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Customer Insights Prozess

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Wie schaut der Prozess für Customer Insights im weiteren Sinne aus? Welche Aspekte sind dabei zu berücksichtigen? Welche technologischen und Architektur-Ansätze können hierbei unterstützen? Angefangen vom Aufbau einer 360 Grad Sicht auf einen eindeutig identifizierten Kunden unter Berücksichtigung des Datenschutzes über die eigentliche Analyse der Kundeninteraktion – in Echtzeit oder nachgelagert – bis hin zur erfolgreichen Nutzung der erzielten Kundenkenntnis in der Interaktion mit dem Kunden.

Publicada em: Tecnologia
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Customer Insights Prozess

  1. 1. Capgemini Week of Innovation Networks 2017 Customer Insights Prozess 28. September 2017
  2. 2. Der Customer Insights Prozess ist eng mit der Customer Journey verzahnt Copyright © Capgemini 2017. All Rights Reserved 2CWIN17-Customer Insights Prozess.pptx Customer Insights Customer Journey
  3. 3. Der Customer Insights Prozess im Überblick Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 3
  4. 4. Ansätze zur Unterstützung des Customer Insights Prozesses  Data Ingestion  Streaming / Event- Processing  Drittanbieterdaten  Open Data  Anonymisierung, Pseudonymisierung  Berichtsprozesse für Regularien  Big Data  NoSQL  Anomalie- erkennung  Stammdaten- Mgmt.  Daten- virtualisierung  Predictive Analytics  Machine Learning  Artificial Intelligence  Business Process Integration  Customer Relationship Mgmt.  User Experience Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 4 Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung
  5. 5. Datenbeschaffung Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 5 Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung  Data Ingestion  Streaming / Event- Processing  Drittanbieterdaten  Open Data
  6. 6. 6Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess Conceptual perspective on reference architecture for Insights&Data Manage Process Analyze Act Information Source data Insight Value Explorative Data Exploration Descriptive Reporting Diagnostic Ad-hoc Querying Predictive Data Mining, Machine Learning Prescriptive Next Best Action Search Retrieval  Data governance and security  Data privacy  Compliance  Collaboration  Value generation  Program delivery  Data-driven culture  Information strategy  Skill development  Master data mgmt  Metadata mgmt  Data quality mgmt  Operations, SLA’s  Orchestration Stream Structured data  IT managed applications (ERP, SCM, CRM)  Master and reference data  Business owned informal data  Third party data Unstructured Data  Social  Documents, mail, images, voice, video Semistructured data  Internet  Internet of Things (machine, sensor)  Server logs  B2B Business ApplicationsBusiness ProcessesDecision makers Data at rest Data in motion Data Warehouse Data Asset Catalog Load Extract Transform Manage Quality Aggregate Historize Data Lake Business rules Predictive modelsBusiness results Alerts Signals Catalog Mask Store Ingest 1) Load ‘as- is’ 2) Distill on demand Prepare Blend Lifecycle Refine 3) Operationalize Planning Forecast, Simulation ETL
  7. 7. 7Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess Lambda, Kappa, Streaming Architecture Objectives  Process infinite data sets  Reduce time-to-insight for those  Flexibility in consumption of this data  Historize data  Recoverable from (logical) errors  “Beat the CAP theorem” • Low latency • High availability   Buzz word alert : Near Real Time system Foundation  Event sourcing  Making use of immutable events  Compose all business logic out of them What is it good for?
