1.
WORKSHOP
Applying BIG DATA to SALES - how you can watch your
revenue grow by using the right data and measurements
BERLIN – FEBRUARY 2018

2.
INTRODUCTION

3.
ABOUT THE SPEAKER
CHRISTIAN PALAU SANZ PROFILE
“ +15 YEARS OF INTERNATIONAL EXPERIENCE
IN THE ONLINE CLASSIFIEDS MARKET”
COMPUTRABAJO BESTJOBS JOBISJOB FOTOCASA SEGUNDAMANO
SCHIBSTED INFOJOBS BRASIL INFOJOBS ITALIA INFOJOBS ESPAÑA
EMAGISTER NIUMBAESPACIO DECO MARKETYOU ENALQUILER
Associated teacher at Business Schools
In-Company
Former
2014 – 2017: SVP BUSINESS DEVELOPMENT EN RED ARBOR: COMPUTRABAJO, BESTJOBS, MUBAWAB, INFOJOBS BRASIL
2012 – 2014: CSO/CMO EN JOBISJOB: Job Aggregator and BIG DATA data solution for the HR sector
2018 – TO DATE: SENIOR DIGITAL STRATEGY CONSULTANT
2013 – 2014: INNOVATION DIRECTOR AT ANUNTIS: FOTOCASA, COCHES.NET, SEGUNDAMANO, INFOJOBS, LECTIVA,...
2009 – 2012: FOTOCASA DIRECTOR: Real Estate top Classified site in Spain
2003 – 2009: MARKETING & STRATEGY DIRECTOR AT INFOJOBS ESPAÑA

4.
https://www.youtube.com/watch?v=iANv_0ZQKDY

5.
WHAT DOES
BIG DATA
MEAN?

6.
WHAT DOES BIG DATA REALLY MEAN?
Extremely big data sets… that need from
computational analysis to reveal patterns,
trends, associations,....
Data alone, has very limited value... Basically the
value of owning it, and not allowing the access to
third...- Economy of information hiding.
Is needed to process it, to transform it into
information that is what really adds value as it is in
what business decisions are taken...
For processing it... We need technology and context to
give sense to the result obtained...
Thought 1
Thought 2
Thought 3

7.
COLLECTING
DATA...
THE BIG PAIN
POINT?

8.
https://www.youtube.com/watch?v=3Sk7cOqB9D k

9.
COLLECTING DATA- THE BIG PAIN POINT
THE OBSESSION OF THE
“1 SHOT COLLECTION”

10.
COLLECTING DATA- THE BIG PAIN POINT
LONG FORMS NOT LEARNING FROM USERSNOT ADDED VALUE FROM INFO1 2 3• Number of fields of information
• Type of fields- UI
• Category of info asked
• Relevance of info versus content
• Non connection between data and benefit
• Feeling of non personalisation
• Asking for info we already should know
• Recurrently asking for the same information
VOLUME OF DATA &
CONSISTENCY OF DATA
MOBILE ADOPTION &
SCREEN LESS EVOLUTION
GDPR - PRIVACY

11.
COLLECTING DATA- THE BIG PAIN POINT
THE CLUE IS…
“GIVE BEFORE OBTAINING”

12.
COLLECTING DATA- THE BIG PAIN POINT
“Users/ customers are overloaded by
information and demands of it…”
So, they approach in front an information demand- form,
call, mail, chat, bot,… is SKEPTICISM!!!
So, every time we try to collect information we should…
PAYS
OFF
IS GOING TO BE
RELEVANT INFO
QUALITY OF THE
COLLECTION
Which is the “driver” that moves
the user/ customer to give us the
information…
Which is the moment we
ask for the information and
how we do it...
Keeping in mind the ”driver” and
the moment... What we’re going to
obtain is differential?...

13.
COLLECTING DATA- THE BIG PAIN POINT
The 4BIG challenges
#
#
#
#
What is the information we need?
Am I able to obtain it?
What is the cost? Does It pay off?
How will we be able to maintain it?

