In “The trifecta of the best data quality management” article, I’ve addressed why a data quality program is needed and what the recurring steps you should always go through are in order to carry on continuous and sustainable efforts to improve and/ or maintain the quality of your
According to the IBM Big Data & Analytics Hub, poor data costs the US economy $3.1 trillion every year. Ovum Research reported that poor data can cost businesses at least 30% of revenues. These numbers are staggering and in my opinion, they should be enough
It’s generally accepted that any organization working with data needs to adopt data governance. Readily available frameworks, tools, and services can be adapted to the requirements and environment of your organization, yet when it comes to big data governance, the options are a bit more
Having accurate and current addresses for your constituents is one of the most important thing in your database. You’re using this information to engage with them, solicit them, inform them, communicate with them, and learn more about them in order to develop and evolve a
If you’ve already started or planning to start a data governance program to support your data quality improvement goals, you need a structured way of tracking your data quality issues and their status. There are different ways of doing this, of course, with either the
Myth #1: It’s all about fixing the data Data cleansing is a very important aspect of improving data quality, but it’s not the only one. In order to have a sustainable data quality program you can’t just fix the data. You need to understand what
Data is an enterprise asset, enabling the organization to make informed decisions. In order to secure data’s position as a highly valuable asset, the organization needs to implement a data quality management program. This will achieve and sustain a high level of quality for the