Duplicate data is a serious problem that affects an organization’s insights, eats up expensive storage space, messes up customer information & leads the business into
Inaccurate data has real-world implications across industries. In law enforcement, inaccurate data could mean booking the wrong person for a crime. In healthcare, it could
Where there is data, there is regulation. Most of all in the financial industry. Banks, insurance companies and financial institutes must deal with a complex
Address data is semi-structured, making it one of the most challenging components in a data matching activity. For long now, manual data matching methods including
Data errors today can easily cascade into millions in losses and poor data quality can cost businesses up to 31% of their revenue. As the
From 2013 to 2015, Facebook and Google were tricked into wiring over $100 million to a scammer’s account who impersonated a trusted vendor. The fraud
Data quality (DQ) and data quality management (DQM) is emerging as a needed business strategy in enterprise-level organizations. Although not a new concept (data quality
Data, an organization’s intellectual asset, must be treated and regularly enriched to remain useful and valuable. Over 80% of companies we’ve worked with, — including
44% of companies lose over 10% of annual revenue due to poor-quality CRM data. This shows how many CRM users’ trust in their data is
Merge purge is the process of combining records from multiple data sources into one dataset, then identifying and resolving the duplicates so what’s left is
Data preparation tools have been around for quite some time now. Most of these tools though require users to be proficient in programming languages &
As companies are spending billions of dollars investing in big data with the hopes of turning data into money, the need for efficient, easy-to-use data preparation
