Customer Information Management
What is Data Quality?
Data quality refers to the completeness, accuracy and timeliness of a piece of data when applied to a specific purpose or set of conditions. In this context, high-quality data is considered information that meets a set of quantitative and qualitative standards for a predetermined scenario, making it a useful asset.
Ensuring data quality requires taking into consideration and remedying a number of variables that have the potential to make data sets incomplete or inaccurate. This involves applying data cleansing and profiling techniques both when the data is initially collected and after it has been determined how the data will be used.
For instance, businesses may employ a platform that monitors for potential errors like data duplication, and flags them to the appropriate party for analysis and correction once these issues are detected. Data quality also involves data enrichment, wherein a similar process of data mining will add details to a piece of data that enhance its value for a given purpose. This can include applying geocoding or geotags to a particular data set, or adding previously collected anecdotal information that gives the data a richer historical context.
Data quality also involves using data discovery tools and techniques to connect information from other sources to a given data set on an ongoing basis. For instance, a business may utilize these discovery tools to monitor for statistics within a particular database to help improve a data set as a project or initiative progresses.
Data quality helps contribute to the larger processes of data governance and master data management by helping to support specific business goals. This includes ongoing quality assurance and issue tracking for database management teams as well as supporting different decision-making initiatives.
Many businesses also utilize an in-house data standardization procedure that acts as a business rules engine to ensure that all data, no matter what its intended purpose, meets baseline qualifications before it can be considered for a specific application. Inclusive of these standards are rules regarding data privacy and use that dictate what information can be accessed to enhance or enrich a data set.
The overall goal in ensuring data quality is to give businesses actionable insights that are informed by accurate and complete findings. Without a vetting process for incoming data, stockpiles of information can become difficult for organizations to manage, leaving the door open for potentially valuable data assets to slip through the cracks.
How it benefits you:
Rather than deal with inaccurate, misleading or incomplete data sets, data quality standards and practices can help automatically and systematically streamline data aggregation so that only useful information is considered for analysis. This feeds into larger master data management initiatives that can help you think smarter about specific business projects and initiatives.
What Pitney Bowes offers:
Pitney Bowes data quality software makes it easier to integrate data quality at the point of entry, or as part of a batch process, giving all users access to accurate and trusted data for better decision making and operational efficiency.