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Workshop- Applying big data to Sales

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Workshop hosted in Berlin on March 2018.
The workshop is specially prepared for the job classifieds sector and analyzes how bog data can contribute to improve the sales

Published in: Business
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Workshop- Applying big data to Sales

  1. 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. 2. INTRODUCTION
  3. 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. 4. https://www.youtube.com/watch?v=iANv_0ZQKDY
  5. 5. WHAT DOES BIG DATA MEAN?
  6. 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. 7. COLLECTING DATA... THE BIG PAIN POINT?
  8. 8. https://www.youtube.com/watch?v=3Sk7cOqB9D k
  9. 9. COLLECTING DATA- THE BIG PAIN POINT THE OBSESSION OF THE “1 SHOT COLLECTION”
  10. 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. 11. COLLECTING DATA- THE BIG PAIN POINT THE CLUE IS… “GIVE BEFORE OBTAINING”
  12. 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. 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. 14. THE DIFFERENCE BETWEEN DATA & INFO
  15. 15. THE DIFFERENCE BETWEEN DATA & INFO ACCESSING TO HUGE AMOUNTS OF DATA IS EASY… WE’RE DATA OVERLOADED
  16. 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. 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. 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. 19. THE DIFFERENCE BETWEEN DATA & INFO - All big players are trying to take positions in the data access and control -
  20. 20. THE DIFFERENCE BETWEEN DATA & INFO 26.2b US$ Late incomers have to pay the price for it..
  21. 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. 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. 23. THE DIFFERENCE BETWEEN DATA & INFO Building a full ecosystem around data acquisition…
  24. 24. THE DIFFERENCE BETWEEN DATA & INFO From an Aggregator to Job Board… and from Job board to full Recruitment player... 80M CV’s
  25. 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. 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. 27. THE DIFFERENCE BETWEEN DATA & INFO 25ºC 10 -3 35 1520 0 ºC ºC ºC ºC ºC ºC But it is not sufficient…
  28. 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. 29. https://www.youtube.com/watch?v=H4SpQqP2zuU
  30. 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. 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. 32. THE CHALLENGE: HOW TO FLOW FROM DATA TO DECISIONS
  33. 33. HOW TO FLOW FROM DATA TO DECISSIONSHOW TO FLOW FROM DATA TO DECISIONS VALIDATIONBuilding trust is an essential issue 1
  34. 34. HOW TO FLOW FROM DATA TO DECISIONS ACCURACYNot finding “Black holes” that put on doubt the rest of the info 2
  35. 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. 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. 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. 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. 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. 40. BIG DATA SPECIFIC PROBLEMS SMART SOLUTIONS
  41. 41. https://www.youtube.com/watch?v=FmMzlehzU8c
  42. 42. SPECIFIC PROBLEMS- SMART SOLUTIONS DIFFERENTIAL VALUE COMPLEXITY/ DIFFICULTY 100% HIGH RISK OF FAILURE NATURAL FORCES - DATA ANALYSIS PARALYSIS -
  43. 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. 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. 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. 46. BIG DATA APPLIED TO THE RECRUITMENT SECTOR
  47. 47. TWO BIG DIFFERENT UNIVERSES B2BB2C Depending on the universe the value proposition based on the data use changes drastically...
  48. 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. 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. 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. 51. TWO BIG DIFFERENT UNIVERSES THE THEORY OF THE GLASS OF WATER
  52. 52. TWO BIG DIFFERENT UNIVERSES - PROGRAMMATIC ADVERTISING -
  53. 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. 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. 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. 56. TWO BIG DIFFERENT UNIVERSES There is a human component... That can be measured through close fields of information...
  57. 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. 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. 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. 60. Let’s see it in Action...
  61. 61. https://www.youtube.com/watch?v=8YD973AbmtM
  62. 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. 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. 64. “There is nothing worse for a seller, not knowing who to call...” TWO BIG DIFFERENT UNIVERSES
  65. 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. 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. 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. 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. 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. 70. TWO BIG DIFFERENT UNIVERSES 2 THERE IS A LACK OF CONFIDENCE THAT KNOWLEDGE CAN COME FROM OUTSIDE CLOSED MENTALITY
  71. 71. TWO BIG DIFFERENT UNIVERSES 3 LACK OF LEADERSHIP INTERNAL DISAGREEMENTS ABOUT WHO HAS TO PILOT THE PROJECT
  72. 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. 73. TWO BIG DIFFERENT UNIVERSES 5 LACK OF VISION OF THE MANAGERS MANY SALES DEPARTMENTS ARE STILL MANAGED IN AN OLD WAY
  74. 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. 75. BIG DATA TRYING TO KILL THE PARADIGMS ABOUT IT
  76. 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. 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. 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. 79. TRYING TO KILL PARADIGMS… OPEN MIND
  80. 80. TRYING TO KILL PARADIGMS… We have to assume that we’re blind...And data can show us a new perspective
  81. 81. TRYING TO KILL PARADIGMS… We have to be prepared... As data can show our weaknesses… People don’t like “finger pointing”
  82. 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. 83. TRYING TO KILL PARADIGMS… Understand that data is dynamic…Generates evolutionary ecosystems
  84. 84. BIG DATA APPLIED TO OUR DAY TO DAY
  85. 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. 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. 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. 88. HIGH ALTITUDE BUSINESS VIEW MARKET: UK PERIOD: JAN 2018 # Job offers evolution last 12 months # Market split by channel CUSTOMER: INDEED
  89. 89. HIGH ALTITUDE BUSINESS VIEW MARKET: DE PERIOD: JAN 2018 # Top advertisers # Market share versus market CUSTOMER: INDEED
  90. 90. HIGH ALTITUDE BUSINESS VIEW MARKET: UK PERIOD: Q4 2017 # Category distribution # Category share variation CUSTOMER: INDEED
  91. 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. 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. 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. 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. 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. 96. BUSINESS RADIOGRAPHY MARKET: UK PERIOD: JAN 2018 # Company with engagement lost CUSTOMER: INDEED
  97. 97. BUSINESS DEVELOPMENT SALES MANAGER / CSO / TEAM LEADERS / SALES TEAM MISSING CUSTOMERS HOT LEADS PRIOR LEADS
  98. 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. 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. 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. 101. APPLIED TO OUR DAY TO DAY… MARKET: DE PERIOD: JAN 2018 CUSTOMER: INDEED # Potential to grow... Companies ordered by potential to grow...
  102. 102. BIG DATA ONE STEP FORWARD
  103. 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. 104. ONE STEP FORWARDONE STEP FORWARD How we traditionally segment customers and assign accounts What traditionally has been known as “swimming pools”
  105. 105. ONE STEP FORWARD Are you taking care of how they’re using your service? How we grow our farm?
  106. 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. 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. 108. CONCLUSIONS
  109. 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. 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. 111. ABOUT
  112. 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

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