Everyone is talking about Big data. But, what does it mean? What can you do with it? Which tools can you use? And what can you do with it. Step by step explanation for marketeers and business executives.

1. Explanation for marketeers
and business executives
Fried
Broekhof
November
2013
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2. Big data’s V’s
Volume
Sta-c
Data
Exabytes
of
exis-ng
data
to
process
Velocity
Dynamic
Data
Streaming
data,
(near)
RT
response
Variety
Different
Data
(Un-­‐)structured
data,
text,
images,
video’s
VeracAty
Right
Data
Integrity
of
data.
Can
the
results
be
trusted?
Value
Valuable
Data
Business
value
of
exploring
data
VolaAlity:
How
long
do
you
need
to
store
this
data?
Validity:
Is
the
data
correct
and
accurate
for
the
intended
usage?
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3. Basic Principles
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4. It all started with ...
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5. It all started with ...
...a single server
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6. and an explosion of
data to be processed
...a single server
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7. We needed...
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8. ...more computing power
and more storage
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9. ... a distributed file system,
to share storage
and computing power
among servers
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10. and...
... a framework for writing applications
that processes large amounts of
structured and unstructed data in
paralel., in a reliable and fult-tolreant
manner.
Core
Hadoop
1.0
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11. but... we needed more...
efficient transfer of bulk data between
Apache Hadoop and structured datastores,
such as relational databases.
Data
Services
Core
Hadoop
1.0
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12. but... we needed more...
a distributed real-time computation system
for processing fast, large streams of data
Data
Services
Core
Hadoop
1.0
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13. ...and...
a non-relational (NoSQL) database that runs on
top of the Hadoop® HDFS, that provides faulttolerant storage and quick access to large
quantities of sparse data. And allows users to
conduct updates, inserts and deletes.
Data
Services
Core
Hadoop
1.0
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14. ...and...
a library of scalable machine-learning
algorithms, implemented on top of Apache Hadoop®
and using the MapReduce paradigm.
Data
Services
Core
Hadoop
1.0
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15. ...but also...
a data warehouse infrastructure built on top of
Apache™ Hadoop® for providing data summarization,
ad-hoc query, and analysis of large datasets.
Data
Services
Core
Hadoop
1.0
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16. ...and...
a simple scripting language that allows users to
write complex MapReduce transformations.
Data
Services
Pig
Core
Hadoop
1.0
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17. And a framework and tools...
...to manage it all...
OperaAonal
Services
Ambari
Falcon
Knox
Zookeeper
Oozie
Data
Services
Flume
Accumulo HCatalog
Core
Hadoop
1.0
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18. Hadoop evolved to
enable a broader array of interaction patterns
for data stored in HDFS beyond MapReduce
meet demands for fast response times and
extreme throughput at petabyte scale
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19. 1 solution doesn’t fit all
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20. There is a lot to choose from
Non-Relational
Relational
Analytic
Dremel
Drill
ParAccel
Operational
Intersystems
Progress
Objectivity
Versant
Teradata
Aster
IBM InfoSphere HP Vertica
Netezza
Oracle
EMC
SAP Hana
Times-Ten
Greenplum SAP Sybase IQ
Hadoop
Horton
Cloudera
MapR
Hadapt
Zettaset
Spark
Oracle
Document MarkLogic
Lotus Notes
McObject
Couchbase
MongoDB
RavenDB
DaaS
MarkLogic
Key
Cloudant
Elasticsearch
Value
App Engine
Couchbase
SimpleDB
Riak
Redis
Big tables
Graph
Membrain
FlockDB
Voldemort
Big table
InfiniteGraph
BerkeleyDB
Hypertable
Neo4j
Dynamo
HBase
AllegroGraph
Cache
Accumulo
Cassandra
Giraph
NoSQL
IBM DB2
Infobright
ParAcell
Calpont
Vectorwise
SQLSVR JustOneDB
MySQL
Sybase ASE
Ingress
ProgreSQL
EnterpriseDB
NewSQL
HandlerSocket
Akiban
MySQL Cluster
Clustrix
Drizzle
GenieDB
SchoonerSQL ScaleBase
ScalArc
Tokutek
CodeFutures NimbusDB
Continuent
VoltDB
Translattice
Spanner
Amazon RDS
SQL Azure
Database.com
Xeround
FanthomDB
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21. How about BI vendors?
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22. BI
vs
Big
data
analyAcs
BI
Big
data
analy,cs
Structured
&
repeatable
IteraAve
&
exploraty
analysis
Business
users
determine
what
to
ask,
in
advance
IT
delivers
a
plaPorm
to
enable
creaAve
discovery
IT
structures
data
to
answer
quesAons
Business
explore
what
use
cases
should
be
confirmed
Monthly
reports,
profitabilty
analysis,
customer
surveys
brand
senAment,
product
strategy,
maximum
uAlizaAon,
segmentaAon
Control
vs
Explore
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23. What can you do with it?
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24. What can you do with it?
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25. What
opportuni-es
do
we
spot?
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26. What hurdles?
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27. Marketing Analytics process
Maturity of Marketing Analytics within organizations
Real-time adaptability
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28. Marketing Analytics process
Maturity of Marketing Analytics within organizations
Real-time adaptability
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29. BD/Marke-ng
analy-cs
is
driving
business
transforma-on
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30. Who’s involved?
Data
Platform
Insights
Transformation
Data consultants
IT
Marketing/Business
Business consultants
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31. Capturing value....
Big data
Predictive and
optimization models
Organizational
transformation
...is an Iterative process
Collaborate
Step by Step
Test & learn
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32. Do’s
•
•
•
•
•
•
Define
use
cases
Ask
the
right
quesAons
Be
creaAve
with
what
you
have
OpAmize
spend
and
impact
across
channels
Keep
reports
&
dashboards
simple
Work
incremental
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33. Cases
• ROI/impact
of
markeAng
campaigns
for
FMCG
– Improved
content
markeAng
– Improved
engagement
(11%)
– Cost
reducAon
of
markeAng
spend
(8%)
• RT
analyAcs
for
RT
bidding
engine
• SegmentaAon/defining
promising
b-­‐b
segments
for
a
lease
company
• Opportunity
spobng
for
health
insurance
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34. Big
Data
Lab
• 5
working
days
• Deliverable;
validated
use
case(s)
for
new
business
opportunity/opportuni-es
•
•
•
•
Steps
Explore
data
Define
uses
cases
Validate
uses
cases
Define
steps
based
on
outcome
valida-on
of
use
cases
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35. Big data in real life
Channel specific
Time-dependent
Personal
based on:
behavior
preferences
conversations
Centralized data
Reward
behavior
sharing
loyalty
participation
co-creation
feed-back
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