Big Data Innovation
- 1. Getting The Most Out Of Big Data Associate Professor Paul Hawking
- 2. “Can the amount of hype about Big Data be considered Big Data?”
- 3. What is Big Data?
- 4. Big Data is Not New Cox & Ellsworth “Data sets are generally quite large, taxing the capacities of main memory, local disk and even remote disk. We call this the problem of big data” 1997 1998 1999 2001 Masey “Big Data… and the Next Wave of Infrastress.” Bryson et al “Very powerful computers are a blessing to many fields of inquiry. They are also a curse; fast computations spew out massive amounts of data.” Laney 3D Data Management: Controlling Data Volume, Velocity, and Variety
- 5. Characteristics – V’s Volume Velocity Variety Voldemort Big Data
- 6. Voldemort – The dark side of Big Data
- 7. Data Sources Big Data Transactions Machines Humans
- 8. What is Big Data? Danah Boyd & Kate Crawford (Microsoft) Big data is “a cultural, technological, and scholarly phenomenon that rests on the interplay of: Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets. Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims. Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.
- 10. Why the increased interest?
- 11. The vendors Prediction: Customers will leverage existing vendors’ technologies
- 13. Business Intelligence Process 1 Identify business issue 2 Formulate business question 3 What information do I need 4 Where do I find the information 5 Retrieve information 6 Analyse Information 7 Report answers 8 Take actions
- 14. Goals of Big Data
- 15. Big Data Analysis Let’s act on it What is the best that can happen? What will happen next? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Reports Ad Hoc Reports Query Drilldown Alerts Statistical Analysis Forecasting Predictive Analysis Optimisation Degree of Intelligence Maturity Competitive Advantage Proactive Decision Making Reactive Decision Making
- 16. Leading Companies Treacy & Wiersema The Discipline of market Leaders
- 17. Core/Context Framework Core Engage Processes that create differentiation that wins customers Context Disengage All other processes
- 18. Big Data Value = Analysis + Context Wisdom Intelligence Knowledge Information Data New business strategies, opportunities Lifetime value of this customer and strategies to deploy to create loyalty What the company has purchased, what other products they may purchase A contact associated to a Company and all back orders A Contact
- 19. Measuring Success and Value Overall Success Implementation Success User Success Operational Success Business Success • Create a formal, continuous process for measuring success and value generated • Identify and measure results of each initiative • Establish realistic goals and expectations based on capability / maturity • On-time, • On-budget • User adoption • Usage tracking • User satisfaction • Productivity improvements • Process efficiency and effectiveness • Return on investment • Economic value add • Revenue increases • Cost Savings • Customer / corporate profits • Enables Business Strategy and Completive Advantage Value Created
- 20. Who?
- 21. Meta Data Management Master Data Management Data Quality Data Integration Beware Big Data
- 23. Topic: Organized by Paul Hawking Associate Professor SAP Academic Programs Director College of Business Telephone: +61-3-99194031 Mobile: +61-419301628 Email Paul.Hawking@vu.edu.au Speaker name: Email ID: UNICOM Trainings & Seminars Pvt. Ltd. contact@unicomlearning.com Paulhawking #SAPVU