Best Data Quality Software include:
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What is Data Quality Software?
Data quality software is designed to ensure that business data is as reliable as possible. Reliable data is essential if it is to provide a solid basis for effective decision-making. Data quality tools improve business data by detecting anomalies across multiple data sources, as well as cleansing and standardizing data for analysis.
Data Quality Software Features & Capabilities
Connectivity to multiple data sources
Data profiling and auditing to help find anomalies, hidden relationships between data elements
Seamless integration with Mister Data Management (MDM) systems
Parsing and standardization of data elements according to pre-defined rules
Match and merge capability
Data format and valid address checking
Why Data Quality Matters
This imperative to improve data quality has become more critical with the emergence of big data strategies. Increasing data volumes make the issue of data quality more important than ever.
The goal is for data to be complete such that, for example, all disparate pieces of data for a single customer are linked. Data also needs to be clean with automatic elimination of typos and abbreviation errors.
Data Quality Improvement Strategies
Among the most common strategies for improving data quality are:
Parsing and standardizing data. Data is broken up into multiple structured elements and then each element is standardized according to predefined rules.
Matching and merging data. Data records that look similar are automatically flagged as possible matches, and if they are found to be matches the records are then merged.
Case management. Some data records will be found to be incomplete or erroneous. This problematic data is automatically flagged and placed in a queue where it can be investigated and remediated later.
Address Verification: Checking format and that address represent a real physical location.
Data Quality Benefits
Availability of high-quality data for business intelligence projects and master data management.
Reduced time to implement data governance or compliance audits.
Consolidated views of customers and households enabling more effective cross and upselling.
Provision of research data for fraud detection and planning.