Some Data Quality issues and, how you will deal with those problems?
1. Duplicate data
Modern organizations face an onslaught of data from all directions – local databases, cloud data lakes, and streaming data. Additionally, they may have application and system silos. There is bound to be a lot of duplication and overlap in these sources. Duplication of contact details, for example, affects customer experience significantly. Marketing campaigns suffer if some prospects get missed out while some may get contacted again and again. Duplicate data increases the probability of skewed analytical results. As training data, it can also produce skewed ML models.
Rule-based data quality management can help you keep a check on duplicate and overlapping records. With predictive DQ, rules are auto-generated and continuously improved by learning from the data itself. Predictive DQ identifies fuzzy and exactly matching data, quantifies it into a likelihood score for duplicates, and helps deliver continuous data quality across all applications.
2. Ambiguous data
In large databases or data lakes, some errors can creep in even with strict supervision. This situation gets more overwhelming for data streaming at high speed. Column headings can be misleading, formatting can have issues, and spelling errors can go undetected. Such ambiguous data can introduce multiple flaws in reporting and analytics.
Continuously monitoring with autogenerated rules, predictive DQ resolves ambiguity quickly by tracking down issues as soon as they arise. It delivers high-quality data pipelines for real-time analytics and trusted outcomes.