Big Data - Chambre de Commerce de Québec
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On se fout du Big Data, on veut des résultats! Comment tirer profit des concepts sans s’embourber dans les détails techniques.
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Big Data - Chambre de Commerce de Québec
- 1. Stéphane Hamel DigitalAnalyticsThought Leader, MBA, Google Product Strategy Expert @SHamel67 Comment tirer avantage des concepts sans s’embourber dans les détails techniques.
- 7. Intelligence d’Affaires (BI) Informatique Back-office Opérationnel Exact et précis Marketing Front office Décisionnel Échantillonnage Adaptif… Digital Analytics
- 9. ANALYTIQUE(S) La science de l’analyse Comment une organisation parvient à une décision optimale et réaliste informée par les données. Activité qui consiste à subdiviser un item complexe en plusieurs sous-éléments pour en comprendre le fonctionnement. ANALYSE
- 10. Technology Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity A visualization of all the Hype Cycle data January 26th, 2013 by Mark Raskino blogs.gartner.com/hypecyclebook/2013/01/26/a-visualization-of-all-the-hype-cycle-data/
- 13. VisiCalc, 1979 Apple II, 8k of memory Ce qui n’entre pas dans Excel
- 14. “Big data is not a market, but a set of conditions and complexities that organizations have to deal with/exploit.” Andrew White, Gartner, August 2015
- 15. Q)If you lost it all - what would be the first thing you would do to try and rebuild your success? A) You have to DEFINE what SUCCESS is. Richard Branson’s Secrets to Success LinkedIn Offical Blog, January 13, 2013
- 16. Création de valeur Big Data – What It Means for Digital Analysts Stephane Hamel, Juin 2012 online-behavior.com/analytics/big-data Augmenter la satisfaction Survie et croissanceRéduire les coûts Augmenter les revenus Engagement Profits
- 17. Transparence Une culture “data informed” encourage l’accès libre par tous les intervenants pertinents.
- 18. Une culture de l’expérimentation permet de découvrir/préciser les besoins, d’exposer les variations et les risques, et d’améliorer la performance. Expérimentation
- 19. Segmentation La granularité et la précision dans la segmentation d’une population (humaine ou non) selon divers critères permettent d’entreprendre des actions spécifiques.
- 20. Support à la décision Remplacer/aider la prise de décision en augmentant la vélocité et en diminuant les risques, tout en découvrant de nouvelles informations et idées qui autrement seraient demeurées cachées.
- 21. Innovation Le Big Data permet de créer de nouveaux produits ou services, d’améliorer ceux existants, en créer de nouveaux ou de raffiner les processus d’affaires.
- 22. Croissance Chacun de ces concepts n’est réalisable qu’avec un réel désir de changement, l’acceptation de prendre des risques, apprendre, et avoir les ressources humaines qualifiées.
- 23. Analytics Center of Excellence Business Outcomes & Actions Enabling CapabilitiesAnalysis Provides: Actionable insights & recommendations Communicate: Business requirement & objectives Supply: Means, tools and data Strategy Goals Technological capabilities & constraints Web development Information architecture User Experience Instrumentation & BI Statistical analysis Problem solving Synthesis of data Communication through reports & dashboards
- 24. “The keepers of big data say they do it for the consumer’s benefit. But data have a way of being used for purposes other than originally intended.” Erik Larson, Harper’s Magazine, July 1989
- 25. Machine Learning 50 ans de recherche Problèmes similaires: facile pour l’humain difficile/impossible pour la machine Images: 93% Voix: 96%
- 26. Intelligence artificielle Intelligence artificielle utilisée pour le jeu Go appliquée aussi pour optimiser l’énergie. 15% d’économie
- 27. Internet des objets Combine voix, contrôle gestuel, projection, cellulaire, GPS, Internet, censeurs…
- 28. Données Pour un distributeur de musique (ex. Sony), disponibilité en tout temps des « Top Charts » à travers tous les players en mode gratuits.
