2025 Publications

  1. DigitalDefynd, “30 interesting CDO quotes [2025],” DigitalDefynd Insights, 2025.
  2. OneData AI Insights, “Unlocking zettabytes of value: Business success in the age of information explosion,” 2025. [Online]. Available: https://onedata.ai/insights/200-zettabytes-of-data-by-2025/
  3. Team DigitalDefynd, “5 digital transformation in FMCG case studies [2025],” DigitalDefynd Insights, 2025. [Online]. Available: https://digitaldefynd.com/IQ/digital-transformation-in-fmcg/
  4. A. Vuocolo, “Experts offer advice on how to avoid ‘pilot purgatory’ with AI rollout,” Retail Brew, Jan. 16, 2025. [Online]. Available: https://www.retailbrew.com/stories/2025/01/16/experts-offer-advice-on-how-to-avoid-pilot-purgatory-with-ai-rollout

2024 Publications

  1. A. Alexis, “Bullish AI spenders report higher ROI rates,” CFO Dive, Dec. 10, 2024. [Online]. Available: https://www.cfodive.com/news/bullish-ai-spenders-report-higher-return-rates-ey/735071/
  2. A. Ali Kidwai, “Top 5 data analytics use cases in FMCG industry,” Polestar Solutions Blog, 2024. [Online]. Updated: Sep. 23, 2024. Available: https://www.polestarllp.com/blog/analytics-use-cases-fmcg-industry
  3. G. V. Hulme, “Machine learning deployments suffer high failure rates,” Digital CxO, May 6, 2024. [Online]. Available: https://digitalcxo.com/article/machine-learning-deployments-suffer-high-failure-rates/
  4. J. Lee, “A quick guide to model explainability,” Experian Insights Blog, Jan. 2024. [Online]. Available: https://www.experian.com/blogs/insights/model-explainability/
  5. J. Ryseff, B. F. De Bruhl, and S. J. Newberry, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI (RR-A2680-1), RAND Corporation, 2024. [Online]. Available: https://www.rand.org/pubs/research_reports/RRA2680-1.html
  6. R. Schmelzer and K. Walch, “Why most AI projects fail: 10 mistakes to avoid,” PMI Blog, Dec. 2024. [Online]. Available: https://www.pmi.org/blog/why-most-ai-projects-fail
  7. E. Siegel, “To deploy machine learning, you must manage operational change—Here is how UPS got it right,” Harvard Data Science Review, Jan. 2024. [Online]. Available: https://hdsr.mitpress.mit.edu/pub/2z4dnhds
  8. E. Siegel, “What leaders should know about measuring AI project value,” MIT Sloan Management Review, Feb. 7, 2024. [Online]. Available: https://sloanreview.mit.edu/article/what-leaders-should-know-about-measuring-ai-project-value/
  9. Talkdesk, “A Talkdesk consumer survey reveals the ethical considerations of AI in retail,” Talkdesk Blog, Jan. 18, 2024. [Online]. Available: https://www.talkdesk.com/blog/ethical-considerations-ai-retail/

