Using Artificial Intelligence and ML in data quality management.
In recent years the technology has become prominent. AI and Machine learning are evolving quickly today. Almost today, everyone will have some interaction with a form of AI daily, some examples like Siri, Google Maps, etc. Artificial Intelligence is an app in which a machine can perform human-like tasks. Same time, ML is a system that can automatically learn and improve from experience without being directly programmed.
As data volumes have grown companies are under pressure to manage and control their data assets systematically. Also, traditional data processing practices are insufficiently scalable and cannot manage ever-increasing data volumes.
AI/ML augmented data quality management platform, can support you in your data management activities
How has AI and ML transformed quality management?
- Automatic Data Collection:
Asides from data predictions, AI helps in data quality improvement by automating data entry through executing intelligent capture. This ensures that all valuable information is captured, and there are no gaps in the system.
- Recognize duplicates:
Twofold entries of data can lead to outdated records, resulting in poor data quality. AI helps organizations to eliminate duplicate records in their database and keep precise records in the database.
- Anomalies are detected:
A small human error can drastically affect the accuracy and the quality of data in a CRM. An AI-enabled system can detect and eliminate flaws in a system. The implementation of machine learning-based anomalies can also improve data quality.
- Fill data gaps:
While many automation can cleanse data based on programming rules, it’s almost impossible for them to fill in missing data gaps without human involvement or additional data source feeds. Machine learning can make calculated assessments on missing data based on how it perceives the situation.
- Match and validate data:
It may take a long time to come up with rules to match data collected from various sources. Machine learning models can be programmed to learn the rules and predict matches for new data.
Most companies look for fast analytics with high-quality insights to deliver real-time benefits based on quick decisions. Many leading data quality tools and solution providers have dabbled in machine learning territory in expectation of increasing the effectiveness of their solutions. As a result, it has the ability to be a game-changer for businesses seeking to improve data quality.