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Optimizing data management with AI: Strategies for improved efficiency and accuracy – A case study
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School of Business |
Master's thesis
Electronic archive copy is available via Aalto Thesis Database.
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en
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62 + 8
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Abstract
In today's digital age, the rapid increase in data presents significant challenges for organizations in maintaining its quality. This thesis explores how Artificial Intelligence can enhance data management practices, particularly in improving the accuracy, completeness, consistency, and timeliness of data.
Organizations face difficulties handling vast amounts of both structured and unstructured data, making data management essential for reliable decision-making and efficient operations. This study begins with a thorough literature review on data quality frameworks and the role of AI in data management. It includes a detailed case study of a company specializing in environmental and industrial measurements, examining their specific data management challenges and assessing the effectiveness of AI-based solutions.
The findings show that AI has great potential in automating and optimizing data management processes. Intelligent automation can improve attribute-level data descriptions, enhance data discovery through various search algorithms, and provide robust data quality monitoring and anomaly detection. These improvements can lead to more accurate, consistent, and timely data, which are important for effective decision-making and operational efficiency.
The discussion highlights AI's potential in data management, emphasizing the importance of domain-specific customization, continuous feedback loops, and seamless integration with existing systems. The study concludes that while AI offers substantial benefits in transforming data quality management, successful implementation requires careful consideration of the organizational context and ongoing adaptation to evolving data needs.