Big data cloud computing
- 1. Dr Kumar Prasoon ( C.I.O Safeer Group)
- 7. In retail, all kinds of connected devices generate a flood of complex structured and unstructured data. POS Device (Point-of-sale Systems, used for receiving payments) Near Field Communication Devices (NFC’s) - the technology being used to establish radio communication between devices by touching them together or bringing them in very close proximity PDT Devices (Used for scanning barcodes, maintaining stock etc.) SOURCES OF DATA
- 8. RFID Chips Video Surveillance Systems Social Media Sites – Used in marketing of products, tracking customer behaviour, trends etc. Used for the purposes of automatically identifying and tracking tags attached to objects with video analytics that record store traffic patterns, employee-customer interactions, and customer- merchandise interactions (such as the dwell time around an end cap) SOURCES OF DATA…
- 9. USES • Monitoring. • Customer Trajectory. HELPS US IN • Promotions. • Shelf allocation. • Shelf life cycle management. • Product positioning CAMERA
- 10. SURVEY/LOYALTY USES • Customer Mind map(GPOMS) • Customer Demographics • Customer preference • Buying pattern • Customer feedback HELPS US IN • Promotions • Effective category management • Product assortment • Personalized customer service
- 12. PDT CAMERAS SURVEY/ LOYALTY CARD SAFEER MEDIA CASH COUNTER BIG DATA SMART DATA SOCIAL NETWORKIN G SITES Data Sources FLOW CHART Analysis
- 13. Supply Analytics Path to Purchase Customer Analytics Retail Analytics • Merchandising • Logistics • Marketing • E-Commerce • Behaviour Analytics • Purchase patterns from point of sale Retail Analytics
- 14. WHY BIG DATA IS NOT JUST AN IT CHALLENGE?
- 15. Travel and Tourism Healthcare Automotive Big Data Analytics Retail Analytics Security Traffic Management
- 16. Smart Data in Security • Prioritizing threats • Stopping crime in its tracks • Visualizing threats http://www.cargosmarton.com/wp-content/uploads/2012/11/data_security.jpg
- 17. Big Data uses the information that customers are already generating to provide travel companies with better more targeted and customized ( and ultimately more profitable) services and products Smart Data in Travel & Tourism http://www.bigdata.amadeus.com/
- 18. To gain new insight in patient care & early indications of disease Smart Data in Healthcare http://www.ibmbigdatahub.com/blog/industry-vertical-analysis-healthcare-and-big-data
- 19. Smart Data Analytics in Traffic Management To improve the everyday life entangled due to our most common problem of sticking in traffic
- 20. Smart Data in Automotive Industry •Optimizing supply chains by ensuring that all components, parts have adequate stock to meet the anticipated demand for replacement parts by predicting when they might fail, how many might fail and where • Analyzing vehicles in the field to predict/anticipate maintenance associated with specific vehicles • Monetizing gathered data by selling raw data to rental companies, insurance companies, public services providers, etc. and selling reworked and aggregated data to weather companies, web analysts, etc.
- 21. CLOUD TOOLS USED FOR ANALYTICS
- 22. Pentaho Pros(+) • Low Cost - Open Source Project • Dashboards and Visualization • Business Query Ad-hoc Reporting • Predictive Analytics Integration • Pixel Perfect Formatting • Application Integration API • Advanced Security Capabilities • Mobile Reporting App Cons(-) • Requires technical resources for implementation • Requires plug-ins to compete with commercial products • User interface will be less intuitive out of the box without customization
- 23. Pros(+) • Data Volume and Scale • Business Query Ad-hoc • Pixel Perfect Formatting • Scheduled Distribution • Dashboards and Visualization • Predictive Analytics Integration • Portal Integration API • Advanced SQL and Metadata • Administration Automation • Advanced Security Capabilities Cons(-) • Specialty BI tools lead in Visualizations • Specialty BI tools have a simplified user interface • Product works best with well defined data model
- 24. Pros(+) • Data Exploration • Advanced Data Visualizations • Intuitive End-User Interface • Web Based Report Development • Mash-ups non-structured data • High business user adoption Cons(-) • Data Scalability • Limited Scheduling/Distribution • Limited Production Reporting • Limited Integration APIs • Limited Stats Integration • Limited Administration Automation
- 25. Pros(+) • Excellent Dashboard Capabilities • Ease of use for data mashups • Little to no data modeling required • Rapid Deployment • Ease of Use for End User • Auto-detect Relationships • In-memory BI • Data Exploration Cons(-) • In-Memory Architecture Data Scalability • Limited direct Query Access • Limited Scheduling/Distribution • Limited Metadata/Object Reuse • Limited Integration APIs • Limited Administration Automation • Enterprise readiness and scalability • No Usages Stats integration