In today’s world, the constant influx of data from various sources makes it nearly impossible for data to be completely error-free. Just about anyone can generate and share data, the danger of putting out “bad” data is on the increase, making it essential to check, and double-check work data, especially when its origin is questionable. Furthermore, organizations are spending on deriving insights without any consensus on the definition of data quality and no systematic way to sustain high-quality data which results in high operational costs and challenges with integration, metadata, curation, governance, and master data management issues. Data Scientists are spending 80% of their effort on data engineering and data preparation challenges versus focusing on model optimization and algorithms.
As part of this session, Raj Joseph, CEO of DQLabs presenting a demo of DQLabs.ai and speaking about how to leverage Artificial Intelligence and Machine Learning to combine processes, technologies to improve, monitor data quality, and prepare “ready-to-use” data.