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6 tools for building a data quality strategy

Determining what type of data quality tools your organization needs depends upon how sophisticated your data quality strategy is. For some companies, a simple data quality approach may be fine, but other organizations may need a very advanced level of data accuracy and data management.

Regardless of your organization’s specific needs, there are a few key universal data quality considerations that are important for any effective approach. Here are six tools that can help with your data quality strategy.

1. Data cleansing: Data cleansing tools are needed when an organization’s data must meet specific domain restrictions, integrity constraints or other business rules. These types of tools provide accurate information for business use. Examples include address verification, email verification, phone validation, etc.

2. Enrichment: Enrichment tools can append additional, third-party information into an organization’s existing database so they can make actionable, strategic decisions based on the enhanced consumer insights.

3. Parsing and standardization: Parsing and standardization tools provide a consistent format that allows for better data consolidation and consistency. These tools help format data values based on industry, local or user-defined standards.

4. Monitoring: Monitoring tools are back-office solutions that monitor an organization’s data to ensure it is fit for purpose over time. These tools track degradation or changes to data and can alert the proper employees if issues arise.

5. Data profiling: Data profiling tools help collect and analyze statistics to provide insight into the quality of an organization’s data. These tools help identify data elements that may need to be updated or replaced.

6. Matching and linkage: Matching and linkage tools help identify and combine pieces of data into a single record. For example, if Richard Wilson and Rich Wilson are in your database and share the same phone number or email address, they may be the same person. This means Wilson's customer records can be combined to better understand who he is and track his interactions with your business.

All of these tools can be combined for a holistic data quality solution, but your company may not require all of these capabilities. Customizing a solution based on your company’s unique needs is the best way to ensure you’re getting the most out of your data.

Check out our data quality buyer’s guide for additional information about choosing the tools that are best suited for your organization’s data quality strategy.

data quality buyer's guide
  • Why quality tools are needed
  • How to definine a custom data quality strategy
  • Ways to identify the best solutions

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