With all the different types of operating systems and platforms that we use today, as well as the amount of data we generate, it’s commonplace for some of that data to contain flaws.
The flawed data contains duplicates, inconsistencies, is unintelligible and incomplete. Poor data quality costs U.S. companies over $600 billion dollars a year. Imagine the legal ramifications of dealing with flawed data within a financial institution.
In fact, poor data quality is one of the quickest way to lose customers since trust would be lost. Data profiling is best done as prevention, before you send out your data; you want to make sure there aren’t any anomalies; this is done by analyzing the data from its source, and collecting that data to make sure of its quality.
In layman’s terms, you want to make sure that the language your computer is speaking is understood to other systems your computer is communicating with, or something could be lost in translation. This is generally the issue with poor data quality, and it could be avoided with some profiling done at the beginning of the communication process.
Even with the best of intentions, operating systems and platforms will still misunderstand or interpret some of that data incorrectly, as much as 25%.
Common data errors include:
Poor data quality takes up valuable storage space, and makes your system run very slow. All of this adds up to costly errors that can impact your business.
This is where Data Ladder’s DataMatch solves duplication problems. It also links records and cleanses data. A good data quality software tool like DataMatch helps maintain operating systems, keeping them clean and error-free. DataMatch is the error watchdog.