Data Science. From Marketplaces to Industry

11 Apr

In our digital age the amount of data generated by various companies is growing exponentially. Vast amounts of information come from various users, social media, IoT devices and other platforms to offer an opportunity to discover valuable data and to proceed with evidence-based decision-making. However, in order to make use of the opportunities contained in this data ‘ocean’ we need efficient tools, data management methodology and analytical techniques.

  • How do Large Language Models (LLM) and generative data models transform businesses today? In which functions/divisions have those models been used successfully to improve performance? (describe particular cases)
  • How do businesses see the future structure of Data Science departments - should we expect reducing the number of ML developers in favor of business analysts due to implementation of LLMs? Maybe, we should have more business analysts in our teams, who know how to use LLMs effectively, rather that having more ML developers?
  • As the companies grow, they develop more and more domains containing knowledge about their users/customers. If often appears that various data sources are poorly aligned, which makes it sometimes impossible to identify the same user across various data bases. Which algorithms and approaches do businesses use to solve this problem?
  • Today, to have users to stay within the contour of our business, be it a bank, a marketplace or a digital ecosystem, it is vital to develop services providing recommendations which would offer relevant products to the user, without all the annoying push-up messages. Which algorithms look more effective to solve this task? How good are graph neural networks? Are there any success stories about launching LLM-based recommendation services?