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Big Data

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Was ist Big Data und wo wird Big Data heute schon eingesetzt? Bitkom klärt die wichtigsten Fragen rund um das Trendthema.

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Big Data

  1. 1. Big Data – Begriff & AnwendungsfälleDr. Thomas Keil, Program Manager Business Analytics, SAS Deutschland Vorstand im AK Big Data des BITKOM Stuttgart, 24.10.2012
  2. 2. Agenda1. Warum ist Big Data wichtig?2. Was ist Big Data – und wo kommt es her?3. Welchen Nutzen stiftet Big Data?4. Wo wird Big Data heute schon eingesetzt?5. Welche Fragestellungen werden neu aufgeworfen? 2
  3. 3. AK Big Data – Neu gewählter Vorstand (20.9.2012) Axel Spaeth, Regional Operations Director, Hewlett- Packard GmbH 3
  4. 4. Leitfaden Big Data am 19. September 2012 veröffentlicht 106 Seiten 20 Autoren, weitere 12 Autoren von Anwendungsbeispielen Veröffentlichungen in „CIO“, „Computerwoche“, „is report“, „silicon.de“ etc. sowie in vielen Blogs und Foren Inhalt: Definitionen, Begriffsklärungen und vor allem 34 Anwendungsbeispiele Download: http://www.bitkom.org/de/publikatione n/38337_73446.aspx 4
  5. 5. 1. Warum ist Big Data wichtig?
  6. 6. Big Data ist ein Trend, kein Hype.
  7. 7. Der globale Markt für Big Data Quelle: Experton Group, Leitfaden Big Data 7
  8. 8. 8
  9. 9. 2. Was ist Big Data – und wo kommt es her?
  10. 10. Big Data hat vielfältige Quellen und ist facettenreich definiert.
  11. 11. Das Datenvolumen wächst rasant. 11
  12. 12. Globale Megatrends sind die Treiber für Big Data. 12
  13. 13. Big Data hat (mindestens) vier Facetten. 13
  14. 14. Definition 14
  15. 15. 3. Welchen Nutzen stiftet Big Data?
  16. 16. Big Data ermöglicht neue Geschäftsfelder und neueVorgehensweisen.
  17. 17. Einsatzszenarien nach Funktionsbereichen1. Marketing & Vertrieb: Wettbewerbsanalysen, Kündigungsgefährdung erkennen, Angebotsoptimierung2. Forschung & Produktentwicklung: Trendanalysen aus Social Media, Effizienzsteigerung in der Pharmaforschung3. Produktion, Service & Support: Produktionsoptimierung mit Maschinen- und Sensordaten, vorausschauende Instandhaltung von Maschinen und Anlagen4. Distribution und Logistik: Optimierung von Lieferketten, Nutzung von Verkehrstelematik5. Finanz- und Risikocontrolling: Echtzeit-Reaktionen auf Geschäftsinformationen, Erkennen von Kreditrisikofaktoren, Betrugserkennung 17
  18. 18. Die Zusammenarbeit von IT und Fachbereich ändert sich. 18
  19. 19. 4. Wo wird Big Data heute schon eingesetzt?
  20. 20. Beispielhafte Big Data-Projekte zeigen ein großes Potenzial auf.
  21. 21. Beispiel 1: Analyse von Website-Traffic Problem Sehr viele Daten fallen in kurzer Zeit an (Milliarden von Datensätzen, zehntausende analytische Abfragen pro Tag) Je genauer und je schneller Surf- und Klickverhalten analysiert werden kann, desto zielgerichteter kann der Kunde bedient werden Lösung In-Memory-Datenbanken (etwa Exasol bei Webtrekk) Neue Indextechnologien und Nutzung von GPUs (Parstream bei Searchmetrics) Nutzen Einbezug aller Daten in eine Echtzeitanalyse des Kundenverhaltens Optimierung des Angebots 21
  22. 22. Beispiel 2: Gesundheitswesen Problem Viele Beteiligte, viele Datenproduzenten, viele Richtlinien und komplexe Einzelprobleme: 1. Überwachung von Frühgeborenen durch Sensoren 2. Suche nach Nebenwirkungen von Medikamenten Lösung & Nutzen Zusammenführen von getrennten und heterogenen Datentöpfen in neuen Big Data- Infrastrukturen 1. Machine-Learing-Algorithmen überwachen den Datenstrom auf kritische Kombinationen (IBM bei der University of Ontario) Reduktion der Frühgeborenensterblichkeit 2. Automatische Analyse von Foren und Posts im Internet nach Beschreibungen von Nebenwirkungen (Cloudera bei Treato / Israel) schnelle Reaktion bei auftretenden, bisher nicht bekannten Nebenwirkungen 22
