Adding a Data Reservoir to Your Oracle Data Warehouse for Customer 360-Degree Analysis

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Adding a Data Reservoir to Your Oracle Data Warehouse for Customer 360-Degree Analysis  Mark Rittman, CTO, Rittman Mead UKOUG Tech’15, Birmingham, December 2015

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com What Is This Presentation About…? •What is a Hadoop-based data reservoir, and why might you add one to a data warehouse? •How do you load, process and integrate one with your data warehouse using Oracle tools? •How can you use it for what’s termed “Customer 360-degree insight?” schema-on-read vs schema on write real-time data ingestion agile data provisioning vs. curated data combining Hadoop, NoSQL and Oracle omni-channel marketing machine learning & decision engines attitudinal vs behavioural data

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com About Me •Mark Rittman, Oracle ACE Director, Oracle BI, DW & Big Data •14 Years Experience with Oracle Technology •Regular columnist for Oracle Magazine •Author of two Oracle Press Oracle BI books •Oracle Business Intelligence Developers Guide •Oracle Exalytics Revealed •Writer for Rittman Mead Blog :  http://www.rittmanmead.com/blog •Past Editor of Oracle Scene Magazine,  BIRT SIG Chair, ODTUG Board Member •Co-founder and CTO for Rittman Mead

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com … Or as I say at Parties…

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com 15+ Years in Oracle BI and Data Warehousing •Started back in 1997 on a bank Oracle DW project •Our tools were Oracle 7.3.4, SQL*Plus, PL/SQL   and shell scripts •Went on to use Oracle Developer/2000 and Designer/2000 •Our initial users queried the DW using SQL*Plus •And later on, we rolled-out Discoverer/2000 to everyone else •And life was fun…

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com The Oracle-Centric DW Architecture •Over time, this data warehouse architecture developed •Added Oracle Warehouse Builder to   automate and model the DW build •Oracle 9i Application Server (yay!)   to deliver reports and web portals •Data Mining and OLAP in the database •Oracle 9i for in-database ETL (and RAC) •Data was typically loaded from   Oracle RBDMS and EBS •It was turtles Oracle all the way down…

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Traditional Three-Layer Relational Data Warehouses Staging Foundation /  ODS Performance /  Dimensional ETL ETL BI Tool (OBIEE)  with metadata  layer OLAP / In-Memory  Tool with data load  into own database Direct  Read Data  Load Traditional structured  data sources Data  Load Data  Load Data  Load Traditional Relational Data Warehouse •Three-layer architecture - staging, foundation and access/performance •All three layers stored in a relational database (Oracle) •ETL used to move data from layer-to-layer

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com ETL Largely Batch-Based and with Single Route through DW •All data lands in Staging layer, processed and then thrown-away ‣Too expensive to store all incoming granular data online - selected data stored as summary •Processed through Foundation layer and then Access and Performance •ETL development an expensive, manual task •But this approach provided accurate numbers  that every could trust, and navigate around

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com And Now … Everyone’s Talking About Big Data •Explosion in volume and variety of data that’s now available •New, cheap and open-source technology  makes it economic to store + process it •Users want more data stored in the DW,   but budgets for IT are getting smaller •Analytics and analysis has gone beyond  tabular reports and dashboards, and requires  new platforms to enable new approaches •Which is actually rather scary…

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Meanwhile, in the real world…

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Who is my customer?

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com More Complete Data About Your Customers Advanced analytics and machine learning More Attributes and Activities Stored at Scale True 360°Customer Profile Connect disparate data Targeted, personalized customer treatment Customer 360-Degree Insight

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Combines transactions + master data with granular behavioural & attitudinal data Adding “Who” and “Why” to Customer Datasets Single Customer View Enriched   Customer Profile Correlating Modeling Machine  Learning Scoring “How”  Interaction Data Voice + Chat Transcripts In-person  dialogs Webserver  logs Blogs Surveys Social Media “Why”  Attitudinal Data “What”  Behavioural Data Transaction  History Retail  Activity Payment  History Basket Analysis Attributes Segments Relationships “Who”  Descriptive Data Demographics

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com But Wait … Isn’t This Just Data Warehousing & Data Mining? •Data warehouses were conceived as a single source of reporting truth •Formally accept, model and integrate data to provide analytical reporting platform •Well-established design patterns for long-term data storage •Stored in structured, indexed, optimised “schema on write” storage •Data moved through layers via formal ETL •Extreme Performance, Highly Secure •Analytic SQL, In-Database Analytics ‣So why not use for this Customer 360 data?

