Showing posts with label Data Culture. Show all posts
Showing posts with label Data Culture. Show all posts

Thursday, October 31, 2024

g-f(2)3149 Data Collaboration Decoded: MIT SMR's Guide to Federated Machine Learning

 


g-f Fishing on the AI Revolution (10/31/2024)


genioux Fact post by Fernando Machuca and Perplexity

Categorization:

  • Type: Bombshell Knowledge, Free Speech
  • Category: g-f Lighthouse of the Big Picture of the Digital Age
  • The Power Evolution Matrix:
    • Foundational pillar: g-f Fishing
    • Power layers: Strategic Insights, Technology & Innovation



Introduction


The article "Know Your Data to Harness Federated Machine Learning" from MIT Sloan Management Review illuminates the transformative potential of federated machine learning in the AI landscape. This innovative approach enables organizations to collaboratively enhance their AI models while maintaining data privacy and ownership. The authors, José Parra-Moyano, Karl Schmedders, and Maximilian Werner, provide crucial insights into how companies can leverage this technology to gain a competitive edge by accessing diverse, high-quality data sets without compromising individual or organizational privacy concerns



genioux GK Nugget


"Federated learning transforms data collaboration, enabling organizations to enhance AI performance through privacy-preserving partnerships, unlocking new competitive advantages and business models." — Fernando and Perplexity, October 31, 2024



genioux Foundational Fact


Federated learning allows organizations to train AI models using data from multiple, decentralized sources without sharing raw data. Combined with encryption methods, this technique enables cross-industry collaborations and even partnerships between competitors, leading to improved AI performance and new data monetization opportunities. Success in federated learning hinges on understanding one's own data status and finding complementary partners to achieve rich, comprehensive datasets.



The 10 Most Relevant genioux Facts


  1. Federated learning sends the algorithm to the data rather than the data to the algorithm, preserving privacy.
  2. Cross-industry collaborations, like Zurich Insurance and Orange, can lead to significant improvements in AI predictions.
  3. Federated learning facilitates cooperation within industries, including between direct competitors.
  4. The approach enables new data-driven business models, such as shared algorithm ownership based on data contributions.
  5. Horizontal federated learning increases the number of samples, while vertical federated learning increases the number of features per sample.
  6. Organizations must assess their data as poor, vertical, horizontal, or rich to determine suitable collaboration strategies.
  7. Vertical data benefits from cross-industry partnerships, while horizontal data is enhanced through same-industry collaborations.
  8. Technical challenges include data structuring and label synchronization across organizations.
  9. Employee buy-in and active engagement are crucial for successful federated learning implementations.
  10. Federated learning presents opportunities for data monetization while maintaining data ownership.



Conclusion


Federated machine learning offers a powerful solution to the challenge of accessing diverse, high-quality data for AI training while respecting privacy concerns. By understanding their data status and identifying complementary partners, organizations can leverage this approach to enhance AI performance, create new business models, and gain competitive advantages in the digital age. As the technology matures, federated learning is poised to become an essential tool for organizations seeking to maximize the value of their data assets while navigating privacy regulations and ethical considerations.



g-f(2)3149: The Juice of Golden Knowledge


Concentrated wisdom for immediate application


"Federated learning empowers organizations to enhance AI performance through privacy-preserving data collaborations. By understanding their data status—poor, vertical, horizontal, or rich—companies can identify complementary partners, either cross-industry or within their sector, to create comprehensive datasets. This approach not only improves AI predictions but also enables new data monetization opportunities while maintaining data ownership. Success hinges on addressing technical challenges, ensuring employee buy-in, and strategically selecting partners based on data complementarity." — Fernando and Perplexity, October 31, 2024



GK Juices or Golden Knowledge Elixirs


REFERENCES

The g-f GK Context


José Parra-Moyano, Karl Schmedders, and Maximilian Werner, Know Your Data to Harness Federated Machine Learning, MIT Sloan Management Review, October 16, 2024.



ABOUT THE AUTHORS


José Parra-Moyano is a professor of Digital Strategy at the International Institute for Management Development (IMD Business School) in Switzerland. His research focuses on the management and economics of data and privacy, with a special focus on how organizations can use data analysis techniques and AI to increase their competitiveness. He is an award-winning teacher, whose research has been published in top-tier academic journals.


