Think big, act small, start now Not only the seemingly endless flow of data but also its variety and complexity are typical for the Digital Era. This evolution offers companies the opportunity to gain new and valuable insights. Some examples of analytics: - A customer segmentation analysis divides customers into several groups, based on specific characteristics. This allows us to target them better, offer them tailor-made products and services or exploit cross-selling and up-selling opportunities more. - Churn prediction even makes real-time prediction possible of which customers are about to leave us. This insight enables us to take proactive action to prevent this. At the same time we are confronted with some new challenges and we need to change the way we handle data. Big data and analytics are the key to gain new insights, which can be incorporated by organizations in their strategic decisions as well as in their operational way of working. The key question is: how do you start? The answer is simple: start with building up the basic competences, start today and keep it simple, prove the added value and add complexity along the way. During this AE foyer two open source solutions (and market standards), R and Hadoop, will be discussed. We will present their characteristics in detail and illustrate (in an accessible way) how to use them and which quick results you can expect. Furthermore a realistic reference architecture will be shown, helping you to make the right choices, based on your needs and ambitions. Don’t miss out and discover how you can take advantage of the opportunities of the Digital Era, in an innovative and pragmatic manner!

1. ©AE 2012  1  
Bram Vanschoenwinkel
Senior Data Scientist, AE
@bvschoen
@AE_NV
R & Hadoop
The perfect marriage for your analytics?
Avondconferentie 19/06/2014

2.  2
Agenda
1. It’s a ( R )evolution
2. Intelligent Decision Support in the Digital Age
3. The R Project for Statistical Computing
4. The World of Hadoop
5. Case: A Customer Intelligence Platform
6. Conclusions

3.  3
It’s a (R)evolution
2000 2010 2015
DATA
VOLUME
TIME
MAJORITY
UNSTRUCTUREDDATA

4.  4
Abundance of Data
BEYOND
WEB
CRM
ERP
PURCHASE DETAIL
PRODUCTION
PAYMENT DETAIL
PLANNING
CONTACT INFORMATION
LEADS
OFFERS
SEGMENTATION
PROSPECTS
CLICK STREAM DATA
WEB SHOPS SOCIAL MEDIA
VIDEO
IMAGES
TEXT
ONLINE SERVICES
AUDIO
OPEN DATA
MOBILE DEVICES
INTERNET OF THINGS
RFID
GPS
SENSORS
USER GENERATED CONTENT
SMART DEVICES
SENSORS
REMOTE MONITORING
CLOUD
MEDICAL
WARABLES

5.  5
Opportunities
OPERATIONAL
EXCELLENCE
INNOVATIVE
BUSINESS MODELS
INSIGHTS, STRATEGY
AND POLICY

6.  6
SHORT LIFESPAN OF THE DATA
FASTMOVINGDATA
FASTDATAPROCESSING
HIGH VARIETY OF DATA
Challenges

7.  7
intelligent decision support in the digital age
WHAT WE SEE
ABUNDANCE OF
HETEROGENOUS DATA
THE WAY WE INTERACT
WITH THE WORLD HAS
CHANGED
OPPORTUNITIES
OPERATIONAL
EXCELLENCE
BETTER DECISION
SUPPORT
CHALLENGES
ANALYSIS GAP
VOLUME, VARIETY,
VELOCITY
INNOVATING BUSINESS
MODELS
COMPETENCES

8.  8
Decision Support in the Digital Age
Facing the Challenges and realizing the
Opportunities
Business
Analytics
Big Data

9.  9
Elements of a Holistic Information Management
Framework
- Data Sources
- Internal & External
- From Data to Information
- Improving data quality
- Integrality of data
- From Information to Knowledge
Intelligent Decision Support:
- Reporting
- Business Analytics
- From Knowledge to Intelligence
DATAInformation
Knowledge
Intelligence
Wisdom/Insight

10.  10
Decision Support in the Digital Age
“Business Analytics is the nontrivial extraction of
implicit, previously unknown, and potentially useful
information from data.”

11.  11
Business Analytics vs Business Intelligence

12.  12
New Insights
8 stoppen
132 stoppen
10 stoppen
53 stoppen
64 stoppen
14 stoppen 4 stoppen
11 stoppen

13.  13
Innovating Business Models
Front-end Application(s)
Security
Analytics (on Hadoop)
Web Click
StreamingSocial Media
Connectivity
External
Application
Integration
Operational Data Processing on Hadoop

14.  14
From Analytics…
Statistics Algorithms
Biology
Psychology
Databases

15.  15
…to Business Analytics

16.  16
Analytics Approach
 Analytics
 Incremental and iterative
 Think big act small
 Proof-of-Concept
 Open source tools
 Architecture & Deployment
 (Non-)funtional requirements
 Information Architecture
 Technology
 Embedded into operations
Two Phase Approach
Analytics
Architecture Deployment

17.  17
Analytics Churn Prediction Example
Invoicing CRM Call Center
Application
John Doe – 43years – Antwerp – Man – 7calls – 3weeks – 30%down invoicing
Jane Dan – 32years – Brussels – Woman – 2calls – 12weeks – 10%up invoicing
…
Operations
CHURN SCORES
REGION
PRODUCT
CHURN SCORES
MANAGEMENT
DASHBOARD
OPERATIONS
DATA DUMP
Analytics
Engine
Data Warehouse

18.  18
Big Data
“Big data is high-volume, high-velocity, high-complexity and
high-variety information assets that demand cost-effective,
innovative forms of information processing for enhanced insight
and decision making.” (Gartner)

19.  19
Four V’s and a C
 Not only volume makes big data big, it’s all about the three V’s:
 High Volume, Variety, Velocity
 High Value!
 In addition the data is very complex in nature, often unstructured:
 Text documents, emails, images and videos, etc.
 Click stream data, social media feed data, etc.