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Back to the real world again…

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Consider How Consumers Used to Be Marketed To… • Marketing used to be generic,   one-way “broadcasting” to public • Then Web 2.0 gave customers  a voice, they could talk back… • But they expected an immediate answer • More work, but more intimate relationship • Big data, smart technology + complex algorithms  makes a “360-degree view of customers   now possible • Customers volunteer much data themselves • But equilibrium of relationship now moved  irrevocably to the customer

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Customer Touch-points Now Cover Many More Channels •The days of a single, high-street retail channel are long gone •Prospects often now find you via web searches, social media connections •Shopping and browsing “on the go”   using mobile devices, wearables •Web increasingly the main sales channel •“Order and go” collection at stores •Call centre helplines, •Customer service desks •Forums, blogs, product reviews  and other user-generated content 1980s 1990s 2000s 2010s Empowered Employees Digital is Humanized Knowledge Everywhere Internet of Things Mobile as Primary Channel Cross-Channel Service WHAT’S NEXT

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Consumers Now Drive Their Own Purchase Decisions

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Datasets for Marketing Need to Reflect Today’s Consumer

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Customers Share Data with You .. With Expectations •Customers now share huge amounts of data willingly, and perhaps unknowingly ‣Through your channels and applications - with potential privacy issue ‣Through tweeting, posting on Facebook and other social networks ‣But they also want to be in control -Ability to delete their data -Understand what data you hold -For what purposes -And how it was collected

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com What is Customer 360-Degree Analysis? •Gather together all meaningful information about the customer (“360-degree view”) •Organizing, matching, profiling & storing every interaction in real time •Matched and combined; factual, interpreted, learned ‣Across all channels, and on public forums and social media •Captures interactions across all-touch points and all channels ‣Including activity on social networks, forums, blogs •Typically stored and processed in a Hadoop “data reservoir” •Dynamic customer profiles with segmentation,   behavioural analysis “at scale” •Downstream feeds into DW, CRM and other systems

T : +44 (0) 1273 911 268 (UK) or (888)

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Connect the Silos, Understand Customers, Drive Decisions execute smarter listen better consumption logs, clickstream & devices demographic, user and credit data customer contacts and service cases transactions and subscriptions content metadata, ratings, comments marketing campaign response social media  activity programmatic  advertising audience acquisition, retention multi-channel  marketing targeted   promotions next best  offer personalized content product & service  strategy content acquisition learn faster Enriched   Customer Profile Correlating Modeling Scoring Micro-Segments History Preferences

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com But … Isn’t This Just CRM? •Typically built for call centres, sales automation •Core data is customer service activity •Supplemented by purchase history •CRM system typically system of record for  service activity, with links to transactions ‣LoB application focused on particular tasks

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Customer 360-Degree view typically used as central data store for digital marketing •Provides key data for real-time decision engines, next-best offer, personalisation Customer 360-Degree View as Driver of Digital Marketing ? ? ?

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Customer 360-Degree view typically used as central data store for digital marketing •Provides key data for real-time decision engines, next-best offer, personalisation Customer 360-Degree View Powering Marketing + Offers Data Transfer Data Access Real-Time Context    Environmental User Journey Offer Feedback Single Customer View Enriched   Customer Profile Correlating Modeling Machine  Learning Scoring Real Time   Offers & Suggestions   Up-Sell / Cross-Sell Decisioning Service Self-Learning  Predictive Models

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Typically Stored on Flexible, Scalable Hadoop + NoSQL Voice + Chat Transcripts Call Center Logs Chat Logs iBeacon Logs Website Logs CRM Data Transactions Social Feeds Demographics Real-time Feeds,  batch and API $50k Hadoop Node $50k Hadoop Node $50k Hadoop Node Hadoop Node Hadoop Node $50k $50k Hadoop Node Hadoop Node $50k Enriched   Customer Profile Modeling Scoring Hadoop Data Reservoir  Raw customer data stored at detail  Enriched and processed for insights $50k

