Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

EDF2013: Selected Talk: Bryan Drexler: The 80/20 Rule and Big Data


Published on

Selected Talk by Bryan Drexler, Vice-President, EMEA, at the European Data Forum 2013, 10 April 2013 in Dublin, Ireland: The 80/20 Rule and Big Data

Published in: Technology
  • Be the first to comment

EDF2013: Selected Talk: Bryan Drexler: The 80/20 Rule and Big Data

  1. 1. European Data Forum 2013: The 80/20 Rule and Big DataBryan Drexler, Vice-President, EMEAApril 10, 2013
  2. 2. The 80/20 RuleOur Observation: The 80/20 rule says that 80% of the revenue comes from 20% of a company’s customer base.Our Question: in the Era of Big Data, does the 80/20 Rule Still Apply?©2013 Jaspersoft CorporationProprietary and Confidential 2
  3. 3. Big Data Definition Data that’s an order of magnitude greater than data you’re accustomed to. -Gartner analyst Doug Laney Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. -Wikipedia ‘3 Vs’  Volume  Velocity  Variety©2013 Jaspersoft CorporationProprietary and Confidential 3
  4. 4. Big Data Analysis in the Real World Major Telco Did a Pay-Per-View event cannibalize the use of another video service? Mobile phone data plan What percentage of the monthly data plan was used up by this Pay-Per-View event?©2013 Jaspersoft CorporationProprietary and Confidential 4
  5. 5. Jaspersoft Big Data Survey General  July 2012  631 completed responses Demographics for filtered responses  80% technical audience with 59% developers  75% responses from 6 industries: Hi-Tech, Financial Services, Pharma/Healthcare/Biotech, Business Services, Government, Telco  Embedded internal 44%, standalone 33%, embedded external 22%, Cloud 11%©2013 Jaspersoft CorporationProprietary and Confidential 5
  6. 6. Jaspersoft Big Data Survey -- Results Big Data deployment  62% already deployed, in development or planning to in next 12 months Volume  86% need Terabytes of data Variety  Enterprise apps most common source, then machine-generated, then text  53% web logs  41% e-commerce data  36% financials  35% CRM Velocity  46% need real-time or near real-time©2013 Jaspersoft CorporationProprietary and Confidential 6
  7. 7. Who is looking for Big Data Analytics? Web/E-Commerce/Internet  Insurance  Integrated website analytics  Customer segmentation Retail  Service response optimization  Competitive pricing  Financial Services  Customer segmentation  Fraud detection analytics  Predictive buying behavior  Risk modeling & analysis  Real-time recommendation generation  Marketing campaign management  Marketing campaign optimization  Manufacturing Government  Inventory optimization   Defense intelligence analysis Threat analytics  Utilities  Customer experience analytics IT  Service quality optimization   Network data analytics Operational intelligence  Media & Cable  Customer satisfaction analytics Healthcare & Pharmaceutical  Truck dispatch optimization  Drug discovery  Marketing performance analytics  Gene/Protein/Molecule sequencing and correlations  Legal Telecommunications  Intellectual property management  Churn / attrition analysis  Regulatory compliance  Customer experience analytics©2013 Jaspersoft Corporation 7Proprietary and Confidential 7
  8. 8. How Can Big Data be Analyzed?Approach Data Exploration Operational Reporting AnalyticsUse Case For data analysts and data scientists For executives and operational For data analysts and operational who want to discover real-time managers who want summarized, managers who want to analyze historical patterns as they emerge from their pre-built daily reports on Big Data trends based upon pre-defined Big Data content content questions in their Big Data contentLatency Low Medium HighBig Data HBase, NoSQL, Analytic DBMS Hive, NoSQL, Analytic DBMS Hadoop, NoSQL, Analytic DBMSConnectivity Native Native, SQL ETLArchitecture Multi-Dimensional Multi-Dimensional Analysis Analysis Reports & Dashboards In-Memory Engine OLAP Engine BI Platform BI Platform BI Platform Native Native SQL ETL BIG BIG Data BIG DATA DATA Mart DATA ©2013 Jaspersoft Corporation. Proprietary and Confidential
  9. 9. Jaspersoft Customer examples Hadoop: Campaign effectiveness metrics on >10 TB Hadoop: Nightly Dashboards to optimize gaming experience MongoDB: Self-Serve embedded visualization & analytics for 2 TB of media management data. Vertica: Ad Hoc access to 8 TB for marketing analytics Vertica: OLAP access to billions of records for Intrusion Detection System©2013 Jaspersoft CorporationProprietary and Confidential 9
  10. 10. The 80/20 RuleOur Observation: The 80/20 rule says that 80% of the revenue comes from 20% of a company’s customer base.Our Question: in the Era of Big Data, does the 80/20 Rule Still Apply?Our Answer: More Than Ever… The volume, variety, and velocity of data and new ways of analyzing them are creating opportunities for greater insights and improved partnerships between customers and clients.©2013 Jaspersoft CorporationProprietary and Confidential 10
  11. 11. Additional Resources Big Data books©2013 Jaspersoft CorporationProprietary and Confidential 11
  12. 12. Thank You and Q & A Contact Information: Bryan Drexler Vice-President, EMEA (0)1 442 83 62©2013 Jaspersoft CorporationProprietary and Confidential 12