Data Quality Assessment
The only way to truly leverage your customer and sales data to further your marketing objectives is to start with quality data. The data quality assessment phase of the data discovery process involves making an evaluation of the quality and completeness of your data set so that it can be cleaned up before analysis, if needed.
Clean data is good data. The reality in business is that almost no data set in existence contains 100% clean, accurate and usable data that is ready for analysis. Almost all databases contain some errors. Examples of unclean data sets include:
- Missing values (e.g., a record lacks a value in the zip code field)
- Incorrect values (e.g., a sales revenue figure is entered as $10,000 instead of $1,000)
- Misaligned values (e.g., the state field contains an address value)
- Data type errors (e.g., the sales units field is designated as a text field rather than as a numeric field)
- Address fields are not CASS-certified
- Extraneous values (e.g., the Name field includes periods, commas, or forward slashes)
Our data quality assessment step involves making a thorough examination of your data while taking a detailed accounting of areas whereby your data?s quality falls short. We can fix most or all of the errors we encounter during the data preparation step.