Data-driven Innovations – supported by the Discovery Lab Harald Erb Oracle Business Analytics
DOAG 2015 Business Intelligence München, 23. April 2015
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» Harald Erb
» Principal Sales Consultant
» Business Analytics Architect Domain Lead - DE/CH Cluster
» Kontakt
+49 (0)6103 397-403
Referent
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Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
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Program Agenda
5
Digital Business, Data-driven Decisioning
Discovery Lab
Oracle Big Data Discovery
Monetizing new Insights
Unified Big Data Management and Analytics Architecture
Digital Business with Oracle
1
2
3
4
5
6
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Digital Business, Data-driven Decisioning
6
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Example: „Hijack“ – A Campaign that starts to sell in competitive stores Digital Business
Video: „HIJACK - MEAT PACK GUATEMALA“Cannes Lions Winner of Bronze & Silver (Mobile Category)
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They ‘Reframe’ Challenges
Looking at them from new perspectives and multiple angles
They Sprint
They work at pace - researching, testing and evaluating current ideas while generating new ones
They Appreciate That
Failure Can Be Good
and are not afraid of new ideas
They Convert Data Into Value
They invest heavily in analyzing their own data and data from external sources to establish patterns and un-noticed opportunities
Characteristics of Digital Business Leaders
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Data-driven Decisions
10
Ide
nti
fy (
bu
sin
ess
) q
ue
stio
n
Become clear about all aspects of the decision to be taken or the problem to be solved.
Try to identify alternatives to your percep-tion
Ve
rify
ear
lier
fin
din
gs
Find out who has investi-gated such or a similar problem in the past and the approach that has been taken
De
sign
of
a so
luti
on
mo
de
l Formulate a detailled hypothesis how specific variables might influence the result of the chosen model
Gat
he
r al
l ne
cess
ary
dat
a
An
alys
e t
he
dat
a
Pre
sen
t & im
ple
me
nt
resu
lts
Gather all available information about the variables of your hypo-thesis. The relevance of a dataset might address your business question directly or needs to be derived
Apply a statistical model and evaluate the correctness of the approach. Repeat this procedure until the right method has been identified.
Source: Thomas H. Davenport, Harvard Business Manager 2013
Frame the results obtained in a comprehensible story. This kind of presentation intends to motivate decision makers and relevant stake-holders to take action
Non-Analysts & Executives should take a closer look on steps 1 and 6 of the analysis process if they plan to make use of statistical analysis.
Knowledge Discovery
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Vertical and Horizontal Data Scientists
Data Warehouse Horizontal Vertical
Deep technical skills
Eigenvalues, Lasso-related regressions
Experts in Bayesian networks, R
Support Vector Machine
Hadoop, NoSQL, Data Modeling, DW
Cross-discipline knowledge
Machine Learning & Statistics
Visualization skills
Domain expertise
Storytelling experts
Programming experience
Aware of pitfalls
& rules of thumb
The Specialist The Unicorn
Look for the individual unicorn
or build a Data Science Team?
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Enabling Data-driven Innovations in Organizations
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Perf.
Mgmt.
Knowledge Discovery
Dynamic Dashboards and Reports
Volume and Fixed Reporting
Knowledge Driven Business Process
Analytical Competence Center (ACC)
» Separate group reporting to CxO
» Not part of a Business Intelligence Competence Center (BICC)
» Mission: broadening the adoption of Analytics across the organization
» Skilled resource pool of Data Scientists, Statisticians and Business Experts with privileged access to the internal Enterprise Data Sources
» Will be assigned to projects for a limited time
» Using scientific methods to solve business problems using available data.
Executive: Decisions effecting
strategy and direction
Business Analyst: Day-to-Day performance
of a business unit
Information Consumer: Reporting on
individual transactions
Automated Process: Decisions effecting
execution of an indiv. transactions
Insight Data Scientist:
Information analysis to meet strategic goals
BICC
ACC
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Discovery Lab
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Information Management – Conceptual View
Discovery Lab
Innovation
Discovery Output
Events & Data
Actionable
Events
Event Engine Data Reservoir
Data Factory Enterprise Information Store
Business
Intelligence
Actionable
Information
Actionable
Insights
Data
Streams
Execution
Structured
Enterprise
Data
Other
Data
Line of governance
Source: Oracle White Paper “Information Management and Big Data – A Reference Architecture”
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» Event Engine: Components which process data in-flight to identify actionable events and then determine next-best-action based on decision context and event profile data and persist in a durable storage system.
