g-f(2)2957 The Right Data and The Right People, The Right Results: A Framework for Fueling AI Success

 

genioux Fact post by Fernando Machuca and Gemini

Introduction:

This webinar "Fuel AI Success With the Right Data and the Right People" emphasizes the vital role of data and people in achieving AI success. It challenges the common overemphasis on technology and algorithms, arguing that organizations often overlook the importance of identifying the right data and involving diverse perspectives.

genioux GK Nugget:

"High-quality data, comprised of both "right data" (relevant and comprehensive) and "data that is right" (accurate and unbiased), is the cornerstone of successful AI initiatives, necessitating strategic leadership and cross-functional collaboration."  — Fernando Machuca and Gemini, September 27, 2024

genioux Foundational Fact:

Progress in AI is often hindered by the lack of attention given to data quality, particularly in identifying and acquiring the right data for specific problems. This issue is compounded by a tendency to rely on readily available data without critically assessing its relevance and potential biases. To address this, a strategic framework emphasizing problem definition, the right data criteria, and data evaluation is crucial.

The 10 Most Relevant genioux Facts:

1. Data Quality's Dual Nature: Data quality involves both having the right data and ensuring that the data is accurate and unbiased.
2. The Right Data Framework: A five-step framework encompassing problem definition, right data criteria, training data assessment, model development, and deployment is crucial for successful AI projects.
3. Problem Definition's Importance: Clearly defining the problem, including the target population, is essential for identifying the right data needed.
4. Foundational Right Data Requirements: Six foundational requirements - relevance, completeness, comprehensiveness, freedom from bias, timeliness, and clear definitions - guide the assessment of training data.
5. Statistics' Role in AI: Statistics provides unique tools like experimental design, process control, and Bayesian methods to address data quality challenges and enhance model interpretability.
6. Basis for Inference Gap: AI's lack of domain knowledge and common sense can lead to inaccurate predictions and a gap in the basis for inference.
7. Statistical Twins: Developing statistical twins alongside AI models can enhance interpretability and provide a basis for understanding model predictions.
8. Managerial Responsibility: Managers are ultimately responsible for ensuring the use of the right data, and they should actively engage in the process instead of delegating it entirely to technical teams.
9. Technical Diversity: AI teams should embrace technical diversity, incorporating statisticians and other experts to augment the skills of data scientists and coders.
10. Proactive Questioning: Asking penetrating questions throughout the AI project lifecycle helps identify potential issues early and ensures the use of the right data.

Conclusion:

Achieving AI success requires a shift in focus from solely relying on technology and algorithms to prioritizing data quality and interdisciplinary collaboration. By understanding the importance of the right data, leveraging statistical tools, and fostering a culture of proactive questioning, organizations can develop AI solutions that are not only effective but also interpretable and grounded in real-world understanding.

REFERENCES

The g-f GK Context

Fuel AI Success With the Right Data and the Right People, MIT Sloan Management Review, YouTube channel, WEBINAR, September 26, 2024.

• 229 views  
• Runtime 0:57:39
• It takes a lot to build and deploy AI models that work well. But when organizations focus too much on the technology and algorithms, they often overlook several essential elements, putting their programs at risk. Managers increase the likelihood of AI success when they start with the right data to train and operate the model – and clear management accountability for that data. They know what questions to ask, and what answers to look for. And they make sure to build a team with diverse skills.

WEBINAR

Roger W. Hoerl, Thomas C. Redman, and Abbie Lundberg, Fuel AI Success With the Right Data and the Right People, MIT Sloan Management Review, September 26, 2024.

ABOUT THE AUTHORS

Roger W. Hoerl is the Brate-Peschel Professor of Statistics at Union College in Schenectady, N.Y., and coauthor of Leading Holistic Improvement With Lean Six Sigma 2.0. 

Thomas C. Redman is president of Data Quality Solutions and author of People and Data: Uniting to Transform Your Organization. 

Abbie Lundberg is editor-in-chief at MIT Sloan Management Review. She moderated the session.

MIT Sloan Management Review Presentation

Having the right data and people in place, and asking the right questions, can make or break your AI deployment.

___________________________________________________________________________________________________________________________

Related Reading

• R.W. Hoerl and T.C. Redman, “What Managers Should Ask About AI Models and Data Sets,” MIT Sloan Management Review, Dec. 19, 2023.
• T.C. Redman and R.W. Hoerl, “AI and Statistics: Perfect Together,” MIT Sloan Management Review, April 16, 2024.

It takes a lot to build and deploy AI models that work well. But when organizations put too much focus on the technology and the algorithms, they often overlook other essential elements, putting their programs at risk.

Business leaders can increase the likelihood that their AI programs succeed by assuming a greater role themselves. They need to start by identifying the right data to train and operate their AI models. They need to understand what questions to ask and what answers to look for. And they need to include the right people — not just technologists and data scientists, but a diverse set of roles with a diverse set of perspectives.

