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The Connected Consumer – Real-time Customer 360

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With Business Data Lake technologies based on EMC’s Big Data portfolio it becomes possible to move away from channel specific analytics towards a 360 customer view.

This presentation will show how technologies like Spark, Hadoop, and Kafka help companies gain a real-time view of everything their customers do and make changes to customer touch points whether mobile, web, in-store, direct marketing or existing transactional systems.

Presented by Steve Jones, Vice President, Insights & Data, Capgemini at EMC World 2016

http://www.capgemini.com/emc

Published in: Technology

The Connected Consumer – Real-time Customer 360

  1. 1. 1© Copyright 2016 EMC Corporation. All rights reserved. 1© Copyright 2016 EMC Corporation. All rights reserved. THE CONNECTED CONSUMER REAL-TIME CUSTOMER 360 MAY 04, 2016 Steve Jones Global VP, Big Data and Analytics steve.g.jones@capgemini.com
  2. 2. 2© Copyright 2016 EMC Corporation. All rights reserved. THE BUSINESS CHALLENGE Become a Digital Data Driven Company Position for the Future Enable Omni- Channel Analytics Capabilities Current State •  Traditional Web based analytics for website. •  Limited, and slow, combination of information between we and other channels •  Unable to integrate internal and external insight in a consistent way Future State •  Omni-Channel Capability: Design, develop and implement a solution to more effectively understand the omni-channel customer experience. •  360 Customer Centric View: Enable the aggregation and analysis of online, offline, customer characteristic, and third-party into an environment that is accessible by analysts and business users, on a near real-time basis. •  Self-Service: Data democratization / Integration / On Demand Access - Develop a trusted data source with extensive drill-down capabilities that enables the users with ease of on-demand data access and addition of new sources •  Ease of Consumption: NRT Reporting / Analytics / Visualization - Design, develop, and implement tools, processes and methods to enable the consumption of data.
  3. 3. 3© Copyright 2016 EMC Corporation. All rights reserved. As companies intends to be able to better analyze the customer omni-channel experience across all functions, it’s important to understand that the Digital Divide depicted below is a limitation to become a full-fledge Digital company. BECOMING A DIGITAL ANALYTICS COMPANY: BRIDGING THE DIGITAL DIVIDE Source: Capgemini Consulting-MIT Analysis – Digital Transformation: A roadmap for billion-dollar organizations; The Digital Advantage (c) 2012 and 2013 Analytics •  Target marketing more effectively •  Personalize marketing communications •  Optimize pricing •  Better qualify sales prospects Process Digitization •  Automating processes •  Monitoring operations in real-time •  Adaptability to external changes Internal Collaboration •  Active knowledge sharing •  Use of internal social networks and video conf. •  Working anywhere, anytime, any device Social Media •  Monitor reputation •  Promote products and services •  Sell products and services •  Provide customer service •  Build customer communities Customer Experience •  Cross-channels consistency •  Personalize the customer experience •  Offer self-service Mobile Channel •  Promote products and services •  Sell products and services •  Provide customer service Data Integration •  Customer Data •  Other data (finance, supply-chain, operations) Customer Engagement Operational Processes D I G I T A L D I V I D E
  4. 4. 4© Copyright 2016 EMC Corporation. All rights reserved. A STANDARD REFERENCE ARCHITECTURE FOR DIGITAL ENGAGEMENT CAPGEMINI’S BLUEPRINT FOR BUSINESS DATA LAKE: Data Movement Design ETL/ELT processes for data movement from heterogeneous data sources to Hadoop and beyond Data masking Design masking process on write (persistent data masking) or on read (dynamic data masking). Analyze Provide data Visualization and Analytics tools, models and frameworks for analytics Enrich Design Enrichment processes to prepare Hadoop Data And Augment It With Descriptive Metadata Prepare Data for Analysis Define strategy for data preparation and provisioning to enable self service BI and advanced analytics Database Design flexible and scalable database to evolve with the Omni-Channel requirements Ingestion Design Ingestion process (batch and near real-time) for structured, semi- structured, and unstructured data sources Discovery Design Data Discovery process for Data Catalogue, Data Profiling and Data Lineage. Insights Provide Analytics at the Point of Relevance through front to back integration of tools Customer 360 Design a trusted data source for 360-degree customer view 1 2 3 4 5 6 8 9 10 7
  5. 5. 5© Copyright 2016 EMC Corporation. All rights reserved. ITS BIG, FAST AND CLEVER Stream Processing Transform Dashboard Ambari Interactive Data Storage Streaming Forwardto spark ETL Informatica BIG DATA Edition Alerts IngesttoHDFS JMS Fast and Batch Ingest Near Real-Time Batch Interactive Load to HDFS SOURCE DATA Email, Call Logs, Documents Click Stream Policy, Quote, Claim Data Customer Account Data/CRM/MDM EDW REST Streaming JMS Analytics Tableau / Qlik / SHINY Distributed DB Query Engine NoSQL HBase SQL Interface Phoenix ETL / MDM Customer 360 SQL on Hadoop Ad-hoc BI Tableau / Qlik Analytics Engine R / SAS Informatica BIG DATA Edition Near Real- Time Tableau / Qlik Alerts Machine Learning Improved Models Streaming Role Based Dynamic Data Masking Adobe Insights (Online & Offline Data) Txt / Csv Import FTP
  6. 6. 6© Copyright 2016 EMC Corporation. All rights reserved. 6© Copyright 2016 EMC Corporation. All rights reserved. DEMO
  7. 7. 7© Copyright 2016 EMC Corporation. All rights reserved. Stream Processing Transform Dashboard Ambari Interactive Data Storage Streaming Forwardto spark ETL Informatica BIG DATA Edition Alerts IngesttoHDFS JMS Fast and Batch Ingest Near Real-Time Batch Interactive Load to HDFS SOURCE DATA Email, Call Logs, Documents Click Stream Policy, Quote, Claim Data Customer Account Data/CRM/MDM EDW REST Streaming JMS Analytics Tableau / Qlik / SHINY Distributed DB Query Engine NoSQL HBase SQL Interface Phoenix ETL / MDM Customer 360 SQL on Hadoop Ad-hoc BI Tableau / Qlik Analytics Engine R / SAS Informatica BIG DATA Edition Near Real- Time Tableau / Qlik Alerts Machine Learning Improved Models Streaming Role Based Dynamic Data Masking Adobe Insights (Online & Offline Data) Txt / Csv Import FTP WHAT YOU SAW 12,500 clicks a second Sessionization and Tokenization Customer Records Digital Profile
  8. 8. 8© Copyright 2016 EMC Corporation. All rights reserved. WHAT IT RUNS ON – WELL THE DEMO RAN ON A SINGLE MACHINE BUT…Technical Orchestration Service Orchestration Vmware V-Realize Cortex Client Specific Tools Hortonworks Data Platform Computer and Memory (EMC Big Data Systems) Storage and Parallelization (Data Lake) Software Defined Network Monitoring&Logging AuditReporting

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