As the amount of data generated and collected increases with every year, we are witnessing the unprecedented rise in popularity of the methods designed to manage and analyze large amounts of data. Data matching is not an entirely new concept and its history goes as far back as 1946. But now, when almost every company is continuously bombarded with large data feeds from many different sources, data matching is more useful than ever before.
For starters, I will only briefly explain what data matching is and where it sits in the largest concepts of data integration and data management.
How to Integrate Data from Different Sources
There are three steps you need to perform to integrate data from different sources:
- Schema matching: it identifies the structures from different databases that point to the same piece of information
- Data matching: it identifies and matches fields from different databases that point to the same real objects. Duplicate detection is a particular case here, where the analysis is performed on a single database.
- Data fusion: it merges and matches data into consistent entities
This should be enough for the theoretical part. Let’s see how you can use data matching to boost your business.
How to Use Data Matching
For many companies, it is usual to send leaflets promoting their products and services. But it often happens that the business mailing list contains several records of the same customer. The reasons are pretty common:
- marriage or divorce
- moving out
- incorrect or inconsistent data entered in different applications
If your business mailing list is not up-to-date, there is an immediate impact. In the first place, your marketing campaign will cost you more money. It is due to the fact that you send several copies to the same persons. Moreover, these customers will certainly not be happy to receive multiple copies of the same marketing product.
Real Life Scenario
If you think that this will not happen to you, just consider a simple example.
Let’s suppose that several months ago you launched your new mobile app. It was a success and the customers are now regularly using it in detriment of the website. So far so good. But the address field on the mobile app is comprised of a single free-form field, with a limit imposed on its length. When a customer that moved out updates his address field, he will abbreviate his address. Most likely, you’ll end up with a duplicate into your business mailing list. WinPure, with its intelligent fuzzy data matching algorithm, can help companies find the most true matches with the least false matches and correct large sets of data in a few seconds. Thus, it could eliminate the waste of resources that usually happens when the business mailing list is not cleansed.
A more complex scenario, but also very common these days, is when several businesses collaborate for a cross-marketing campaign. Just think about the fact that a social media campaign collects only data available on Facebook or Twitter, while a phone campaign collects customer’s cell and maybe the home number.
Obviously, it is not trivial to get a single customer of view out of this and a small detail can make the difference. As Justin Honaman, former VP of retail sales at Coca-Cola summed up for TechTarget: ”There’s definitely an alienation component. I could not tell you the number of times we would get replies where people were irate: ‘I’m not so and so. You sent this to me but that’s my ex-husband’s email address’.”
In this article, we discussed only two cases where data matching can improve your business outcomes. But you should consider that data matching can help in a variety of situations, from managing customer service information to improving internal workflows.
These days, almost all companies do rely on vast amounts of data. Since the data is not static but continuously changing, it is impossible to manually keep it clean, up-to-date, and meaningful. Download the free trial and see for yourself how our fuzzy and phonetic data matching algorithm can help you save valuable time.