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Architected using “Data Reservoir” Design Pattern •Data for customer 360 system typically landed into a Hadoop & NoSQL-based •Applies aggregation, joining and machine-learning processes to extract insights Data Transfer Data Access Data Factory Data Reservoir Business Intelligence Tools Hadoop Platform File Based Integration Stream Based Integration Data streams Discovery & Development Labs Safe & secure Discovery and Development environment Data sets and samples Models and programs Marketing / Sales Applications Models Machine Learning Segments Operational Data Transactions Customer Master ata Unstructured Data Voice + Chat Transcripts ETL Based Integration Raw Customer Data Data stored in the original format (usually files) such as SS7, ASN.1, JSON etc. Mapped Customer Data Data sets produced by mapping and transforming raw data

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Data Transfer Data Access Data Factory Data Reservoir Business Intelligence Tools Hadoop Platform File Based Integration Stream Based Integration Data streams Discovery & Development Labs Safe & secure Discovery and Development environment Data sets and samples Models and programs Marketing / Sales Applications Models Machine Learning Segments Operational Data Transactions Customer Master ata Unstructured Data Voice + Chat Transcripts ETL Based Integration Raw Customer Data Data stored in the original format (usually files) such as SS7, ASN.1, JSON etc. Mapped Customer Data Data sets produced by mapping and transforming raw data

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com So What is a Data Reservoir?

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com What Does it Do?

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com And Does it Replace My Data Warehouse?

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com A technical digression…

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Data from Real-Time, Social & Internet Sources is Strange Single Customer View Enriched   Customer Profile Correlating Modeling Machine  Learning Scoring •Typically comes in non-tabular form •JSON, log files, key/value pairs •Users often want it speculatively ‣Haven’t though through final purpose •Schema can change over time ‣Or maybe there isn’t even one •But the end-users want it now ‣Not when your ETL team are next free

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Data Warehouse Loading Requires Formal ETL and Modeling $1m Analytic DBMS Node ETL Data Model ETL  Developer Data Modeller Curated Data ETL Development takes time, is fragile, but results in well-curated data But what about data whose schema is now known? Or final use has not yet been determined? Dimensional data modelling gives structure to the data for business users But also restricts how that data can be analysed What if the end-user is better placed to apply that schema?

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com … And Are Limited in What They Can Store (Economically) $1m Analytic DBMS Node DB Instance Compute ETL Data Model ETL  Developer Data Modeller $1m Analytic DBMS Node Compute $1m Analytic DBMS Node Compute $1m Analytic DBMS Node Single DB Instance Compute

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Analytic DBMS Node Analytic DBMS Node Sharded Databases Can Scale Further - At Even More Cost $1m Analytic DBMS Node Compute Data Model Compute Compute DB Shard DB Shard DB Shard Complex Shard-Aware ETL A-F O-R S-T $1m $1m Analytic DBMS Node Compute DB Shard Analytic DBMS Node Compute DB Shard Analytic DBMS Node Compute DB Shard Analytic DBMS Node Compute DB Shard $1m $1m $1m $1m G-J K-N U-W X-Z .. and adding more nodes means re-sharding the dataset Also rules out mixed-workload DBs with OLTP

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Exadata Combines Best of Both … Again At Cost Data Model Compute DBMS Node Compute Storage Cell Storage Compute Offload Query offloading Filtered, projected columns only returned Storage Cell Storage Compute Offload Storage Cell Storage Compute Offload Storage Cell Storage Compute Offload Storage Cell Storage Compute Offload Storage Cell Storage Compute Offload Storage Cell Storage Compute Offload Storage Cell Storage Compute Offload ETL Compute DBMS Node Compute Compute DBMS Node Compute Single DB Instance

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Introducing Hadoop - Cheap, Flexible Storage + Compute •A new approach to data processing and data storage •Rather than a small number of large, powerful servers, it spreads processing over  large numbers of small, cheap, redundant servers •Spreads the data you’re processing over   lots of distributed nodes •Has scheduling/workload process that sends   parts of a job to each of the nodes •And does the processing where the data sits •Shared-nothing architecture •Low-cost and highly horizontal scalable Job Tracker Task Tracker Task Tracker Task Tracker Task Tracker Data Node Data Node Task Tracker Task Tracker

