We’ve all heard the saying “you are what you eat” in reference to our morning donut and coffee. But did you ever consider that the same principle applies to your organization’s data collection processes? Much like the saturated fats from bad-for-you food that clog up your arteries, unchecked bad data can enter your database and compromise your ability to draw on that information in the future. That’s why it’s important to put measures in place to validate and correct bad data at every point of entry. Before your database has a cardiac event (and increases your stress levels), let’s talk about your data’s diet.
Knowing what kind of address you’re mailing to can be important and often will influence the amount you pay for postage and shipping. Shipping to residential addresses, for example, tends to be more expensive than shipping to commercial ones. There are residential and commercial addresses, mixed addresses, as well as post office boxes. Post office boxes (P.O. Box) are traditionally used by individuals to receive mail in areas where mail may not be delivered directly to their homes. People also use P.O. boxes for security, privacy, the need for quicker delivery, and to maintain a permanent mailing address.
According to a study by 451 research, the cloud storage market is expected to double from 2016 to 2017, with cloud storage costs jumping from $19 billion to nearly $40 billion. As organizations prepare to invest even more in storing their data, it is important to remember that storing vast amounts of data is only valuable when you know what to do with that data. Storing data you do not use can cost more than you may think.
As digital marketers, the technology available to help us get the word out about our products seems limitless. Whether you’re focused on hyper-personalization, segmentation, or building out models to target ideal customers, the most tried and true channel has been, and for the foreseeable future will remain, email marketing. Why do we love it so much? It’s fast and efficient and inexpensive compared to other channels.
After collecting email addresses through various channels and validating only the form and syntax of those collected through their Ecommerce channel, Cabela’s had amassed a large number of suspected “bad” email addresses over the years. Experian Data Quality's batch email cleansing solution helped them recover a significant portion of email addresses to include in email marketing communications.
A lot goes into confirming that an address is valid. For the United States Postal Service (USPS), there are several pieces of information needed to verify an address. When you use address validation software, it checks the information entered against USPS’s database and requirements. A couple of terms to know when it comes to address validation are CASS and DPV. The Coding Accuracy Support System (CASS) is a certification process used by USPS to evaluate the accuracy of address correction/matching software. Delivery Point Validation (DPV), which is one of the USPS-sourced files included in CASS data, is used to check the status of a specific address within the United States.
There are many different variations in the way addresses can be written, depending on which country you're in, which country you're mailing to, where you got the address information (a business card, a postal authority, an email signature, etc.), how recent the addresses are, and whether they have all the required parts of a complete address. All these factors contribute to potential areas of inaccuracies in address data.
Here's what you can do to verify your address data:
The costs of shipping can vary based on the type of address to which you’re sending. We incorporate three datasets into our address verification solutions to help businesses get a better idea of what kind of address they are shipping to (business or residential), and that helps them make more cost effective shipping decisions. To help you decide which dataset is best for you, we’ve outlined the differences between ATI, RDI, and RTI.
This week I was able to attend the Gartner Data and Analytics Summit in Dallas. For those of you who weren’t able to join this meeting of data professionals, I thought I would share a few of my key take-aways from the event.
When it comes to data management practices, government agencies today have their work cut out for them. As the breadth and volume of data entering organizations continue to grow, harnessing this information for strategic initiatives becomes increasingly elusive—even for the most advanced agencies. One such area where data is heavily relied upon is in the transportation sector, which is typically responsible for maintaining roadways and airways in addition to issuing permits for both residents and companies. As you can imagine, transportation departments have a lot of information that requires strong data management practices.