Generating actionable consumer insights from analytics - Telekom R&D
Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.com
Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.comThank you to our sponsor & partners!Gold SponsorSupported byOrganised by
Generating actionable consumerinsights from analyticsShazri, researcher from email@example.com
talk roadmapinformation & data sources trendschallenges and roadblocks in big data andanalytics implementationopportunities toouse cases in health and telecommunication
information and data sources trendmore powerful tabs - increased data vol. perperson.more real time network elements reportingunstructured and structured relational sources
challenges and roadblocks in big data andanalytics implementation
challenges and roadblocks in big data andanalytics implementationdifferent db sources have differentowners, - to target higher level ofanalytics abstraction.interfacing with unstructuredsources speeddemand of output speedsecurity ?
Zoom in - SecurityThe following lists the security challenges in Big Data. The list was taken from Top Ten Big Data Security andPrivacy challenges, by Cloud Security Alliance.• Secure computations in distributed programming frameworks-Untrustworthy data mapper.• Security best practices for non-relational data stores-No in database security yet, now depends on middleware.• Secure data storage and transactions logs-No tier-ing strategies to differentiate type of data.• End-point input validation/filtering-Veracity, how do you make sure data is trustworthy.-many factor validation/verification.• Real-time security/compliance monitoring-what is anomalous in Big Data framework.-baseline signature?• Scalable and composable privacy-preserving data mining and analytics-some analytics results can be correlated with other external results that can infer identity.• Cryptographically enforced access control and secure communication-access must be encrypted.• Granular access control-Giving access to the right people, without being too troublesome.• Granular audits-What happened? When?• Data provenance-Meta data security and speed of processing.