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The presentation discusses the significance and challenges of working with large data.
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> Small vs. Big Data < What the heck? What does it all mean and how does it help me?
> Smart data driven marke5ng
June 2012 © Datalicious Pty Ltd 2
Media A8ribu5on & Modeling
Op5mise channel mix, predict sales
Tes5ng & Op5misa5on Remove barriers, drive sales
Boos5ng ROI
Targe5ng & Merchandising Increase relevance, reduce churn
“Using data to widen the funnel”
June 2012 © Datalicious Pty Ltd 3
Twi8er @datalicious
> Wikipedia: Big data In informaAon technology, big data consists of datasets that grow so large that they become awkward to work with using on-‐hand database management tools. DifficulAes include capture, storage, search, sharing, analyAcs, and visualizing. This trend conAnues 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 zeMabytes of data.
June 2012 © Datalicious Pty Ltd 4
June 2012 © Datalicious Pty Ltd 5
Big data = bo8lenecks
> Big data analy5cs bo8lenecks
June 2012 © Datalicious Pty Ltd 6
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 boMleneck: I/O
> Power vs. distributed compu5ng
June 2012 © Datalicious Pty Ltd 7
Adding more supercomputers is difficult as they are complex and expensive but adding machines to a distributed compuAng network is fairly cheap and ‘easy’.
June 2012 © Datalicious Pty Ltd 8
Big data = hype?
> Importance of research experience
June 2012 © Datalicious Pty Ltd 9
The consumer decision process is changing from linear to circular.
Considera5on set now grows during (online) research phase which increases importance of user experience during that phase
(Online) Research
> The consumer data journey
June 2012 © Datalicious Pty Ltd 10
To reten5on messages To transac5onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
Campaign response data
> Single customer view is key
June 2012 © Datalicious Pty Ltd 11
Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
> Maximise iden5fica5on points
20%
40%
60%
80%
100%
120%
140%
160%
0 4 8 12 16 20 24 28 32 36 40 44 48
Weeks
−−− Probability of idenAficaAon through Cookies
June 2012 12 © Datalicious Pty Ltd
> Tradi5onal single customer view
June 2012 © Datalicious Pty Ltd 13
Vendor data feed #2
Website data
Call center data
Customer data
Reports and dashboards
Vendor data feed #1
Vendor data feed #3
Targeted campaigns
Transac5on data warehouse
Repor5ng data warehouse
Data import (ETL) process
> Tradi5onal single customer view
June 2012 © Datalicious Pty Ltd 14
Vendor data feed #2
Website data
Call center data
Customer data
Reports and dashboards
Vendor data feed #1
Vendor data feed #3
Targeted campaigns
Transac5on data warehouse
Repor5ng 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
Splunk instance on dedicated AWS server
> Splunk single customer view
June 2012 © Datalicious Pty Ltd 15
3rd party campaign execu5on
Splunk saved searches and dashboards
Splunk Forwarder for data import
Website data
Call center data
Customer data
Splunk regex builder and data exports
SuperTag integra5on for real-‐5me data
3rd party data mining and repor5ng
> Key Splunk advantages § Powerful data mining – Structured and unstructured data
§ Easy sharing of insights – Online dashboards and reports
§ Short project duraAon – Quick implementaAon and 1st insights
§ IntegraAon with other plaeorms – Regex builder and data extracts
§ Low technology and resource costs – ImplementaAon and maintenance
June 2012 © Datalicious Pty Ltd 25
June 2012 © Datalicious Pty Ltd 26
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Data > Insights > Ac5on