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Introducing Hadoop - Cheap, Flexible Storage + Compute •Hadoop & NoSQL better suited to exploratory analysis of newly-arrived data ‣Flexible schema - applied by user rather than ETL ‣Cheap expandable storage for detail-level data ‣Better native support for machine-learning and  data discovery tools and processes ‣Potentially a great fit for our new and emerging  customer 360 datasets, and great platform for analysis

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Hadoop Designed for Real-Time Storage of Raw Data Feeds $50k Hadoop Node Voice + Chat Transcripts Call Center Logs Chat Logs iBeacon Logs Website Logs Real-time Feeds Raw Data

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Supplement with Batch + API Loads of ERP + 3rd Party Data $50k Hadoop Node Voice + Chat Transcripts Call Center Logs Chat Logs iBeacon Logs Website Logs Real-time Feeds CRM Data Transactions Social Feeds Demographics Batch Loads APIs, Web Service Calls Raw Data

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Supplement with Batch + API Loads of ERP + 3rd Party Data $50k Hadoop Node Voice + Chat Transcripts Call Center Logs Chat Logs iBeacon Logs Website Logs CRM Data Transactions Social Feeds Demographics Raw Data Customer 360 Apps Predictive   Models SQL-on-Hadoop Business analytics Real-time Feeds,  batch and API

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Hadoop Node Hadoop Node Hadoop Node Hadoop Node Supplement with Batch + API Loads of ERP + 3rd Party Data Voice + Chat Transcripts Call Center Logs Chat Logs iBeacon Logs Website Logs CRM Data Transactions Social Feeds Demographics Real-time Feeds,  batch and API Hadoop Node Compute Hadoop Node Compute Compute Compute $5k Compute Compute $50k Hadoop Node Raw Data across Cluster Filesystem Compute

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Hadoop-Based Storage & Compute : A Better Logical Fit Voice + Chat Transcripts Call Center Logs Chat Logs iBeacon Logs Website Logs CRM Data Transactions Social Feeds Demographics Real-time Feeds,  batch and API $50k Hadoop Node $50k Hadoop Node $50k Hadoop Node Hadoop Node Hadoop Node $50k $50k Hadoop Node Hadoop Node $50k Enriched   Customer Profile Modeling Scoring Hadoop Data Reservoir  Raw customer data stored at detail  Enriched and processed for insights $50k

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Typically Stored on Flexible, Scalable Hadoop + NoSQL Voice + Chat Transcripts Call Center Logs Chat Logs iBeacon Logs Website Logs CRM Data Transactions Social Feeds Demographics Real-time Feeds,  batch and API $50k Hadoop Node $50k Hadoop Node $50k Hadoop Node Hadoop Node Hadoop Node $50k $50k Hadoop Node Hadoop Node $50k Enriched   Customer Profile Modeling Scoring Hadoop Data Reservoir  Raw customer data stored at detail  Enriched and processed for insights $50k

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Oracle Engineered system for big data processing and analysis •Start with Oracle Big Data Appliance Starter Rack - expand up to 18 nodes per rack •Cluster racks together for horizontal scale-out using enterprise-quality infrastructure Oracle Big Data Appliance Starter Rack + Expansion • Cloudera CDH + Oracle software • 18 High-spec Hadoop Nodes with InfiniBand switches for internal Hadoop traffic, optimised for network throughput • 1 Cisco Management Switch • Single place for support for H/W + S/W  Deployed on Oracle Big Data Appliance Engineered System Oracle Big Data Appliance Starter Rack + Expansion • Cloudera CDH + Oracle software • 18 High-spec Hadoop Nodes with InfiniBand switches for internal Hadoop traffic, optimised for network throughput • 1 Cisco Management Switch • Single place for support for H/W + S/W  Enriched   Customer Profile Modeling Scoring Infiniband

