Inaccurate data has real-world implications across industries. In law enforcement, inaccurate data could mean booking the wrong person for a crime.
Category: Data management
Data quality (DQ) and data quality management (DQM) is emerging as a needed business strategy in enterprise-level organizations. Although not a
Data, an organization’s intellectual asset, must be treated and regularly enriched to remain useful and valuable. Over 80% of companies we’ve
Customer personalization is a digital business imperative that requires clean, updated, enriched customer data. The data enrichment process, part of a
In a world fascinated with the limitless opportunities of AI, ML, and predictive analysis, data quality has become a significant challenge.
You may have often heard of ZIP+4 address verification when implementing a data cleansing and matching activity. But do you know
It won’t be possible to give an overview of data management trends 2020 without conducting an analysis of the clients and
Using Wordsmith to Remove Noise and Standardize Data in Bulk for Greater Matching Confidence The data that flows into your organization
“It takes $1 to verify a record as it’s entered, $10 to cleanse and dedupe it, and $100 if nothing