Karl Schmedders is a professor of finance at the International Institute for Management Development (IMD) in Lausanne, Switzerland. He is an expert in the field of finance and contributes to research on innovative topics such as federated machine learning and its applications in the financial sector. Schmedders collaborates with other scholars to explore how organizations can leverage new technologies to gain competitive advantages in the digital age. His work focuses on the intersection of finance, technology, and data-driven decision-making, particularly in the context of AI and machine learning applications in business and finance.


Maximilian Werner is an associate director and research fellow with the Venture Asset Management initiative at the International Institute for Management Development (IMD) in Lausanne, Switzerland. His work focuses on innovative financial technologies and strategies, particularly in the realm of AI and machine learning applications in business and finance. Werner collaborates with other scholars to explore cutting-edge topics such as federated machine learning and its potential to transform data utilization in various industries. His research contributes to the understanding of how organizations can leverage new technologies to gain competitive advantages in the digital age.



Classical Summary of the Article


The article "Know Your Data to Harness Federated Machine Learning" discusses the transformative potential of federated learning in enhancing AI performance while preserving data privacy. This approach allows organizations to train AI models using data from multiple, decentralized sources without sharing raw data.


Key points of the article include:


  1. Federated learning enables cross-industry collaborations, as demonstrated by Zurich Insurance Group and Orange, leading to significant improvements in AI predictions.
  2. The technique facilitates cooperation within industries, even between competitors, creating new data-driven business models.
  3. Organizations must assess their data as poor, vertical, horizontal, or rich to determine suitable collaboration strategies.
  4. Vertical data benefits from cross-industry partnerships, while horizontal data is enhanced through same-industry collaborations.
  5. Technical challenges include data structuring and label synchronization across organizations.
  6. Employee buy-in and active engagement are crucial for successful federated learning implementations.
  7. The article emphasizes that to harness federated learning effectively, organizations need to understand their own data status and find complementary partners. This approach not only improves AI performance but also presents opportunities for data monetization while maintaining data ownership.


The article emphasizes that to harness federated learning effectively, organizations need to understand their own data status and find complementary partners. This approach not only improves AI performance but also presents opportunities for data monetization while maintaining data ownership.



José Parra-Moyano


José Parra-Moyano is a distinguished Professor of Digital Strategy at the International Institute for Management Development (IMD Business School) in Switzerland. His academic and professional journey is marked by a deep focus on the management and economics of data and privacy, and how firms can create sustainable value in the digital economy³.


José holds a Bachelor's degree in Economic Science from the University of Zurich². His research has been published in top-tier academic and practitioner journals, highlighting his contributions to the field of digital strategy⁴. He is also an award-winning teacher, recognized for his innovative approach to education and his ability to inspire students¹.


In addition to his academic achievements, José is an entrepreneur. He founded his own successful startup and has been actively involved in the World Economic Forum’s Global Shapers Community of young leaders¹. His work emphasizes the importance of ethical considerations in the use of digital technologies, particularly in the areas of data privacy and management³.


José Parra-Moyano's contributions to the field of digital strategy and his commitment to ethical practices make him a prominent figure in the digital economy landscape.


¹: [UZH Blockchain Center](https://www.blockchain.uzh.ch/members/jose-parra-moyano/)

²: [Profile - José Parra-Moyano](https://www.parramoyano.com/html/profile.html)

³: [IMD Business School](https://www.imd.org/faculty-profile/jose-parra-moyano/)

⁴: [José Parra-Moyano](https://www.parramoyano.com/)


Source: Conversation with Copilot, 9/27/2024


(1) José Parra Moyano - IMD Business School. https://www.imd.org/faculty-profile/jose-parra-moyano/.

(2) Profile - José Parra-Moyano. https://www.parramoyano.com/html/profile.html.

(3) José Parra-Moyano. https://www.parramoyano.com/.

(4) Prof. Dr. José Parra Moyano - UZH Blockchain Center. https://www.blockchain.uzh.ch/members/jose-parra-moyano/.



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



Additional Context:


This genioux Fact post is part of:
  • Daily g-f Fishing GK Series
  • Game On! Mastering THE TRANSFORMATION GAME in the Arena of Sports Series



g-f Lighthouse Series Connection



The Power Evolution Matrix:



Context and Reference of this genioux Fact Post



“genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)3149, Fernando Machuca and Perplexity, October 31, 2024, Genioux.com Corporation.


The genioux facts program has established a robust foundation of over 3148 Big Picture of the Digital Age posts [g-f(2)1 - g-f(2)3148].