2023 Publications

  1. G. Aue, P. Cafferata, R. Drapeko, M. Penwarden, and V. Sinha, “Scaling AI for success: Four technical enablers for sustained impact,” McKinsey Tech Forward Insights, Sep. 27, 2023.
  2. M. Berndtsson, A.-C. Jonsson, M. Carlsson, and T. Svahn, “A strategy for scaling advanced analytics,” Communications of the ACM, vol. 66, no. 12, pp. 29–31, 2023. [Online]. Available: https://doi.org/10.1145/3582075
  3. I. I. Bojinov, “Keep your AI projects on track,” Harvard Business Review, vol. 101, no. 6, pp. 53–59, Nov.–Dec. 2023.
  4. N. Bryant and L. Resmerita, “Foundational considerations in mitigating AI data risk,” IAPP News, Aug. 2023. [Online]. Available: https://iapp.org/news/a/foundational-considerations-in-mitigating-ai-data-risk
  5. Deloitte, “Many executives uncertain if their organizations have ethical standards for generative AI: State of ethics and trust in technology report,” Press Release, Oct. 2023. [Online]. Available: https://www2.deloitte.com/us/en/pages/about-deloitte/articles/press-releases/many-executives-uncertain-if-their-organizations-have-ethical-standards-for-generative-ai.html
  6. L. Dominguez, “P&G’s AI factory: Scaling data science innovations,” Consumer Goods Technology, Oct. 19, 2023. [Online]. Available: https://consumergoods.com/pg-investing-ai-scalability-eyeing-end-end-applications
  7. Evidently AI Team, “Accuracy, precision, and recall in machine learning: What’s the difference?,” Evidently AI Blog, 2023. [Online]. Accessed: Mar. 3, 2025. Available: https://www.evidentlyai.com/classification-metrics/accuracy-precision-recall
  8. K. Gibson, “5 ethical considerations of AI in business,” Harvard Business School Online Insights Blog, 2023. [Online]. Available: https://online.hbs.edu/blog/post/ethical-considerations-of-ai
  9. IBM Cloud Blog, “Why data governance is essential for enterprise AI,” Aug. 23, 2023. [Online]. Available: https://www.ibm.com/think/topics/data-governance-for-ai
  10. National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF) 1.0 (NIST AI 100-1), U.S. Dept. of Commerce, Jan. 2023. [Online]. Available: https://nvlpubs.nist.gov/nistpubs/AI/NIST.AI.100-1.pdf
  11. N. Pappu, “AI ethics 101: Comparing IEEE, EU, and OECD guidelines,” Zendata Blog, 2023. [Online]. Available: https://www.zendata.dev/post/ai-ethics-101
  12. M. Raza, “Explainable vs. interpretable artificial intelligence,” Splunk Blog, 2023. [Online]. Available: https://www.splunk.com/en_us/blog/learn/explainability-vs-interpretability.html
  13. A. Saxena, “Quote on cultural change in data-driven initiatives,” DigitalDefynd – 30 Interesting CDO Quotes, 2023.
  14. ScatterPie Solutions, “FMCG analytics case study: Improving data-driven decision making,” 2023. [Online]. Available: https://www.scatterpie.io/data-analytics-case-studies/fmcg-analytics-case-study/
  15. xMap Insights, “Leveraging digital analytics to boost your FMCG business in 2023,” xMap Blog, 2023. [Online]. Available: https://www.xmap.ai/blog/leveraging-digital-analytics-to-boost-your-fmcg-business-in-2023
  16. A. Zharovskikh, “AI in FMCG: Top use cases,” InData Labs Blog, Jun. 29, 2023. [Online]. Available: https://indatalabs.com/blog/ai-in-fmcg-top-use-cases

2022 Publications

  1. Allstate Identity Protection, “6 lessons from the Cambridge Analytica breach,” Allstate Identity Protection Business Insights, 2022. [Online]. Available: https://www.allstateidentityprotection.com/business/content-hub/6-lessons-from-the-cambridge-analytica-breach
  2. R. Bean and T. H. Davenport, Data and AI Leadership Executive Survey 2022: The Quest to Achieve Data-Driven Leadership. NewVantage Partners (Wavestone), 2022.
  3. Datadog, “How to monitor ML pipelines effectively,” 2022.
  4. A. Edquist, L. Grennan, S. Griffiths, and K. Rowshankish, “Data ethics: What it means and what it takes,” McKinsey Digital, Sep. 23, 2022. [Online]. Available: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/data-ethics-what-it-means-and-what-it-takes
  5. Gartner, Gartner Top Strategic Technology Trends for 2022, 2022.
  6. Google Cloud, “Continuous monitoring in ML on Google Cloud,” 2022.
  7. IBM Cloud Education, “What is model drift?,” IBM Cloud Learn Hub, 2022. [Online]. Available: https://www.ibm.com/think/topics/model-drift
  8. IDC, IDC FutureScape: Worldwide AI and Automation 2022 Predictions, 2022.