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Flexible, Low-Cost Resilient Storage : Hadoop Distributed FS •The filesystem behind Hadoop, used to store data for Hadoop analysis ‣Unix-like, uses commands such as ls, mkdir, chown, chmod •Fault-tolerant, with rapid fault detection and recovery •High-throughput, with streaming data access and large block sizes •Designed for data-locality, placing data closed to where it is processed •Accessed from the command-line, via internet (hdfs://), GUI tools etc [oracle@bigdatalite mapreduce]$ hadoop fs -mkdir /user/oracle/my_stuff [oracle@bigdatalite mapreduce]$ hadoop fs -ls /user/oracle Found 5 items drwx------ - oracle hadoop 0 2013-04-27 16:48 /user/oracle/.staging drwxrwxrwx - oracle hadoop 0 2012-09-18 17:02 /user/oracle/moviedemo drwxrwxrwx - oracle hadoop 0 2012-10-17 15:58 /user/oracle/moviework drwxrwxrwx - oracle hadoop 0 2013-05-03 17:49 /user/oracle/my_stuff drwxrwxrwx - oracle hadoop 0 2012-08-10 16:08 /user/oracle/stage

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Apache Hive : SQL Access + Table Metadata Over HDFS •Apache Hive provides a SQL layer over Hadoop, once we understand the structure (schema) of the data we’re working with •Exposes HDFS and other Hadoop data as tables and columns •Provides a simple SQL dialect for queries called HiveQL •SQL queries are turned into MapReduce jobs under-the-covers •JDBC and ODBC drivers provide  access to BI and ETL tools •Hive metastore (data dictionary)  leveraged by many other Hadoop tools ‣Apache Pig ‣Cloudera Impala ‣etc SELECT a, sum(b)  FROM myTable  WHERE a<100  GROUP BY a Map  Task Map  Task Map  Task Reduce  Task Reduce  Task Result

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com NoSQL Databases •Family of database types that reject tabular storage,   SQL access and ACID compliance •Focus is on scalability, speed and schema-on-read ‣Oracle NoSQL Database - speed and scalability ‣Apache HBase - speed, scalability and Hadoop ‣MongoDB - native storage of JSON documents •May or may not run on Hadoop, but associated with it •Great choice for high-velocity data capture •CRUD approach vs write-once/read many in HDFS

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Best Data Store for Customer 360 Data : Hadoop & NoSQL •Data for customer 360 system typically landed into a Hadoop & NoSQL-based •Applies aggregation, joining and machine-learning processes to extract insights Data Transfer Data Access Data Factory Data Reservoir Business Intelligence Tools Hadoop Platform File Based Integration Stream Based Integration Data streams Discovery & Development Labs Safe & secure Discovery and Development environment Data sets and samples Models and programs Marketing / Sales Applications Models Machine Learning Segments Operational Data Transactions Customer Master ata Unstructured Data Voice + Chat Transcripts ETL Based Integration Raw Customer Data Data stored in the original format (usually files) such as SS7, ASN.1, JSON etc. Mapped Customer Data Data sets produced by mapping and transforming raw data

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Interfaces with CRM Tools, and Accessible ‣CRM can be a consumer of data from the Hadoop-based Customer 360 ‣And provide key customer attributes and sales events from CRM activity ‣Allows CRM tools to focus on their core strengths ‣With ability to interface with the Customer 360 system as appropriate Data Reservoir   Business Intelligence Tools CRM System Models Machine  Learning Segments Raw   Customer Data Data stored in the original format (usually files) such as SS7, ASN.1, JSON etc. Mapped Customer Data Data sets produced by mapping and transforming raw data Data Transfer Data Access

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Combine with DW for Big Data Management Platform

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Combining Oracle RDBMS with Hadoop + NoSQL •High-value, high-density data goes into Oracle RDBMS •Better support for fast queries, summaries, referential integrity etc •Lower-value, lower-density data goes into Hadoop + NoSQL ‣Also provides flexible schema, more agile development •Successful next-generation BI+DW projects combine both - neither on their own is sufficient

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Today’s Oracle Information Management Ref Architecture Actionable  Events Event Engine Data   Reservoir Data Factory Enterprise Information Store Reporting Discovery Lab Actionable Information Actionable  Insights Input Events Execution Innovation Discovery Output Events   & Data Structured  Enterprise Data Other Data

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Next-Generation Layered Data Warehouse Architecture Virtualization &   Query Federation Enterprise Performance Management Pre-built &   Ad-hoc   BI Assets Information  Services Data Ingestion Information Interpretation Access & Performance Layer Foundation Data Layer Raw Data Reservoir Data   Science Data Engines &   Poly-structured   sources Content Docs Web & Social Media SMS Structured Data  Sources •Operational Data •COTS Data •Master & Ref. Data •Streaming & BAM Immutable raw data reservoir Raw data at rest is not interpreted Immutable modelled data. Business Process Neutral form. Abstracted from business process changes Past, current and future interpretation of enterprise data. Structured to support agile access & navigation Discovery Lab Sandboxes Rapid Development Sandboxes Project based data stores to support specific discovery objectives Project based data stored to facilitate rapid content / presentation delivery Data Sources