In this webinar, you will learn:

• What we mean by “the right data” — and why it is essential for success with AI.
• The questions managers must ask as models are developed and deployed.
• How statisticians can fill critical gaps on your AI team.
• How managers can build their own — and their organization’s — capabilities.

Classical Summary of the WEBINAR:

The YouTube video "Fuel AI Success With the Right Data and the Right People" emphasizes the crucial role of data and talent in achieving success with artificial intelligence (AI). It highlights the importance of having not just any data, but the right data that is relevant, high-quality, and diverse. The video underscores that AI models are only as good as the data they are trained on, and having a robust data strategy is essential for building effective AI solutions.

Moreover, the video stresses the significance of having the right people involved in the AI journey. It emphasizes the need for collaboration between data scientists, domain experts, and business leaders to ensure that AI initiatives align with strategic objectives and deliver tangible business value. It also highlights the importance of fostering a culture of experimentation and learning, where teams are encouraged to explore new ideas and approaches to AI development.

In essence, the video underscores that successful AI implementation requires a holistic approach that combines the right data with the right talent. By prioritizing data quality, fostering collaboration, and encouraging experimentation, organizations can unlock the full potential of AI and drive innovation and growth.

Roger W. Hoerl

Roger W. Hoerl is the Brate-Peschel Professor of Statistics at Union College in Schenectady, New York¹. He has a distinguished career in both academia and the private sector, with significant contributions to the field of statistics.

Educational Background:

• B.S. from Elizabethtown College
• M.S. and Ph.D. from the University of Delaware¹

Professional Experience:

• Roger Hoerl has a rich background in regression analysis, particularly in shrinkage estimators¹. 
• His work in the private sector deepened his appreciation for experimental design methods¹.
• He has recently focused on Big Data analytics, exploring how statistical engineering can provide effective strategies for tackling Big Data problems¹.

Academic Contributions:

• At Union College, he teaches a variety of courses, including introductory statistics, engineering statistics, design of experiments, regression analysis, and Big Data analytics¹.
• He has been promoted to Associate Professor upon receiving tenure in 2018 and has held the Brate-Peschel Professorship since 2023¹.

Publications and Research:

• Roger Hoerl has authored several influential books and articles, including "Leading Six Sigma: A Step-by-Step Guide Based on Experience with GE and Other Six Sigma Companies"².
• His research interests include statistical thinking, performance improvement, and the application of statistical methods to real-world problems³.

Roger W. Hoerl's work continues to impact the field of statistics, particularly in the areas of Big Data and statistical engineering. His dedication to teaching and research makes him a valuable asset to Union College and the broader academic community.

¹: [Union College](https://www.union.edu/mathematics/faculty-staff/roger-hoerl)

²: [Google Scholar](https://scholar.google.com/citations?user=c-UCZ4kAAAAJ)

³: [Union College Publications](https://www.union.edu/sites/default/files/mathematics/202107/publications.pdf)

Source: Conversation with Copilot, 9/30/2024

(1) Roger Hoerl | Mathematics - Union College. https://www.union.edu/mathematics/faculty-staff/roger-hoerl.

(2) ‪Roger Wesley Hoerl‬ - ‪Google Scholar‬. https://scholar.google.com/citations?user=c-UCZ4kAAAAJ.

(3) Publication List Roger W. Hoerl July, 2021 - Union College. https://www.union.edu/sites/default/files/mathematics/202107/publications.pdf.

(4) Roger W. Hoerl - Union College. https://www.union.edu/sites/default/files/mathematics/202311/hoerl-cv.pdf.

Thomas C. Redman

Thomas C. Redman, also known as "the Data Doc," is the President of Data Quality Solutions¹². He is a renowned innovator, advisor, and teacher in the field of data quality and analytics¹².

Educational and Professional Background:

• Dr. Redman has a Ph.D. in Statistics and has dedicated his career to improving data quality and helping organizations leverage data for better decision-making¹².
• He was the first to extend quality principles to data and information in the late 1980s¹.
• Over the years, he has developed a comprehensive set of tools, techniques, and roadmaps that enable organizations to achieve significant improvements in data quality¹.

Career Highlights:

• As the President of Data Quality Solutions, Dr. Redman assists start-ups, multinational corporations, senior executives, chief data officers, and leaders in navigating their paths to data-driven futures²³.
• He emphasizes the importance of data quality, organizational structure, and analytics in achieving these goals²³.

Publications:

• Dr. Redman is the author of several influential books, including "People and Data: Uniting to Transform Your Organization"⁵. This book explores the critical relationship between non-data professionals and data, highlighting how their collaboration can unlock an organization's full potential⁵.

