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Friday, September 27, 2024

g-f(2)2958 Winning with AI: Data Quality, Team Diversity, and Leadership Oversight



genioux Fact post by Fernando Machuca and ChatGPT


Introduction:


In the MIT Sloan Management Review webinar "Fuel AI Success With the Right Data and the Right People," experts Roger W. Hoerl and Thomas C. Redman explore the crucial roles of data quality and team diversity in ensuring successful AI implementations. While the technical aspects of AI, such as algorithms, often take center stage, this webinar emphasizes the importance of selecting the right data and involving a diverse team with both technical and business expertise to enhance AI’s effectiveness. Hoerl and Redman argue that a strong leadership focus on data quality and problem definition is key to AI success, encouraging managers to take active ownership of the AI process and ensure it aligns with organizational goals.



genioux GK Nugget:


"AI success depends not just on technology but on selecting the right data and involving diverse, cross-functional teams to ensure meaningful outcomes." — Fernando Machuca and ChatGPT, September 27, 2024



genioux Foundational Fact:


The webinar highlights that AI initiatives can fail when organizations overlook the importance of high-quality, relevant data and the right team to manage it. Effective AI models require both the “right data”—meaning the most relevant and comprehensive data for the problem at hand—and the engagement of diverse team members who can ask critical questions, understand the business context, and align AI goals with organizational objectives. This leadership responsibility cannot be delegated to technical teams alone; managers must actively oversee the entire process.



The 10 Most Relevant genioux Facts:


  1. The Right Data Matters More Than Big Data: Success with AI is not about collecting vast amounts of data but ensuring that the data is directly relevant to the problem being solved.
  2. Data Quality Has Two Aspects: AI relies on both having the right data (relevant to the problem) and making sure that the data is right (accurate and timely).
  3. AI Needs Human Oversight: Managers should take ownership of AI initiatives, rather than leaving data selection and model building to technical teams. Leadership is critical to ensuring that the AI aligns with business goals.
  4. Statisticians Bring Unique Value: The tools and methods statisticians use, such as the design of experiments and statistical process control, can improve the quality of data used in AI, especially in selecting the right data and understanding variability.
  5. Team Diversity Drives Success: Successful AI teams combine technical skills (e.g., coding and data science) with business acumen, problem-solving, and statistical expertise to ensure robust solutions.
  6. Defining the Problem Clearly: Clear problem definition is the foundation of successful AI. Teams must understand the business challenge before selecting data or building models.
  7. Bias in Data Can Undermine AI: AI models trained on biased or incomplete data can produce biased results. Freedom from bias is essential in selecting training data to ensure fairness and accuracy.
  8. Model Development Is Just One Step: Developing a robust AI model requires more than just building the algorithm; it depends on having the right data, ongoing evaluation, and testing in real-world scenarios.
  9. The Role of Future Data: AI models must not only perform well on training data but also adapt to future, real-world data that may differ from the original data set, requiring continuous monitoring and adjustments.
  10. Managerial Responsibility Cannot Be Delegated: While technical experts are crucial, managers are ultimately responsible for the success or failure of AI initiatives. They must ask tough questions throughout the process to ensure alignment with strategic goals.



Conclusion:


The webinar stresses that achieving AI success requires more than just powerful algorithms or large data sets. It hinges on leadership, the selection of relevant data, and building diverse, technically proficient teams. By focusing on the right data and diverse expertise, organizations can ensure their AI systems deliver actionable, reliable insights aligned with business objectives. Leaders must take an active role, asking critical questions and overseeing AI projects to guarantee meaningful and lasting success in a rapidly evolving digital landscape.



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



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:


In the MIT Sloan Management Review webinar "Fuel AI Success With the Right Data and the Right People," experts Roger Hoerl and Tom Redman discuss the critical importance of data quality and team composition for successful AI implementations. While much focus is typically placed on technology and algorithms, the speakers emphasize that the right data and diverse teams are equally essential for AI’s effectiveness. They highlight that business leaders must take ownership of AI initiatives rather than delegating these responsibilities to technical teams.


The webinar outlines two key aspects of data quality: ensuring that the data is accurate and timely (the data is right) and selecting data that is directly relevant to the business problem at hand (the right data). The speakers also stressed the importance of diverse teams that include not only data scientists and technologists but also statisticians and business experts who can ask critical questions, understand variability in data, and align AI efforts with organizational goals.


Hoerl and Redman argue that AI models can only succeed if managers clearly define the business problem and ensure that the data collected is appropriate for addressing it. They recommend that leaders embrace a collaborative approach by combining technical expertise with domain knowledge and statistical skills to create well-rounded AI teams.


The webinar concludes by reiterating that leadership, data quality, and diverse, cross-functional teams are the key factors for AI success. Managers must actively engage in the AI process, ensuring that AI systems are built on a solid foundation of relevant data and aligned with strategic business objectives.


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/.



The categorization and citation of the genioux Fact post


Categorization


This genioux Fact post is classified as Bombshell Knowledge which means: The game-changer that reshapes your perspective, leaving you exclaiming, "Wow, I had no idea!"


Type: Bombshell Knowledge, Free Speech



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REFERENCES



“genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)2958, Fernando Machuca and ChatGPT, September 27, 2024, Genioux.com Corporation.


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