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Oracle Engineered system for big data processing and analysis •Start with Oracle Big Data Appliance Starter Rack - expand up to 18 nodes per rack •Cluster racks together for horizontal scale-out using enterprise-quality infrastructure Oracle Big Data Appliance Starter Rack + Expansion • Cloudera CDH + Oracle software • 18 High-spec Hadoop Nodes with InfiniBand switches for internal Hadoop traffic, optimised for network throughput • 1 Cisco Management Switch • Single place for support for H/W + S/W  Deployed on Oracle Big Data Appliance Engineered System Oracle Big Data Appliance Starter Rack + Expansion • Cloudera CDH + Oracle software • 18 High-spec Hadoop Nodes with InfiniBand switches for internal Hadoop traffic, optimised for network throughput • 1 Cisco Management Switch • Single place for support for H/W + S/W  Enriched   Customer Profile Modeling Scoring Infiniband

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Oracle Big Data Discovery for Cataloging Customer 360 Data •Access a rich, interactive catalog of all   data in Customer 360 data reservoir •Familiar search and guided navigation   for ease of use •See data set summaries, user annotation   and recommendations •Add personal and enterprise data to   Customer 360 datasets via self-service ‣Make sense of the wider customer  data now loaded into data reservoir

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Back to the real world again…

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com What is Customer 360-Degree Analysis? •Gather together all meaningful information about the customer (“360-degree view”) •Organizing, matching, profiling & storing every interaction in real time •Matched and combined; factual, interpreted, learned ‣Across all channels, and on public forums and social media •Captures interactions across all-touch points and all channels ‣Including activity on social networks, forums, blogs •Typically stored and processed in a Hadoop “data reservoir” •Dynamic customer profiles with segmentation,   behavioural analysis “at scale” •Downstream feeds into DW, CRM and other systems

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com The Data Integration Challenge •Data from all the sources will need to be integrated to create the single customer view ‣Hadoop technologies (Flume, Kafka, Storm) can be used to ingest events, log data ‣Files can be loaded “as is” into the HDFS filesystem ‣Oracle/DB data can be bulk-loaded using Sqoop ‣GoldenGate for trickle-feeding transactional data •But nature of new data sources brings challenges •May be semi-structured or unknown schema ‣Joining schema-free datasets •Need to consider quality and resolve incorrect,   incomplete, and inconsistent customer data Voice + Chat Transcripts Batch Load  from files, DB:  Easy Stream from  APIs, HTTP:  Moderate Load raw text from files:  Easy Data Reservoir Raw   Customer Data Data stored in the original format (usually files) such as SS7, ASN.1, JSON etc. Mapped Customer Data Data sets produced by mapping and transforming raw data Cleanse,  enrich and  obfuscate raw files:  Lots of work..! Join structured+ semi/ unstructured:  How..?

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Landing raw data is easy; then the real work needs to be done - can be > 90% of project •Four main tasks to take raw data and apply schema and combine together 1. Apply Schema to Raw and Semi-Structured Data 2. Remove Sensitive Data from Any Input Files 3. Identify joins, further enrichments and transforms 4. Store as “mapped” data in data reservoir Ingesting Raw Customer Data : Two Key Challenges

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Data enrichment tool aimed at domain experts, not programmers •Uses machine-learning to automate   data classification + profiling steps •Automatically highlight sensitive data,  and offer to redact or obfuscate •Dramatically reduce the time required  to onboard new data sources •Hosted in Oracle Cloud for zero-install ‣File upload and download from browser ‣Automate for production data loads Raw Data Data stored in the original format (usually files) such as SS7, ASN.1, JSON etc. Mapped Data Data sets produced by mapping and transforming raw data Voice + Chat Transcripts Oracle Big Data Preparation Cloud Service

