Data Quality is key for effective business growth. The way that data quality is achieved is through data quality tools and techniques that will improve business value. Here are five key aspects to data quality management:
-Address Data Quality
-Record Linkage and Matching
First up is data cleansing. Data cleansing combines the definition of business rules in concert with software designed to execute those rules. The approach taken here is to integrate rules into a data cleansing rules engine, and then present strings to be corrected through the engine. In some cases, a little bit more control is needed in order to effectively transform and correctly correct the data.
Next up is addressing quality. This involves reviewing a lot of the existing documentation that has been collected from a number of different operational systems, as well as reviewing the business processes to see where location data is either created, modified, or read.
Thirdly, we have address standardization. In the US, an address contains a street name and number, as well as a city, state, and postal code. The refinement can begin with the state, then resolve down to the city, state, and a postal code.
Then there is data enhancement. Due to most business applications being designed to serve a specific purpose, the amount of data either collected or created is typically just enough to get the specific job done. That causes the "degree of utility" to be limited to that single business application. Data sets can be enhances and there are numerous ways for that to happen. However, a challenge that tends to emerge is that the data collected is not sufficient quality for secondary uses. Luckily, this can easily be addressed through the process of adding information to data sets to improve its potential utility, known as data enhancements.
Lastly, we have record linkage and matching. Electronic footprints are really broad due to the growth of online interactions. There are many distributed sources of information about customers, and each individual piece of collected data holds a little bit of value. When these distributed pieces of data are merged together, they can be used to reconstruct an incredibly insightful profile of the customer.
If you are interested in learning more about how using data quality tools and techniques can improve your business value click here to read the full whitepaper.By Natalia Crawford