  23. 23. Beispiel 3: Risikocontrolling bei Banken Problem Viele, sich ständig ändernde Marktparameter aus unterschiedlichen Quellen Komplexe Berechnungen von Risikoportfolios Lösung In-Memory Analytics (SAS bei einer europäischen Großbank) Nutzen Echtzeit-Risikoportfolio erlaubt die optimierte Hinterlegung von Eigenkapital bei Finanztransaktionen Risiken und Chancen werden früher erkannt („permanentes Stresstesting“) 23
  24. 24. 5. Welche Fragestellungen werden neu aufgeworfen?
  25. 25. Darf man das, was man kann? Wenn ja, haben wir die Leute, diedas umsetzen? Betrifft das nicht nur die ganz Großen?
  26. 26. Big Data und Datenschutz „Nach deutschem Datenschutzrecht sind Big-Data-Methoden in einer ganzen Reihe von Fällen zulässig.” (Leitfaden, Kapital 8) Kontroverse Beispiele: Darf Facebook eine Funktion zur Gesichtserkennung anbieten? Die EU sagt Nein. Dürfen Social Media-Daten durch die Schufa ausgewertet werden? Die Öffentlichkeit sagt Nein. 26
  27. 27. Big Data und Ausbildung Eine McKinsey-Studie vom Mai 2011 weist darauf hin, dass es bereits heute ein "gap of analytical talent" gibt, das sich in den nächsten Jahren verschärfen wird. Welche Qualifikationen werden gefragt sein? Und wie beheben? Neue Technologien: Hadoop, Hive, Pig, High-Performance Analytics Neue Vorgehensweisen: Data Scientist / Big Data Analysts 27
  28. 28. Big Data und Mittelstand Datendimensionen im Petabyte-Bereich bewältigen heute nur die ganz Großen: ebay, Google, Amazon, Facebook. Dann kommen sicher die großen Konzerne – was aber ist mit dem Mittelstand? Wie kann der Mittelstand von Big Data profitieren? Datenmarktplätze entstehen Cloud-Infrastrukturen ermöglichen zukünftig „Analytics as a Service“ Hard- und Software wird immer günstiger, heutige High-End-Verfahren sind morgen vielleicht schon „commodity“ 28
  29. 29. Vielen Dank für Ihre Aufmerksamkeit.Dr. Thomas Keil Dr. Mathias WeberProgram Manager Business Bereichsleiter IT Services,Analytics, SAS Deutschland BITKOM06221.415-1268 030.27576-121 m.weber@bitkom.orgthomas.keil@ger.sas.com
  30. 30. Was macht eigentlich der AK Big Data?
  31. 31. Der AK Big Data will Unternehmen (und besonders den globaltätigen deutschen Mittelstand) dabei unterstützen, das riesigePotenzial von Big Data zu heben.
  32. 32. Schwerpunkt 2012: Erschließung des Themas 27.1. Gründungsveranstaltung bei IBM in Frankfurt Vorträge: TU Berlin, EMC, Forrester 18.4. Zweite Sitzung bei SAS in Heidelberg Vorträge: SAS, IBM, Blue Yonder, Attensity, Experton 28.6. Dritte Sitzung bei THESEUS in Berlin Vorträge: Fraunhofer, Exasol, Splunk, fun communications, Autonomy, Teradata 20.9. Vierte Sitzung bei Empolis in Kronberg Vorträge: Empolis, IBM, SAP, TU Berlin, Fraunhofer 14.11. Fünfte Sitzung bei HP in Kronberg Vorträge: Experton, HP, Cloudera, Graf von Westphalen 32
  33. 33. Projekte 2013: Praxisfragen adressieren, Öffentlichkeit schaffen Erstellung von Publikationen wie Leitfäden, Analysen, Reports, Positionen Präsenz auf Messen, Branchen-Events und Veranstaltungen Presse- und Öffentlichkeitsarbeit Entwicklung politischer Positionen und Positionspapiere zu Big Data Mitwirkung bei der Entwicklung eines Innovations- und Schulungszentrums für Big Data Inhaltliche Themen (Auswahl): Technologie & Referenzarchitektur Aus- und Weiterbildung Datenschutz 33
  34. 34. Leitfaden II zur Umsetzung von Big Data-Projekten in Arbeit Neues Autorenteam Schwerpunkte: Technologie, Projektvorgehen, Checklisten Management von Big Data-Projekten 34

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