social networks tracking Application

29. Description:
Search the network content and publications on
specific subjects of our interest, to detect, filter,
collect and process relevant information in semi-
real time or batch.
Associated with the semantic analysis this allows
the detection and classification of the contents
effectively.
Advantages:
Allows the generating of sites in a dynamic way
without any intervention or exhaustive searches,
with the contents collected and categorized.
Unifies in a single web all the tasks that users have
to do manually, so it saves them money and
generates loyalty.
Web Content Crawling and Scraping

30. EXAMPLES

31. +160% clicks
vs. one size fits all
+79% clicks
vs. randomly selected
+43% clicks
vs. editor selected
Recommended links News Interests Top Searches
Description:
Customizing homepages based on user navigation
Analysis and customization of the homepage and site in
real time for each user based on their browsing
Modification of contents, highlights, ads, in real time
based on user history
Advantages:
Over 300% increase in clickthrough
Creating millions of web pages in real time
Increasing Conversions
Increase in sales
Cost ten times lower than other solutions
Marketing online: Customizing Web Sites (Behavioral
Customization)

32. Description:
Newsletter development, email-marketing or any
other sent material segmented by individual
preferences
Analyzes and takes into account:
•Financial information and user data
•Navigation and usage information from previous
marketing shipments
•Mobile app data (GPS, payments, browsing of
offers…)
• Users’ information from the social networks
Advantages:
Increased clickthrough
Increase in conversions and sales
Natural language processing – semantics and
sentiments
Combines private and public data
Marketing offline: Personalized Marketing with Big Data

33. NH Quality Focus:
Complementing the internal data of a company by
combining the structured and the unstructured
data, with the data generated by the web and
social networks, allows us to determine the validity
of the data of our brand, product or company.
The comparison and analysis of internal and
external data (web) increases the value of our data
and allows us to gain a competitive advantage over
our competitors.
Advantages:
 It allows sales improvement.
Improves loyalty.
Increases Conversions.
Detects errors or data manipulation.
 SEO improvement with regards to the users and
the public data.
Improves marketing and product boosting with
regards to trends.
Marketing through private structured data with unstructured
public data

34. Description:
Allows you to label and categorize automatically and
massively, any type of content or information.
Advantages:
Allows searching, categorization, clustering, and be
able to extract value out of information otherwise
hardly findable and usable.
Utilizes state of the art tools to identify entities, NED
systems, NERD. These tools combined with the use of
disambiguation of entities using a Big Data system
containing the Wikipedia and other sources of
information.
Speed processing capabilities and data volume​​
superior to that of other systems.
Massive information tagging

35. TECHNOLOGY AND THE FUTURE OF BIG
DATA

36. COMBINATION AND SPEED
COUCHDBCOUCHDB
Combine all type of data and past, present and future
“Cross Data Spark” main mission is:
•To facilitate the use of data stored in different noSQL databases and data containers
•To allow combining stored data (past), real-time data (present), and future data
(predictive).

37. MACHINE LEARNING AND ALGORITHMS
USING ONLY SPARK FOR ALL PROCESSING:
BATCH, INTERACTIVE AND STREAMING
CROSSDATA SPARK:
Stratio is able to
combine, in one query,
stored data with
streaming data entering
in the system
Polyglots: Spark
integrated with the main
noSQL databases, starting
with Cassandra & Mongo
DB.

38. Lean = Easier deployment, management,
and use of the system
Former Hadoop or
Hybrid Hadoop-Spark Platforms
Stratio Platform
SIMPLE AND EASY

39. SIMPLIFICATION
Simplify Building
Process

40. No te puedes quedar mirando

41. Arriesga, innova,
reinventate

42. Hazlo ahora,
si no puede ser tarde

43. No hay nada mas
arriesgado
que no arriesgarse

44. Enjoy with “Big Data”

45. Q&A

46. “the best way to predict the future is to create it”
THANKS
Óscar Méndez, CEO de Stratio,