March 18 marks a bit of an awkward holiday: Awkward Moments Day. It's a day that celebrates those times that make you cringe, moments that make you face-palm, and stories that wake you up at 4 in the morning thinking, "Ugh! Why did I do that?" Although these moments can be uncomfortable, it's helpful to remember that everyone has them, and if they say they don't … We know they’re lying.  It's also helpful to remember that these embarrassing stories can help us learn and do better the next time around.

At Avengers headquarters, Tony Stark is busily creating gadgets for Spider-Man as a thank-you for his dedication to superheroism. Stark places the final touches and debates how to deliver the package. He could suit up and deliver it himself, convince Doctor Strange to teleport it, or use the boring, everyday mail system.  Stark runs all these scenarios through his head— Doctor Strange's focus has been a little divided lately, so he'll probably mess up the location and drop it on some poor lady’s head.

On October 21, we celebrate Marty McFly Day, dedicated to honoring the time-traveling teen's contribution to the world of time travel in the "Back to the Future" universe. In the spirit of this iconic day, we embark on a journey to learn from Marty's adventures and apply those lessons to address data quality in the business world.  Just as Marty's actions set off ripples through time, we'll explore how address data accuracy—or the lack thereof—can create a ripple effect with profound consequences.

Handling address data is such a routine occurrence for health insurance providers that it’s not the first thing that would come to mind as being a liability. However, the reality is that discrepancies and inaccuracies in address data can have far-reaching consequences, posing serious privacy and compliance risks. Healthcare fraud, reputational damage, and financial penalties are just a few examples of the potential hazards. In this article, we’ll delve into three common types of privacy and compliance risks resulting from incorrect or incomplete address data.

When it comes to physical health, sometimes the smallest things can have a big impact. A microscopic virus or a small injury can develop into a life-threatening infection before you even know it’s there — and conversely, a tiny pill or ointment can save a life. The little things matter when it comes to health data, too. In particular, address data is a small part of a patient profile that can have far-reaching effects if it is incorrect or incomplete. Address data is a key information point at the center of billing and communication with patients, and even a slight mistake in an address—a missing unit number or a misspelled street name—can make mail undeliverable.

Ever since the first Blue Cross health insurance plan was provided to Texas schoolteachers in 1929 for a monthly fee of $6, health insurance providers have formed the backbone of the American healthcare system. Today, as that system faces ongoing scrutiny, health insurance providers are still at the heart of the action, striving to connect patients to the highest possible quality of care while navigating significant changes including rising costs, digital transformation, medical advances, customer expectations, and ever-present regulations.

Every business executive knows that good data is critical. When you’re developing your sales strategy and talking about how to increase revenue, you probably turn to data to answer questions like these: - What areas of growth can we exploit? - Which products are performing well? - Is mobile or desktop driving more sales? Where is traffic increasing? - How do we identify the “low-hanging fruit” customers? - Where can we reduce costs? But how do you know if your data is accurate? What steps do you need to take to ensure your data is actionable and driving revenue? In our free ebook, 4 Steps to Unlocking Latent Revenue and Driving ROI with Your Address Data?, we show you how to get clean address data, and then how to use that address data to unlock latent revenue and drive ROI.

Typos, typoes or typo's? No matter how you spell the word, typos are a problem. Even the most accurate typist occasionally misspells a word, leaves off a letter at the end, or just types a different word than intended. Typos in a text to your friend aren't a big deal. They'll figure out what you mean using context clues. But in a business data scenario, if you're following the logic of traditional address matching, where an address is either an exact match or not a match, you can't account for different formats or misspellings.

We recently released a free ebook, 4 Steps to Unlocking Latent Revenue and Driving ROI with Your Address Data, in which we show you how to use your company’s address data to unlock latent revenue and drive ROI. In our last article, we went over address parsing. Today, we’ll explain address normalization and why this matters for data cleansing and analytics. Address Normalization (or making an address look normal) is the process of formatting an address so that it matches the proper address format of the national postal authority, such as the USPS for those in the United States.

Our global economy comprises many industries—healthcare, financial services, insurance, commercial development, and more. But one thing all companies have in common, no matter the industry? They’re all chasing a bigger and better return on investment (ROI). While there are many drivers of ROI, have you ever considered the role that address validation plays? Clean address data is essential for many business functions. Consider eCommerce, one rapidly growing example. Forecasting shows this retail channel will account for 22 percent of all global retail sales by 2024.

There are many problems you may run into if you're working with address data. In our recent webinar, our own Adam Charlton went over some of the challenges he's encountered the most, and how to deal with them. Here’s some of what he talked about. You can view the full recording at the link below. Standardization Standardizing addresses helps solve many of the problems inherent with data blending (combining two or more address databases together). In order to properly blend different address databases together you need to either have an external ID correlate the two sources, or have identically formatted address fields.