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References

Every figure quoted on this site comes from a named source. They are listed here in full, so any claim can be checked.

Publication details are given rather than links. A citation with author, title, publisher and year stays findable for as long as the work exists; a URL does not. Current locations are held in Elliptica's reference library and are available on request.

SOURCES CITED  ·  COMPILED 6 AUGUST 2026
  1. Board Intelligence. Effective Board Reporting.
    Industry report  ·  2020
  2. Boston Consulting Group. Flipping the Odds of Digital Transformation Success.
    Industry report  ·  October 2020
  3. Boston Consulting Group. From Potential to Profit: Closing the AI Impact Gap. AI Radar.
    Industry report  ·  January 2025
  4. Boston Consulting Group and University of California, Riverside. Study of AI tool use and knowledge-worker productivity.
    Harvard Business Review  ·  March 2026
  5. Challapally, A., Pease, C., Raskar, R. and Chari, P. The GenAI Divide: State of AI in Business.
    MIT NANDA  ·  2025
  6. DataCamp. The State of Data and AI Literacy.
    Industry report  ·  2026
  7. Eckerson Group and BARC. Strategies for Driving Adoption and Usage with BI and Analytics: A Global Study.
    Industry study  ·  2022
  8. Henke, N., Levine, J. and McInerney, P. “Analytics Translator: The New Must-Have Role.”
    Harvard Business Review  ·  2018
  9. Infoxchange. Digital Technology in the Not-for-profit Sector Report: 10th Anniversary Edition.
    Sector report  ·  November 2025
  10. Koloski, D., Porter, C., Almand-Hunter, B., Gatchell, S. and Logan, V. “Data Literacy in Industry: High Time to Focus on Operationalization Through Middle Managers.”
    Harvard Data Science Review  ·  January 2025
  11. Rudin, C. “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.”
    Nature Machine Intelligence  ·  preprint at arXiv:1811.10154  ·  September 2019
  12. Suazo-Galdames, I. C., Saracostti, M. and Chaple-Gil, A. M. “Scientific Evidence and Public Policy: A Systematic Review of Barriers and Enablers for Evidence-Informed Decision-Making.”
    Frontiers in Communication  ·  July 2025
  13. Tabassi, E. Artificial Intelligence Risk Management Framework (AI RMF 1.0).
    National Institute of Standards and Technology (U.S.), NIST AI 100-1  ·  January 2023

Each of these is cited where it is used: against the six checks that make up decision mapping and against the evidence behind each service line. Where a figure on this site is attributed to two sources, both appear above. If you want the current location of any of these, or the underlying data behind a figure, ask and we will send it.