This document discusses big data and the single customer view. It defines big data as large datasets that are difficult to work with using typical database tools due to their size. It also discusses how distributed computing networks can help analyze these large datasets more efficiently compared to supercomputers. Additionally, it outlines how a single customer view that combines all customer data sources is important for understanding customer behavior and interactions over time to improve marketing campaigns and the customer experience.

1. > Small vs. Big Data <
What the heck? What does it all
mean and how does it help me?

2. > Wikipedia: Big data
In information technology, big data consists of datasets
that grow so large that they become awkward to work
with using on-hand database management tools.
Difficulties include capture, storage, search, sharing,
analytics, and visualizing.
This trend continues because of the benefits of working
with larger and larger datasets allowing analysts to spot
business trends, prevent diseases, combat crime.
Though a moving target, current limits are on the order
of terabytes, exabytes and zettabytes of data.
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Big data = bottlenecks

4. > Big data analytics bottlenecks
June 2012 © Datalicious Pty Ltd 4
Fast laptops now have up to 8GB of
RAM, that means you can compute
up to 6GB of raw data very fast in
memory thus bypassing the
biggest bottleneck: I/O

5. > Power vs. distributed computing
June 2012 © Datalicious Pty Ltd 5
Adding more supercomputers is
difficult as they are complex and
expensive but adding machines to
a distributed computing network
is fairly cheap and ‘easy’.

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Big data = hype?

7. > Importance of research experience
June 2012 © Datalicious Pty Ltd 7
The consumer decision process is changing from linear to circular.
Consideration
set now grows
during (online)
research phase
which increases
importance of
user experience
during that phase
(Online) Research

8. > The consumer data journey
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To retention messagesTo transactional data
From suspect to To customer
From behavioural data From awareness messages
TimeTime
prospect

9. Campaign response data
> Single customer view is key
June 2012 © Datalicious Pty Ltd 9
Customer profile data
+ The whole is greater
than the sum of its parts
Website behavioural data

10. > Maximise identification points
20%
40%
60%
80%
100%
120%
140%
160%
0 4 8 12 16 20 24 28 32 36 40 44 48
Weeks
Cam
paign
response
Em
ailsubscription
Online
purchase
Repeatpurchase
Confirm
ation
em
ail
Em
ailnew
sletter
W
ebsite
login
Online
billpaym
ent
−−− Probability of identification through Cookies
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App
dow
nload/access

11. > Traditional single customer view
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Vendor
data feed #2
Website
data
Call center
data
Customer
data
Reports and
dashboards
Vendor
data feed #1
Vendor
data feed #3
Targeted
campaigns
Transaction
data warehouse
Reporting
data warehouse
Data import
(ETL) process

12. > Traditional single customer view
June 2012 © Datalicious Pty Ltd 12
Vendor
data feed #2
Website
data
Call center
data
Customer
data
Reports and
dashboards
Vendor
data feed #1
Vendor
data feed #3
Targeted
campaigns
Transaction
data warehouse
Reporting
data warehouse
Data import
(ETL) process
Challenge #1:
Rigid database
schema requires
extensive planning
and maintenance
Challenge #2:
Data feeds require
constant updates
and maintenance
Challenge #3:
Increasing number
of (unstructured)
data sources

13. Splunk instance
on dedicated
AWS server
> Splunk single customer view
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3rd party
campaign
execution
Splunk saved
searches and
dashboards
Splunk
Forwarder for
data import
Website
data
Call center
data
Customer
data
Splunk regex
builder and
data exports
SuperTag
integration for
real-time data
3rd party data
mining and
reporting

23. > Key Splunk advantages
§ Powerful data mining
– Structured and unstructured data
§ Easy sharing of insights
– Online dashboards and reports
§ Short project duration
– Quick implementation and 1st insights
§ Integration with other platforms
– Regex builder and data extracts
§ Low technology and resource costs
– Implementation and maintenance
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Contact us
cbartens@datalicious.com
Learn more
blog.datalicious.com
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25. Data > Insights > Action