» Data Reservoir: Economical, scale-out storage and parallel processing for data which does not have stringent requirements for formalisation or modelling. Typically manifested as a Hadoop cluster or staging area in a relational database.
» Data Factory: Management and orchestration of data into and between the Data Reservoir and Enterprise Information Store as well as the rapid provisioning of data into the Discovery Lab for agile discovery.
» Enterprise Information Store: Large scale formalised and modelled business critical data store, typically manifested by an (Enterprise) Data Warehouse. When combined with a Data Reservoir, these form a Big Data Management System.
» Reporting: BI tools and infrastructure components for timely and accurate reporting.
» Discovery Lab: A set of data stores, processing engines, and analysis tools separate from the everyday processing of data to facilitate the discovery of new knowledge of value to the business. This includes the ability to provision new data into the Discovery Lab from outside the architecture.
» Execution: Flow of data for execution are tasks which support and inform daily operations
» Innovation: Flow of data for innovation are tasks which drive new insights back to the business
» Arranging solutions on either side of this division (as shown by the red line) helps inform system requirements for security, governance, and timeliness.
Information Management – Conceptual View
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Source: Oracle White Paper “Information Management and Big Data – A Reference Architecture”
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Discovery Lab: Design Pattern
» Specific focus on identifying commercial value for exploitation
» Wide range of tools and techniques applied
» Iterative development approach – data oriented NOT development oriented
» Data provisioned through Data Factory or own ETL processes
» Typically separate infrastructure but could also be unified Reservoir if resource managed effectively
» Small group of highly skilled individuals (aka “Data Scientists” or Analytical Competence Center, ACC)
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Discovery Lab: Activity Cycles
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Discovery Lab: Sandbox Provisioning
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Analysis Processing & Delivery
Discovery Lab & Development Environment
Data
Science
(Primary
Toolset)
Statistics Tools
Data & Text Mining Tools
Faceted Query Tools
Programming & Scripting
Data Modelling Tools
Query & Search Tools
Pre-Built
Intelligence
Assets
Intelligence
Analysis
Tools
Ad Hoc Query & Analysis Tools
OLAP Tools
Forecasting & Simulation Tools
Reporting Tools
ACC
Virtu
alis
atio
n &
Info
rma
tion S
erv
ices
Data Factory flow
ACC may quickly develop new reporting through mashups from any available internal and external sources and may used advanced analytical tools for innovative analysis
Data Quality & Profiling
Graphical rendering tools
Dashboards & Reports
Scorecards
Charts & Graphs
Sandbox – Project 3
Sandbox – Project 2
Sandbox – Project 1
Data store Analytical Processing
General BI flow
1
2
BICC
The majority of BI development activity will be from existing sources – performed by the BICC developing new reports to existing or new channels
External Data
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Discovery Lab: Need To Get Analytic Value Fast
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Tool Complexity
» Early Hadoop tools only for experts
» Existing BI tools not designed for Hadoop
» Emerging solutions lack broad capabilities
80% effort typically spent on evaluating and preparing data
Data Uncertainty
» Not familiar and overwhelming
» Potential value not obvious
» Requires significant manipulation
Overly dependent on scarce and highly skilled resources
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Oracle Big Data Discovery: The Visual Face of Hadoop
find explore transform discover share
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Oracle Big Data Discovery. The Visual Face of Hadoop
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find explore transform discover share See the potential in big data
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Catalog
22
» Access a rich, interactive catalog of all data in Hadoop
» Familiar search and guided navigation for ease of use
» See data set summaries, user annotation and recommendations
» Provision personal and enterprise data to Hadoop via self-service
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Explore
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» Visualize all attributes by type
» Sort attributes by information potential
» Assess attribute statistics, data quality and outliers
» Use scratch pad to uncover correlations between attributes
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Oracle Big Data Discovery. The Visual Face of Hadoop
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find explore transform discover share Quickly make big data better
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» Intuitive, user driven data wrangling
» Extensive library of powerful data transformations and enrichments
» Preview results, undo, commit and replay transforms
» Test on sample data then apply to full data set in Hadoop
Transform
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Oracle Big Data Discovery. The Visual Face of Hadoop
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find explore transform discover share Unlock big data not only for Data Scientists
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» Join and blend data for deeper perspectives
» Compose project pages via drag and drop
» Use powerful search and guided navigation to ask questions
» See new patterns in rich, interactive data visualizations
Discover
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» Share projects, bookmarks and snapshots with others
» Build galleries and tell big data stories
» Collaborate and iterate as a team
» Publish blended data to HDFS for leverage in other tools
Share
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Oracle Big Data Discovery: Deployment
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Diagram Source: RittmannMead Blog, 2015
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Oracle Big Data Discovery: Components
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Oracle Big Data Discovery Workloads
Hadoop Cluster (Oracle Big Data Appliance or Commodity Hardware with
Cloudera CDH 5.)
