We've used this analogy many times here at Experian Data Quality, but that's only because it makes a lot of sense when referencing data standardization. What analogy am I talking about? The one where we discuss how a robust data management strategy relies on a methodical, step-by-step approach—much like how you'd approach building a house.
So you’re tasked with your organization’s next big data migration. Maybe you’re moving to a new CRM system. Maybe you’ve just acquired another company and need to integrate their data into your system. Whatever the reason, data migrations are critical processes that many businesses go through. The continuing explosion in the volume of data businesses collect, store and use suggests that the trend of most companies engaging in some data migration project isn’t letting up anytime soon. According to a recent data migration study, 91% of companies engage in data migration projects.
Whether you’re working on a large data migration project, or simply trying to answer business questions with your data and having issues, Experian Pandora should be part of the conversation. We often hear from customers that they have disparate databases, leverage excel to house important data, or just have no idea how bad their data really is. This often leads to us running data tests on a subset of their data, which typically cannot tell the whole story, or is not statistically significant based on the total size of their database.
As a Health and Human Services provider, you know how important health is. Connecting people with the resources they need to improve their lives is a top priority. But do you ever consider the health of your organization? The same way that healthy people enjoy fuller lives, healthy organizations are better able to provide their constituents with excellent services. In order to make the right strategic decisions, you need your data to be healthy too. So what does it mean to have healthy data and how can you make sure you do?
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.
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