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Identification of recipients to fulfill disclosure obligations under Article 15 of the GDPR
Projektdauer: 6 Monate
Kurzbeschreibung
The goal of the project is to develop a recipient search tool that reliably identifies relevant candidates despite incomplete, incorrect, or inconsistent address data. The client benefits from a significant reduction in the workload associated with processing requests for information under Article 15 of the GDPR and can query operational systems in a targeted and efficient manner. PTA models the database, implements ELT/ETL processes for the normalization, standardization, and indexing of addresses, and develops a user-friendly interface.
Ergänzung
The system environment consists of Snowflake as a cloud data warehouse and a Python-based Streamlit app as the user interface. PTA designs the data model, identifies relevant address data, and develops ELT/ETL pipelines for cleaning, normalizing, and standardizing the addresses. In addition, addresses are indexed through tokenization and text embeddings via Snowflake Cortext AI to enable robust search results despite spelling variations or errors. PTA integrates mechanisms to ensure compliance with retention periods and implements a GDPR-compliant authorization concept. The Streamlit app provides the search function in a clear and intuitive manner.
Fachbeschreibung
The product is designed to efficiently fulfill the obligations to provide information under Article 15 of the GDPR by reliably identifying potential recipients of personal data. Authorized employees are provided with a tool that standardizes heterogeneous, incorrect, or incomplete address data and derives relevant matches from it. The solution enables precise candidate selection, allowing operational systems to be queried specifically via exact match and making information disclosure processes significantly more efficient.
IT-Projektdaten
- Branchen Logistik
- Fachaufgaben Big Data, Business Analytics, business intelligence, Data Analytics, Data Cleansing, data protection, data warehouse, GDPR, GDPR implementation, search engine
- DV-Aufgaben Data Warehouse
- Systemumgebungen Artificial Intelligence (AI), Business Intelligence, Cloud Computing
- Vorgehensmodell customer standard
- Programmiersprachen SQL
- Datenbanken Snowflake
Projektzeitraum01.11.2025 - 30.04.2026
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