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 program collects data from multiple sources and verifies its accuracy 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 alone are insufficient. 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 duplicate entries are among the many things to address.
The next big challenge is IT architecture. IT relies on a variety of hardware platforms and software solutions. Their mesh-up could cause data issues if not done properly. Additionally, failing to update systems as the Enterprise grows could lead to 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 adversely affect a business. Knowing the types of dirty data that commonly enter enterprise systems can help you improve your data quality.
Download this free e-book to understand the impact of dirty data and how to tackle it to improve decision-making.
In this, tackling the challenges of Dirty Data, 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
Schedule a call today for Customer Analytics, Marketing Analytics, Business Intelligence, Data Cleaning, AI-Apps, Visualization solutions, and services.
