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Thesis: Knowledge Management Systems Thesis Studio: Fall 2009 Amber Krishan | MFA DT
Domain Knowledge management Wearable and computer vision technologies Competitive Advantage Imperative competitive Advantage Efficient information collection, processing & dispersion Embracing modern technologies Financial Consultancies
What is Knowledge Management? Information Collection from real world experiences Processing information into knowledge Contextual Knowledge Distribution ORGANIZATION WORLD
Why is It Important? KM = Competitive  Advantage ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Rough Information Ecosystem
What do consultants carry?
What is coming tomorrow?
What do I want to do? ,[object Object],[object Object]
Early Concept 1 – Bridging existing systems ,[object Object],Raw Data collection Processing raw data into knowledge Contextual Knowledge distribution
Early Concept 2 – The KM room ( Projected interactive knowledge systems) ,[object Object]
Next Steps Identify target organization (possibly Ernst & Young) Contact available consultants Conduct data gathering exercise with the consultants Identify needs and requirements Validate and narrow the scope Continue further research based on existing studies Further ideation Explore possible technologies early on

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Thesis Concept Km

  • 1. Thesis: Knowledge Management Systems Thesis Studio: Fall 2009 Amber Krishan | MFA DT
  • 2. Domain Knowledge management Wearable and computer vision technologies Competitive Advantage Imperative competitive Advantage Efficient information collection, processing & dispersion Embracing modern technologies Financial Consultancies
  • 3. What is Knowledge Management? Information Collection from real world experiences Processing information into knowledge Contextual Knowledge Distribution ORGANIZATION WORLD
  • 4.
  • 7. What is coming tomorrow?
  • 8.
  • 9.
  • 10.
  • 11. Next Steps Identify target organization (possibly Ernst & Young) Contact available consultants Conduct data gathering exercise with the consultants Identify needs and requirements Validate and narrow the scope Continue further research based on existing studies Further ideation Explore possible technologies early on