Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges

The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without ch...

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Bibliographic Details
Published in:Applied sciences Vol. 13; no. 12; p. 7082
Main Authors: Aldoseri, Abdulaziz, Al-Khalifa, Khalifa N., Hamouda, Abdel Magid
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2023
Subjects:
Accuracy
Algorithms
Artificial intelligence
Artificial Intelligence (AI)
Big Data
challenges and opportunities
Competitive advantage
Computational linguistics
Computer software industry
Cost reduction
data strategies and learning approaches
Datasets
Decision making
Deep learning
Information management
Language
Language processing
Machine learning
Natural language interfaces
Natural language processing
Privacy
Problem solving
Robotics
Trends
Voice recognition
New York
United Kingdom
California
United States
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain competitive advantage in the digital age. It is expected, since this review article provides and discusses various strategies for data challenges for AI over the last decade, that it will be very helpful to the scientific research community to create new and novel ideas to rethink our approaches to data strategies for AI.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app13127082