Big data datacrunch
1 like641 views
Big data refers to vast datasets that are challenging to manage with traditional tools, posing difficulties in areas like capture, storage, and analysis. The document discusses the evolution of big data, highlighting its exponential growth due to increased information capture from multimedia, social media, and IoT, and emphasizes the need for effective data handling in business. Additionally, it explores the implications of big data on customer behavior, requiring companies to adapt to complex consumer interactions while leveraging data for enhanced insights and decision-making.
Big data datacrunch
- 1. BIG DATA What ? Issues Business Opportunies How ?
- 2. Definition big data consists of datasets that grow so large that they become awkward to work with using on-hand database management tools. Difficulties include capture, storage, search, sharing, analytics, and visualizing. « WIKIPEDIA » « The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future ». McKinsey Global Institute, http://bit.ly/tE7fiZ
- 3. Data Deluge : Evolution 2010-2015 e bas Data X 11 3, 4 milliards 4 50 000 d’abonnés pétaoctects 3G en 2015 contre 500 250 000 millions en pétaoctects s 2010 F ile Ericson ail s 30 000 X 10 M pétaoctects X6 1 pétaoctet (Po)= 1 000 To= 1 000 000 000 000 000 d'octets International Communication Union 1 zettaoctect (Zo) = 10puissance 21 octets.
- 4. Non Structured Data Données non structurées Données structurées
- 5. Available DATA files mail … Mobile Social Network Apps…. Comportemental DATA RFM Socio-psycho-demographic Text DATA DATA Life Instant Non structured
- 7. DATA Road Map -----> -2015---- ----------- 2013------------- 2012
- 8. BIG DATA : Theorical & practical Landscape Human Societi es IT system? Customers, Co nsumers ? Privacy ? Knowledges, H abilities ? Management, W ork Organisation ?
- 9. BIG DATA : A Thermodinamic Evolution Species (Human societies..) produce energy & modify environment (human competition, natural resources..) The Red Queen Energy Information effect : Run Maximization memorization faster to stay in place!
- 10. IT ecosystem Industries Data Created & duplicated
- 11. IT system, today Competitivity Réduce Cost (not Only) 4 1 Environment Environment impact 3 adaptation 5 2 Information 6…. Memorization Resources Moore Law
- 12. Like that, the future is a BIG DATA CRUNCH In a thermodynamic Schéma, BIG DATA Is Increase STORAGE + Inscrease TREATMENT + Increase DATA PRODUCTION +… Energy Dissipation critical threshold ??
- 13. IT System Tomorrow Entropy Management DATA sens Reduce Energie consumption
- 14. A BIG DATA Process DATA Management Multi canal (today) Cross Canal (Tomorrow) Life Instants Comportemental web mail Emission DATA … voice Capture …. Network & real Time Storage Silo traitment Predictive Actions Analysis making Reuse Contextual information PUSH
- 15. Consumers, customers Voice Professionnal Consumer
- 16. Privacy by Design Visibility Security Transparency Prevention
- 17. BIG DATA = A Co-adaptativ Environment E Hyperstructure X T Technologic Model I N C T Specialization I O N E V U C Decentralized O S E collective L E N intelligence U R T Memetic T R Evolutiv I Environment I O N c
- 18. Data Hominem = BIG DATA Knowledges DATA Specialists who know collect, analyze and reuse efficiency the data in a business way
- 19. Few Ways BIG DATA Capture Voice = Life Instant & Comportemental DATA Understanding = Few to one, One to One. Interaction = ATAWAD « any time, any where, any device »
- 20. Industrialize singularity Several years, Customers have good technology and consumption habits since they use different Medias to behave and stay informed: internet, mobile, touch pad, interactive interface, so on. The ways of consumption could be defined as a set of situations (probably circumstances) experienced by the customers. We observe an increase of the used medias combinations during the purchase process. So it becomes very difficult to understand real customers needing in only using statistical indicators. And however it’s the goal of everyone in the company. So, how can we measure the customer experience especially to understanding their new purchases habits? Customer reality would be elusive? Our process should be as complex as their behavior? No, smarter but not especially complex. Sometimes, expanded uses methods can be a good alternative. Finally, understanding user experience within customer centricity seems the best way to industrialize singularity. ME…
- 21. Business in Progress Transaction space between financial and retail actors
- 22. BIG DATA, an opportunity for the Retail
- 23. Find DATA Connexity : Ex. WallMart Lab with Social Genome wihtout Social Genome
- 24. DATA Matching : Ex. DATALIFT R&D Project In order to see the Web of data emerge, it is necessary to provide methods and tools all along the semantic lifting process. The main objective of DATALIFT is to bootstrap semantic lifting of raw data on the Web. Interconnexion des données avec d'autres jeux de données Publication sur le web de données Conversion des données en RDF en rapport avec la ou les ontologies selectionnées Sélection des ontologies pouvant décrire les données
- 25. BIG DATA Interaction : a diagram of Customer evolution Bring a lot of information on current changes and emerging phenomena. Communication on the efficiency and performance will focus on the essentials iteration. The dashboard of the future can not be compared, but it will tell us that is most important in analysis and decision making.
- 26. Build a BIG DATA vision 1. Capture Voice Life Instants 2. Understanding Comportemental DATA F O N … C T I Scoring O N Predictiv N Analysis I Expert T rules Y GRANULARITY 3. Interaction Diagram of Dashboard evolution 360° Alerts …
- 27. HOW ? DATACRUNCH, 13 APRIL 2012, LILLE, France RESEAU & ABLE www.reseaunable.net