Kevin W. McCarthy is a Director of Product Marketing on the Global Product Marketing team at Experian Data Quality. He has spent more 20 years in the data quality and data management space, working directly with clients worldwide in nearly every vertical, including financial services, retail, and manufacturing. In previous roles, Kevin has served as a data quality implementer, architect and marketer. He is a lifelong Massachusetts resident, graduating from Merrimack College with a dual degree in Computer Science and Psychology, and he now resides on the North Shore with his wife and two boys.
Interested in more insights from Kevin? Check out his posts on Dataversity.
My colleague and I had a great time presenting (and conversing) about data quality and data management at the 13th Annual MIT Chief Data Officer and Information Quality Symposium (MITCDOIQ). Read the recap about how we were able to explore the rich insights of our Global Data Management Report and the trends and impact that data management initiatives are having on today’s businesses.
Data is a critical but often overlooked component of delivering superior customer experiences. Most of us know firsthand how bad data creates a negative experience. Customers, rightfully, want to be treated as if they are special. They expect the companies they do business with to know them, understand them and remember them. Delivering on that expectation requires great data.
Last week, myself and members of the Experian team attended the MDM and Data Governance Summit in Chicago. The main topics of this conference were MDM (Master Data Management) and DG (Data Governance), although at many times, it was difficult to tell the difference. MDM and DG are starting to meld together as one topic, with MDM being the data repository for all (or as much as possible) corporate data, and DG being the documentation and “GPS” for navigating the data (in this case, GPS means “Gain Perspective Simply”.
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 two of three in a mini-series we are calling The art and science of matching your data.
Matching data should be simple, right? Well, that depends on your perspective. As much as processes can be automated these days, when it comes to record matching, the results still depend on the context in which you want to view the relationships.
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.
In schoolyard terms, data migrations are the equivalent of the old “Telephone” game that you may have played as a kid. You get a line of people together, and the first person in the line whispers a sentence to the second person – “The quick brown fox jumped over the lazy dogs.” The second person then whispers this phrase to the next person, and so on, until they get to the end of the line. At that point, the last person says what the sentence is – in this case, “The slick clown’s socks slumped over the crazy bogs.” As you can see, the end result may be similar to the start, but it’s definitely not the same!
Data quality can be boring. Yes, I said it. And this comes from someone who has worked in the data quality space for more than 20 years. When I’m at a social gathering, I dread the inevitable “so what do you do?” question. My short answer is usually “boring computer stuff.” Heaven forbid they try to dig deeper! Then it becomes this awkward explanation about reducing the amount of junk mail they get or some pseudo-relatable data activity, and then watching their eyes begin to glaze over. At the end of the day, I use my tried-and-tested conversation changer, “enough about me, what about you?” I double majored in Computer Science and Psychology, and while Computer Science ultimately provided the foundation of my career, I’ve found that the psychology background is often more valuable for dealing with people and sticky situations.