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Big Data Challenges
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The presentation discusses the significance and challenges of working with large data.
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Big Data Challenges
1.
> Small vs.
Big Data < What the heck? What does it all mean and how does it help me?
2.
> Smart data
driven marke5ng “Using data to widen the funnel” Media A8ribu5on & Modeling Op5mise channel mix, predict sales Targe5ng & Merchandising Increase relevance, reduce churn Tes5ng & Op5misa5on Remove barriers, drive sales Boos5ng ROI June 2012 © Datalicious Pty Ltd 2
3.
Twi8er @datalicious June
2012 © Datalicious Pty Ltd 3
4.
> 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
5.
Big data =
bo8lenecks June 2012 © Datalicious Pty Ltd 5
6.
> Big data
analy5cs bo8lenecks 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 June 2012 © Datalicious Pty Ltd 6
7.
> Power vs.
distributed compu5ng 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 7
8.
Big data =
hype? June 2012 © Datalicious Pty Ltd 8
9.
> Importance of
research experience The consumer decision process is changing from linear to circular. Considera5on set now grows during (online) (Online) Research research phase which increases importance of user experience during that phase June 2012 © Datalicious Pty Ltd 9
10.
> The consumer
data journey To transac5onal data To reten5on messages From suspect to prospect To customer Time Time From behavioural data From awareness messages June 2012 © Datalicious Pty Ltd 10
11.
> Single customer
view is key Website behavioural data Campaign response data + The whole is greater than the sum of its parts Customer profile data June 2012 © Datalicious Pty Ltd 11
12.
> Maximise iden5fica5on
points 160% 140% 120% 100% 80% 60% −−− Probability of idenAficaAon through Cookies 40% 20% 0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks June 2012 © Datalicious Pty Ltd 12
13.
> Tradi5onal single
customer view Website Vendor Reports and Repor5ng data data feed #1 dashboards data warehouse Call center Vendor Targeted Transac5on data data feed #2 campaigns data warehouse Customer Vendor Data import data data feed #3 (ETL) process June 2012 © Datalicious Pty Ltd 13
14.
> Tradi5onal single
customer view Challenge #3: Increasing number Website Vendor Reports and Repor5ng of (unstructured) data data feed #1 data warehouse dashboards data sources Challenge #1: Rigid database Call center Vendor Targeted data data feed #2 schema requires Transac5on campaigns data warehouse extensive planning and maintenance Challenge #2: Customer Data feeds require Vendor Data import data constant u3 data feed # pdates (ETL) process and maintenance June 2012 © Datalicious Pty Ltd 14
15.
> Splunk single
customer view SuperTag Splunk saved Website integra5on for searches and data real-‐5me data dashboards Splunk Splunk regex Call center Splunk instance Forwarder for builder and data on dedicated data import data exports AWS server 3rd party data 3rd party Customer mining and campaign data repor5ng execu5on June 2012 © Datalicious Pty Ltd 15
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
> 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
26.
Contact us
cbartens@datalicious.com Learn more blog.datalicious.com Follow us twi8er.com/datalicious June 2012 © Datalicious Pty Ltd 26
27.
Data > Insights
> Ac5on
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