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How to Identify Missing Data, Ensure Data Completeness & Maintain the Accuracy of Your Data
Research shows that businesses can lose up to $3 of every $10 of revenue due to poor data quality. With incomplete data known to be
What is Data Accuracy, Why it Matters and How Companies Can Ensure They Have Accurate Data.
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
Why Banks Need Powerful, Agile Data Preparation Solutions for Accurate and Timely Regulatory Reporting
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 Matching Does Not Have to be a Resource-Draining Challenge. Here’s How You Can Do it Better.
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 Profiling vs. Data Cleansing – Key Differences, Use Cases & Importance in Current Business Environments
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
Classify Product Data: Classification Standards and How to Implement them Painlessly
[vc_row][vc_column][vc_column_text]Accurate product data forms an integral part of a consumer’s purchase decision. From search results to conversions, from brand recognition to comparative product evaluations to
Product Matching: The Key Factor to Accurate Sales and Marketing Intelligence
Product matching is the process of matching identical products from various sources through deep learning technologies. In a competitive landscape, where the customer’s spending habit
The Importance of Product Data Governance for Retail Businesses
Unlike traditional stores, digital retailers rely on the effective categorization and dissemination of their product data on their web pages. So having accurately categorized product data on their
Matching Evolution: Finding Matches Across the Enterprise and Fine Tuning Results the Modern Way
As data undergoes a paradigm shift, so do the systems, processes, and approaches involved. Legacy systems are dying. Batch ETL pipelines are slowly becoming obsolete. Ownership is moving away from IT to
