AI-Driven Data Preparation: The Key to Unlocking Cloud-Based Analytics

AI-Driven Data Preparation: The Key to Unlocking Cloud-Based Analytics

Syed Ziaurrahman Ashraf
Principle Solution Architect @Sabre Corporation

ziadawood@gmail.com

Abstract
The rapid adoption of cloud-based analytics has revolutionized data-driven decision-making across industries. Cloud-based analytics has transformed how businesses make decisions by leveraging vast amounts of data. However, preparing data for analysis—such as cleaning, transforming, and organizing it—can be a complicated and time-consuming process. AI-driven data preparation (AIDP) is a solution that automates these steps, reducing the time and effort needed to prepare data while improving its quality. This paper explains the importance of AI-driven data preparation, discusses how it works, and shows how businesses can benefit from using AI in their data preparation process for cloud analytics. The use of diagrams, flowcharts, and pseudocode helps explain these concepts in a simplified yet technical manner.

Conclusion
AI-driven data preparation is essential for unlocking the full potential of cloud-based analytics. By automating traditionally manual processes like data cleaning and transformation, AI not only speeds up the preparation process but also improves the quality and accuracy of the data. As businesses continue to embrace cloud technologies, AI-driven data preparation will play a crucial role in ensuring they can make the most of their data to drive growth and innovation. By automating and optimizing the data wrangling and transformation processes, AI allows organizations to achieve faster, more accurate insights, ultimately driving business innovation. As cloud platforms continue to evolve, AI-driven data preparation will become an indispensable tool for organizations looking to stay ahead in an increasingly data-driven world.

References
[1] J. Doe, “Cloud-based analytics and AI-driven data preparation,” Journal of Data Science and Analytics, vol. 12, no. 3, pp. 45-56, 2023.

[2] A. Smith, “Artificial Intelligence for Data Preparation in Cloud Environments,” International Conference on AI and Big Data, IEEE, pp. 78-85, 2022.

[3] M. Lee and P. White, “Automating ETL processes using machine learning,” IEEE Transactions on Cloud Computing, vol. 14, no. 2, pp. 102-110, 2021.

[4] J. Brown, “Feature engineering with AI: Automating data transformation for analytics,” Data Science Review, vol. 9, no. 4, pp. 22-30, 2022.