Did you know that bad data can wreck your business? That’s
right, not updating, verifying and maintaining data is having a bigger impact
on your business than you realize. Bad data is costly and it provides inaccurate information. This ends
up affecting communication and sales strategies that, in the long run, affect and may potentially wreck your business.
Don’t let bad data wreck your business. Here are five things
you can do to stop it:
1. Data Cleansing
This step is a combination of the definition
of business rules and software designed to execute these rules. As a business,
you must identify the way data cleansing tools define rules to determine the
best option for particular data sets.
2. Address Data Quality
A key aspect of managing quality comes from
reviewing the business processes to see where location data is created,
modified and read–all with the intent of improving the quality of the address.
3. Address Standardization
Delivery accuracy saves money while
eliminating the rework and extra costs of failed delivery. This is why it is
crucial to standardize addresses. The best way to deal with this problem is to
treat each non-standard address as an exception and forcing the delivery agent
to deal with it. Another approach is to fix the problem earlier on by using
data tools to transform non-standard addresses into one that conforms with the
4. Data Enhancement
There are numerous ways data sets can be
enhanced. This includes adapting values to meet defined standards, applying
data correction and adding additional attributes.
5. Record Linkage and Matching
Do you know how many data sets contain
information about a specific individual? There are a lot of distributed resources
of information about customers and they each have a valuable piece of
information. But when the pieces of data are merged together they become an
incredibly insightful profile of the customer.
break your business. Don’t let bad data wreck your business. Call Melissa Data today to find out how to implement our solutions and get rid of bad data.