BDD node
data node
data node
data node
data node
name node Data Processing, Workflow & Monitoring • Profiling: catalog entry creation, data type &
language detection, schema configuration • Sampling: dgraph (index) file creation • Transforms: >100 functions • Enrichments: location (geo), text (cleanup,
sentiment, entity, key-phrase, whitelist tagging)
Self-Service Provisioning & Data Transfer
• Personal Data: Upload CSV and XLS to HDFS
In-Memory Discovery Indexes • DGraph: Search, Guided Navigation, Analytics
Studio
• Web UI: Find, Explore, Transform, Discover, Share
Hadoop 2.x
Filesystem (HDFS)
Workload Mgmt (YARN)
Metadata (HCatalog)
Other Hadoop Workloads
MapReduce
Spark
Hive
Pig
Oracle Big Data SQL (Oracle Big Data Appliance only)
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Oracle Big Data Discovery: Data Ingestion Workflow Overview
1M of 100M
Diagram Source: RittmannMead Blog, 2015
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User friendly,... Oracle Big Data Discovery: Data Preparation
Preferred method for the Business Analyst
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...and flexible (based on Groovy Programming Language) Oracle Big Data Discovery: Data Preparation
Preferred Method for IT / Data Engineer / Data Scientist / …
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Working the Data Analyzing the Data
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Data Discovery & Analytics Lifecycle
Data Select Data
Prepare Data
Transform Data
See Patterns
Interpret & Evaluate Knowledge
Oracle Advanced Analytics
Oracle Big Data Discovery
Time
f(x)
?
a = A
80% 20% 20% 80%
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More Data Variety available – Better Results
35
Response Modelling Example: Getting „lift“ on responders
Data Mining-based Prediction results with Big Data and hundreds of input variables including:
Naïve Guess or Random
100 0 Population Size (% of Total Cases)
% o
f P
osit
ive R
esp
on
ders
Model with 20 variables
Model with 75 variables
Model with 250 variables
» Demographic data » Purchase POS
transactional data » Polystructured data,
text & comments » Spatial location data » Long term vs. recent
historical behaviour » Web visits » Sensor data » …
100
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Oracle R Enterprise (ORE)
» Allows distributed processing of huge data volumes
» Benefits from DB features, e.g. Security and SQL access
» R Studio = GUI for Data Analysts
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Oracle Data Mining (ODM)
» Implemented in the Oracle Database kernel
» Direct access via PL/SQL API and SQL operators
» Oracle Data Miner GUI embedded in SQL Developer
Oracle Advanced Analytics Native SQL Data Mining/Analytic Functions + High-performance R Integration
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Discovery Lab: End of Research Phase
Oracle Advanced Analytics
Oracle Big Data Discovery
Apply statistical & predictive models
No Data Movement; Bring algorithms to the data
Utilize Oracle R and Data Mining for Massive Computing Scalability on Hadoop or Oracle
Integrated with SQL and BI tools
Find data for analytics & data science projects
Explore the shape and quality of the data
Transform data for analytics
Discover and visualize insights in data sets
Share insights with analysts and downstream systems
Share Insight
Interpret & Evaluate
Select, Prepare & Transform
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Acceptable Results Probability: A Clearer View
38
Storytelling / Infographics
Discovery Lab: Explanation & Validation of the Results
Individuals of the Analytical Competence Center need to frame the results obtained in a comprehensible story. This kind of presentation intends to
motivate decision makers and relevant stake-holders to take action
Result of 1000 simulations of a $100 million investment in a new factory: Estimation expects an annual return of 20% over a 10-year lifespan, but the risk to loose invested money is still 8% Big Data Discovery – Gallery feature documents all discovery
steps taken to achieve new insights
Individually created infographic explaining the key findings
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Monetizing New Insights
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Discovery and monetising steps have different requirements Making sense from diverse data
Research & Development
» Unbounded discovery
» Self-Service sandbox
» Wide toolset
» Agile methods
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Discovery and monetising steps have different requirements Making sense from diverse data
Promotion to Data-driven Services
» Commercial exploitation
» Narrower toolset
» Integration to operations
» Non-functional requirements
» Code standardisation & governance
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Usage of Data-driven Innovations: Predictive BI
Oracle Business Intelligence: Dashboards, Alerts,...
