Elections in Norway are happening, a lot of people have already delivered their vote and the deadline to deliver your vote is around the corner (deadline on September the 9th, go vote!). In this post, we will look into a little discovery we did while exploring the data regarding the candidates in the Norwegian local elections for 2019. We will introduce the idea that data, and especially bad data, can change and has changed the course of history. This, in the context of big data and especially digitalization, can have some serious consequences.
How can (bad) data change the course of history?
Let us start by introducing the concept of the ripple effect. This effect refers to one small happening having ramifications that expand far beyond and can be combined with the effect of other small happenings. These small, inciting happenings, when combined and let “in the free” can have incrementally important consequences.
In the context of the Big Data deluge, the pebble causing the ripple is bad data, meaning data with no veracity. Along history, there are several examples of how bad data can have a major ripple effect on the course of history. A few examples include:
- Christopher Colombus’ discovery of the Americas
- The presence of tetraethyllead in bensin
- The Mars orbiter disaster
- The Enron Scandal …
See the figure below for a really cool infographic with some more cases of the ripple effect changing the course of history and some details about them.
We just wanted to calculate the candidates political churn!
A friend and I were interested in understanding the phenomenon of political churn. Political churn of candidates means that a candidate in party A changes to party B. This led us to analyze the data set from the Norwegian local elections in 2019. We used the data set with the information about the candidates participating in the elections. This data set can be found at:
The data looks like this:
And this is where it gets very interesting! We created a few extra features in the data set in order to identify candidates that changed parties. The resulting data set was something like this:
As you can see we created 4 extra columns,
- Name + birth year: This, in order to try to create a unique identifier
- kommune + party: This was just to calculate the rank of the politician
- changed: Another column to calculate the rank of the politician
- Rank: The rank of the politician (the higher the rank the higher the chances for him to win a post in the elections)
Then our little discovery came:
We found 14 people that had, presumably, registered for the same elections under distinct parties.
The list of candidates looked like this:
We started looking case by case and this is what we found…
There are people born on the same year and with the same name and they are participating in the 2019 local elections. However there are people that have registered under two different parties in the same elections!
Accident? Bad data? Or bad intentions?
After careful analysis, we found out that 3 persons out of the 14 duplicates where actually registered under two different parties or municipalities…
John Helge Larse, born 1956 is participating in the elections in Fjord (ranked 5 under Arbeiderpartiet) and in the elections in Loppa (ranked 10 under Arbeiderpartiet).
Ann-Hege Lervåg born in 1974 is participating in the elections in Vega (ranked 3 under Senterpartiet) and in the elections in Namsos (ranked 8 under Senterpartiet).
Adiam Menghisteab Haile born in 1972 is participating in the elections in Kristiansand both under Arbeiderpartiet and Miljøpartiet (ranked53 and 30, respectively).
This issues should be looked upon by the right authorities to determine the intent of the candidates or to correct these types of errors in order to avoid ripple effects!
If you want to warm up to election day… Play with some interactive data visualizations!
Click on the image below and get ready to explore the demographics of the candidates in the local 2019 Norwegian elections. The dashboard is interactive and runs on the browser. It works on PCs, Tablets and Mobiles. The dashboard is interactive and every dimension in the tables and charts functions as a filter. Among the things you can explore are:
- Which party attracts the most youngsters
- Which party has the best gender balance
- Which party has the most women registered as top candidates
- How are political parties distributed over Norway
- Etc!
Perhaps in a future post, we will present some of this fun facts. Most of the statistics from SSB and a few news papers can be reproduced with this dashboard. Feel very free to play with it 😀
About the writer
Arturo is a technologist born in Veracruz, Mexico and has been living in Norway since 2010. He completed his BSc and MSc in Mexico and his PhD in Trondheim, Norway. He is taking the last semester of his Executive MBA at BI in Oslo, to be concluded in September 2020.
During his experience as a scientific researcher, which started with the publication of a couple of papers in international journals after his BSc thesis, he has participated in several research projects. The results of these projects can be found in the 6 research papers shared below. The topics of these projects vary from theoretical physics, mathematics, statistics, and applications of Big Data technologies to understand human behavior. Part of Arturo’s training as an academic includes the use of different programming languages and scientific tools such as Mathematica, Matlab, C++, being Python one of his most common go-to tools.
After concluding his PhD work at NTNU, Arturo made a transition into the private sector in 2015. He started leading the development of the Mobility Analytics service in the business division of Telenor Norway. Arturo led the development of this service, from strategic aspects and all the way to the technical aspects of the development. Not only did Arturo work in tasks such as developing Go To Market strategies but also with hands-on development of GIS data visualizations and development of the code-base to power the Mobility Analytics Service.
The extensive experience as a leader and communicator in the scientific and private sector fields has made Arturo a firm believer that it is not often that leaders and executives make decisions based on hard, cold numbers, but that it is necessary to communicate the results from the analytic work via great story-telling and understanding of the business mechanisms in which value can be created. This realization made Arturo take the decision to start his EMBA program in 2019.
During his EMBA studies with a specialization in managing and developing digital enterprises, and which include a leadership program, Arturo has expanded his business knowledge to include fields such as digitalization strategies, innovation frameworks (such as design thinking lean and agile), entrepreneurship, HR management, and value creation and capture.
As his professional track shows, Arturo has a mind eager to always learn and apply his knowledge. He has taken several courses and certifications within Machine Learning and Artificial Intelligence and has virtually never stopped studying, even after concluding his doctoral degree.
Arturo enjoys mentoring and helping colleagues and people in general. He is always open to grab a coffee, a slice of pizza or a pint of beer, so… feel free to send him a message and connect with him, even if you haven’t met him in person.
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