Tijdens de vierde sessie van de vierdelige reeks Master Minds on Data Science hield Eric van Tol een presentatie over businesscases en verdienmodellen.

1. Eric van Tol
evtol@dataXL.eu

3. Intro Obama Data Science Officer
1.30 min

5. Big Data sources (1)
Users: applications (Web 2.0)
and Social Media

6. Big Data sources (2)
Machines
with sensors and IP adres
mobile devices “internet of things”

7. Telco xDRs Call Detail Records
Bank ATMs and credit-card transactions
Retailer point-of-sale transactions.
Utility energy meters.
Dotcom web-click streams and social-media interactions
Big Data (internal) sources (3)

8. ." But ask anyone today what comes to mind when you say "CRM," and you'll
hear "frustration," "disaster," "expensive," and "out of control." It turned out to
be a great big IT wild-goose chase.
And I'm afraid we're heading down the same road with Big Data.
Peter Fader, codirector of the Wharton
Customer Analytics Initiative at the
University of Pennsylvania

9. "In the long term, they expect $3 to $4 return on investment for
every dollar.
But based on our analysis, the average company right now is
getting a return of about 55 cents on the dollar,"
Jeffrey F. Kelly, Wikibon. 2013

10. How Can You Avoid Big Data?
Pay cash for everything!
Do not play games online
Avoid surveillance camera's
Never use toll highways
Don’t pay tax
Never go online!
Don’t use a telephone!
Don’t use Albert Heijn bonus cards!
Don’t fill any prescriptions!
Never leave your house!

11. Data Driven Enterprise?

20. Point Break

21. The promise of Big Data…
targeted, localised and personalized services
• detailed insight of consumer behaviour (real time & predictive)
• process optimisation

22. The fear to
• embrace new technology and adapt legacy
• share valuable data
• tackle privacy and trust issues (reputation damage)

23. Point Break

24. 1997IBM Deep Blue beats G. Kasparov
Calculate all combinations

25. Point Break

26. After 72 hours (on 20 core Intel Xeon) playing against itself,
reached a rating equivalent to the top 2.2% of players.
Self learning chess computer September 2015
Matthew Lai student
Imperial College London

27. Point Break

28. Machine learning
algorithm applied by
Google on You-Tube
stills
taught itself to
recognize cats

29. running 16,000 processors across a thousand computers for 10 days without pause 2012

30. Predict behavior of
student mistakes
or
defects car parts

31. App recognises your pictures better than yourself

32. Image
Text
Numeric
More data - better interpretation

34. Retailer Amazone
5 billion cloud services of
22.3 billion

35. CEO Google
Largest database
on earth

37. Big Data
Technology

38. Target: Mobile consumer profiling
Apps: Visualisation, Machine Learning, Statistics
In memory: Real time, Streaming
On disk: Batch processing & scalable storage

39. Big data pipe line
80%
Intake &
Storage
Extract &
Clean
Aggregation,
Analyses &
Modelling
Interpretation
&
Collaboration
Visualization

40. Big Data
Management
&
Business

41. 12-02-2012
Beurskoersvoorspeller wint
opnieuw pitchwedstrijd
Vincent van Leeuwen
Aandeelwaarde voorspellen door sentiment te meten

42. Statcom 2013 New York
Corr = 0.88
Piet Daas, Statistics Netherlands
consumer confidence with social media
N=1500 enquête
Facebook/twitter

43. French campings near water :
689 records
data set contains websites of French
campings near a beach or a lake.
www.camping-le-mas-de-la-plage.fr
www.palmirabeach.fr
www.mairie-telgruc.fr
www.camping-lesdunes.fr
www.campingdespins.fr
Marc Noët

44. “Netflix is now working to perfect its personalization technology
The recommendation engine will be so finely tuned that it will show users “one or two
suggestions that perfectly fit what they want to watch now.”
Netflix’s Neil Hunt

45. SlideShare Rob Winters

46. Digitalization
Integration
Personalization
(Big) Data

47. Digitalization
Integration
Personalization
Real Time & Predictive
(Big) Data
Consumer

48. Digitalization
Integration
Personalization
Real Time & Predictive
Agri Food
Cow or Tomato
Precision agriculture
Smart Diary Farming

49. Digitalization
Integration
Personalization
Real Time & Predictive
Online Gaming
Game
Advice
Influencing behaviour
Recommendation

50. Digitalization
Integration
Personalization
Real Time & Predictive
Patient
Self-diagnoses
Influencing behaviour
Prevention
Personalized Medicine
Healthcare

51. Digitalization
Integration
Personalization
Real Time & Predictive
User
Predict network & user
behaviour
Location based services
Telecom

52. Digitalization
Integration
Personalization
Real Time & Predictive
Predict reader/viewer behaviour
Context based services
Media