  8. 8. 8Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess Lambda architecture (2011) – What does it look like? https://www.mapr.com/developercentral/lambda-architecture https://www.oreilly.com/ideas/questioning-the-lambda-architecture
  9. 9. Datenschutz  Anonymisierung, Pseudonymisierung  Berichtsprozesse für Regularien Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 9 Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung
  10. 10. EU General Data Protection Regulation (GDPR) in a nutshell, enforced at end of May 2018 Objectives • harmonize data protection across Europe • strengthen data protection for EU Citizens Scope • EU citizen’s data • Entities from all countries processing EU citizen’s data Sensitive Data • racial, ethnic origin • political opinions • philosophical, religious beliefs • health or sex life • sexual orientation • genetic, biometric Data Breach • third party gains unauthorised access to personal data • must be reported without undue delay Responsibilities • data protection officer • Data protection impact assessment • workforce privacy awareness training • data protection policies • Data protection by design • Record of data processing activities Penalties for non-compliance • up to 4% of worldwide turnover or 20 million € Rights • Erase records • Data portability • Explain automated decisions • Dissent data profiling Consent • permits types of processing • to be provided explicitly (opt-in) • can be withdrawn Principles • Lawful, fair, transparent • Purpose limitation • Data minimisation • Accuracy • Storage limitation • Integrity, confidential • Accountability Data Processing • Collecting, storing • Altering , deleting • Retrieving • Transmitting Data Subject natural person • Client • employee Supervisory authority Identification • name, id. number, location or physical, physiological, genetic, mental, economic, cultural or social identity factors Personal Data • any information relating to an identified or identifiable natural person Data Controller Data Processor • Service provider
  11. 11. Datenhaltung  Big Data  NoSQL Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 11 Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung
  12. 12. 12Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess Manage Process Analyze Information Source data Data ExplorationReporting Ad-hoc Querying Search, Retrieval Structured data  tables Unstructured Data  Text, speech, … Semistructured data  JSON, XML, … Implementation perspective on reference architecture for Big Data Analytics Data Warehouse Data Asset Catalog  Index  Tags  Metadata Data Lake Analytical Sandbox NoSQL databases Key value store Document store Column store Graph store NLP SQL databases Row based Column based Streaming, Event Processing File system Analytics Base Tables Data Mining, Machine Learning Next Best Action High level ingestion and data preparation Low level ingestion ETL/ELT Adv. VisualizationSQL SQL Java Scala Python R Batch Processing Data virtualization Eventbased MicroBatch
  13. 13. 13Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess Mapping components for data storage to the reference architecture for Big Data Analytics HDFS, MapR-FS, IBM GPFS HBase, Cassandra, Cloudera Kudu, Accumulo JackRabbit Giraph Google file system Google Big Table, MemSQL MongoDB, CouchDB, MarkLogic, IBM Cloudant Redis, Voldemort, BerkeleyDB, Oracle NoSQL, MemcacheDB Neo4J, OrientDB, Titan, Virtuoso, ArrangoDB, Oracle Spatial, Teradata Aster
  14. 14. Datensicherheit  Anomalie- erkennung Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 14 Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung
  15. 15. SIEM and GRC can not prevent any type of data breach - we need a different approach Anomalous Behavior Traditional approaches need to be complemented – SIEM, GRC are still needed GRC says what is approved – the tasks you can do, the gates you can go through. Abnormal Behavior Detection says whether you should have Extend using Anomalous Behavior Detection: This approach: 1. Learns what is normal [the difference between approved and allowed] 2. Identifies what is anomalous and categorizes the risk 3. Alerts so you can react before it becomes a problem. New Outcomes are Possible It is an extension of current security approaches that enables a reduction in GRC and can identify threats that GRC cannot  It shows where “allowed” is not “normal” and the scope of the deviation from the norm.  Detect social engineering attacks as well as network level detections  Minimize the exposure time and loss  Potentially predict the leakage areas ahead of the attack  This can be applied to both GRC areas (Snowden) and non-GRC areas (networks, non-controlled information) to build up a broader pattern of behavior.
