Understanding the data quality process is important for many businesses trying to sort out their databases. Inevitably the need arises for a good data quality tools that can clean, deduplicate, and match data from various sources. Reviews of various data quality tools provide an overview of features important to the business user. In a recent independent study, Data Ladder’s data cleansing software DataMatch Enterprise outperformed companies such as IBM and SAS on both accuracy and speed.
Data Quality Tools: Get a Plan
Data quality tools can save a lot of money and time for business users trying to sort out their data. From validating addresses to linking records from different databases, data quality tools can make all the difference for a company trying to optimize a marketing campaign or merge several databases.
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 a data quality program on track:
- 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.
- 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.
- 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.
- 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.
- 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.
Before using data quality software, it’s important to understand that there are four critical aspects to good data quality:
- Accuracy: data that has been recorded and input correctly
- Uniqueness: data is input once as necessary
- Timeliness: data is kept up to date
- Consistency: information is uniform across all applications
Identify and profile 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.
DataMatch: Your Data Quality Software Solution
- – Detect and link records within and between data sets with multiple customizable fuzzy matching techniques.
- – Identify and remove 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
- – See graphical reports on the number of records with potential linkages
Data Ladder has been able to help several companies with their data quality needs. Learn more on our case studies page.