Joint research by IBM and Carnegie Mellon University found that 90% of data in an organization is never successfully used for any strategic purpose. McKinsey Consulting found that an average business user Key AI Patterns spends Improving Data two hours a day looking for the right data? and according to Experian Data Quality? bad data costs 12% of the company’s revenue [2].
So? what can enterprises do improve data quality? While there are many solutions and options to improve data quality? AI is a very viable option. AI can significantly enhance data quality in several ways. Here are 12 key use cases or patterns from four categories where AI can help in improving the data quality in business enterprises.
Figure 1: 12 Key Use Cases for Improving Data Quality with AI
Data Profiling and Cleansing
Data profiling involves analyzing and understanding philippines whatsapp number data the structure? content? and relationships associated with data. Data cleansing includes formatting? de-duping? renaming? correcting? improving accuracy? populating empty attributes? aggregating? blending and/or any other data remediation activities that help to improve data quality. AI can help in data profiling and cleansing through:
Automated Data Cleaning: AI can identify and correct errors typos? identify and purge duplicate? incomplete? and irrelevant records? and resolve data inconsistencies? especially on nomenclature and job function library taxonomy based on pre-defined rules. AI can also help in data transformation including data normalization? standardization? and de-duping. Data normalization is adjusting the data values to a common scale. For example? converting data from text to numeric. AI can standardize dates? addresses? and units of measurement to a defined standard. AI can also help in data deduplication by eliminating duplicate copies of repeating data.
Proactive Data Remediation
Machine learning and predictive analytics algorithms afb directory can be trained to recognize common patterns of data errors or inconsistencies and apply suitable corrections. For example? AI models can be used to predict and fill in missing values? use contextual information to provide more accurate imputations? and more.