14.
THE
DIFFERENCE
BETWEEN
DATA & INFO

15.
THE DIFFERENCE BETWEEN DATA & INFO
ACCESSING TO HUGE
AMOUNTS OF DATA IS EASY…
WE’RE DATA OVERLOADED

16.
THE DIFFERENCE BETWEEN DATA & INFO
Every day we generate 2.5 quintillion bytes of data…
3.8bPeople connected
5.2b daily Google searches
4M hours of content uploaded to Youtube every day
21.9b daily messages sent
67M daily photos uploaded to Instagram
1.0b daily facebook users
4.3b daily messages posted on Facebook
267b daily mails sent
373M daily Amazon sales

17.
THE DIFFERENCE BETWEEN DATA & INFO
We’re completely
overloaded by data…
In this context it becomes more
relevant to be able to differentiate
data from info, and to choose the
right source of information
https://www.domo.com/learn/data-never-sleeps-5?aid=ogsm072517_1&sf100871281=1

18.
THE DIFFERENCE BETWEEN DATA & INFOTHE DIFFERENCE BETWEEN DATA & INFO
DATA OBSOLESCENCE
90% of available data has
been generated in the last
24 months…
So, it is not only relevant to know which info we want, but to be able to access it…
It is very important to be able to feed from it in a constant mode...

19.
THE DIFFERENCE BETWEEN DATA & INFO
- All big players are trying to take positions in the data access and control -

20.
THE DIFFERENCE BETWEEN DATA & INFO
26.2b US$
Late incomers have to
pay the price for it..

21.
In fact, all players are
afraid of a new incomer...
+2.000M profiles
#1
Worldwide in audience
and consumed online time
THE DIFFERENCE BETWEEN DATA & INFO

22.
They have already won the
space of our private life...
But...
Will they also win the
professional one?
THE DIFFERENCE BETWEEN DATA & INFO
25%Facebook users
search for a job over
the platform

23.
THE DIFFERENCE BETWEEN DATA & INFO
Building a full
ecosystem
around data
acquisition…

24.
THE DIFFERENCE BETWEEN DATA & INFO
From an Aggregator to
Job Board… and from
Job board to full
Recruitment player...
80M CV’s

25.
OBTAIN THE CONTENT- PARTNER AGREEMENTS
SEO + SEM
AFFILIATION PROGRAM
DIRECT EMPLOYERS RELATION
ATTRACT CV’s
BUILD BRAND
ENGAGE PRODUCTS
BLOCK JOB BOARDS
RECRUITMENT
SERVICES
ACTING AS AN
AGGREGATOR
ACTING AS A
JOB BOARD
ACTING AS AN
HR COMPANY
Evolution inside
the value chain...
THE DIFFERENCE BETWEEN DATA & INFO

26.
THE DIFFERENCE BETWEEN DATA & INFO
“DATA IS ONLY A SEED TO
OBTAIN INFORMATION”
For obtaining
information we need
the combination of
several points of
data…

27.
THE DIFFERENCE BETWEEN DATA & INFO
25ºC
10
-3 35
1520
0 ºC
ºC
ºC
ºC
ºC
ºC
But it is not sufficient…

28.
THE DIFFERENCE BETWEEN DATA & INFO
We need to give context… data when gets context is transformed
and allows us to build an interpretation over it...
CONTEXT A
SUMMER
13PM
SUNNY DAY
CONTEXT B
TEMPERATURE AT DIFFERENT ALTITUDE
PYRENEES

29.
https://www.youtube.com/watch?v=H4SpQqP2zuU

30.
THE DIFFERENCE BETWEEN DATA & INFO
DATA INFOCONTEXT
INTERPRETATION = (S + K) x E
S: SKILLS K: KNOWLEDGE E: EXPERIENCE
Once we have an interpretation… we can take a decision...

31.
THE DIFFERENCE BETWEEN DATA & INFO
The BUS PARADIGM
80% OF KIDS UNDER 10 YEARS OLD DIDN’T HAVE PROBLEM TO SAY IN WHICH WAY THE BUS GOES…

32.
THE CHALLENGE:
HOW TO FLOW
FROM DATA TO
DECISIONS

33.
HOW TO FLOW FROM DATA TO DECISSIONSHOW TO FLOW FROM DATA TO DECISIONS
VALIDATIONBuilding trust is an essential issue
1

34.
HOW TO FLOW FROM DATA TO DECISIONS
ACCURACYNot finding “Black holes” that put
on doubt the rest of the info
2

35.
HOW TO FLOW FROM DATA TO DECISSIONSHOW TO FLOW FROM DATA TO DECISIONS
PROCESSINGRough data needs to ne “cooked”,
de-duplicated, categorized & standardized…
3

36.
HOW TO FLOW FROM DATA TO DECISIONS
CONSTANT FLOWOnce we build the trust we need to guarantee
the constant access over the data
4

