In the real world, data is never 100% percent perfect, 100% accurate, and 100%
complete. By nature, data is full of errors: omissions, inconsistencies, and duplicates.
That’s why understanding the data quality process is important for many businesses trying to sort out their data. Inevitably the need arises for a good data quality tools that can clean, deduplicate, and match data from various sources.
Without the right tools, removing duplicates can often betime-consuming…and cause even more errors!One of the critical pieces of eliminating duplicates is survivorship. If you have duplicate records, which one should stay (survive) and which one should go?
Truth be told, managing customer data in any size organization can be a real challenge. From home moves to major life changes such as marriage, keeping a streamlined database with clean, up to date information isn’t always an easy task. And, statistics show that a customer’s information becomes redundant as soon as these life changes occur. So, for those in the data quality field, what’s the best way to manage all of this information?
Well, let’s get to the bottom of the issue. There are several reasons for these redundancies:
- Customer data changes occur too late
- Islands of information aren’t synchronized and integrated properly
- Data is not complete in time to make important business decisions
Before using data cleansing software, it’s important to understand that there are four critical 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
Identifying and profiling data sources from various formats (such as Excel, Access, XML, SQL Database) and implementing a system that adequately handles the amount and type of data for analyzing is critical to a good data governance program.
So how can one ensure data that is free from duplicates? There are a few steps one can take to reduce the possibility of having duplicate records exist. Here’s a short checklist:
- Be sure to enter all data (addresses, phone numbers, email addresses) accurately through the various opt-in methods, and check for misspells and duplicate entries.
- After running email marketing campaigns, regularly manage data hygiene through removing duplicates, inactive or wrong addresses. Use of a data cleansing software can help with this task.
- Create a streamlined process at your company or organization that enforces standards for inputting data.
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.
Real World Case Studies
With the substantial growth in data cleansing activities in industries such as healthcare and education over the last several years, there has been increasing demand for high performing data cleansing software tools.West Virginia University was recently tasked with assessing the long-term impacts of certain medical conditions on patients over an extended period of time. Through using Data Ladder’s record linkage software, researchers were able to link two groups of records together to make the determination on whether previous medical conditions affected long-term health and patient care.
Data Ladder also worked with Zurich Insurance on their record linkage activities. In the insurance industry, it is critical to have payee names aggregate and match for the functioning of various payment processes. The constant need to monitor data requires clean, usable data due to the stringent requirements of the industry.
Read some of Data Ladder’s case studies across various industries:
|EDP Consulting Inc.
|Power & Energy
|Printing & Graphics
|Quick Reliable Printing
DataMatch Enterprise, Data Ladder’s premier data cleansing software, helps companies detect duplicates across numerous sources. In a recent independent study, DataMatch Enterprise outperformed companies such as IBM and SAS on both accuracy and speed.
Through special record linkage algorithms, Data Ladder’s DataMatch data cleansing 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
- See graphical reports on the number of records with potential linkages
- Free consultation with our record linkage software experts
Unfortunately, normal duplicate removal software routines can delete vital business data. Data Ladder’s DataMatch Enterprise does not delete any information from the source files. All information is kept temporarily in memory where you can test different removal settings without consequence.
What was once a lengthy manual process has now become more intelligent and smarter, without the use of standardization or rule sets. The Data Ladder Decision Engine learns from human input on what is and what not a match to make decisions is. Very large data sets can be loaded across multiple machines and sorted quickly.
Keep in mind that getting started with a data management project takes strategy. Whether you are looking for short-term fixes or need a major overhaul, your approach will determine whether your company sees changes on an operational front or from a marketing perspective.
Here are some key things to look for when searching for the right data management tool:
- Smooth integration with databases and other systems
- Hierarchy management capabilities
- The ability to match and merge data across various sources