In recent years technology has become prominent, both at work and at home. Machine Learning (ML) and Artificial Intelligence (AI) are evolving quickly today. Almost everyone will have some interaction with a form of AI daily. Some common examples include Siri, Google Maps, Netflix, and Social media (Facebook/Snapchat).AI and ML have popularly used buzzwords right now, often used interchangeably. Most experimentation has been geared to finding specific solutions to specific problems. Artificial Intelligence (AI) is an application in which a machine can perform human-like tasks. At the same time, Machine Learning (ML) is a system that can automatically learn and improve from experience without being directly programmed.
Data quality refers to how relevant information is for use. If the information isn’t suitable, you won’t be able to make the right decisions. Data quality is determined by several factors, including; accuracy, completeness, reliability, relevance, and timeliness. If there’s a missing factor or is lower than other factors, your data quality won’t be very high. Read more about what is data quality and why is it important.
Increased data volumes have put companies under pressure to manage and control their data assets systematically. Also, standard data management practices lack sufficient scalability and cannot manage ever-increasing data volumes. Companies, therefore, need to rethink their data management. The good news is that substantial progress in artificial intelligence (AI) and machine learning (ML) through entities such as DQLabs.ai – AI/ML augmented data quality management platform, can support you in your data management activities.
How has AI and ML transformed quality management?
Besides data predictions, AI helps improve data quality by automating data entry through executing intelligent capture. This ensures all the valuable information is captured, and there are no gaps in the system.
Twofold entries of data can lead to outdated records that result in bad data quality. AI helps eliminate duplicate records in an organization’s database and keeps precise gold keys in the database. It is hard to identify and remove recurring entries in a big company’s repository without implementing sophisticated mechanisms. An organization can combat this by having intelligent systems that can detect and remove duplicate keys.
A small human mistake can drastically affect the utility and the quality of data in a CRM. An AI-enabled system removes defects in a system. Data quality can also be enhanced through the implementation of machine learning-based anomalies.
Apart from correcting and maintaining data integrity, AI can improve data quality by adding to it.
