Recently, we hosted a webinar on the modernization of data quality, featuring David Menninger of Ventana Research. While replaying the webinar on-demand, I discovered some really interesting points that are worth diving into. Let me share a bit of what he spoke about, and give you the opportunity to not only view the webinar yourself, but also get your hands on some valuable content pieces.
One of the points David made that resonated with me was that no matter who you are, or who you work for, you need data quality standards. He spoke about the old data adage (that a lot of people know) garbage in, garbage out. So how does someone identify this poor quality data before it gets into the system?
Please realize, it’s a challenge. Most of the folks that are dealing with missing, incomplete, and just plain ugly data don’t realize anything is wrong until they attempt to gain some intelligence by analyzing that data. Then they find out rather quickly how trustworthy their data is. It all comes down to this:
Without high quality data you can’t have confidence in your results
Data prep is something that you can do to minimize the amount of “garbage in”. This is a very common, time-consuming activity, thus making it a big part of the analytic process. Individuals can prep data by experimenting with ways to improve data quality, and export cleansed, individual data sources to the enterprise.
A Ventana Research study says that only 22% of businesses report they are confident in their data preparation efforts. So it’s not unreasonable to see why the #1 data prep benefit is improved quality of information.
Another activity you can do to ensure that your data analytics are working for the people that are using it is data governance. First-rate governance is making sure your data has high quality and is in compliance. With good data governance you can develop rules that you can slowly test and roll out, to define the automated processes that you will be implementing in the enterprise later on.
These are just two of the use cases that you can use to convince your management to invest in a data quality program. There are others, including IoT, AI, and Machine Learning where data quality works to help organizations of any size. David touches on these topics and more, and also provides some recommendations that are only available in the webinar.
Don’t miss out, get this valuable content here.