Machine learning applications have the potential to transform business strategy in a significant way. They are showing up in obvious and not so obvious platforms and applications, including healthcare. Information Age brought this interesting topic to our attention in their article, “How to sharpen machine learning with smarter management of edge cases.”
The key to successful use of machine learning comes down to data. Data quality is often overlooked as important. However, it’s crucial for accurate and relevant forecasts. If data is mislabeled or annotated incorrectly, all the predictions will be based on misconceptions.
Data quality cannot be compromised due to a heavy reliance on data models to make business decisions. Poor data quality can not only lead to negative consequences and heavy financial losses, but it can also potentially tarnish the repetition of the entire organization due to weak business strategies and errors in marketing campaigns.
Data quality is ensured by data completeness, data consistency, data currency and absence of redundancy. These are the major attributes that a good quality data set has and that make it trustworthy and reliable.
Metadata makes digital content findable. However, findability works only when a proper taxonomy is in place. Proper indexing against a strong standards-based taxonomy increases the findability of data. Access Innovations is one of a very small number of companies able to help its clients generate ANSI/ISO/W3C-compliant taxonomies.
Melody K. Smith
Sponsored by Access Innovations, changing search to found.