Contributions to the Field:

• Dr. Redman is known for his visionary approach to the data landscape, combining deep expertise in data quality, data science, and analytics⁴.
• He has been instrumental in helping organizations tackle tough issues such as departmental silos and upskilling the workforce to maximize the value of their data⁹.

Thomas C. Redman's work continues to shape the field of data quality and analytics, making him a pivotal figure in helping organizations transform into data-driven entities.

¹: [eLearningCurve](https://ecm.elearningcurve.com/Tom-Redman-s/127.htm)

²: [DATAVERSITY](https://www.dataversity.net/contributors/thomas-redman/)

³: [Forbes](https://www.forbes.com/sites/thomascredman/)

⁴: [Data Quality Solutions](https://www.dataqualitysolutions.com/meet-the-data-doc)

⁵: [Amazon](https://www.amazon.co.uk/People-Data-Uniting-Transform-Business/dp/1398610879)

⁹: [Kogan Page](https://www.koganpage.com/hr-learning-development/people-and-data-9781398610828)

Source: Conversation with Copilot, 9/27/2024

(1) Tom Redman - eLearningCurve. https://ecm.elearningcurve.com/Tom-Redman-s/127.htm.

(2) Thomas Redman - DATAVERSITY. https://www.dataversity.net/contributors/thomas-redman/.

(3) Thomas C. Redman - Forbes. https://www.forbes.com/sites/thomascredman/.

(4) People and Data: Uniting to Transform Your Business. https://www.amazon.co.uk/People-Data-Uniting-Transform-Business/dp/1398610879.

(5) Meet "the Data Doc" — Data Quality Solutions. https://www.dataqualitysolutions.com/meet-the-data-doc.

(6) People and Data | Kogan Page. https://www.koganpage.com/hr-learning-development/people-and-data-9781398610828.

(7) People and Data: Uniting to Transform Your Business. https://www.amazon.co.uk/People-Data-Uniting-Transform-Business/dp/1398610828.

(8) People and Data: Uniting to Transform Your Business - Skillsoft. https://www.skillsoft.com/book/people-and-data-uniting-to-transform-your-business-d0819511-7eaa-430f-9e04-d9875342e5df.

(9) People and Data: Uniting to Transform Your Business. https://www.amazon.com/People-Data-Strategies-Business-Performance/dp/1398610828.

Abbie Lundberg

Abbie Lundberg is a distinguished editorial leader, content strategist, and professional speaker with over 30 years of experience in reporting and commenting on tech-enabled business strategy, leadership, transformation, and change²³. She currently serves as the Editor-in-Chief at MIT Sloan Management Review².

Educational and Professional Background:

• Abbie Lundberg has a rich background in journalism and editorial leadership, having founded **Lundberg Media LLC** in 2009 to provide insights into tech-enabled business strategy and transformation for C-level audiences¹.
• She has been instrumental in expanding MIT Sloan Management Review's influence as a digital-first, integrated media brand².

Career Highlights:

• As Editor-in-Chief, she leads the editorial strategy and oversees both print and digital operations at MIT Sloan Management Review².
• Her work focuses on connecting the world's leaders and managers with the trends, systems, and theories that power successful organizations².

Publications and Contributions:

• Abbie Lundberg has authored numerous articles and research papers on topics related to business strategy, leadership, and digital transformation².
• She is known for her ability to moderate high-level discussions and sessions, bringing valuable insights and fostering meaningful conversations among industry leaders².

Abbie Lundberg's extensive experience and dedication to her field make her a pivotal figure in the world of business journalism and editorial leadership.

¹: [PR Newswire](https://www.prnewswire.com/news-releases/mit-sloan-management-review-names-abbie-lundberg-its-next-editor-in-chief-301345633.html)

²: [MIT Sloan Management Review](https://sloanreview.mit.edu/abbie-lundberg/)

³: [Lundberg Media](http://lundbergmedia.com/about)

Source: Conversation with Copilot, 9/27/2024

(1) Abbie Lundberg - MIT Sloan Management Review. https://sloanreview.mit.edu/abbie-lundberg/.

(2) About Abbie. - Lundberg Media. http://lundbergmedia.com/about.

(3) MIT Sloan Management Review names Abbie Lundberg its next editor in chief. https://www.prnewswire.com/news-releases/mit-sloan-management-review-names-abbie-lundberg-its-next-editor-in-chief-301345633.html.

(4) About Abbie. - Lundberg Media. https://bing.com/search?q=Abbie+Lundberg+biography.

(5) Abbie Lundberg - Metis Strategy. https://www.metisstrategy.com/interview/abbie-lundberg/.

(6) Into the Fray | Abbie Lundberg - MIT Sloan Management Review. https://sloanreview.mit.edu/article/into-the-fray/.

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REFERENCES

“genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)2957, Fernando Machuca and Gemini, September 27, 2024, Genioux.com Corporation.

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