Download - Big Data Challenges
![Page 1: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/1.jpg)
> Small vs. Big Data < What the heck? What does it all mean and how does it help me?
![Page 2: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/2.jpg)
> 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”
![Page 3: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/3.jpg)
June 2012 © Datalicious Pty Ltd 3
Twi8er @datalicious
![Page 4: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/4.jpg)
> 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
![Page 5: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/5.jpg)
June 2012 © Datalicious Pty Ltd 5
Big data = bo8lenecks
![Page 6: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/6.jpg)
> 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
![Page 7: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/7.jpg)
> 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’.
![Page 8: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/8.jpg)
June 2012 © Datalicious Pty Ltd 8
Big data = hype?
![Page 9: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/9.jpg)
> 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
![Page 10: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/10.jpg)
> 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
![Page 11: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/11.jpg)
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
![Page 12: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/12.jpg)
> 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
![Page 13: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/13.jpg)
> 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
![Page 14: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/14.jpg)
> 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
![Page 15: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/15.jpg)
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
![Page 16: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/16.jpg)
![Page 17: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/17.jpg)
![Page 18: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/18.jpg)
![Page 19: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/19.jpg)
![Page 20: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/20.jpg)
![Page 21: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/21.jpg)
![Page 22: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/22.jpg)
![Page 23: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/23.jpg)
![Page 24: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/24.jpg)
![Page 25: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/25.jpg)
> 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
![Page 26: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/26.jpg)
June 2012 © Datalicious Pty Ltd 26
Contact us [email protected]
Learn more
blog.datalicious.com
Follow us twi8er.com/datalicious
![Page 27: Big Data Challenges](https://reader036.vdocuments.us/reader036/viewer/2022081323/54167d518d7f722f6c8b4ac4/html5/thumbnails/27.jpg)
Data > Insights > Ac5on