solution to large data image.jpg

Every successful business scales and grows. As a result, so
does its database. And as data grows so do problems including: faulty customer
entry, breakdowns in data migration, disparate upstream legacy, and big data
systems. All of these problems lead to one thing: bad data. High quality data
is key to uncovering meaningful insights and new opportunities. Bad data, on
the other hand, poses missed opportunities and bad decisions.

You don’t have to be misinformed due to bad data. In fact,
you can have smart data that poses smart insights and opportunities. Melissa
Data has recently released a new tool, Data Profiler, to help companies dealing
with large quantities of data that need a full spectrum of solutions that work. 

Data Profiler is made up of two modules:

Module 1: Discovery

An analysis of data before it is loaded into a data warehouse, ensuring consistency in formatting as well as
entry on all fields to avoid problems downstream.

Module 2: Monitoring

The continual analysis of warehoused data that ensures
consistent data quality over time. Data Profiler can be integrated in any place that data is
streaming in or out and has the ability to assess the results of data quality
efforts. Check out our video to learn more.

Gain business value from your customer data. Our data profiling helps you discover existing weaknesses in your database so you can create and enforce business rules on incoming records and maintain data quality. Profiling is an important step in empowering full lifecycle data management. For more contact data solutions visit our website.

1 thought on “The Solution to Large Quantities of Data

  1. The Solution to Large Quantities of Data

    Every successful business scales and grows. As a result, so does its database. And as data grows so do problems including: faulty customer entry, breakdowns in data migration, disparate upstream legacy, and big data systems. All of these problems lead…

Leave a Reply

Your email address will not be published. Required fields are marked *