Disrupting Data Governance by Laura B. Madsen

Author:Laura B. Madsen [Laura B. Madsen]
Language: eng
Format: epub
Publisher: Technics Publications
Published: 2019-12-31T16:00:00+00:00

Right brain left brain

I have come to think about it like this. If artificial intelligence is the thing that gets all the attention, takes what we know and turns it into decisions, actions, art, or music, it’s like our right brain. The machine learning takes the data and categorizes it for us, indexes it into constructs that are easy for us to navigate. Think about when you learn something new: you scan first, then categorize, and finally, you develop a new way of thinking. Machine learning is our left brain, scanning and categorizing the mammoth amount of information that is available today.

In order to break down the feasibility of using ML to improve our enterprise data quality I did two things: determined what we meant by data quality and then reviewed the science and statistics behind the machine learning algorithms.

In order to help our ML algorithms train the data to improve, I first needed to know what good data quality looks like. To understand this, I turned to Steve Johnson, professor at the University of Minnesota. Steve is on a personal mission to use data to solve some of the world’s biggest problems. Not long after setting this goal, however, he realized that he wouldn’t get far until the data improved. As such, he took a slight detour from his original mission to clearly define and operationalize what we meant by “Good Data Quality” (GDQ). Steve and a group of other researchers have been at it for over two years now. They have agreed on a standard subset of elements that make up good data quality. 3

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