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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:

-Data Cleansing

-Address Data Quality

-Address Standardization

-Data Enhancement

-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. For more information, read our whitepaper

By Natalia Crawford

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