53. Big Data
“Management”

54. A few comuters
generate data

55. And then..
Everybody and everything
generates data

56. And then..
Structured
Unstructured

57. Internal
Periodic
External
Ad Hoc
Bring together…

58. Manage KPIs:
ERP, DWH, RDBMS
Business Intelligence
Realtime explore data:
NO SQL, Hadoop, Spark
Hyper Agile, Sandbox
Bring together…

59. Transistion
ICT systems support humans
Humans support
ICT systems
to learn

60. Battle for unambiguity
Fact, truth, undisputed data ..
Likely,
perspective,
impure,..

61. The best predictor?
Hedgehog
Fox

62. Fox or Hedgehog?

63. Foxes know many small things which they bring to
bear in their analyses in a dynamical and flexible way

64. The hedgehog is said to know
one thing and know it well
Tedlock

65. Low entry barrier to start
Low cost of technology
• Open Source Software
• Cloud
• Sensors

66. Low entry barrier to start
Availabity of data:
• Social Media
• Crowdsourcing
• Open Data

67. Create a sanctuary for experimenting with digital data
A pirate island for innovation

70. Think Big

71. Start small

72. Show the money

73. The promise of Big Data…
targeted, localised and personalized services
• detailed insight of consumer behaviour (real time & predictive)
• process optimisation

74. The promise of Big Data…
• Change the business
• Run the business

75. how the publisher can use data to better inform and serve
audiences and journalists?
Know your audience
• Data analytics for digital subscriptions
• Programmatic advertising and real-time bidding
Find the news
• Outliers and trends
• Data journalism – Data visualisation
• Automating journalism with data

76. Big Data teams corporates
versus
Start Ups

77. Start small

79. convergence between media and telco industry before 2000?
IT
Media
Content
Fixed
Mobile
Who is eating who know?

80. DHL & Big Data

81. China’s Alibaba is the biggest e-retailer in the world and has
more online transactions than eBay and Amazon combined

82. 2013, eBusiness Review, a Chinese print publication modeled after the Harvard Business review.
2014, 40% stake in Huxiu , one of China’s leading technology and business blogs,
2015, China Business Network, a Bloomberg-esque financial news and data provider.
2015, Alibaba partnered with financial magazine Caixin and the Xinjiang government to launch Wujie
Media, an online-only news provider .
2015, Alibaba partnered with the parent company of domestic newspaper Sichuan Daily
Alibaba’s broader media portfolio also includes streaming video, feature film production, and in
Snapchat.
Amazon CEO bought The Washington Post.
Alibaba buys Hong Kong-based newspaper South China Morning Post?
Alibaba also Publisher

83. The SMILE platform will provide Indian SMEs access to global business trading
financing, logistics, inspections and certifications, technology and SME trade-linked education.
As per Alibaba, more than 4.5 Mn Indian SMEs are listed on its platform.
Connect Indian manufacturers with Chinese suppliers, provide Indian sellers trading support,
and facilitate the global sales of Indian products through Alibaba.
7 December 2015
Alibaba launches online platform SMILE for Indian SMEs

84. The Sacramento Bee is a daily newspaper published in Sacramento,
California, in the United States. Since its founding in 1857, The Bee has
become the largest newspaper in Sacramento, the fifth largest
newspaper in California, and the 27th largest paper in the U.S.

86. Sacramento Bee makes “big data” available to small businesses
March 24, 2014 / Jim Bonfield
Posted By: Darrell Kunken
Through our relationship with CustomerLink, we can offer an SMB learn more about:
• Who their most valuable customers are.
• Where they can find more of them.
• And how they can develop programmes and processes to keep bringing them back to buy more.
What does The Sacramento Bee want out of this relationship?
The opportunity to have a conversation with the business decision-maker about how to connect the dots between the
report on their most valuable customers and the local media channels that can be best leveraged to drive business.
Sacramento Bee makes Big data” available to small businesses

87. Archant, a community media company active in
the fields of regional newspaper and magazine
publishing and Internet communications. UK
based.

88. Capture subscriber
Archant uses Cxense Insight a Norwegian
company to capture all relevant traffic
and events across desktop, tablet and
mobile devices, and display the
information in dashboards.
Cxense provides real-time analytics, data
management, search and personalization
solutions to help brands deliver more
engaging online experiences.

