Data Digest: Focus on Data Quality and Data Integration
Tips for combating poor data quality, how enterprises can justify their use of data integration tools, and how to get the best insight from your data.
- By Quint Turner
- March 18, 2016
The underlying understanding of big data is 'garbage in, garbage out'; as in, if you analyze data of poor quality, your results will be of poor quality. Making sure your data is of high quality is difficult, but this article has plenty of tips for combating poor data quality.
Read more at Information Age
A common way to improve data quality is through integration. Bringing together many strands of data exponentially increases data's condition. Most enterprises use a tool for this step, but is it worth it? This article looks at how a few enterprises justify their use of data integration tools and offers its own advice on the matter.
Read more at Tech Target
The end goal of practically all big data projects is actionable insight. However, making sure your data is of high enough quality to be mined is an important first step. This article covers five best practices for making sure your enterprise can turn data into insight.
Read more at CIO
Quint Turner is an editorial intern at TDWI and an undergraduate English student at Skidmore College. Follow his blog at pungry.com.