Data Cleansing

The process of cleaning up incomplete or duplicate data has become a major need for businesses of all sizes. Reviews and comparisons of major data cleansing tools provide business users an overview of the best data quality tools on the market today. Data Ladder’s data cleansing tool DataMatch Enterprise outperformed companies such as IBM and SAS on both accuracy and speed in a recent independent study.

Resolving data quality problems is usually one of the largest IT undertakings for companies. Many new users to data cleansing often use the basic “find and replace” or regexes functions through text editors, or may use spreadsheets to sort and aggregate data. These old school methods often take an enormous amount of time and energy, and usually don’t capture all the errors and inconsistencies often found in the large data sets of companies.

Data cleansing tools can eliminate many common human errors, such as incorrect spelling, improper formatting, or missing zip codes. They can also save a lot of money and time for a company.

First, understand that there are four important aspects to good data quality:

  1. Accuracy: data that has been recorded and input correctly
  2. Uniqueness: data is input once as necessary
  3. Timeliness: data is kept up to date
  4. Consistency: information is uniform across all applications

 

There is a general methodology to starting a data cleansing process. Start by identifying and profiling data sources from various formats (such as Excel, Access, XML, SQL Database) and implement a system that adequately handles the amount and type of data for analyzing.

The basic outline for data cleansing process is as follows:

  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. Exceptions to rules also need to be set up, that way the data cleansing process will be easier.
  3. Execute: It’s time to 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: Audit and correct data that cannot be automatically corrected, such as phone numbers or emails. Define certain fix procedures for future use.
  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 cleansed data, such as bounced emails or returned postcards through reporting functions.

 

Data cleansing tools such as DataMatch Enterprise can cleanse a large amount of data. Data Ladder’s DataMatch software suite helps the user:
— Detect and link records within and between data sets with multiple customizable fuzzy match techniques.
— Identify duplicates
— Import and export from Excel, Access, Text Files, ODBC, and other file types.
— Clean data with Data Ladder’s special libraries on nicknames, abbreviations, states, advanced pattern recognition and more
— Correct and clean email addresses
— Parse addresses, email, and other data with customizable parsing tools

The latest version for 2015 also includes improvements to speed and performance, mid-process cancellation, merge purge capabilities, and improved matching statistic reports. Contact us today for a free trial.