91. Archant brings in advertising revenue with topic-based apps
11 November 2013 · By Miller Hogg
Almost two years into the media company’s venture into apps, results look good. Topic-led apps have brought in £600,000
in ad revenues, and unique visitors to the company’s replica apps average 2.2 visits and more than 100 pages per month,
per user. Next up? An app factory.
Archant shares 10 lessons to bringing in video revenue
10 October 2014 · By Marek Miller and Mariell Raisma
Paying attention to what print sales people can and can’t do, what role journalists can play in video production, and how
content marketing could be a game changer are key to increasing video revenue
Read more: http://www.inma.org/blogs/ideas/post.cfm/archant-brings-in-advertising-revenue-with-topic-based-apps#ixzz3tXkPLa7o
Read more: http://www.inma.org/blogs/conference/post.cfm/archant-shares-10-lessons-to-bringing-in-video-revenue#ixzz3tXktbFp0

92. Automatic News Alert

93. Automated journalism

94. Waisda?: Video Labeling Game
Crowdsourcing

95. Data Journalism like Thomas van Linge
Start small

96. Real Time Crises Mapping
2010 manual in NY for Haiti earth quake victims
2011 Tsunami and earth quake in Japan 2011, 300,000 tweets per
minute
Automatic Twitter en SMS classification irevolution.net/category/crisis-mapping
Started small

104. Type A: ‘Free data collector and aggregator’
CO Everywhere :app filter social media activity by location
Coosto: sentiment with twitter and facebook
Dataprovider: webcrawl 23 countries-digital economic activity measure
Type B: ‘Analytics-as-a-service’
Granify: identify the points in an e-commerce transaction where users are most likely to convert
Algoritmica: predictive maintenance
Type C: ‘Data generation and analysis’
Swarmly: Waze for people. Where everyone is in realtime
GoSquared, Mixpanel or Spinnakr, provide a Web analytics service
Automated Insights, software service that turns structured data into readable narratives
Type D: ‘Free data knowledge discovery’
GitHub or Google Code
Type E: ‘Data-aggregation-as-a-service‘
AlwaysPrepped: monitor students’ performance by aggregating data from multiple education programmes and websites.
Type F: ‘Multi-source data mash-up and analysis’
Welovroi, a Web-based digital marketing monitoring allows tracking of a large number of different metrics based on data
provided by customers and external data.

105. We also argue that creating a business model for a
data-driven business involves answering six
fundamental questions:
1. What do we want to achieve by using big data?
2. What is our desired offering?
3. What data do we require and how are we going to acquire it?
4. In what ways are we going to process and apply this data?
5. How are we going to monetize it?
6. What are the barriers to us accomplishing our goal?

106. • Freemium “free” and “premium”
• Advertisement
• Subscription
• Usage fees
• Licensing IP copyright (incumbents – no start ups)
• Commission fees intermediaries for B2C markets
Revenue models in data ecosystem

107. Three keys to building a data-driven strategy
Executives should focus on targeted efforts to source data, build models, and transform
organizational culture.
March 2013 | byDominic Barton and David Court
1. Choose the right data
Source data creatively
Get the necessary IT support
2. Build models that predict and optimize business outcomes
3. Transform your company’s capabilities
Develop business-relevant analytics that can be put to use
Embed analytics in simple tools for the front lines
Develop capabilities to exploit big data

108. Commit2Data

113. Think Big

114. Coca cola – exponential quotient of 62 out of 84
The Guardian – exponential quotient of 62 out of 84
General Electric – exponential quotient of 69 out of 84
Amazon – exponential quotient of 68 out of 84
Zappos – exponential quotient of 75 out of 84
ING Direct Canada – exponential quotient of 69 out of 84
If you want to be exponential?
“exponential organization”
introduced and defined in 2014 by Ismail, Michael S. Malone and Yuri van Geest in their book
Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, Cheaper
Than Yours (and What to Do About It).

115. book Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, Cheaper Than Yours (and What to Do About It)

116. If you are Aspirational: Assemble the best people and resources to make the case for
investments in analytics. To get sponsorship for initial projects, identify the big business
challenges that can be addressed by analytics and find the data you have that fits the
challenge.
If you are Experienced: Make the move to enterprise analytics, and manage it by
keeping focus on the big issues that everyone recognizes. Collaborate to drive
enterprise opportunities without compromising departmental needs while preventing
governance from becoming an objective unto itself.
If you are Transformed: Discover and champion improvements in how you are using
analytics. You’ve accomplished a lot already with analytics but are feeling increased
pressure to do more. Focus your analytics and management bandwidth to go deeper
rather than broader, but recognize it will be critical to continue to demonstrate new
ways of how analytics can move the business toward its goals.
http://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/ December 2010

117. Data Detective
Hadoop Hacker
Data Operating System
Applications
Enterprise processes
Hardware
Skills

118. Ontwikkelaar en engineer
Bouwen en onderhouden met Big Data gereedschappen en methoden
Onderzoeken , Ontdekken en Onderhouden
Hadoop Hacker
Domein expert en analist
Vertalen business vraag naar Big Data vraag
Ongearticuleerde behoefte omzetten in specifieke vragen…
Data Detective

119. Video
Dutch Image recognition and
data visualization companies