2021 Publications

  1. Accenture, “Logistics Innovation: Route Optimization with AI,” 2021.
  2. Azure ML, ML Model Registry Best Practices, 2021.
  3. R. Bean, “Why culture is the greatest barrier to data success,” MIT Sloan Management Review, 2021. [Online]. Available: https://sloanreview.mit.edu/article/why-culture-is-the-greatest-barrier-to-data-success/
  4. Deloitte, Retail Analytics and AI: Boosting Operational Efficiency, 2021.
  5. KPMG, KPMG Survey: Risk Management in AI Implementation, 2021.
  6. T. Mayor, “15 quotes and stats to help boost your data and analytics savvy,” MIT Sloan – Ideas Made to Matter, Mar. 2021.
  7. McKinsey, The State of AI in 2021: McKinsey Global Survey, 2021.
  8. F. Rossi, “Why you should hire a chief AI ethics officer,” World Economic Forum Agenda, Sep. 2021. [Online]. Available: https://www.weforum.org/stories/2021/09/artificial-intelligence-ethics-new-jobs/
  9. SAS, “How to align business and analytics teams for AI success,” 2021.
  10. World Economic Forum, Data-Driven Economies in 2025 and Beyond, 2021.
  11. L. Zhang, “End-to-end MLOps pipelines for scalable ML,” IEEE Cloud Computing, vol. 8, no. 3, pp. 45–58, 2021.

2020 Publications

  1. AWS, AWS Machine Learning Lens: Well-Architected Framework, 2020.
  2. BCG, Transforming the FMCG Supply Chain with AI, 2020.
  3. R. Bean, “Why culture is the greatest barrier to data success,” MIT Sloan Management Review, Sep. 30, 2020. [Online]. Available: https://sloanreview.mit.edu/article/why-culture-is-the-greatest-barrier-to-data-success/
  4. F. Cabitza, A. Campagner, and C. Balsano, “Bridging the ‘last mile’ gap between AI implementation and operation: ‘data awareness’ that matters,” Annals of Translational Medicine, vol. 8, no. 7, p. 501, 2020. [Online]. Available: https://doi.org/10.21037/atm.2020.03.63
  5. R. Duggal, “Enterprise AI adoption: Key roles and responsibilities,” 2020. [Online blog].
  6. IBM, Analytics in Action: IBM Case Studies, 2020.
  7. Ponemon Institute, Cost of a Data Breach Report, 2020.
  8. Prosci, Change Management Best Practices, 2020.
  9. U.S. Food and Drug Administration, Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan, 2020.

2019 Publications

  1. A. Cam, M. Chui, and B. Hall, “Global AI survey: AI proves its worth, but few scale impact,” McKinsey & Company Report, Nov. 2019.
  2. ISACA, Data Governance and Management: ISACA Insights, 2019.
  3. M. Kuhn and K. Johnson, Applied Predictive Modeling, Springer, 2019.

2018 Publications

  1. Adimo, “How will the GDPR impact FMCG brands, and how can they prepare?,” Adimo News, Feb. 21, 2018. [Online]. Available: https://www.adimo.co/news/2018-02-21-how-will-the-gdpr-impact-fmcg-brands-and-how-can-they-prepare
  2. P. Bisson, B. Hall, B. McCarthy, and K. Rifai, “Breaking away: The secrets to scaling analytics,” McKinsey & Company Insights, May 22, 2018.
  3. K. Carlsson, “Five best practices to scale data science across the enterprise,” Forrester Research Blog, Jun. 2018.
  4. J. Dastin, “Amazon scraps secret AI recruiting tool that showed bias against women,” Reuters News, Oct. 2018. [Online]. Available: https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG
  5. T. H. Davenport and R. Bean, “Big companies are embracing analytics, but most still don’t have a data-driven culture,” Harvard Business Review (Digital Article), Feb. 2018. [Online]. Available: https://hbr.org/2018/02/big-companies-are-embracing-analytics-but-most-still-dont-have-a-data-driven-culture
  6. J. Lu, A. Liu, J. Gama, G. Zhang, et al., “Learning under concept drift: A review,” IEEE Trans. on Knowledge and Data Engineering, vol. 31, no. 12, pp. 2346–2363, 2018.

