3#18 - Bridging the Gap: Data Science Education and Industry Collaboration (Nor)

MetaDAMA - Data Management in the Nordics

"Det var jo veldig urealistisk å tenke kanskje at en haug med folk som har matematisk eller Computer Science bakgrunn, skal komme inn og skjønne forretningen. / It was very unrealistic to think that maybe a bunch of people with a mathematical or computer science background would come in and understand the business."
Join us on Metadama as we welcome Erlend Aune, an accomplished data science expert with a rich background in both academia and industry. Through real-world examples from the Norwegian industry, we illustrate how successful research collaborations and technology transfers can stimulate innovation and create value. Despite the promising advances, we also candidly address the cultural and operational challenges businesses encounter when integrating AI research into their workflows.
What practical steps can bridge the gap between theoretical education and real-world application? Our conversation further explores the intersection of business development and the practical application of machine learning and data science. We emphasize the need for environments that foster hands-on experience for students, such as hackathons and industry-linked thesis projects. Additionally, we discuss the importance of tailored training development within organizations, focusing on understanding trainee characteristics to achieve meaningful training outcomes. Tune in to gain valuable insights and actionable advice on nurturing the next generation of data scientists and enhancing organizational capabilities.
Here are my key takeaways:
Data Science and Business Development

• Data science needs a strong connection to business development
• You need to embed Data Science in a cross-functional environment
• Business acumen needs to be ingrained in the work with data
• Data Science needs to start from a Business side - ensure that you work on the problems that generate value for your organization.
• Data Science works with probability, not certainty - this notion is not yet understood by everyone in business.
• Data organizations are often build on an engineering mindset, that can be contradictive to an exploratory mindset.
• Even when designing Data Warehouse, you need to understand the business impact, have a business development mindset.

Norway & AI

• Norway has a great AI and ML research community.
• The public discourse on AI portraits a quite narrow view, that doesn’t reflect the broad application and research done in the field.

Research & Business

• Responsible AI is not a one-size fits all. Different organizations have different needs, for either certainty, security, reliability of outcome, etc. So a rAI approach needs ton be tailored to the business need.
• Startups and companies that have products related to the AI research environment, have the advantage that products are improved in tact with research development.
• In addition to in-house R&D, organizations can collaborate directly with research environments at universities.
• You cannot do R&D just as a pocket of excellence, if you want to operationalize results in your organization.
• We need to shorten the distance between R&D and operations.

For the Data Science Student

• If you apply knowledge on different challenges, you will get an intuition on how to solve a broad variety of challenges.
• When selecting a task within an organization as a Master thesis, make sure the task is delimited.
• Traits to succeed as a student working in industry:
  • Interest in your discipline
  • Interest in the organization and its sector
  • Problemsolving
  • Creativity

Rozdziały

1. Industry-Academia Collaboration in Data Management (00:00:00)

2. Challenges in Implementing AI Research (00:16:44)

3. Business Development and Data Science Interface (00:26:21)

4. Exploring Training Development in Organizations (00:37:24)