37.
HOW TO FLOW FROM DATA TO DECISIONS
VISUALISATIONThe consumption of information has
to be easy… If we don’t put focus
on it... However the info could be
very useful... It becomes a
showstopper...
5

38.
HOW TO FLOW FROM DATA TO DECISIONS
Help to understand the information, giving
references that make it easy to assume it
6 GIVE CONTEXT

39.
But sometimes...
However much we follow the 6
points... “magic” doesn’t happen...
And this is basically due to a “miss
gap” of the size of the project...
Differential value vs. Complexity

40.
BIG DATA
SPECIFIC
PROBLEMS
SMART
SOLUTIONS

41.
https://www.youtube.com/watch?v=FmMzlehzU8c

42.
SPECIFIC PROBLEMS- SMART SOLUTIONS
DIFFERENTIAL
VALUE
COMPLEXITY/
DIFFICULTY
100%
HIGH RISK
OF FAILURE
NATURAL FORCES
- DATA ANALYSIS PARALYSIS -

43.
SPECIFIC PROBLEMS- SMART SOLUTIONS
APPROACH A
We collect data, but we’ve not defined
a specific problem... So, although we
have sufficient information... We’re
not able to give it a clear value...
LOW VALUE = LOW USE

44.
SPECIFIC PROBLEMS- SMART SOLUTIONS
APPROACH B
We define a huge problem... Which means a big complexity
in terms of time & resources... If we don’t achieve a
tangible output quickly, the project loses traction...
HIGH COMPLEXITY= HIGH CHURN RATE

45.
SPECIFIC PROBLEMS- SMART SOLUTIONS
“When we introduce big data Philosophy/ Methodology inside the
company becomes critical to clearly define objectives &
problems to solve and assume that it is a learning path...”
It’s impossible to go
from blindness to
starlight in 1 jump...
The figure of the CDO
CHIEF DATA OFFICER
“Could be a good catalyser...”
“But if he doesn’t have the empowerment of the
top management and he is not able to give value
to all the levels of the organisation...
Definitely he will fail...”

46.
BIG DATA
APPLIED TO THE
RECRUITMENT
SECTOR

47.
TWO BIG DIFFERENT UNIVERSES
B2BB2C
Depending on the universe the value proposition
based on the data use changes drastically...

48.
TWO BIG DIFFERENT UNIVERSES
B2C APPROACH
The main impact of big
data is related to the
definition of business
models...
PAY
x
PUBLISH
PAY
x
PERFORMANCE
EVOLVING

49.
TWO BIG DIFFERENT UNIVERSES
The supports are not so open to
changing their business
model...
Performance is a less secure
business model than pay & pray
The change is
starting in the
publisher side...

50.
TWO BIG DIFFERENT UNIVERSES
Optimisation of the
exposure of my ads
Obtaining a better distribution of
the investment between platforms
Obtaining a better optimisation of
the investment inside a platform
Obtaining a better optimisation of
the “talent acquisition”
Searching a clear improvement of the
investment in terms of ads x euro
paid or effective distribution

51.
TWO BIG DIFFERENT UNIVERSES
THE THEORY OF THE
GLASS OF WATER

52.
TWO BIG DIFFERENT UNIVERSES
- PROGRAMMATIC ADVERTISING -

53.
TWO BIG DIFFERENT UNIVERSES
PROGRAMMATIC
ADVERTISING
Platforms (SaaS) that apply big data, deep learning and
AI strategies to maximize the return on the investment
of the advertisers
80%
Is expected
that the
Of the jobs will be posted
under this model in 2020
- However it is not a perfect system... -

54.
TWO BIG DIFFERENT UNIVERSES
We’re talking
about people...
We’re talking about
on-boarding them in
our company
The solution is a little bit more
“tricky” than when we buy
visits/ users to our sites...

55.
TWO BIG DIFFERENT UNIVERSES
The solution is a little bit more
“tricky” than when we attract visits
to our site to sell shoes...
HR
“we’re buying talent”
BUSINESS > CONSUMER BUSINESS < CONSUMER$¿ ¿

56.
TWO BIG DIFFERENT UNIVERSES
There is a human
component... That
can be measured
through close fields
of information...