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Step 1: Apply Schema to Raw and Semi-Structured Data NLP Embedded Information in  unstructured text Entities Embedded Information  No reliable patterns Invalid and missing data  Sensitive data Invalid  emails Stream from  APIs, HTTP:  Moderate Batch Load  from files, DB:  Easy Load raw text from blog entries,  reviews

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Step 2: Remove Sensitive Data from Any Input Files •Automatically profile and analyse datasets •Use Machine Learning to spot and obfuscate sensitive data automatically

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Step 3 : Identify Common Keys and Joins using BDD •Data ingest process automatically applies some enrichments - geocoding etc •Can apply others from Transformation page - simple transformations & Groovy expressions

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Step 4 : Process Joined and Enriched Data Back to Hadoop •Data joined and transformed within Big Data Discovery can be saved back to Hadoop •Export to HDFS, register with Hive (optional) •Supports creation of Data Reservoir Raw   Customer Data Data stored in the original format (usually files) such as SS7, ASN. 1, JSON etc. Mapped Customer Data Data sets produced by mapping and transforming raw data Cleanse,  enrich and  obfuscate raw files:  Lots of work..! Join structured+ semi/unstructured:  How..?

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Further Transforming & Managing Customer 360 Data •Oracle Data Integration Suite offers a wider set of products for managing Customer 360 data ‣Oracle GoldenGate ‣Oracle Enterprise Data Quality ‣Oracle Data Integrator ‣Oracle Enterprise Metadata   Management -All Hadoop enabled -Works across Big Data,  Relational and Cloud

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Data Factory for file, event and DB transaction batch and streaming ingestion •Big Data Management Platform for combined Hadoop + RDBMS data storage •Discovery Labs for innovation and sandboxing Solution Component Logical View

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Discovery Labs and Sandboxes : Key to Innovation •For Customer 360 systems to innovate beyond the initial roll-out, these are critical •Allows innovation and new subject areas to be developed separate from IT governance ‣Then migrated to production when appropriate • Support agile development of BI • Demand management through BICC • Automated provisioning / de-provisioning • Data sourced from any data layer or off-platform • Standardised use of BI tooling • Careful governance required once work is complete • Measure: Engineering backlog and size of shadow-IT • Support agile discovery in data • Demand management through Analytical CC • Automated provisioning / de-provisioning • Data sourced from any data layer or off-platform • Broad range of analytical tools • Governance step required to operationalise insights • Measure: value and rate of new insights to business

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com And now the clever bit…

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T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com I’m too sexy…

T : +44 (0) 1273 911 268 (UK) or (888)

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com • Complete view of customers • Micro-segmented customer profiles • Predictive models

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Customer 360-Degree view typically used as central data store for digital marketing •Provides key data for real-time decision engines, next-best offer, personalisation Customer 360-Degree View as Driver of Digital Marketing ? ? ?

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Customer 360-Degree view typically used as central data store for digital marketing •Provides key data for real-time decision engines, next-best offer, personalisation Customer 360-Degree View Powering Marketing + Offers Data Transfer Data Access Real-Time Context    Environmental User Journey Offer Feedback Single Customer View Enriched   Customer Profile Correlating Modeling Machine  Learning Scoring Real Time   Offers & Suggestions   Up-Sell / Cross-Sell Decisioning Service Self-Learning  Predictive Models

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Customer 360-Degree view typically used as central data store for digital marketing •Provides key data for real-time decision engines, next-best offer, personalisation Customer 360-Degree View as Driver of Digital Marketing Data Transfer Data Access Real-Time Context    Environmental User Journey Offer Feedback Single Customer View Enriched   Customer Profile Correlating Modeling Machine  Learning Scoring Real Time   Offers & Suggestions   Up-Sell / Cross-Sell Decisioning Service Self-Learning  Predictive Models

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Customer 360 systems can use machine learning across all data to build predictive models •Decision engines (for example, Oracle RTD) can use its data as source •Wider range of attributes and faster velocity •Aim to process data and not just to store it -Identify customers likely to defect,   work proactively to retain -foster enhanced engagement •increase revenue •pass events and contextual data to   real-time decisioning engines Use Machine Learning and Real-Time Decisions to add Value Real Time   Offers & Suggestions   Up-Sell / Cross-Sell Decisioning Service Self-Learning  Predictive Models Real-Time Context    Environmental User Journey Offer Feedback Single Customer View Enriched   Customer Profile Correlating Modeling Machine  Learning Scoring Operational Data Transactions Customer  Master Data Unstructured Data Voice + Chat Transcripts Touch Points Store Web Service

