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The Complete Guide to Data Cleaning Tools, Solutions & Best Practices for Enterprise Level
Fewer than half (49%) of data practitioners have high trust in their data. The rest are navigating critical business decisions with incomplete, inconsistent, and inaccurate
What is a Postal Code and Why is it Important for Address Verification and Validation?
Imagine sending out thousands of marketing materials only to discover that a significant portion never reaches its intended audience. The result? Wasted resources, missed opportunities,
Key Components that Should be Part of Your Operational Efficiency Goals
Operational efficiency remains a key challenge for enterprise-level businesses, especially in an age when customers have very little patience and the competition is high. It
How Transaction Matching Software Can Empower Your Financial Institution & Improve Operational Efficiency
Do your banking officers or insurance agents still manually compare spreadsheets to make sense of data? Do you still have manual journal entries as part
Data Management Trends 2020 – An Overview by Data Ladder
It won’t be possible to give an overview of data management trends 2020 without conducting an analysis of the clients and the projects we worked
Your Complete Guide to List Matching Software and Approaches
Most companies now understand that new technologies and applications must be implemented in order to upscale business operations. But implementing a data migration of a
Name Matching Software vs Algorithms: Which is Best for Your Business?
Imagine sending out a promotional email to one of your best customers, William Rogers, that opens with “Dear Willy Rog.” It may seem like a
Using Wordsmith to Remove Noise and Standardize Data in Bulk for Greater Matching Confidence
The data that flows into your organization comes in a variety of formats: inconsistent capitalization, punctuation, obscure acronyms, alpha-numeric characters living in fields they shouldn’t
What Does Data Quality Mean for Your Data Warehouse?
Every year, organizations invest millions into data warehousing projects, often to see them falter due to bad data. Whether it’s inaccurate entries, inconsistent formats, or