57.
TWO BIG DIFFERENT UNIVERSESTWO BIG DIFFERENT UNIVERSES
“The only way is to share internal info... To
enrich the algorythm... And teach it about what
matches or not with the organisation...”
And always remember...
“WE CAN OPTIMIZE MACHINE LEARNING... BUT WE
CAN’T FORGET HUMANS DON’T ACT AS MACHINES”

58.
TWO BIG DIFFERENT UNIVERSES
Introduces Predictive
Sourcing Tool
“Put your sourcing
on autopilot”
• 500 million candidate profiles,
aggregated from 50+ data sources
• They examine how these profiles
change over time, then use AI to
predict future change. In other
words, the technology uses
candidate activity patterns to
determine how likely someone is
to leave a job, thus, how
“recruitable” the candidate is.

59.
TWO BIG DIFFERENT UNIVERSES
They’re able to analyse emotions
just by watching you...
Through the use of AI-
analysing the look ad
facial factions, they’re
able to detect patterns of
behaviour/ preference/
engagement,...
Once you have this info... You
can take decisions based on the
behaviour

60.
Let’s see it
in Action...

61.
https://www.youtube.com/watch?v=8YD973AbmtM

62.
TWO BIG DIFFERENT UNIVERSES
B2B APPROACH
Is where the big
competence is happening...
All players are fighting
to attract business to
their site... So knowledge
is a competitive advantage
that can make all the
difference...

63.
In fact... Since a long time ago...
The B2B side has tried to apply “big
data” acquisition techniques...
PRESS
SCRAPING
WEB
SCRAPING
APP
SCRAPING
’90’’90’s
’00’s
’10’s
TWO BIG DIFFERENT UNIVERSES

64.
“There is nothing worse for a seller,
not knowing who to call...”
TWO BIG DIFFERENT UNIVERSES

65.
NO LEADS
NO QUALIFIED LEADS
NO MANAGED LEADS
NO PROPOSALS
NO SALES
NO SALES PAID
NO SELLER
NO BUSINESS
NO COMPANY
- It is critical to maintain the wheel turning round -
TWO BIG DIFFERENT UNIVERSES

66.
Market segmentation and lead
discovering is something that,
traditionally, companies have been
doing “doors inside”- in a secret
way, with a lot of hand making a
very tailored to each department...
Knowledge is not flowing easily inside the organisations...
TWO BIG DIFFERENT UNIVERSES

67.
Business based on “hiding
information” are losing sense...
The greatness is that data is
there... Most cases “public”...
And you only have to know how
to access it...

68.
If data is there... And the
use of it is so great...
Why don’t companies
put focus on it?
The 6 main reasons...
TWO BIG DIFFERENT UNIVERSES

69.
TWO BIG DIFFERENT UNIVERSES
MEANS A CHANGE IN THE
WAY OF WORKING...
1st HUMAN REACTION
VERSUS CHANGE IS...
NEGATION
TWO BIG DIFFERENT UNIVERSES
1

70.
TWO BIG DIFFERENT UNIVERSES
2
THERE IS A LACK OF
CONFIDENCE THAT
KNOWLEDGE CAN
COME FROM OUTSIDE
CLOSED MENTALITY

71.
TWO BIG DIFFERENT UNIVERSES
3
LACK OF LEADERSHIP
INTERNAL
DISAGREEMENTS
ABOUT WHO HAS TO
PILOT THE PROJECT

72.
TWO BIG DIFFERENT UNIVERSES
4
WHY TO IMPROVE IF THIS
ALREADY WORKS
WE’RE IN THE MIDDLE OF A
“BLUE OCEAN” WITH
PLENTY OF OPPORTUNITIES

73.
TWO BIG DIFFERENT UNIVERSES
5
LACK OF VISION OF
THE MANAGERS
MANY SALES
DEPARTMENTS ARE STILL
MANAGED IN AN OLD WAY

74.
TWO BIG DIFFERENT UNIVERSES
6
SALES ARE EVOLVING FROM
AN ART TO A SCIENCE
AN EXCESS OF FOCUS ON THE
TACTIC– SPEECH/ CHANNELS
VERSUS STRATEGY

75.
BIG DATA
TRYING TO KILL
THE PARADIGMS
ABOUT IT

76.
LET’S BE HONEST…
HOW MANY OF YOU THINK THAT
SALES HEADCOUNT DECISIONS ARE
BASED ON FEELINGS?
AND HOW MANY DO IT BASED
ON INTERNAL DATA?