2017 Publications

  1. ACM US Public Policy Council, “Statement on Algorithmic Transparency and Accountability,” 2017. [Online]. Available: Online Resource
  2. M. Asay, “85% of big data projects fail, but your developers can help yours succeed,” TechRepublic, Nov. 10, 2017. [Online]. Available: https://www.techrepublic.com/article/85-of-big-data-projects-fail-but-your-developers-can-help-yours-succeed/
  3. T. H. Davenport and J. Harris, Competing on Analytics: The New Science of Winning, Harvard Business Review Press, 2017.
  4. B. Marr, “The amazing ways Coca-Cola uses AI and big data to drive success,” Forbes, Sep. 18, 2017. [Online]. Available: https://www.forbes.com/sites/bernardmarr/2017/09/18/the-amazing-ways-coca-cola-uses-artificial-intelligence-ai-and-big-data-to-drive-success/

2016 Publications

  1. N. McAlone, “Why Netflix thinks its personalized recommendation engine is worth $1 billion per year,” Business Insider, Jun. 14, 2016. [Online]. Available: https://www.businessinsider.com/netflix-recommendation-engine-worth-1-billion-per-year-2016-6

2015 Publications

  1. D. Lazer and R. Kennedy, “What we can learn from the epic failure of Google flu trends,” WIRED, Oct. 1, 2015. [Online]. Available: https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/
  2. T. Saito and M. Rehmsmeier, “The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets,” PLOS ONE, vol. 10, no. 3, p. e0118432, 2015. [Online]. Available: https://doi.org/10.1371/journal.pone.0118432
  3. D. Sculley et al., “Hidden technical debt in machine learning systems,” in Advances in Neural Information Processing Systems (NIPS), 2015.

2013 Publications

  1. I. MacKenzie, C. Meyer, and S. Noble, “How retailers can keep up with consumers,” McKinsey & Company Insights (Retail), Oct. 2013.
  2. F. Provost and T. Fawcett, Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking, O’Reilly Media, 2013.

2012 Publications

  1. T. H. Davenport and D. J. Patil, “Data scientist: The sexiest job of the 21st century,” Harvard Business Review, vol. 90, no. 10, pp. 70–76, Oct. 2012.
  2. J. Kotter, Leading Change, Harvard Business Press, 2012.

2011 Publications

  1. E. Brynjolfsson, L. M. Hitt, and H. H. Kim, “Strength in numbers: How does data-driven decision making affect firm performance?,” in Proceedings of the 32nd Int. Conf. on Information Systems (ICIS 2011), Shanghai, China, Dec. 2011.
  2. S. LaValle, E. Lesser, R. Shockley, M. S. Hopkins, and N. Kruschwitz, “Big data, analytics and the path from insights to value,” MIT Sloan Management Review, vol. 52, no. 2, pp. 21–32, Winter 2011.

2007 Publications

  1. T. H. Davenport and J. G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.

2001 Publications

  1. D. Laney, “3D data management: Controlling data volume, velocity, and variety,” META Group (now Gartner), Feb. 2001.

2000 Publications

  1. P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, and R. Wirth, CRISP-DM 1.0: Step-by-Step Data Mining Guide, SPSS Inc./CRISP-DM Consortium, 2000. [Online]. Available: https://the-modeling-agency.com/crisp-dm.pdf
  2. C. Shearer, “The CRISP-DM model: The new blueprint for data mining,” Journal of Data Warehousing, vol. 5, no. 4, pp. 13–22, 2000.