20.  20
Innovative Forms of Information Processing
 Traditional methods don’t suffice anymore.
 New forms of information processing have emerged.
DISTRIBUTED DATA
STORAGE
COMPUTATION
NoSQL DATA STORES

21.  21
Innovative Forms of Information Processing

22.  22
The R Project for Statistical Computing
 R is a dialect of the S language
 S was developed by John Chambers and others at Bell Labs
 S was initiated in 1976
 Now owned by TIBCO and sold under the name S-PLUS
INTERACTIVE NOT
PROGRAMMING
PROGRAMMING
WHEN SYSTEM
ASPECTS BECOME
IMPORTANT
GRADUALLY MOVING INTO

23.  23
Advantages of R
 Most widely used data analysis software
 Created and used by 2M+ data scientists, statisticians and analysts
 Most powerful statistical programming language
 Flexible, extensible & comprehensive for productivity, +4800 packages
 Create beautiful and unique data visualizations
 As seen in New York Times, Twitter and Flowing Data
 Thriving open-source community
 Leading edge of analytics research
 Fills the talent gap
 New graduates prefer R

24.  24
Drawbacks of R
Steep learning curve
Objects must be
stored in physical
memory, little
thought to memory
management
Functionality is
based on consumer
demand and user
contributions
Documentation is
sometimes patchy
and terse, and
impenetrable to the
non-statistician
Vibrant community
to help you
Recent
advancements to
deal with this
If a package is
useful to many
people, it will
quickly evolve into a
robust product
Vibrant community
to help you

25.  25
Exploding growth and Demand for R
 R is the highest paid IT skill
 – Dice.com, Jan 2014
 R most-used data science language
after SQL
 – O’Reilly, Jan 2014
 R is used by 70% of data miners
 – Rexer, Sep 2013
 R is #15 of all programming languages
 – RedMonk, Jan 2014
 R growing faster than any other data
science language
 – KDnuggets, Aug 2013
 More than 2 million users worldwide

26.  26
Great Adoption of R by Many Companies
 Commercial vendors offering general support and developing
specific R based products, e.g.: Oracle, RevolutionAnalytics.
 Companies using R for advanced statistics and analytics, e.g.:
Thomas Cook, Google, Twitter.
 Also in the AE customer base we see different companies looking
into R as an alternative or complement to the traditional tools.

27.  27
Example Packages
 twitteR: Provides an interface to the Twitter web API.
 tm: Provides Text Mining functionalities like word stemming,
stopword removal, etc.
 wordcloud: Provides methods for producing wordclouds in
different forms, shapes and colors.

28.  28
Apache Hadoop
 Open-source software framework.
 Storage and large-scale processing of data on clusters of commodity hardware.
 Apache top-level project built and used by a global community.
 Two core components:
1. Hadoop Distributed File System (HDFS)
2. MapReduce

29.  29
Apache Hadoop
 MapReduce/HDFS based on Google's MapReduce and Google File System.
 Other components are:
 Hadoop Common – libraries and utilities needed by other Hadoop modules
 Hadoop YARN – a resource-management platform
 The entire Apache Hadoop “platform” is now commonly considered to consist
of a number of related projects as well: Pig, Hive, Hbase,…
 Created by Doug Cutting and Mike Cafarella at Yahoo in 2005 originally to
support distribution for the Apache Nutch search engine project.
All the modules in Hadoop are designed with a fundamental
assumption that hardware failures (of individual machines, or
racks of machines) are common and thus should be
automatically handled in software by the framework.

30.  30
The World of Hadoop

31.  31
Key Properties Apache Hadoop
 Transforms commodity hardware into a service that:
 Stores petabytes of data reliably.
 Allows huge distributed computations.
 Key Properties:
 Designed for batch processing.
 Write-once-read-many access model for files.
 Extremely powerful.
 Scalability:
• Scales linearly with cores and disks.
• Machines can be added and removed from the cluster.
• Write code once, same program runs on 1, 1000, 4000 machines.
 Reliable and fault-tolerant:
• Failed tasks/data transfers are automatically retried.
• Data replication, redundancy.