77.
LET’S BE HONEST…
HOW MANY OF YOU THINK THAT
SALES OBJECTIVES ARE BASED ON
PAST PERFORMANCE?
WITHOUT KNOWLEDGE OF THE
REMAINING MARKET POTENTIAL...

78.
LET’S BE HONEST…
HOW MANY OF YOU THINK THAT
SELLERS OBTAIN THE 80% OF
THE TOTAL POTENTIAL VALUE OF
THE CUSTOMERS?
THEY REALLY CONTROL CUSTOMER VALUE

79.
TRYING TO KILL PARADIGMS…
OPEN MIND

80.
TRYING TO KILL PARADIGMS…
We have to assume
that we’re blind...And data can show us a new perspective

81.
TRYING TO KILL PARADIGMS…
We have to be prepared...
As data can show our weaknesses…
People don’t like “finger pointing”

82.
TRYING TO KILL PARADIGMS…
We have to be creative
We have to sell the information inside the company
Data is 30%
Information is 40%
Visualisation is 30%

83.
TRYING TO KILL PARADIGMS…
Understand that
data is dynamic…Generates evolutionary ecosystems

84.
BIG DATA
APPLIED TO
OUR
DAY TO DAY

85.
APPLIED TO OUR DAY TO DAY…
THE RULE OF
“COFFEE FOR ALL”
It doesn’t work... Every
hierarchic level of the
organisation should obtain
a specific benefit

86.
APPLIED TO OUR DAY TO DAY…
HIGH ALTITUDE
BUSINESS VIEW
CEO / OWNERS / GENERAL MANAGERS/ VP’s /
MARKET ANALYSTS / INVESTMENT FIRMS /
RESEARCH FIRMS
HOW BIG IS THE MARKET MARKET COMPOSITION MARKET SPLIT
IS THERE A BUSINESS OPPORTUNITY?

87.
APPLIED TO OUR DAY TO DAY…
# What are the top recruiting channels? (free/ paid- offers & advertisers)
# How many companies are posting offers? (recruiting)
# What is the size of the market
- Published offers
- Active offers
# Level of activity of the market (offers x advertiser)
# Content distribution (x category, location, job title, salary,…)
# Advertisers distribution (x category, location, job title, salary,…)
# Top customer profiles (size, split & share of them)

88.
HIGH ALTITUDE BUSINESS VIEW
MARKET: UK
PERIOD: JAN 2018
# Job offers evolution
last 12 months
# Market split by channel
CUSTOMER: INDEED

89.
HIGH ALTITUDE BUSINESS VIEW
MARKET: DE
PERIOD: JAN 2018
# Top advertisers
# Market share versus
market
CUSTOMER: INDEED

90.
HIGH ALTITUDE BUSINESS VIEW
MARKET: UK
PERIOD: Q4 2017
# Category distribution
# Category share
variation
CUSTOMER: INDEED

91.
HIGH ALTITUDE BUSINESS VIEW
MARKET: UK
PERIOD: Q4 2017
# Salary distribution
# Compared to one of
the top players of the
market
CUSTOMER: INDEED

92.
APPLIED TO OUR DAY A DAY…APPLIED TO OUR DAY TO DAY…
BUSINESS
RADIOGRAPHY
BIZ DEVELOPMENT / GROWTH MANAGER / CMO / COO / CSO
/ SALES DIRECTOR / SALES MANAGER / TEAM LEADERS
CUSTOMER SHARE CAPACITY OF GROWTH BUSINESS EVOLUTION
HOW HEALTHY IS OUR BUSINESS
HOW IS
CONTROLLING
A SPECIFIC NICHE
IS THIS NEW PLAYER
REALLY A
COMPETITOR
ARE WE LOOSING
ENGAGEMENT W/
CUSTOMERS

93.
APPLIED TO OUR DAY TO DAY…
# How many companies post with us and how much content?
# Which is our market share?
- Advertisers
- Content
# Trends- evolution over time
- By company
- By location
- By job title
# Share by customer- % of total content and competitive fragmentation
# Share by category, location and job title
# Gain/ lost customers/ categories/ location
# Overlap with other players based on advertisers or offers

94.
BUSINESS RADIOGRAPHY
MARKET: UK
PERIOD: JAN 2018
CUSTOMER: INDEED
# Analysis of the advertising profile of one of the TOP customers of the customer
# Share of customer by channel, and strategic position by category, location and job title

