Data quality is an essential component for most businesses today, as reliable, accurate information provides insights into consumer behavior and trends, allowing businesses to make decisions to move their company forward. But many companies are stumped when it comes to making sure the highest quality data possible is being used for business decisions. That’s why on Thursday, September 2017, Dan Adams, Vice President, Data Product Management Pitney Bowes, Linda Brendish, Contributor Forbes Insights, and Anthony Scriffignano Senior Vice President, Chief Data Scientist Dun & Bradstreet will discuss tips and best practices for getting the most out of your data.
During the session, experts from the field will discuss the risks associated with using poor-quality data in your business, where data accuracy and quality data count the most, tips on improving data quality, and more. Here are a few ways the experts at Pitney Bowes say you can can get started using high-quality and accurate information to improve business outcomes:
- Data Cleansing and Profiling. By applying data cleansing and profiling techniques like de-duplication efforts and putting flags in place when issues are detected, data may be researched and corrected for use as-needed, ensuring accuracy and reliability.
- Data Enrichment. Data mining techniques like geocoding or geotags may enhance data sets, or add previously collected anecdotal information that gives the data a richer historical context.
- Data Discovery Tools. Tools and techniques may be used to connect information from other sources to a given data set on an ongoing basis. For instance, a business may utilize discovery tools to monitor for statistics within a particular database to help improve a data set as a project or initiative progresses.
Ensuring data quality requires taking into consideration and remedying a number of variables that have the potential to make data sets incomplete or inaccurate. 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. The overall goal in ensuring data quality is so businesses will gain actionable insights that are informed by accurate and complete findings. If data is an integral part of your business strategy, you won’t want to miss this informative session about why data is a differentiator to most businesses today.