» Understandable prediction results, Self-service BI
» Making analysis results available to every business user, i.e. potential cross-selling effects to responsible Buyers
» Operated by Business Analysts / BICC, etc.
Predictive Query Example
SELECT cust_income_level, cust_id
, ROUND(probanom,2) AS probanom
, ROUND(pctrank,3)*100 AS pctrank
FROM (SELECT cust_id, cust_income_level, probanom
, PERCENT_RANK()
OVER (PARTITION BY cust_income_level
ORDER BY probanom DESC) AS pctrank
FROM (SELECT cust_id, cust_income_level
, PREDICTION_PROBABILITY(OF ANOMALY,0 USING *)
OVER (PARTITION BY cust_income_level)
AS probanom
FROM customers
)
)
WHERE pctrank <= .05
ORDER BY cust_income_level, probanom DESC;
Oracle 12c In-Database Mining / Statistics
» Operationalize Data Mining Models as part of Oracle BI Dashboards, calculated on-the-fly
» Available query types: Classification & regression (incl. Multi-target problems), clustering, anomaly detection, feature extraction
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Example: Oracle Human Capital Management Usage of Data-driven Innovations: Forward-looking Apps
Characteristics:
» Includes Oracle Advanced Analytics factory-installed Predictive Analytics:
» Employees likely to leave & predicted performance
» Top reasons, expected behavior
» Real-time "What if?" analysis
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Example: Next Best Offer Usage of Data-driven Innovations: Real-Time Decisoning
1 Channel: Web 1
2 Placement: Homepage 2
3 Creative-Content: "Expert Tennis Tips" 3
4 Slot Type: Articles 4
5 Slot: Center Middle 5
7 Tags: Tennis | Tips | Pros 7
6 Offer: Discount on Tennis Lessons 6
Leads to multiple model updates and discovery of associated correlations across the graph
Rules & Predictive Models
Performance Goals
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Intelligent User Experience Usage of Data-driven Innovations: Event Processing
iBeacons
» Bluetooth Low Energy (BLE)
» Optimized for small bursts of data.
» Impressive battery Life
» Ideal for sensors
Requirements
» Find purchase pattern from data of shopper’s purchase history
» Leverage all the data, including real-time context from Beacon, CRM data, purchase history data, to improve the relevance of the offer
» Leverage predictive models to alleviate the reliance on the rule based models
» Being able to understand customer’s feedback on Beacon marketing
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Usage of Data-driven Innovations: Event Processing Solution Architecture
Analysis and Offering
Decision Engine
Unstructured Text Analysis (VOC analysis)
Rule Based Statistical
Model-based
Modeling Processing
Real Time Offering
Qualitative indices
Text Mining
Data Dictionary
Text Analysis
Collection
Batch collection
Real Time Collection
Web Crawling
Open API
Storage and processing Utilization
ETL
Treatment Store
Hadoop File
Reduce Map
HDFS
Datafile#1 HDFS
Datafile#2 HDFS
Datafile#n HDFS
NoSQL DB Transaction (Key-Value)
Stores
Big Data Connectors
Mobile Apps
Unstructured Data Visualization
Coupon
Mileage
…..
New information
Keywords Visualization
Search Vigan Visualization
Dash Board
Mobile
Real Time
Formal & Informal
Integration
Source system
Other internal and external systems
Beacon
Time
Phone Number
Distance
Beacon MAC
Customer
…..
Martial Status
Customer Type
Customer ID
…..
Num of Children
Occupation
Gender
Purchase
Amount
Product
Customer ID
…..
Quantity
Date
Smart App
Web
VOC
SNS
ODS DW
Advanced Analytics on
Purchase Pattern
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Usage of Data-driven Innovations: Event Processing Solution Architecture – Product View
Analysis and Offering
Decision Engine
Unstructured Text Analysis (VOC analysis)
Rule Based Statistical
Model-based
Modeling Self-Learning
Real Time Offering
Qualitative indices
Text Mining
Data Dictionary
Text Analysis
Collection
Batch collection
Real Time Collection
Web Crawling
Open API
Storage and processing Utilization
ETL
Treatment Store
Hadoop File
Reduce Map
HDFS
Datafile#1 HDFS
Datafile#2 HDFS
Datafile#n HDFS
NoSQL DB Transaction (Key-Value)
Stores
Big Data Connectors
Mobile Apps
Unstructured Data Visualization
Coupon
Mileage
…..
New information
Keywords Visualization
Search Vigan Visualization
Dash Board
Mobile
Real Time
Formal & Informal
Integration
Source system
Other internal and external systems
Beacon
Time
Phone Number
Distance
Beacon MAC
Customer
…..