Monthly Compilations Context October 2024

  • Strategic Leadership evolution
  • Digital transformation mastery


genioux GK Nugget of the Day


"genioux facts" presents daily the list of the most recent "genioux Fact posts" for your self-service. You take the blocks of Golden Knowledge (g-f GK) that suit you to build custom blocks that allow you to achieve your greatness. — Fernando Machuca and Bard (Gemini)



Power Matrix Development


September 2024

  • g-f(2)3003 Strategic Leadership in the Digital Age: September 2024’s Key Facts
  • g-f(2)3002 Orchestrating the Future: A Symphony of Innovation, Leadership, and Growth
  • g-f(2)3001 Transformative Leadership in the g-f New World: Winning Strategies from September 2024
  • g-f(2)3000 The Wisdom Tapestry: Weaving 159 Threads of Digital Age Mastery
  • g-f(2)2999 Charting the Future: September 2024’s Key Lessons for the Digital Age


August 2024

  • g-f(2)2851 From Innovation to Implementation: Mastering the Digital Transformation Game
  • g-f(2)2850 g-f GREAT Challenge: Distilling Golden Knowledge from August 2024's "Big Picture of the Digital Age" Posts
  • g-f(2)2849 The Digital Age Decoded: 145 Insights Shaping Our Future
  • g-f(2)2848 145 Facets of the Digital Age: A Month of Transformative Insights
  • g-f(2)2847 Driving Transformation: Essential Facts for Mastering the Digital Era


July 2024


June 2024


May 2024

g-f(2)2393 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (May 2024)


April 2024

g-f(2)2281 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (April 2024)


March 2024

g-f(2)2166 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (March 2024)


February 2024

g-f(2)1938 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (February 2024)


January 2024

g-f(2)1937 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (January 2024)


Recent 2023

g-f(2)1936 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (2023)



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Wednesday, July 31, 2024

g-f(2)2687 Ethical AI in Action: Scotiabank's Blueprint for Responsible Innovation

 


genioux Fact post by Fernando Machuca and Perplexity



Introduction by Fernando and Perplexity:


Welcome to "g-f(2)2687 Ethical AI in Action: Scotiabank's Blueprint for Responsible Innovation," a pivotal genioux Fact that illuminates the path to ethical AI implementation in the corporate world. In the rapidly evolving landscape of the g-f New World, where artificial intelligence is reshaping industries and business practices, understanding how to develop and maintain an ethical AI culture is crucial for those aiming to win the g-f Transformation Game (g-f TG).


This genioux Fact, inspired by Scotiabank's groundbreaking approach as detailed in the MIT Sloan Management Review, offers invaluable insights into building a robust ethical framework for AI adoption. It demonstrates how a major financial institution has successfully integrated AI technologies while prioritizing ethical considerations, employee engagement, and stakeholder trust.


The importance of "g-f(2)2687" for winning the g-f Transformation Game cannot be overstated. As players in this game, business leaders and organizations must recognize that ethical AI is not just a compliance issue, but a strategic imperative. Scotiabank's blueprint provides a roadmap for responsible innovation, showing how to harness AI's transformative power while maintaining integrity and building trust.


This genioux Fact offers practical strategies for developing an ethical AI culture, including establishing comprehensive governance structures, fostering cross-functional collaboration, and maintaining transparency. By mastering these concepts, players in the g-f TG can position themselves at the forefront of responsible AI adoption, gaining a significant competitive advantage in the digital age.


"g-f(2)2687" empowers players to navigate the complex ethical landscape of AI, ensuring that their AI initiatives align with organizational values and societal expectations. This knowledge is essential for those seeking to lead rather than follow in the ongoing digital revolution, making it a crucial component in the strategy to win the g-f Transformation Game and thrive in the g-f New World.



Introduction


The MIT Sloan Management Review article "How Scotiabank Built an Ethical, Engaged AI Culture" provides an in-depth look at the strategies and practices Scotiabank employed to foster a responsible and engaged approach to artificial intelligence (AI) within the organization. This analysis extracts the golden knowledge (g-f GK) from the article, offering valuable insights for business leaders and organizations aiming to develop ethical AI practices in the g-f New World.



genioux GK Nugget


"Building an ethical and engaged AI culture requires a strategic vision, a robust ethical framework, cross-functional collaboration, and continuous improvement." — Fernando Machuca and Perplexity, July 31, 2024



genioux Foundational Fact


Scotiabank's approach to developing an ethical AI culture involves establishing a comprehensive ethical framework, implementing a robust governance structure, engaging employees, fostering cross-functional collaboration, and maintaining transparency. These efforts ensure that AI initiatives align with the bank's values and ethical standards, enhancing trust and credibility with stakeholders. By continuously reviewing and updating their practices, Scotiabank demonstrates a commitment to responsible AI use, setting a benchmark for other organizations.



