#142 Is Data Science Still the Sexiest Job of the 21st Century?
The episode covers the impact of data science, its adoption across industries, the evolution of data science roles, challenges faced in the field, the adoption and impact of AI, applications of generative AI, and its potential impact on various sectors. It emphasizes the need for a data-driven culture, education in data science, and ethical considerations in AI adoption. The episode also explores the potential of generative AI in revolutionizing knowledge management and transforming industries like real estate and law.
Data science is essential in every boardroom
Data science has become institutionalized and is now a must-have for every boardroom.
Financial services lead in data science adoption
Financial services organizations have adopted data science quicker than other industries due to their access to large amounts of data.
Cultural change is crucial for data-driven decision making
Investing in cultural change and education is crucial for creating a more data-driven culture. Tailoring programs to different parts of the business and incorporating face-to-face interactions are necessary.
Specialization vs generalization in data science roles
The debate in the data science space is whether specialization or generalization is the optimal path for success.
AI-driven organizations require a holistic approach
To be an AI-fueled organization, you need a variety of AI technologies, support from senior executives, investment, talent, ethical frameworks, and governance structures.
Generative AI has transformative potential
Generative AI has the potential to significantly impact various areas such as writing, coding, R&D, law, and professional services.
Ethical considerations in generative AI
Ethical issues such as misinformation, deep fakes, transparency, and bias need to be addressed in relation to generative AI.
Generative AI applications in real estate and law
Custom-designed models exist for specific fields of law or industries like real estate, enabling more efficient processes and decision-making.
Experimentation and starting small are key in generative AI
Start experimenting aggressively and get your content in shape in the generative AI space. Think big about how AI can transform your organization, but start small if needed.
- Data Science and its Impact
- Adoption of Data Science
- Evolution of Data Science
- Challenges in Data Science
- AI Adoption and Impact
- Generative AI and its Applications
- Applications of Generative AI
Data Science and its Impact
00:00 - 07:55
- Data science has become more institutionalized and is now a must-have for every boardroom.
- Technologies like AI and chat GPT are transforming data science tasks.
- Tom Davenport, co-author of the article 'Data Scientist, the Sexiest Job of the 21st Century,' shares his perspective on data science.
- Data science is still desirable, but the job has fragmented and there is a lack of clarity in organizations about who is a data scientist.
- There has been an increase in investment in data science by organizations across various industries.
- However, in most companies, there is still not a data-driven culture and decision-making is not based on data.
- Financial services organizations have adopted data science quicker than other industries.
- Data-rich industries tend to adopt data science faster than others.
Adoption of Data Science
07:26 - 15:08
- Data science adoption varies by industry, with financial services organizations leading the way due to their access to large amounts of data.
- Telecom companies also have a significant amount of data and are starting to embrace data science.
- The pharmaceutical industry is heavily involved in data science, with many companies showing interest and participation.
- Manufacturing has been slower to adopt data science, but some high-tech manufacturing companies are using it for tasks like predictive maintenance.
- Professional services, including audit, tax, and law, are starting to incorporate AI and data-driven decision making.
- The cultural component is a major obstacle for organizations in adopting a data-driven culture.
- Senior executives need to be committed to data science and analytics for the rest of the organization to follow suit.
- Investing in cultural change and education is crucial for creating a more data-driven culture.
- While providing tools and literacy programs is important, it's not enough on its own. Tailoring programs to different parts of the business and incorporating face-to-face interactions are necessary.
- Cultural change programs should include one-on-one sessions with senior executives, community development within the organization, and behavior change initiatives during meetings.
Evolution of Data Science
14:51 - 22:44
- Developing healthy data skepticism within the organization is crucial.
- Data science has evolved with the emergence of specialized roles like machine learning engineers, data product managers, and MLOps engineers.
- The debate in the data science space is whether specialization or generalization is the optimal path for success.
- Education in data science has seen a proliferation of programs, but it can be challenging for students to understand what they are getting.
- One-year programs may create excellent citizen data scientists but may not be enough to develop advanced skills.
- There is a need for more PhD programs in data science to foster advanced talent.
- Universities struggle with combining different skills from various disciplines in their data science programs.
Challenges in Data Science
22:16 - 30:04
- Data science is a multi-disciplinary activity, but universities tend to focus on specific skills rather than combining them.
- Organizations should develop categorizations and certification programs for different data science roles.
- Software engineering has well-defined levels, but data science lacks a standardized leveling system.
- Certification programs like CAP exist, but more are needed in the data science field.
- Less than 1% of large organizations consider themselves AI-driven.
- To be an AI-fueled organization, you need a variety of AI technologies, support from senior executives, investment, talent, ethical frameworks, and governance structures.
- There are three strategic archetypes for becoming AI-driven: doing something new, operational transformation, and changing customer behavior.
AI Adoption and Impact
29:38 - 37:32
- Operational transformation is the most common use case for AI, followed by changing customer behavior and then generative AI.
- When prioritizing use cases, organizations should consider the potential cost savings and viability of implementing AI solutions.
- Developing a human side and fostering an AI-driven culture is crucial for success in AI adoption.
- An AI-driven culture should involve democratizing AI skills across the organization, even for non-data scientists.
- Generative AI has the potential to significantly impact various areas such as writing, coding, R&D, law, and professional services.
- Large organizations should experiment with generative AI applications while also considering production-level implementations.
- Morgan Stanley is using generative AI to provide financial advisors with high-quality answers based on their analysts' recommendations.
Generative AI and its Applications
37:06 - 44:45
- Generative AI is being used by companies to provide high quality answers and recommendations to financial advisors.
- Generative AI has the potential to revolutionize knowledge management.
- The economy will be impacted by generative AI in the next few years.
- Professionals should embrace these tools and use them to increase productivity.
- Leaders should incorporate both humans and generative AI into their organizational strategy.
- Automation may become more prevalent with the advancement of generative AI, but human involvement is still necessary at this stage.
- Managers should encourage employees to experiment with generative AI and form communities of practice within the organization.
- Ethical issues such as misinformation, deep fakes, transparency, and bias need to be addressed in relation to generative AI.
- Generative AI front ends will be integrated into various software products, replacing command line interfaces and point-and-click interfaces.
- Conversational interfaces and useful chatbots will become more common in online activities.
- Customized models for specific fields of law or industries will be developed using generative AI.
Applications of Generative AI
44:22 - 46:14
- Different countries or regions have different versions of real estate law.
- Individual law firms have their own unique models of real estate law.
- Custom-designed models exist for vacation planning in specific locations like Peru.
- Companies like Morgan Stanley and Deloitte have been working with OpenAI for 18 months on code generation.
- Start experimenting aggressively and get your content in shape in the generative AI space.
- Think big about how AI can transform your organization, but start small if needed.