  16. 16. Detection of Anomalous Behavior – from Insight to Action Structured data Machine learning defines “normal” across user base Inform management Adjust policies Lockdown SIEM AD HR Unstructured data Images Social Email Video Automated response based on level of deviation and system criticality Users accessing key systems within role as defined by GRC Deviation from norm triggers action
  17. 17. 1 Out of policy access In policy but extremely abnormal access 3 2 In policy but abnormal access Examples – SIEM, GRC and Detection of Anomalous Behavior User tries to access what they shouldn’t GRC says “no”, notifies SIEM SIEM collates, alerts, may reduce privileges via GRC/IAM User accesses single item out of norm but in policy GRC says “yes” AB ‘but that isn’t normal’, alert to SIEM SIEM collates, alerts, may reduce privileges via GRC/IAM User accesses multiple areas out of ordinary but in policy GRC says “yes” AB ‘this is the ONLY person EVER to do this!’ alert to SIEM Shutdown of user access + manager alerts
  18. 18. 360 Grad Sicht  Stammdaten- Mgmt.  Daten- virtualisierung Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 18 Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung
  19. 19. Alternative Architekturansätze zur Bildung einer 360 Grad Sicht Copyright © Capgemini 2017. All Rights Reserved 19CWIN17-Customer Insights Prozess.pptx Master Data Management Data Virtualization Big Data / Data Lake
  20. 20. Erkenntnisse durch Analytics  Predictive Analytics  Machine Learning  Artificial Intelligence Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 20 Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung
  21. 21. The terms “AI”, “Machine Learning” and “Deep Learning” form an abstraction hierarchy Artificial Intelligence Machine Learning Neural Networks Deep Learning Artificial Intelligence General term Machine Learning Sub-field of research Neural Networks Computational model building approach Deep Learning Class of algorithms for specific kinds of NNs Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 21
  22. 22. Cognition Deep learning Image analysis Knowledge engineering Natural language generation Machine learning Robotics Sensory perception Natural language processing Think Act Sense Speech recognition Artificial Intelligence has different technological building blocks working together to form a learning cycle Copyright © Capgemini 2017. All Rights Reserved 22CWIN17-Customer Insights Prozess.pptx
  23. 23. Ethische Aspekte der Nutzung von Daten und abgeleiteten Erkenntnissen Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 23 Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung
  24. 24. Bei der Nutzung von Erkenntnissen aus Big Data Analytics müssen ethische Aspekte geprüft werden Copyright © Capgemini 2017. All Rights Reserved 24 Mit Daten sollte sparsam umgegangen werden Die Big Data Anwendung muss transparent gemacht werden Pauschalisierung darf Einzelpersonen nicht benachteiligen Betroffenen sollte Mitsprache bezüglich der eigenen Daten ermöglicht werden Die Anwendung muss Nutzen für Betroffene, Kunden oder Gesellschaft stiften Daten der Betroffenen müssen vor Missbrauch geschützt werden Gesetzestreue ist Grund- voraussetzung für ethisch korrektes Verhalten Nutzen Individuelle Prüfung Ethisch korrekte Anwendung von Big Data Transparenz Chancen- gleichheit Datensparsamkeit Mitbe- stimmung Gesetz Daten- sicherheit Nutzen
  25. 25. Der Customer Insights Prozess im Überblick Touch- point Daten- beschaffung Daten- schutz Daten- haltung Daten- sicherheit 360 Grad Sicht Erkenntnis Ethik Erkenntnis- verwendung Copyright © Capgemini 2017. All Rights Reserved CWIN17-Customer Insights Prozess.pptx 25
  26. 26. Über Capgemini Mit mehr als 140.000 Mitarbeitern in über 40 Ländern ist Capgemini einer der weltweit führenden Anbieter von Management- und IT-Beratung, Technologie-Services sowie Outsourcing-Dienstleistungen. Im Jahr 2013 betrug der Umsatz der Capgemini-Gruppe 10,1 Milliarden Euro. Gemeinsam mit seinen Kunden erstellt Capgemini Geschäfts- wie auch Technologielösungen, die passgenau auf die individuellen Anforderungen zugeschnitten sind. Auf der Grundlage seines weltweiten Liefermodells Rightshore® zeichnet sich Capgemini als multinationale Organisation durch seine besondere Art der Zusammenarbeit aus – die Collaborative Business ExperienceTM. Rightshore® ist eine eingetragene Marke von Capgemini Die in der Präsentation enthaltenen Informationen sind Eigentum. Copyright © 2016 Capgemini. Alle Rechte vorbehalten. www.de.capgemini.com

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