Data Quality
Correct, standardize and validate your enterprise information.
Unlock the potential of your data
After identifying your data issues in the data profiling phase, it’s time to begin correcting these issues. In the data quality phase, you will start to improve the quality of information throughout the enterprise by creating business rules to correct, standardize and validate your data.
DataFlux Methodology Stage 2 of 5 – Data Quality
High-quality data is essential to successful business operations. When you're working from a true picture of your organization, you can understand your business environment, more accurately reach your customers, maximize profitability and reduce costly operational inefficiencies. Data quality helps you:
- Plan and prioritize data correction initiatives to begin to build more consistent, accurate and reliable data across business systems
- Parse data into separate components to help identify and resolve problematic data
- Standardize, correct and normalize data to create a more unified view of corporate information
- Verify and validate data accuracy to improve the overall accuracy of customer records, product data and other information
- Apply business rules across the enterprise to ensure all corporate data reflects business needs
Standardize and Transform Your Data
There are three components that combine to ensure the quality and integrity of data during the data quality phase.
| Data Rationalization |
Automatically validate data and consolidate similar information
- Create an automatic data rationalization framework
- Validate and consolidate similar information.
- Consolidate customer records into identifiable customer groups
- Utilize sophisticated fuzzy matching technology and innovative householding methodologies
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| Data Standardization |
Transform inconsistent data into one common product representation, using out-of-the box or customized rules to:
- Standardize information
- Enforce uniform abbreviations
- Correct spelling
- Format patterns
- Create rules to enforce standards
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| Data Transformations |
DataFlux's natural language parsing engine allows you to break apart single fields that contain multiple values to unlock the potential of data, automatically identifying:
- Address information
- Measurements
- Quantities
- Packaging information
- Manufacturer names
- Product IDs
- Customized strings
- Any other common customer or product data element
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The Next Step: Data Integration
After you have corrected, standardized, validated and verified data from your data sources, you will have standardized and high-quality throughout your systems. But you still need to make all of this data work together. This comes in the next phase, data integration >>