The 10 Most Relevant genioux Facts





  1. Strategic Vision: Scotiabank's leadership recognized AI's potential to transform banking and committed to integrating it ethically.
  2. Ethical Framework: The bank established principles such as fairness, accountability, transparency, and privacy to guide AI development and deployment.
  3. Governance Structure: A robust governance structure, including an AI ethics committee, oversees AI initiatives to ensure adherence to ethical guidelines.
  4. Employee Engagement: Scotiabank engaged employees at all levels through training programs, workshops, and discussions about AI's ethical implications.
  5. Cross-Functional Collaboration: Collaboration across IT, legal, compliance, and business units ensures a holistic approach to AI ethics.
  6. Transparency and Communication: Regular communication with stakeholders about AI projects' goals, progress, and ethical considerations enhances transparency.
  7. Continuous Improvement: The bank regularly reviews and updates its AI ethical framework and practices to keep pace with technological advancements and evolving standards.
  8. Case Studies and Examples: Specific examples demonstrate how Scotiabank applied its ethical AI principles in practice, showing tangible benefits.
  9. Trust and Credibility: Building an ethical AI culture enhances trust and credibility with customers, employees, and other stakeholders.
  10. Model for Others: Scotiabank's proactive approach serves as a model for other organizations aiming to harness AI responsibly and ethically.



Conclusion


The insights from "How Scotiabank Built an Ethical, Engaged AI Culture" provide essential golden knowledge for organizations seeking to develop responsible AI practices. By establishing a strategic vision, ethical framework, robust governance, and fostering engagement and collaboration, Scotiabank has set a benchmark for ethical AI use. As we navigate the g-f New World, these strategies will empower organizations to harness AI's transformative potential responsibly, enhancing trust and driving sustainable success.





REFERENCES

The g-f GK Context


Thomas H. Davenport and Randy Bean, How Scotiabank Built an Ethical, Engaged AI Culture, MIT Sloan Management Review, July 31, 2024.



ABOUT THE AUTHORS


Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a fellow of the MIT Initiative on the Digital Economy, and senior adviser to the Deloitte Chief Data and Analytics Officer Program. He is coauthor of All in on AI: How Smart Companies Win Big With Artificial Intelligence (Harvard Business Review Press, 2023) and Working With AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022). Randy Bean (@randybeannvp) is an adviser to Fortune 1000 organizations on data and AI leadership. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).



Classical Summary of the Article


The MIT Sloan Management Review article "How Scotiabank Built an Ethical, Engaged AI Culture" examines the steps taken by Scotiabank to develop a responsible and engaged approach to artificial intelligence (AI) within the organization. Here are the key points:


  1. Strategic Vision: Scotiabank's leadership recognized the potential of AI to transform the banking industry and committed to integrating AI in a way that aligns with the bank's ethical standards and values.
  2. Ethical Framework: The bank established a comprehensive ethical framework to guide AI development and deployment. This framework includes principles such as fairness, accountability, transparency, and privacy.
  3. Governance Structure: Scotiabank implemented a robust governance structure to oversee AI initiatives. This includes an AI ethics committee responsible for ensuring that AI projects adhere to the established ethical guidelines.
  4. Employee Engagement: The bank focused on engaging employees at all levels to foster a culture of ethical AI. This involved training programs, workshops, and open discussions about the ethical implications of AI.
  5. Cross-Functional Collaboration: Scotiabank encouraged collaboration across different departments, including IT, legal, compliance, and business units, to ensure a holistic approach to AI ethics.
  6. Transparency and Communication: The bank prioritized transparency in its AI initiatives, regularly communicating with stakeholders about the goals, progress, and ethical considerations of AI projects.
  7. Continuous Improvement: Scotiabank adopted a mindset of continuous improvement, regularly reviewing and updating its AI ethical framework and practices to keep pace with technological advancements and evolving ethical standards.
  8. Case Studies and Examples: The article highlights specific examples of how Scotiabank applied its ethical AI principles in practice, demonstrating the tangible benefits of their approach.