32.  32
Rack 2 Rack 3Rack 1
A Typical Hadoop Cluster
Client
DATA ASSIGNMENT TO NODES
DATA READ
DATA WRITE
METADATA FOR
BLOCK INFO
Task Tracker
Task Tracker
Map Reduce
Map Reduce
Job Tracker
Data Node
Data Node
Task Tracker
Map Reduce
Data Node
Task Tracker
Task Tracker
Map Reduce
Map Reduce
Data Node
Data Node
Task Tracker
Map Reduce
Data Node
Task Tracker
Task Tracker
Map Reduce
Map Reduce
Data Node
Data Node
Task Tracker
Map Reduce
Data Node
Master
Node
Slave
Nodes
Slave
Nodes
Slave
Nodes
Name Node
JOB
ASSIGNMENT
TASK ASSIGNMENT
1. Client
2. Master Node
 Name Node
 Job Tracker
3. Slave Nodes
 Data Nodes
 Task Trackers
 Map / Reduce

33.  33
1. Client consults Name Node
2. Client writes block to Data Node
3. Data Node replicates block
4. Cycle repeats for next blocks
Rack 2 Rack 3Rack 1
Hadoop File System (HDFS)
Data Node 1 Data Node 4 Data Node 7
Data Node 2 Data Node 5 Data Node 8
Data Node 3 Data Node 6 Data Node 9
Name Node
Client
FILE
FILE
DATA ASSIGNMENT TO NODES
DATA READ
DATA WRITE
METADATA FOR
BLOCK INFO
Rack 1:
Data Node 1
Data Node 2
…
Rack 2:
Data Node 3
…

34.  34
MapReduce
the, 1
quick, 1
brown, 1
fox, 1
the, 1
fox, 1
ate, 1
the, 1
mouse, 1
how, 1
now, 1
brown, 1
cow, 1
the, 1
the, 1
the, 1
fox, 1
fox, 1
quick, 1
brown, 1
brown, 1
ate, 1
mouse, 1
how, 1
now, 1
cow, 1
the, 3
fox, 2
quick, 1
brown, 2
ate, 1
mouse, 1
how, 1
now, 1
cow, 1
the, 3
fox, 2
quick, 1
brown, 2
ate, 1
mouse, 1
how, 1
now, 1
cow, 1
Input Splitting Map Shuffle
Sort
Reduce
Output
The Map function processes one line at a time,
splits it into tokens seperated by a withespace
and emits a key-value pair <word, 1>.
The Reducer function just sums up the values,
which are the occurence counts for each key
(i.e. words in this example).

35.  35
Hadoop Distributions
 Fully equipped, scalable and flexible cloud solutions.
 Also different on premise solutions are being offered.
 Choice depends on specific requirements.
 Data Privacy, Scalability, Security, Data Mastership, Configuration, Flexibility,
Price-Performance Ratio, Automation,…
 How to get started?
 Free to download!
 Business model is based on training, consulting, support and additional
“tooling” (Enterprise Editions).
 Many free trial cloud versions available to play around with.
 Many tutorials, trainings, blogs, user groups etc.

36.  36
RHadoop
 A collection of four R packages that allow users to manage and
analyze data with Hadoop:
 rmr: Hadoop MapReduce functionality in R
 rhdfs: file management of the HDFS from within R
 rhbase: database management for the HBase distributed database
 Recently a new package plyrmr was relased providing a familiar interface
while hiding many of the MapReduce details (like Hive, Pig and Mahoot).
 R and all RHadoop packges should be installed on all nodes in
the Hadoop cluster.
Combining the advantages of R with the
power of Hadoop.

37.  37
MapReduce Wordcount Example in R
Map function.
Reduce function.
Reading the input from
HDFS from.dfs().
Writing the results back
to HDFS to.dfs().

38.  38
Case: A Customer Intelligence Platform
* Non Disclosure Agreement: Contact AE via www.ae.be/contact for more information

39.  39
Conclusions
 The Digital Age brings many opportunities but also challenges.
 Big Data and Analytics can face the challenges and realize the
opportunities.
 It is within anyone’s grasp, do it incremental and iterative.
 R and Hadoop:
 Open source software, active user groups and support.
 A great way to start exploring!
 Combined power gives you the advantage of 1 + 1 =3.
 Sometimes alternatives are better.

40.  40
Conclusions
 Don’t always need Big Data to do Analytics, it depends on the
requirements.
 Hadoop cloud solutions are scalable, flexible and cost-efficient,
but sometimes limited in functionality (or not standardized).
 Many differences between Hadoop distributions, constantly
evolving (and getting better).
 Need for good Data Scientists in a mixed team of competences to
make the right choices.

41.  41
What’s next?
 Ask yourselves following questions:
 What opportunities do I see for myself?
 What strategic and competitive advantages can I realize?
 Is Analytics the right solution for me? Do I need Big Data?
 What about my Data Warehouse environment?
 And what about the quality of my operational data?
 Do I have the right infrastructure in place?
 Do I have the right competences in house?
 Now you should know what’s in it for you, but also the challenges
your most probably will be facing.

42.  42
What’s next?
 You have a case you would like to discuss…?
 You have any questions…?
 Please feel free to contact me:
 Bram Vanschoenwinkel
 Bram.Vanschoenwinkel@ae.be
 +32 478 741738
@bvschoen
be.linkedin.com/in/bramvanschoenwinkel/

43. @bvschoen / @ae_nv
www.ae.be
blog.ae.be