Data collection and the use of algorithms to parse through and determine meaning in the information, has always been a subject I find interesting.  How corporations use this data is a topic that should be discussed and understood, not only by students in Digital Technology and Culture, but by the general public as well. Technological literacy is probably the most important skill that needs to be acquired for current generations and the upcoming generations.  Privacy concerns and a lack of transparency are probably the biggest concern for users right now. Understanding how the technology we use works, what the information it gathers on us is and how it is used, is an extremely important part of how our technology works, how companies make money, and how our lives are run.  

Who is collecting the data, who they are giving it to, what they are using it for, and how much are they collecting.  How do we come to trust corporations that do this?

Over the course of this semester I have submitted 3 Multimodal Analyses, two of which covered data collection and the other trust.  In my first Multimodal Analysis I discussed Google Maps, and the collection of your location data. In this analysis, how this information was used outwardly to the public and to the user was discussed.  My second Analysis was about corporate trust and the factors that create trust in a consumer base. I determined that the leadings factor in trust are quality and choice. The existence of a true competitive market is what allows a consumer to find a corporation they like and trust.  When you take away choice by creating monopolies, the amount of trust in your consumer base plummets. My third analysis was about credit scores and how data collection has affected risk assessment for lenders and lendees. As data collection has improved algorithmic bias has resulted in credit scores becoming once again what they were created to overcome.

Multimodal Analysis 1: 28 Minutes to Work

The title is a reference to the banner notification that users receive when they have location services turned on for Google Maps.  When you frequent a location consistently on the same days and time, Google will automatically tell you how long it take to get to that location.Overall, I use Google Maps to discuss the collection of  your location data, how it is used, and how it is collected. The overall theme being about the consumer being the product when a free service is provided, how this information was used outwardly to the public and to the user was discussed.

Multimodal Analysis 2: All the World is Faith and Trust and Pixie Dust

Trust is the most valuable trait to a consumer base, if a consumer can trust a corporation to tell them the truth and not screw them over, then you have created a loyal consumer base.  To determine what constitutes trust I looked at the most and least trusted companies, and through this I determined that the leading factor in trust are quality and choice. The existence of a true competitive market is what allows a consumer to find a corporation they like and trust.  When you take away choice by creating monopolies, the amount of trust in your consumer base plummets, since choice in product is eliminated the quality of service is compromised as well. Certain institutions require more trust than others: banks, lenders, etc. things that hold livelihoods in their hands, but as security breaches happen, and events such as the 2008 financial crisis, how is is possible to trust these entities.

Multimodal Analysis 3: Please Don’t End Up Selling Fish to Tourists in T-Shirts

The title of this analysis is in reference to a Free Credit Report dot Com commercial that has always stuck with me.  The idea behind this commercial is, you need to watch your credit because if someone steals your identity or a mistake is made on your report, then your livelihood could be so affected that, “you could end up selling fish to tourists in t-shirts”.  So this analysis goes through the history of the credit report and all the elements that go into your 3-digit number. The way the credit score has evolved over-time has become cyclical rather than linear, we have seen an attempt to remove bias in lending and the return to it.