By building an ethical and engaged AI culture, Scotiabank not only mitigates risks associated with AI but also enhances trust and credibility with customers, employees, and other stakeholders. The bank's proactive approach serves as a model for other organizations looking to harness the power of AI responsibly and ethically.






Thomas H. Davenport


Thomas H. Davenport (born October 17, 1954) is a renowned American academic and author specializing in analytics, business process innovation, knowledge management, and artificial intelligence. He is currently the President’s Distinguished Professor in Information Technology and Management at Babson College, a Fellow of the MIT Initiative on the Digital Economy, Co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics¹².


Education and Early Career

Davenport initially trained as a sociologist, earning a BA in Sociology from Trinity University in 1976, a Master's degree in Sociology from Harvard in 1979, and a Ph.D. in Sociology from Harvard in 1980¹. After completing his Ph.D., he worked as an academic before transitioning to a research and consulting role at Index, where he became the Director of Research¹.


Contributions and Achievements

Davenport has written, coauthored, or edited twenty books, including pioneering works on analytical competition, business process reengineering, and achieving value from enterprise systems¹². His book "Competing on Analytics: The New Science of Winning" (coauthored with Jeanne Harris) is particularly notable for providing guidelines on basing competitive strategies on business data analysis¹.


He has also written over 250 articles for prestigious publications such as Harvard Business Review, MIT Sloan Management Review, and the Financial Times². Davenport has been recognized as one of the world’s top three analysts of business and technology and one of the top 50 business school professors by Fortune Magazine².


Personal Life

Davenport has two sons: Hayes Davenport, a television comedy writer and podcaster, and Chase Davenport, who makes surfboards and researches artificial intelligence¹.


Would you like to know more about any specific aspect of Thomas H. Davenport's work or contributions?


¹: [Wikipedia](https://en.wikipedia.org/wiki/Thomas_H._Davenport)

²: [Tom Davenport's Official Website](https://www.tomdavenport.com/about/)


Source: Conversation with Copilot, 7/31/2024

(1) Thomas H. Davenport - Wikipedia. https://en.wikipedia.org/wiki/Thomas_H._Davenport.

(2) About - Tom Davenport. https://www.tomdavenport.com/about/.

(3) Thomas Davenport | Electric Car, Automobile Engineer & Ironworker. https://www.britannica.com/biography/Thomas-Davenport.

(4) 토머스 H. 데이븐포트 - 위키백과, 우리 모두의 백과사전. https://ko.wikipedia.org/wiki/%ED%86%A0%EB%A8%B8%EC%8A%A4_H._%EB%8D%B0%EC%9D%B4%EB%B8%90%ED%8F%AC%ED%8A%B8.

(5) Thomas H. Davenport – Wikipédia, a enciclopédia livre. https://pt.wikipedia.org/wiki/Thomas_H._Davenport.



Randy Bean


Randy Bean is a prominent advisor to Fortune 1000 organizations on data and AI leadership. With over three decades of experience, he has established himself as a thought leader, author, and speaker in the field of data-driven business leadership².


Career and Contributions

Randy Bean is the Founder and CEO of NewVantage Partners, a consultancy specializing in data and AI strategy². He also serves as an Innovation Fellow for Data Strategy at Wavestone, a Paris-based consultancy². Bean is a regular contributor to prestigious publications such as Forbes, Harvard Business Review, and MIT Sloan Management Review²³.


Notable Work

Bean is the author of the bestselling book **"Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI"** (Wiley, 2021)²³. The book provides valuable insights and lessons on how organizations can leverage data and AI to drive innovation and achieve success in a rapidly changing business environment.


Thought Leadership

Throughout his career, Bean has been a vocal advocate for the importance of data and AI in business transformation. He has advised numerous Fortune 1000 companies on how to harness the power of data to gain a competitive edge². His work has been instrumental in shaping the data strategies of many leading organizations.


Would you like to know more about any specific aspect of Randy Bean's work or contributions?


²: [NACD](https://www.nacdonline.org/speaker-bios/randy-bean/)

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


Source: Conversation with Copilot, 7/31/2024

(1) Randy Bean Bio | NACD. https://www.nacdonline.org/speaker-bios/randy-bean/.

(2) Randy Bean - Forbes. https://www.forbes.com/sites/randybean/.

