This article summarizes some of the advice that we believe will be valuable for anyone that plans to start or build a career in data science, data analytics or people analytics.
Category - Business
AIAW Podcast Episode 066 – Peter Sarlin
Episode Description In this episode of the weekly Artificial Intelligence After Work (AIAW Podcast), we have the pleasure to welcome Peter Sarlin, Co-founder and CEO of Silo AI, the largest private AI lab in the Nordics...
Accelerating Diversity, Equity and Inclusion through Use of Data: Interview with Jacob Nielsen
Having a diverse blend of talents is the key to developing new innovative and sustainable solutions, both for the business and society.
Data Monetization Requires both Data Products and Services: Interview with Jarkko Moilanen
How can organizations utilize their data and design data products to maximize internal reuse and partner network value?
Solving Business Challenges with Data Warehouse Automation Tool: Interview with Terje Vatle
Excel sheets, a business intelligence tool and no data warehouse. When is a data warehouse typically introduced in an organization?
The Future of Data Platforms is Truly Hybrid Lake-House Architecture: Interview with Liangfeng Hu
In the next few years, exciting upcoming technology is the hybrid lake-house architecture of data platforms, thinks Liang Feng from The AI Framework.
Benefits of Open Source as a Platform for Innovation: Interview With Heikki Nousiainen
Video interview with Heikki Nousiainen, CTO and Co-founder of Aiven.
The Time to Install Trust throughout the AI Life Cycle is Now: Interview with Seth Dobrin
The development of AI must be both human-centric and trustworthy if organizations and societies want the technology to reach its full potential.
Why Does Self-Service BI Fail and What Could Enterprises Do to Turn the Tide?
Investing in Self-Service Business Intelligence (BI) does not guarantee that companies are able to crack the code on becoming data-driven organizations.
Algorithmic Risk Management: A Framework for Identifying, Assessing, Controlling, and Mitigating Risks in AI Development and Operations
In this article, we attempt to understand risk management as a unified concept and discuss a 6-step risk management process using algorithmic risk management tools.