Clean, reliable records are non-negotiable for data teams. The real question is which approach to data quality better fits their needs. For some, that means
OpenRefine is an impressive open-source tool – but it’s not built for enterprise-scale data quality issues. If you’re tidying up small datasets or fixing column
Everyone says data is power. But let’s be honest: in most companies, data is politics. It’s locked in tools no one understands, hoarded by teams
In 2024, National Public Data, a prominent background check and fraud prevention service, made headlines for all the wrong reasons. Over 2.7 billion records containing
Duplicate records in a company’s datasets cause serious roadblocks to business success. 92% of organizations claim to suffer from the nightmare of data duplication. Nowadays,
92% of organizations claim that their data sources are full of duplicate records. To make things worse, valuable information is present in every duplicate that
With hundreds of data sources and an average of 259 distinct software applications and systems, organizations are drowning in information and tools. This, coupled with
Did you know poor data quality can drain as much as 25-31% of a company’s revenue? While there are multiple factors that can affect data
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
Part of doing business – whether locally or worldwide involves ensuring you have the right customer address data. Without a strong address standardization process in
A data cleansing tool is perhaps the most powerful and yet the most underestimated solution. Companies spend millions of dollars in procuring cloud solutions and
Let’s face it – it’s embarrassing when you send out mails to the wrong audience set. More so, it is costly. Millions of dollars go
Data Ladder offers an end-to-end data quality and matching engine to enhance the reliability and accuracy of the enterprise data ecosystem without friction.