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Real-time decisioning engine from Oracle, part of Oracle BI product family •Predictive and Personalised Real-Time Recommendations ‣Behaviour-based models that take into  account activity across all channels ‣At very low levels of granularity ‣Micro-segmentation to individual customer ‣Multi-contextual recommendations   based on predicted customer needs •Real-time offers, delivered to any channel •Feedback loop to improve recommendation Oracle Real-Time Decisions

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Decision engines and business processes use models to predict customer behavoir •Traditional CRM-driven decision engines only consider what happened •Big Data + real-time feeds can dramatically improve model performance •Model with “Big Data” and potentially thousands of input variables: •Customer sentient data •Competitors data •Environmental data •Spatial location data •Long term vs. recent historical behavior •Sensor data More Data + Variety Data -> Better Predictive Models 1980s 1990s 2000s 2010s Empowered Employees Digital is Humanized Knowledge Everywhere Internet of Things Mobile as Primary Channel Cross-Channel Service WHAT’S NEXT

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Event-Driven Personalised Marketing & Offers •Vastly increased size, time-relevance and scope of customer data into decision models Touch Points Store Web Service Operational Data Events & Workflow Transactions Customer  Master Data Profile-Based (Demographic) Prioritization   Offers & Suggestions   Guided Search Guided Search Pricing Marketing Engagement Real Time   Offers & Suggestions   Up-Sell / Cross-Sell Decisioning Service Self-Learning  Predictive Models Single Customer View Enriched   Customer Profile Correlating Modeling Machine  Learning Scoring Real-Time Context    Environmental User Journey Offer Feedback Relevant  Personalized  Experiences •Then close the loop with real-time context back into decision engine

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Connect the Silos, Understand Customers, Drive Decisions execute smarter listen better consumption logs, clickstream & devices demographic, user and credit data customer contacts and service cases transactions and subscriptions content metadata, ratings, comments marketing campaign response social media  activity programmatic  advertising audience acquisition, retention multi-channel  marketing targeted   promotions next best  offer personalized content product & service  strategy content acquisition Single Customer View Enriched   Customer Profile Correlating Modeling Machine  Learning Scoring

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com •Start with pilot for area of the business that needs a single view of customers •Then, over time, iterate and build out the Customer 360-degree view Delivering a Successful Customer 360-Degree View Start with a business area that  needs a single   customer view Obtain clear understanding of customer online & offline behaviour Build out   Predictive Models  and Decision Engines  to deliver value now Build out Hadoop Data Reservoir, Feeds  and link to DW + CRM Iterate and Build-out,  add new integrations,  incrementally building  capability Develop and Implement Strategy, Deliver Business Value Build DevOps Capability Pilot & Quick Win Create Full Production Infrastructure Pilot (Virtualised / Commodity) Hadoop Infrastructure

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631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Packaged Customer 360 Applications •Packaged Customer 360 applications seem a good way to start…? •Risks around degree of fit and integration limits, but can be useful for mature projects See everything together – comparisons with a Set defined by you, and evolving trend scores for each customer From Data to DNA – 1000s of metrics determine individual DNA – common, industry and customer metrics

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Rittman Mead Customer 360 + Real-Time Decisions Projects Start with a business area that  needs a single   customer view Obtain clear understanding of customer online & offline behaviour Build out   Predictive Models  and Decision Engines  to deliver value now Build out Hadoop Data Reservoir, Feeds  and link to DW + CRM Iterate and Build-out,  add new integrations,  incrementally building  capability

T : +44 (0) 1273 911 268 (UK) or (888)
631-1410 (USA) or   +61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India) E : [email protected] W : www.rittmanmead.com Adding a Data Reservoir to Your Oracle Data Warehouse for Customer 360-Degree Analysis  Mark Rittman, CTO, Rittman Mead UKOUG Tech’15, Birmingham, December 2015

Adding a Data Reservoir to Your Oracle Data Warehouse for Customer 360-Degree Analysis

Adding a Data Reservoir to Your Oracle Data Warehouse for Customer 360-Degree Analysis

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