Master Thesis: AI-Assisted Identification of Critical Data Elements Using Agentic AI

Technology, Data & Digital · Data, AI & Analytics · Data Science · Artificial Intelligence

In short

This Master's thesis project explores AI-assisted identification of Critical Data Elements (CDEs) using agentic AI in Snowflake CoWork. It evaluates AI's accuracy, strengths, and limitations in enterprise data governance, aiming to provide recommendations for adoption and insights into scalable data quality practices.

Responsibilities

Review literature on data governance, data quality, Critical Data Elements, and agentic AI.
Explore how Snowflake CoWork can support AI-assisted identification of business-critical data.
Evaluate AI-generated recommendations using selected datasets and business scenarios.
Compare AI results with expert assessments and existing practices.
Analyze strengths, limitations, risks, and improvement opportunities.
Develop recommendations for integrating AI-assisted CDE identification into enterprise data governance processes.
Present findings and document work in a written thesis report.

Requirements

Master's student in Computer Science, Artificial Intelligence, Data Science, Information Systems, Business Analytics, Information Management, or related field.
Strong analytical and problem-solving skills.
Interest in Data & AI, data governance, and enterprise analytics.
Knowledge of databases, data modelling, business intelligence, or information management.
Curiosity about Generative AI and agentic AI technologies.
Ability to work independently and collaborate with technical and business stakeholders.

Desired Qualifications

Experience with SQL, Python, Snowflake, or cloud data platforms is beneficial.

Benefits

Outstanding opportunity to use skills and imagination to push boundaries.
Build solutions for tough problems.
Challenged in a team of diverse innovators.
Opportunity to craft what comes next.
Opportunity to work at either Linköping or Stockholm office.

Skills

SQLPythonSnowflake

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