Data scientists, data engineers and BI
analysts are in for a fun ride.
1. Data engineering will evolve—and be highly valued—in an AI world.
– Data engineers’ concerns about job displacement by AI are unfounded; instead, their skills will be highly valued.
– The expertise of data engineers is crucial for organizing data and ensuring its proper intake, a prerequisite for leveraging the power of Large Language Models (LLMs).
– Data engineers will play a vital role in connecting with LLMs through data pipelines, automating value extraction. Their expertise will evolve to solve unique challenges and oversee work now handled routinely by generative AI.
2. Data scientists will have more fun.
– Data scientists face evolving challenges with the advent of generative AI, shifting from mundane tasks like sentiment analysis to addressing new issues like contextual data input and minimizing hallucination in Large Language Models (LLMs).
– Generative AI is expected to make data science jobs more appealing by automating repetitive tasks, leading to increased interest among students.
– To adapt, data science leaders must adjust their skill sets, transitioning from traditional roles to selecting and integrating external vendors of AI models. The role of data scientists as accurate intermediaries between raw data and consumers remains crucial.
3. BI analysts will have to uplevel.
– Analysts currently create reports and answer executive queries. In the future, executives will prefer self-service interaction with summarized data, freeing analysts for more profound work.
– Snowflake CIO Sunny Bedi sees this shift as inevitable, urging analysts to enhance skills. The choice between dashboards and natural language querying highlights a trend of upleveling roles.
– BI professionals, adapting to self-service trends, move beyond dashboards to address complex issues, contributing to their professional development.
4. Developers expect to be 30% more efficient using generative AI assistants.
– Bedi’s dev team estimated 30% of their code could be handled by a gen AI tool, potentially a game changer in efficiency.
– Beyond initial efficiency gains, AI-generated code offers reusability, enhancing overall project efficiency.
– Testing and quality assurance could be assisted by AI agents, leading to faster, higher-quality deployments, though coding skills remain essential.
– In the near term, AI tools focus on quickly executing tasks, but predicting AI output supervision needs beyond five years is uncertain due to rapid advancements.