How do you Decide which Data Provides the Best ROI?

How do we view Data as an Asset?

How much is too much when it comes to Collecting Data?

Information Capabilities Framework (ICF)

Credits: Albert Santiago, Alex, Arsalan Khan, Guneet Gill, and Maryam Moussavi

ICF 1 - Introduction
ICF 2 - Abstract
ICF 3 - Key Findings
ICF 4 - Recommendations
ICF 5 - Overview
ICF 6 -.Market Maturity
ICF 7 - Technology in Depth
ICF 8 - What ICF aims to do
ICF 9 - How NOT to use ICF
ICF 10 - Market Clock
ICF 11 - Hype Cycle
ICF 12 - Implementation Approach
ICF 13 - Adoption
ICF 14 - Magic Quadrant
ICF 15 - Deployment Risks
ICF 16 - Competitive Advantage
ICF 17 - Implementation Timeline
ICF 18 - Bottom Line
ICF 19 - Recommended Reading

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2 Takeaways from the 2018 Spring Meetings by the International Monetary Fund (IMF) and the World Bank

Every year the IMF and the World Bank hold a conference-style event that is referred to as the Spring Meetings. These Spring Meetings bring together central bankers, ministers of finance and development, private sector executives and academics to discuss global issues such as global economy, international development, and the world’s financial markets.

This year I had the opportunity to attend the 2018 Spring Meetings where discussions were held about threats and opportunities of technological changes as it affects global economies and policies. Here are 2 takeaways from the 2018 Spring Meetings focused on technology and innovation including some of my related articles:

  1. TECHNOLOGICAL ADVANCEMENTS AND JOBS
    •  Industrialization Paradigms
      • Typical Industrialization: Agriculture → Manufacturing → Services
      • Current Industrialization: Agriculture → Services
    • Impacts of Technology
      • Technological Changes → Job loss → Re-skill → New Jobs
      • Some jobs will never be recovered
      • The flow of technology and expertise doesn’t flow easily across countries
      • Even within countries, technological impacts are uneven causing inequality
      • A good balance between data privacy and business models is needed that benefits societies at a larger scale
      • Depending upon where innovation (internal or external) to the organizations is can impact society at different levels
      • A good balance of foundations and advance education is needed
      • Specialized knowledge can negatively impact holistic societal impacts
    • Artificial Intelligence (AI)
      • Dystopian Views: AI will take over most human activities and would rule over humans
      • Middle Ground Views: AI will augment and enhance human activities but never replace humans
      • Utopian Views: AI will take over most human activities that would free up time for humans to do other things
    • The Brave New World of Data
      • Data quality issues are borderless
      • Standard data definitions of economic data has to be agreed upon and used
      • Data is being used to build economic policies
      • Data is being used to create multinational economic blocs
      • Data is being used to assess the humming of the global economy
      • Data Standardization and Harmonization àData Transparency àData Accountability
  2. PARTNERSHIPS
    • For economic prosperity, no organization, country, region is an island in of itself
    • Bridges need to be created across, public, private, academic, non-profit and shareholders
    • Regulations are slow to adapt to technological advancements and can be too heavy-handed or light-touch if not properly understood by policymakers
    • Grassroots changes are affecting how governments function and adapt
    • Technology and innovation should have executive level consideration across all branches of government and not just a ministry or a few people

Bonus: IMF’s Innovation Lab (iLab)

IMF has created the iLab whose goal seems to be to look at how technology and innovation are affecting the global economy and economic policies in various countries.

Related Articles:

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  2. WHERE IS MY BIG DATA COMING FROM AND WHO CAN HANDLE IT
  3. 5 QUESTIONS TO ASK ABOUT YOUR INFORMATION
  4. 5 QUESTIONS TO ASK ABOUT PREDICTIVE ANALYTICS
  5. 5 QUESTIONS TO ASK ABOUT YOUR BIG DATA
  6. 5 QUESTIONS TO ASK ABOUT PRESCRIPTIVE ANALYTICS
  7. IDENTIFYING ORGANIZATIONAL MATURITY FOR DATA MANAGEMENT
  8. UNDERSTANDING AND APPLYING PREDICTIVE ANALYTICS
  9. 5 OBSERVATIONS ON BEING INNOVATIVE (AT AN ORGANIZATIONAL LEVEL)
  10. 5 OBSERVATIONS ON BEING INNOVATIVE (AT AN INDIVIDUAL LEVEL)
  11. HOW DO YOU COMMUNICATE?
  12. 5 QUESTIONS TO ASK ABOUT YOUR BUSINESS PROCESSES
  13. 75 QUESTIONABLE THOUGHTS ABOUT ORGANIZATIONAL TRANSFORMATION
  14. 35 CONCEPTS THAT AFFECT ORGANIZATIONAL TRANSFORMATION EFFORTS
  15. SPICE FOR BUSINESS TRANSFORMATION
  16. 5 QUESTIONS TO ASK ABOUT YOUR CULTURE
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