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.
Data quality is something organizations know they need---eighty--nine percent of U.S. companies plan to make data quality solutions a priority in the next 12 months. But the question is, how do you get to a state of better data quality? What are the right solutions? Can’t data just magically appear accurate and fit for a given purpose?
In today’s data-driven economy, we all want data at our finger tips and we use it in a wide variety of ways across the business, from marketing automation, operations, consumer insights and so much more.
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.
Data is truly at the heart of every organization. We use it to back up business proposals and initiatives, prepare forecasts and projections, pinpoint areas for improvement, and substantiate cases we try to build. We can’t rely solely on instinct and gut feeling because they are intangible, and with the amount of information collected in today’s data-driven society, most businesses have come to expect the credibility that data brings and are investing in that power.
The major consumer credit bureaus expect for data furnishers to report on their data in a single, standardized format, known as Metro 2®. While the Metro 2® standards are designed to make it easier to keep credit information up-to-date, many organizations still face many challenges with their Metro 2® reporting. From lack of resources to manual, time-consuming processes, many organizations currently struggling to comply with Metro 2® regulation take a reactionary approach to their reporting. As consumers become more well informed about their credit, through various ease-of-access channels, and as disputes grow exponentially, many data furnishers are looking for ways to ease their Metro 2® reporting.
Last week, I had the opportunity of attending the NASWA UI Directors’ Conference and IT/Legal Issues Forum in Orlando, Florida. The conference was a forum to collaborate and discuss innovative ways to improve customer service and business decision making, while fighting fraud within state workforce agencies. At this conference, I had the ability to connect with leadership to discuss the impact that quality data can have on their systems and processes.
In today’s highly competitive business landscape, the data an organization collects is expected to deliver insight and value back to the business. Therefore, there is an increased focus on the accuracy and reliability of data collected, while there is also the apparent need for business users to have direct access to their data. We are seeing organizations express their commitment to making data-driven decisions, and this is only possible when business users are directly able to understand and leverage data to make these decisions. Despite this growing need, a common problem presents itself when IT is the keeper of an organization’s data, and business users have to wait for insight from the IT that they can understand.
When you build something, the final product is only as strong as the foundation it was built upon. Building a company is no different. It’s not uncommon for startups, in the pursuit of rapid growth and higher valuations, to accidentally allow the basics to become an afterthought.