(3) Obituary: Randy Bean | AspenTimes.com. https://www.aspentimes.com/obituaries/obituary-randy-bean/.

(4) Randy Bean Data. https://www.randybeandata.com/.



The categorization and citation of the genioux Fact post


Categorization


This genioux Fact post is classified as Breaking Knowledge which means: Insights for comprehending the forces molding our world and making sense of news and trends.



Type: Breaking Knowledge, Free Speech



g-f Lighthouse of the Big Picture of the Digital Age [g-f(2)1813, g-f(2)1814]

  • Daily g-f Fishing GK Series
  • Game On! Mastering THE TRANSFORMATION GAME in the Arena of Sports Series


Angel sponsors                  Monthly sponsors



g-f(2)2687: The Juice of Golden Knowledge



GK Juices or Golden Knowledge Elixirs



REFERENCES



“genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)2687, Fernando Machuca and Perplexity, July 31, 2024, Genioux.com Corporation.


The genioux facts program has established a robust foundation of over 2686 Big Picture of the Digital Age posts [g-f(2)1 - g-f(2)2686].



List of Most Recent genioux Fact Posts


genioux GK Nugget of the Day


"genioux facts" presents daily the list of the most recent "genioux Fact posts" for your self-service. You take the blocks of Golden Knowledge (g-f GK) that suit you to build custom blocks that allow you to achieve your greatness. — Fernando Machuca and Bard (Gemini)


June 2024


May 2024

g-f(2)2393 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (May 2024)


April 2024

g-f(2)2281 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (April 2024)


March 2024

g-f(2)2166 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (March 2024)


February 2024

g-f(2)1938 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (February 2024)


January 2024

g-f(2)1937 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (January 2024)


Recent 2023

g-f(2)1936 Unlock Your Greatness: Today's Daily Dose of g-f Golden Knowledge (2023)


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Monday, August 30, 2021

g-f(2)458 THE BIG PICTURE OF THE DIGITAL AGE (8/30/2021), HBR, How Data Literate Is Your Company?




ULTRA-condensed knowledge


"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital age, Data Literate (8/30/2021)  g-f(2)426

Opportunity, Building data literacy, HBR
  • By investing in data literacy across the enterprise, businesses can bring more divergent and creative perspectives to bear on both mitigating the risk of algorithmic bias — and identifying other efficiencies and opportunities that data can often reveal.
  • Data literacy is about much more than machine learning and data science. And it’s about more than AI. Data literacy is simply about humans coping better in a data-infused world.
Lesson learned, A look at the data tells us that most companies are still struggling to build data literacy, HBR
  • Ninety percent of business leaders cite data literacy as key to company success, but only 25% of workers feel confident in their data skills.
Lesson learned, Specific practices implemented to make data literacy a reality, HBR
    • Make data literacy an organization-wide priority, not just among people within the technology org. Data literacy is not a technical skill. It is a professional skill.
    • Develop an internal common language for speaking about data, how it intersects with your business and industry, and how it is changing specific roles at your company. The world of data is big, filled with buzzwords and misunderstanding.
    • Create spaces within your organization for workers to connect business concepts and data concepts. Empower employees to generate new business ideas that apply their data literacy. 
    • Create incentive structures to reward data-driven decision making. Take your current process for approving ideas or setting budgets. Then add mechanisms that reward data-driven thinking.
    • Deploy L&D programs that teach data literacy in the context of your business problems — and that actually engage your employees. Not everyone needs to know how to code. But soon everyone will need data literacy. 

          Genioux knowledge fact condensed as an image


          Condensed knowledge


          Opportunity, Data literacy is a skill that everyone has to have now, HBR
          • As companies rely more and more on data, and it creeps into more parts of business, data literacy is a skill that everyone has to have now.


          Some relevant characteristics of this "genioux fact"

          • Category 2: The Big Picture of the Digital Age
          • [genioux fact deduced or extracted from HBR]
          • This is a “genioux fact fast solution.”
          • Tag Opportunities those travelling at high speed on GKPath
          • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
          • Type of validity of the "genioux fact". 

            • Inherited from sources + Supported by the knowledge of one or more experts.



          References




          ABOUT THE AUTHORS


          Rasheed Sabar


          Rasheed Sabar is co-founder and co-CEO of Correlation One, a technology company focused on data-skills training for enterprises. The company is building a more inclusive data ecosystem, including programs to bring more women and underrepresented groups into data jobs.



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