If you ask senior leaders of your company if they consider data to be an asset, chances are that they’re going to say yes. If you ask them how confident they are in the accuracy of that data however, the answers quickly start to vary.
It’s not uncommon for challenges that are considered “just the way it is” to be truly simple fixes if you attack them from a data quality standpoint. Doing a cleanse of your customer database can make a world of difference for your customer communications, customer experience, and cost control.
This blog post is the third and final post in a mini-series we are calling The art and science of matching your data. In the previous matching articles, we talked about the fundamentals of data matching, and both the art and the science of building matching rules based on the context of your end goal. In this final section, I want to discuss some of the more advanced aspects of record matching, and how they can provide business value.
This blog post is part one of three in a mini-series we are calling The art and science of matching your data.
Matching is a term used commonly throughout data management, but it is also known by several other terms: linking, deduplication, joining, aggregation, and so on. For the purposes of this discussion, let’s define matching at the process in which I can determine a relevant association between two or more individual data records.
We live in an era of healthy living (whether we like it or not). Much to my dismay, I find my doctor constantly telling me to eat more fruits and vegetables, whereas I would rather be eating a cheeseburger and fries. And that’s not all – drink more water, cut out carbohydrates, take the stairs, get more sleep – it’s endless! The reality is, my doctor is right—and if I want to live a long and prosperous life, I need to take a comprehensive approach to my healthy lifestyle. Eating a green bean occasionally isn’t going to do the trick. I must see how I can incorporate as many aspects of healthy living as I can into my everyday life.
“Boo!” Is that a ghost or ghoul? No—it’s something much spookier: bad customer contact data. Did you know that less than half of retailers trust their data to make important business decisions? In fact, 57 percent of retailers say that they rely on educated guesses or gut feelings to make decisions based on their data. While blood and guts may have a place in horror movies, gut feelings are simply not enough to go on for important business decisions. Accurate, reliable data to drive decision-making is a far stronger retail strategy.
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.