Since 2020 is a U.S. presidential election year (a sharply heated one), successful data management and reporting have never been more important. As evidenced from the system-wide clusterfuck that was the Iowa Caucuses, it’s apparent that there remains a long way to go in effectively collecting and processing a lot of data when it concerns voting workflows.
I recently attended another informative talk by modern-skillset school, Promotable, called “Common Data-Diven Mistakes,” presented by Jeanette Shutay, Senior Director of Analytics at HAVI, a logistics and supply chain company. She reinforced two fundamental elements in the digital milieu that were at the forefront of political procedures (aftermath ongoing) and reporting at the time. These factors exceeded the boiling point:
- Scale
- Data Quality
Jeanette’s Promotable talk on avoiding common data-driven mistakes must serve as a guide to help the Iowa Caucus’ next (and second) attempt at adopting tech. Her directive “Don’t let the machines make the decision for you” is especially apt with regards to the design and usage of voting tech which was (to be painfully obvious) never meant to get in the voter’s way.
Thanks again to Promotable who proactively connect their data-driven coursework with timely perspectives through their organizing of talks!
My resolution → To get demystified about big data. This is matched by my intent → To attend more events held by Promotable.
To view Jeanette’s talk about common data-driven mistakes and more, go to Promotable’s YouTube channel.
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Check out this well-written and designed report → “Here’s a List of Everything That Went Wrong at the Iowa Caucuses” by Lauren Leatherby, Lazaro Gamio and Keith Collins, New York Times (2-4-2020).
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