There are many definitions of data quality, but the commonly understood meaning is: the degree or extent to which an organization’s data meets certain quality characteristics such as: accuracy, completeness of data, validity, uniqueness, consistency, integrity, timeliness and up-to-date. Eventually, data must be able to meet with user and customer demands for efficiency.
High quality data, therefore, is data that can satisfy the requirements of its intended use. An example of high-quality data is data that has the right names and addresses for a company to use in a marketing campaign.
Industries most affected with bad data are healthcare, finance, education, and other public-service institutes where bad data can wreak havoc in people’s lives. Therefore, it’s imperative for organizations to focus on implementing data quality initiatives.
A data quality tool like Data Ladder’s Data Match will help you achieve important data quality management objectives. Some of these common objectives are:
Accuracy: Data that conveys true information without errors (such as invalid address or email fields) is considered accurate data. Accuracy affects every outcome of a data’s intended use. Be it for analytics, for reporting, for research, for marketing or for business intelligence – you need data that you can rely on to complete a task.
Completeness: Data completeness differs according to your respective business needs. However, complete data in the general sense refers to data that has up-to-date contact information.
Relevancy: Your organization may be dealing with terabytes of data each day, but how much of that data is relevant to your business needs? More importantly, how much of that data is duplicated or redundant? Irrelevant, redundant, obsolete data take up much needed storage space and prevents you from obtaining accurate reporting and analysis.
Up-to-Date: Historical data acquired haphazardly needs to be made useful and up-to-date to meet with automation, AI, modern business intelligence and consumer demand requests.
The primary goal of data quality tools is to help you improve data quality and ensure that you meet your data quality management goals so you no longer incur losses due to bad data.
Data Ladder’s DataMatch Enterprise is a full-fledged data quality solution that enables organizations to perform key data management operations – including, but not limited to: data profiling, data cleansing, data preparation, data standardization and most importantly, data matching.
Data Ladder’s data quality software is an automated commercial solution that does not require the need to learn or possess any specific talent or skill. You also don’t need to spend hundreds of millions of dollars on hiring or retaining talent, when you can spend significantly lesser in an automated solution.
Fix, update and maintain data quality.
Data transformation bottlenecks.
Merge data from disparate data sources to get a complete source of truth.
Deduplicate redundant data.
Typos, invalid, incomplete, null or inaccurate data fields.
Fully visual, intuitive interface.
Affordable package; costs 95% less than comparable solutions.
Support for disparate data sources for record linkage.
Semantic matching for unstructured data.
Complete set of data cleansing tools.
During your 30-day trial, you can access DataMatch Enterprise risk-free. The data preparation software is user-friendly and easy to install – what you see is what you get! However, we recommend a 30 to 60-minute non-obligatory online consultation with one of our subject matter experts to help you get the most out of your free trial.