- 30. Data Centralization Data Visualization Analysis Automation Business Intelligence Waves of Innovation Faster Answers Better Answers Better Questions
- 31. Tableau Software Data visualization • Slice & Dice • Exploration • Reports & dashboards • Collaboration Qlik Sense Microsoft Power BI Tibco Spotfire
- 32. Outlier.ai Business analytics automation • Data monitoring • Machine learning • Visualisation • Collaboration IBM Watson Google Automated Insights Adobe Sensei Domo Mr. Roboto
- 33. Outlier.ai Business analytics automation • Data monitoring • Machine learning • Visualisation • Collaboration IBM Watson Google Automated Insights Adobe Sensei Domo Mr. Roboto
- 34. En résumé Analyse/analytique Transparence Expérimentation Segmentation Support à la décision Innovation Croissance Vie privé Centralisation, visualisation, automatisation
- 35. (the fine prints) Copyright © 2017 by Stéphane Hamel. All rights reserved. No part of this document may be used or reproduced in any manner whatsoever without the express permission of the author, except in brief quotations embodied in critical articles and reviews (with proper citation of source). No commercial use of this material is permitted. Stéphane Hamel PO BOX 1123 St-Pierre, Qc G0A4E0 Canada https://StephaneHamel.net me@StephaneHamel.net
- 36. Alors? Big Data ou pas? me@StephaneHamel.net
Notes de l'éditeur
- We have been doing it all wrong… the idea was to gather business requirements from stakeholders, define KPIs, and create a solution design. The thing is… either your stakeholders (if client side) or clients (if agency) do not have a clue or they do not know how to properly articulate their needs. Do not ask them! Instead, take the lead - be the expert; Show them the light; pave the way! In this presentation, Stéphane Hamel, a recognized industry leader and author of the Digital Analytics Maturity Model, will propose a radical new approach to digital analytics. He will share tricks and examples that could transform the way you do your job. Utopia or Nirvana? It will be yours to decide! --- « Marketing strategy », « Big Data », « digital analytics », Internet des Objets… en voulez-vous du data, en v’là! En principe, l’analytique devrait permettre aux gestionnaires marketing et autres de prendre des décisions plus éclairées. Pourtant, après des années à définir des objectifs et des indicateurs de performance, à mesurer, à produire des rapports et des tableaux de bord, la majorité des entreprises tirent le diable par la queue. Peut-être est-ce le temps de changer notre façon de faire?
- Placeholder for personal intro Why I don’t have a job title… What is a « thought leader », what is a « consultant » vs « academic » vs « practitionner »?
- Circa 1991 System administrator, DBA, software engineer TDSB: Traitement des Données de Surveillance des Barrages Hundred of dam constructions, 40 years, dozens if not hundred of instruments (quantitative: temperature, pressure, humidity, inclination; qualitative: observations, cracks, scree and fallen rocks) Real-time polling through… slow modems… Data distributed in multiple Oracle databases Representation of observations on GIS (Geographic Information Systems) and 2D blue-prints Volume: The quantity of generated and stored data. The size of the data determines the value and potential insight- and whether it can actually be considered big data or not. Variety: The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Velocity In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Variability Inconsistency of the data set can hamper processes to handle and manage it. Veracity The quality of captured data can vary greatly, affecting accurate analysis. Source: Wikipedia
- http://blogs.gartner.com/hypecyclebook/ “In 2013 Gartner analysts published over 90 Hype Cycle reports covering different technology domains. There were more than 1900 technology profiles within those charts. I had always wanted to see all that data in one place, at one time.”
- http://blogs.gartner.com/hypecyclebook/ “In 2013 Gartner analysts published over 90 Hype Cycle reports covering different technology domains. There were more than 1900 technology profiles within those charts. I had always wanted to see all that data in one place, at one time.”
