AIMIA Big Data Challenges Splunk
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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.
AIMIA Big Data Challenges Splunk
- 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. June 2012 © Datalicious Pty Ltd 2
- 3. June 2012 © Datalicious Pty Ltd 3 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’.
- 6. June 2012 © Datalicious Pty Ltd 6 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 June 2012 © Datalicious Pty Ltd 8 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 June 2012 10© Datalicious Pty Ltd App dow nload/access
- 11. > Traditional single customer view June 2012 © Datalicious Pty Ltd 11 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 June 2012 © Datalicious Pty Ltd 13 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 June 2012 © Datalicious Pty Ltd 23
- 24. June 2012 © Datalicious Pty Ltd 24 Contact us cbartens@datalicious.com Learn more blog.datalicious.com Follow us twitter.com/datalicious
- 25. Data > Insights > Action