Posted on

The Essential Data Cleansing Checklist

Data quality issues, such as missing, duplicate, inaccurate, valid, and inconsistent values, cause headaches in finding and using data sets. Having a suitable data cleansing procedure handles this bad data and makes it suitable for other people and systems.

A helpful data cleansing process standardizes data, fixes, or removes erroneous values, and formats records to be readable. You get these adequate results from data cleansing when you know your data’s original purpose and visualize the good data you require to meet new goals. You need to create a good foundation and run through the essential data cleansing checklist in this article to achieve your objectives.

Posted on

7 Data Management Pitfalls To Avoid

Introduction

Most businesses are aware of why migrating applications and workloads to the cloud is beneficial. Cloud technology is a necessity in today’s big data world. However, with change comes risk. When IT systems go down or aren’t managed effectively, the entire business suffers. 

Big data investment is clearly advantageous, but poor management can mean a big mess. Averting a data management crisis is easier when you’re aware of the common mistakes others have made. That way you can be ready with a solution instead of spending time on the phone or on a video call with your team.