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.

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. Cleveland, W. S. and McGill, R. “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods.”
   Journal of the American Statistical Association  ·  September 1984
7. DataCamp. The State of Data and AI Literacy.
   Industry report  ·  2026
8. Eckerson Group and BARC. Strategies for Driving Adoption and Usage with BI and Analytics: A Global Study.
   Industry study  ·  2022
9. Henke, N., Levine, J. and McInerney, P. “Analytics Translator: The New Must-Have Role.”
   Harvard Business Review  ·  2018
10. Infoxchange. Digital Technology in the Not-for-profit Sector Report: 10th Anniversary Edition.
    Sector report  ·  November 2025
11. 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
12. Nielsen, J. “Enhancing the Explanatory Power of Usability Heuristics.”
    Proceedings of ACM CHI ’94  ·  April 1994
13. 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
14. 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
15. 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
16. Tufte, E. R. The Visual Display of Quantitative Information. Second edition.
    Graphics Press  ·  2001
17. World Wide Web Consortium. Web Content Accessibility Guidelines (WCAG) 2.2.
    W3C Recommendation  ·  October 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.