Redefining Data Products in the Age of Artificial Intelligence and Deep Learning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P105Keywords:
Artificial Intelligence, Deep Learning, Data Products, Real-time Data Processing, Machine LearningAbstract
AI and deep learning technologies are rapidly evolving and these advanced technologies are altering the approaches towards generating, maintaining, and using data solutions. The traditional data products were thus initially catered for descriptive analytics especially on past data. However, the integration of AI and DL has caused a shift to normal from where the organization can achieve predictive and prescriptive analytics and decision-making, as well as learning systems that can adapt themselves. This paper focuses on how technological advancements and their subtopics concerning the data docket have changed due to the rise of AI-powered data products. With the help of indexing, issues related to data quality and bias, interpretability of AI, and data security are discussed in the context of the AI ecosystem. Cloud computing and edge AI, as well as federated learning, are examined in order to understand the effect they have on today’s data product architecture. Moreover, there is a transition to real-time data processing and intelligent automation, and the need for organizations to employ the AI-native architecture is outlined. Thus, with the help of redefining data products, organizations can make more business sense, deliver exceptional customer experience, and facilitate the creation of new data solutions. This paper discusses the emerging trends and the guidelines needed to support organizations to achieve the optimal functionalities of AI and Deep Learning in developing the next generation of intelligent data products
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