- 28.02.2026
Data Cleaning: A comprehensive guide for data scientists and business leaders in Saudi Arabia to achieve 99% accuracy in AI models
Data cleaning: An Advanced Practitioner's Guide to Achieving AI Accuracy and Efficiency in the Saudi Market Clean Data: The secret to reliable decision-making and competitive advantage in the Saudi market In the era of digital transformation and Vision 2030, data is no longer just an administrative record, it has become the most valuable asset for businesses in Saudi Arabia. With the influx of Big Data from multiple sources, there is an urgent need to ensure the quality of this data. Decisions worth billions of riyals in vital sectors such as finance, energy, and e-commerce must be based on solid foundations. That foundation is Data Cleaning. Cleaning is not just about correcting errors, it is a strategic process that gives Saudi companies a critical competitive advantage by enabling them to derive real and reliable insights instead of drowning in the chaos of unstructured data. What is data cleaning? A comprehensive definition of the importance of data quality Data cleaning, also known as Data Scrubbing, is the systematic process of identifying and correcting or removing errors, inconsistencies, duplicates, and missing values from a dataset. The primary goal is not just to "make the data look good," but to ensure that the data is accurate, complete, consistent, and uniform enough to be used for analysis, statistical modeling, or training AI algorithms. This process involves careful steps to address spelling errors, standardize inconsistent formats (such as date or currency), deal with
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