Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it’s up to date. Measuring data quality levels can help organizations identify data errors that need to be resolved and assess whether the data in their IT systems is fit to serve its intended purpose. But what happens when the analytics aren’t accurate? This interesting information came to us from DazeInfo in their article, “The Big Reasons Your Data Analytics Is Consistently Wrong.”
As more businesses undergo digital transformations, more will understand and leverage the power of data analytics. Unfortunately, this means that even more companies will get it wrong.
Bad data can have significant business consequences for companies. Poor-quality data is often pegged as the source of operational pitfalls, inaccurate analytics and ill-conceived business strategies.
Accurate data is important. Finding that data is necessary. Making data accessible is something we know a little about. Whatever you are searching for, it is important to have a comprehensive search feature and quality indexing against a standards-based taxonomy. Choose the right partner in technology, especially when your content is in their hands. Access Innovations is known as a leader in database production, standards development, and creating and applying taxonomies.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, changing search to found.
