Uploaded byMedia Perspectives
Eric van Tol - Businesscases & Verdienmodellen
The document discusses the various sources, challenges, and promises of big data in business, illustrating both the potential for enhanced consumer insights and operational efficiency, as well as the frustrations associated with implementing data-driven strategies. It emphasizes the importance of understanding customer behavior through real-time analytics and personalization while also addressing privacy concerns and the necessity for effective data management. Furthermore, it highlights the growth of companies like Alibaba and Amazon in the digital marketplace, showcasing the integration of big data into their business models.
More Related Content
Similar to Eric van Tol - Businesscases & Verdienmodellen
Eric van Tol - Businesscases & Verdienmodellen
- 1.
- 3. Intro Obama DataScience 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 CallDetail 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 askanyone 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 longterm, 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 YouAvoid 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.
- 20.
- 21. The promise ofBig 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.
- 24. 1997IBM Deep Bluebeats G. Kasparov Calculate all combinations
- 25.
- 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.
- 28. Machine learning algorithm appliedby Google on You-Tube stills taught itself to recognize cats
- 29. running 16,000 processorsacross a thousand computers for 10 days without pause 2012
- 30. Predict behavior of studentmistakes or defects car parts
- 31. App recognises yourpictures better than yourself
- 32. Image Text Numeric More data -better interpretation
- 34. Retailer Amazone 5 billioncloud services of 22.3 billion
- 35.
- 37.
- 38. Target: Mobile consumerprofiling Apps: Visualisation, Machine Learning, Statistics In memory: Real time, Streaming On disk: Batch processing & scalable storage
- 39. Big data pipeline 80% Intake & Storage Extract & Clean Aggregation, Analyses & Modelling Interpretation & Collaboration Visualization
- 40.
- 41. 12-02-2012 Beurskoersvoorspeller wint opnieuw pitchwedstrijd Vincentvan Leeuwen Aandeelwaarde voorspellen door sentiment te meten
- 42. Statcom 2013 NewYork Corr = 0.88 Piet Daas, Statistics Netherlands consumer confidence with social media N=1500 enquête Facebook/twitter
- 43. French campings nearwater : 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 nowworking 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.
- 46.
- 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.
- 54.
- 55. And then.. Everybody andeverything generates data
- 56.
- 57.
- 58. Manage KPIs: ERP, DWH,RDBMS Business Intelligence Realtime explore data: NO SQL, Hadoop, Spark Hyper Agile, Sandbox Bring together…
- 59. Transistion ICT systems supporthumans Humans support ICT systems to learn
- 60. Battle for unambiguity Fact,truth, undisputed data .. Likely, perspective, impure,..
- 61.
- 62.
- 63. Foxes know manysmall things which they bring to bear in their analyses in a dynamical and flexible way
- 64. The hedgehog issaid to know one thing and know it well Tedlock
- 65. Low entry barrierto start Low cost of technology • Open Source Software • Cloud • Sensors
- 66. Low entry barrierto start Availabity of data: • Social Media • Crowdsourcing • Open Data
- 67. Create a sanctuaryfor experimenting with digital data A pirate island for innovation
- 70.
- 71.
- 72.
- 73. The promise ofBig Data… targeted, localised and personalized services • detailed insight of consumer behaviour (real time & predictive) • process optimisation
- 74. The promise ofBig Data… • Change the business • Run the business
- 75. how the publishercan 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 teamscorporates versus Start Ups
- 77.
- 79. convergence between mediaand telco industry before 2000? IT Media Content Fixed Mobile Who is eating who know?
- 80. DHL & BigData
- 81. China’s Alibaba isthe 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 platformwill 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 Beeis 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 communitymedia company active in the fields of regional newspaper and magazine publishing and Internet communications. UK based.
- 88. Capture subscriber Archant usesCxense 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 inadvertising 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.
- 93.
- 94. Waisda?: Video LabelingGame Crowdsourcing
- 95. Data Journalism likeThomas van Linge Start small
- 96. Real Time CrisesMapping 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: ‘Freedata 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 arguethat 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 tobuilding 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.
- 113.
- 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 areAspirational: 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 DataOperating System Applications Enterprise processes Hardware Skills
- 118. Ontwikkelaar en engineer Bouwenen 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 recognitionand data visualization companies