- There’s a big chasm when shifting from Excel to the next level. Small Data is fundamentally the same as Visicalc… invented nearly 35 years ago! Cited in “The Devil’s Data Dictionary” by Jim Sterne, 2015
- Source: http://blogs.gartner.com/andrew_white/2015/08/20/the-end-of-big-data-its-all-over-now/
- Making data accessible to relevant stakeholders in a timely manner.
- Enabling experimentation to discover needs, expose variability, and improve performance. As more transactional data is stored in digital form, organizations can collect more accurate and detailed performance data.
- More granular segmentation of populations can lead to customize actions.
- Replacing/supporting human decision making with automated algorithms which can improve decision making, minimize risks, and uncover valuable insights that would otherwise remain hidden.
- Big Data enables companies to create new products and services, enhance existing ones, and invent or refine business models. Google took the machine learning that won Go and applied it to data center cooling. Achieved 15% energy saving after trying to do it by hand for decades. Build system that find patterns in data. https://vimeo.com/195062332 8:20
- Each of those important outcomes can only become reality if sufficient and properly trained human capital is available.
- Source: https://bits.blogs.nytimes.com/2013/02/01/the-origins-of-big-data-an-etymological-detective-story/?_r=0 http://harpers.org/archive/1989/07/what-sort-of-car-rt-sort-am-i-junk-mail-and-the-search-for-self/
- After 50 years of work , computer vision systems got this right 72% of the time. A whole class of similar problems – easy for people and hard/impossible for computers. Consensus: decades more work. Then, in 2012, machine learning… Image recognition: 28% error rate now 7% Speech recognition: 26% error rate now 4% How it used to work: codify perception vs massive data & applied math Build system to look for ears, eyes, noses. Hire linguists and create grammar rules. Try to codify human intelligence Take 10k pictures of dogs, 10k pictures without dogs. Learn rahter than rules. Now possible because we have 1m times more computer power & data Face recognition: Facebook, Google, airports, crowd… Source: https://vimeo.com/195062332
- Big Data enables companies to create new products and services, enhance existing ones, and invent or refine business models. Google took the machine learning that won Go and applied it to data center cooling. Achieved 15% energy saving after trying to do it by hand for decades. Build system that find patterns in data. https://vimeo.com/195062332 8:20
- Big Data enables companies to create new products and services, enhance existing ones, and invent or refine business models. Google took the machine learning that won Go and applied it to data center cooling. Achieved 15% energy saving after trying to do it by hand for decades. Build system that find patterns in data. https://vimeo.com/195062332 8:20
- Big Data enables companies to create new products and services, enhance existing ones, and invent or refine business models. Google took the machine learning that won Go and applied it to data center cooling. Achieved 15% energy saving after trying to do it by hand for decades. Build system that find patterns in data. https://vimeo.com/195062332 8:20
- Big Data enables companies to create new products and services, enhance existing ones, and invent or refine business models. Google took the machine learning that won Go and applied it to data center cooling. Achieved 15% energy saving after trying to do it by hand for decades. Build system that find patterns in data. https://vimeo.com/195062332 8:20
- Source: https://medium.com/@outlierai/the-end-of-business-intelligence-80ee439e7669
- Stéphane Hamel is a seasoned consultant and distinguished thought leader in the field of digital analytics. Named Most Influential Industry Contributor of 2012 by the Digital Analytics Association, he has made significant contributions to the industry, including creating the Digital Analytics Maturity Model, the Web Analytics Solution Profiler (WASP) quality assurance tool as well as other tools and concepts. Stéphane has previously held board positions with the DAA, taught hundreds of students enrolled in the UBC Award of Achievement in Digital Analytics and graduate-level marketing classes at Laval University. In addition, Stéphane co-manages the largest Google Analytics user community on Google Plus and serve on a number of advisory boards, including award-winning digital data analytics firm Cardinal Path and dashboard solution Klipfolio. He is frequently called upon to speak at conferences around the world and an often-quoted media contributor on the topic of digital intelligence and analysis. Stéphane holds an MBA in eBusiness and work from home in an historical heritage area called Île d’Orléans, near Québec-city, Canada.