Martial Status
Customer Type
Customer ID
…..
Num of Children
Occupation
Gender
Purchase
Amount
Product
Customer ID
…..
Quantity
Date
Smart App
Web
VOC
SNS
ODS DW
Advanced Analytics on
Purchase Pattern
Oracle Big Data Appliance
Oracle Event
Processing
Endeca
Information Discovery
Oracle Advanced Analytics
Ora
cle
Dat
abas
e
Oracle Big Data Connectors
Oracle Data Integrator
Oracle Golden Gate
Oracle Data Integrator
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Unified Big Data Management and Analytics Architecture
48
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Productize, Secure & Govern
Experiment, Prototype & Collaborate
Data Reservoir
Poly
stru
ctu
red
D
ata
Data Warehouse
Oracle Database
Stru
ctu
red
Dat
a
Oracle Big Data Discovery
Oracle Big Data SQL
Hadoop (HDFS)
Oracle R for Hadoop
Oracle Advanced Analytics (Data Mining, Oracle R Enterprise)
Tables in Hadoop
Tables in DB
SQL join
In-Memory Appliance
Oracle BI Foundation Suite (ROLAP/MOLAP, Mobile,…)
Oracle SQL Queries
Exalytics
Exadata
BDA
Unified: Big Data Management and Analytics…
Experiment, Prototype, Collaborate
» Quickly find, explore, transform, analyze and share discoveries in Big Data Discovery
» Publish results to the Hadoop Distributed File System (HDFS)
» Use to build predictive models with Oracle R for Hadoop
Productize, Secure, Govern
» Connect published HDFS files to secure Oracle DB using Oracle Big Data SQL
» No data movement required
» Seamlessly extends existing DWH and BI investments with non-traditional data in Hadoop
49
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Example: „Pattern Matching“ Feature over Hadoop and DWH Data Sources Oracle Big Data SQL – Enables Full Use of Oracle 12c
Example Table Definition
CREATE TABLE movieapp_clicks
(click VARCHAR2(4000))
ORGANIZATION EXTERNAL
(TYPE ORACLE_HIVE
DEFAULT DIRECTORY Dir1
ACCESS PARAMETERS
(com.oracle.bigdata.tablename logs
com.oracle.bigdata.cluster mycluster
)
)
REJECT LIMIT UNLIMITED
Oracle Business Intelligence Dashboard
External Database Table accessing a Hive Table logs in Hadoop Cluster
named mycluster
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…powered by Oracle Engineered Systems
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Complete: Oracle Unified Information Architecture
52
Data Center Example
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Digital Business with Oracle
53
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Market, Sell & Service
Choice Of Deployment
Digital Product Development
End-to-end Visibility
Marketing Systems
Commerce Systems
Sales Systems
Service Systems
Private Cloud IaaS / Paas / SaaS
On_Premise
Public Cloud
Moblle & Device Security Identity Mgmt. Authorization Gateways
Digital Content
Process Integration Analytics
Service/Events Integration
Data Integration
Web / Mobile / IoT Development Framework
Real Time Data & Analytics
Platform Access APIs
Social Things Mobile Web
Learn
& A
dap
t Consume & Access
Multi-Channel
Protect & Secure
Oracle’s Digital Business Platform
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Insert Information Protection Policy Classification from Slide 13 55
Nike A Digital Example of an
Oracle Customer
Product & Digital Innovation is a Constant State for Nike
2012
2012
2009
2008
2006
2005
2002
2000
1987
1982
1979
1974
Flyknit upper technology introduced
Nike’s Fuelband is launched
Pro Combat apparel is launched
Lunar Foam and Flywire Technology launched
Nike+ (8 million+ members)
Nike Free launched
Golf clubs introduced
Shox technology introduced
Air Max shoe launched
Air Force 1 Basketball shoe
Tailwind – 1st Nike Air shoe
Waffle Trainer introduced
Copyright © 2012, Oracle and/or its affiliates. All rights reserved. Insert Information Protection Policy Classification from Slide 13 57
“Where most initiatives focus on enticing
consumers to complete a purchase, Nike+
continues to engage the consumer long
after the transaction has occurred, keeping
Nike+ runners motivated & connected, with
each other & with the Brand.”
Nick Law, R/GA EVP/Chief Creative Officer
Digital is enabling relationships &
community
+ +
Product + Content + Community = Premium Consumer
Experience
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Show Me
Ext. Data Analytics
Connect
Context
Content
Configure
Always Connected, Sharing & Aware
C l o u d
Digital Business Platform - Overview
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