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Tackling The Challenges Of Dirty Data

| 16 Apr 2021

Tackling the Challenges of Dirty Data: We all know that Big Data must be viewed through the lens of business context in order to be utilized. This is where master data management comes in. This is a program that collects data from multiple sources and determines its veracity in order to ensure data consistency.

A business’s first step is usually to inventory its data assets. It establishes the baseline for relative values, uniqueness, and validity of all incoming data. Based on these ratings, all incoming data will be evaluated going forward.

Sounds good, right? However, data asset inventories by themselves are simply not sufficient. As an organization grows, it encounters hurdles, and the greater the growth, the greater the chances of compromising data quality.

Dirty data can be caused by many factors. Human error is at the top of the list. Typos, erroneous entries, and duplications of entries are among the many things to be addressed.

The next big challenge is IT architecture. IT relies on a variety of hardware platforms and software solutions. Their mesh-up could cause data problems if it is not done properly. Additionally, not updating systems as the Enterprise grows could also result in consistency errors.

Then there is the problem of data decay, which not many businesses factor in.

The list is long.

Each year, dirty data costs businesses thousands of dollars. Data entry errors can have an adverse effect on business. Knowing the types of dirty data that commonly make it into enterprise systems can help you improve your data quality. Download this free e-book to know how to tackle dirty data.

In this tackling the challenges of Dirty Data e-book you will get to read about:

* What Is Dirty Data
* Areas Of Operations That Get Impacted Because Of Dirty Data
* How To Ensure Master Data Management
* The Use Of AI In Cleaning Data

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