95.
BUSINESS RADIOGRAPHY
MARKET: COL
PERIOD: JAN 2018
# Company distribution
# Compared to the
market… give us
vision of the weight of
the short/ long tail
CUSTOMER: INDEED

96.
BUSINESS RADIOGRAPHY
MARKET: UK
PERIOD: JAN 2018
# Company with
engagement lost
CUSTOMER: INDEED

97.
BUSINESS
DEVELOPMENT
SALES MANAGER / CSO / TEAM LEADERS / SALES TEAM
MISSING
CUSTOMERS
HOT
LEADS
PRIOR
LEADS

98.
APPLIED TO OUR DAY TO DAY…
# How many companies post with us and how much content?
# Which is our market share?
- Advertisers
- Content
# Trends- evolution over time
- By company
- By location
- By job title
# Share by customer- % of total content and competitive fragmentation
# Share by category, location and job title
# Gain/ lost customers/ categories/ location
# Overlap with other players based on advertisers or offers

99.
APPLIED TO OUR DAY TO DAY…
MARKET: UK
PERIOD: JAN 2018
# Hot leads…
vacancies being posted
on paid sites multiple
times with periods of
up to 2 weeks in-
between.
CUSTOMER: INDEED

100.
APPLIED TO OUR DAY TO DAY…
MARKET: UK
PERIOD: JAN 2018
# Missing customers...
Companies that are not
publishing with us...
But YES with other
market players...
# Also gives
information of which of
them were with us in
the last 12 months
CUSTOMER: INDEED

101.
APPLIED TO OUR DAY TO DAY…
MARKET: DE
PERIOD: JAN 2018
CUSTOMER: INDEED
# Potential to grow...
Companies ordered by
potential to grow...

102.
BIG DATA
ONE
STEP
FORWARD

103.
ONE STEP FORWARD…ONE STEP FORWARD
EXTERNAL
SOURCES
TRAFFIC SOURCES
SOCIO-DEMOGRAPHIC
MOBILITY
ECONOMICAL
To evaluate what a leadership position in traffic means
To evaluate the potential of a zone/ region
To evaluate the potential of a segment/ profile/ sector
To evaluate the impact of field sales
One key point is the capacity of evolve easily the ”big data ecosystem”
to adapt it to business needs...

104.
ONE STEP FORWARDONE STEP FORWARD
How we traditionally segment
customers and assign accounts
What traditionally has been known as “swimming
pools”

105.
ONE STEP FORWARD
Are you taking care
of how they’re
using your service?
How we grow
our farm?

106.
ONE STEP FORWARD
But do we know the
potential of the customers?
Do we work with customers with high potential?
Are the higher potential customers assigned to the best sellers?
- THE DYNAMIC ACCOUNT ASSIGMENT -

107.
ONE STEP FORWARD-THEDYNAMICACCOUNTASSIGMENT-
3
STEPS
PROCESS
BLOC 1 BLOC 2 BLOC 3
-COMPANY INFO - - SERVICE USE - - COMPANY POTENTIAL-
COMPANY SIZE
INDUSTRIAL SECTOR
LOCATION
CONTRACT STATUS
Nº OFFER PUBLISH LAST 12M
ACTIVE PRODUCT
LAST LOGIN
LAST PRODUCT BOUGHT
CUSTOMER VALUE (12M LTV)
IS MISSING CUSTOMER
GROWING POTENTIAL
CUSTOMER SHARE
COMPETITORS USE
ALGORITHM
CUSTOMER
VALUE
CUSTOMER
POTENTIAL POTENTIAL
GAP
“Leads/ customers are prioritized based on their
potential GAP and assigned to the seller based on their
capacity of management and conversion scoring…”
Life process… not closed segments... The system
optimizes resources and ROI of sales activity

108.
CONCLUSIONS

109.
CONCLUSIONS Data and the capacity to transform it into
relevant information has become a
critical issue for companies
1
DATA IS
POWER
INFO IS
AUTHORITY
DECISIONS ARE
GREATNESS

110.
CONCLUSIONS All ”Big Data” process landing inside
a company requires…2
# Time
# Clear goals
# All company implication
# Consistent data sources
&... AN OPEN MIND!!!

111.
ABOUT

112.
CHRISTIAN PALAU SANZ
SENIOR STRATEGIC BUSINESS DEVELOPMENT ADVISOR
TELF: +34 620 89 31 62
MAIL: CPALAU@JOBISJOB.COM
SKYPE: CERKDTI
Visit us- www.jobmarketinsights.com