Data quality: The unseen villain of machine learning

A modern machine learning (ML) engineer's role extends far beyond just building models and analyzing data. Efficient data use is crucial for successful businesses, requiring data to be acquired, securely shared, and analyzed throughout its lifecycle. The rise of cloud computing and enterprise ML adoption has facilitated the beginning and end of this data journey, but middle stages often face issues related to data quality. Poor quality data burdens data users, often preventing data scientists from effectively building models and performing analyses. Data scientists spend a significant portion of their time cleaning data to ensure reliable outcomes, which can be frustrating and inefficient. Clean data is essential for ML projects, as it ensures models remain effective against changing data landscapes. Effective data management involves continual evaluation and handling of data drift to maintain model accuracy. Aligning the entire organization around data-driven practices, including non-technical stakeholders, is critical to avoid data quality issues. Organizations that prioritize data quality can drive higher AI effectiveness and achieve reliable business outcomes, avoiding the high failure rates seen in AI projects due to poor data quality.