Data Standardization: Define, Test, and Transform

While organizations shift towards establishing a data culture across the enterprise, many are still struggling to get their data right. Pulling data from disparate sources and getting varying formats and representations of what is supposed to be the same information – causes serious roadblocks in your data journey. Teams experience delays and mistakes while carrying out their routine operations or extracting insights from datasets. Such problems force businesses to introduce a data standardization mechanism –

How To Validate An Email Address With Regular Expressions (Regex)

Virtually every programming language supports regular expressions nowadays. While some developers don’t like them, they truly are a best practice as they typically perform functions like validation extremely fast with fewer server resources. Email addresses are a perfect example… where they can be easily checked to ensure they’re properly formatted. Keep in mind that validation is not verification. Validation simply means that the data passed follows a standard format that is properly constructed. Some interesting

Deduplication: Best Practices For Avoiding Or Correcting Duplicate Customer Data

Duplicate data does not only reduce the accuracy of business insights, but it compromises the quality your customer experience as well. Although the consequences of duplicate data are faced by everyone – IT managers, business users, data analysts – it has the worst impact on a company’s marketing operations. As marketers represent the company’s product and service offerings in the industry, poor data can quickly deface your brand reputation and lead to delivering negative customer