Determining what type of data quality tools your organization needs depends upon how sophisticated your data quality strategy is. For some companies, a simple data quality approach may be fine, but other organizations may need a very advanced level of data accuracy and data management.
Regardless of your organization’s specific needs, there are a few key universal data quality considerations that are important for any effective approach. Here are six tools that can help with your data quality strategy.
1. Data cleansing: Data cleansing tools are needed when an organization’s data must meet specific domain restrictions, integrity constraints or other business rules. These types of tools provide accurate information for business use. Examples include address verification, email verification, phone validation, etc.
Data is an increasingly important part of organizations today. It can be leveraged for traditional operations and efficiency, but now is also being used to gain a better understanding of the consumer, prompt personalized marketing messages and determine a host of new product innovations.
The increasing use of data is putting a greater spotlight on information and how it is used. But many companies have issues with their data. In fact, a recent Experian Data Quality study found that U.S. companies believe on average a quarter of the information in their databases is inaccurate.
This is anti-autoresponder, death of the automatic email reply.
Digital marketers have relied on the autoresponder for years. It has commonly been used as an engagement tool to send automatic messages to users when they sign up for a free trial, attend a webinar or request a monthly newsletter.
Autoresponse: A brief history
To understand where it has all gone wrong, we should first understand why it was created in the first place. Initially, the autoresponder was created in mail transfer agents to create ‘bounce back’ emails so users knew when an email wasn't getting to its intended recipient.