Enterprise Data Cleansing

Enterprise data cleansing can be a challenge for users. From validating addresses to correcting typos and matching zip codes, there is a lot of data that must be sorted and optimized. A data cleansing software package can make all the difference for a company working with hundreds of thousands of records.

Implementing a data quality plan for your company should become standard operating procedure. While it may take some adjustments, creating a single view of your data will help you gain a better understanding of your business.

Here are some steps to getting your enterprise data quality program on track:

  1. Define and Plan: Identify the data that is important in the day to day process of your operation. Define certain validation rules for standardizing your data, identifying fields that are critical to your goals. This may include job title, email address, or zip code.
  2. Assess: Understand what needs to be cleaned up, what information is missing, and what can be deleted. Make the data cleansing process easier by creating exceptions to rules.
  3. Execute: Run the cleansing process! Create workflows to standardize and cleanse the flow of data to make it easier to automate the process. Investigate, standardize, match, and survive data sets as necessary.
  4. Review: Define certain fix procedures for future use. Be sure to audit and correct data that cannot be automatically corrected, such as phone numbers or emails.
  5. Manage and Monitor: Evaluating the database is important after the cleansing process is complete. Track the results of any campaigns that ran with the updated, cleansed data, (bounced emails or returned mail pieces) through reporting functions.

 

DataMatch Enterprise is a full service data cleansing solution for large amounts of data. Our software tools provide:

- Matching and deduplication
- Enrichment
- Standardization and categorization of data based on business rules
- Validation of data against standard rules or patterns such as zip codes

Download a free trial of DataMatch Enterprise today!