Data Managment

During the course of an ordinary work day I took it upon myself to record all of the instances in which I was required to produce any amount of personalized electronic data. The list not only astounded me, but frightened me as well. My day started at six in the morning with a swipe of my electronic key card at my gym. The data in the key card is attached to my social security number, phone number, address, birthdate, and credit card. On my way home I stopped at a coffee shop and paid with a debit card thereby producing another record of my location at an exact time and date. At home while getting ready for work, I logged into a podcast of my favorite National Public Radio show. My login information is connected to a credit card and all of my personal demographics. The time stamp I left while streaming online radio will most likely be saved on a massive server, available for recall for years on end. As disturbing as this seems, this is only the beginning, as my day to day job requires constant surveillance, both by proximity card, audio and video surveillance. Most people can tell you that if you desire privacy at your workplace, do not pursuit a career in corrections. In one eight hour day, my proximity card, which links to my personnel record, is recorded over fifty times a day. In order to enter or exit any door or hallway, one must swipe their proximity card, which logs the activity into a massive database that is backed up into a cloud server on an hourly basis. If you really wanted to know, you could find out exactly how many times I went to the bathroom today and for how long during each trip. To add insult to injury, upon leaving work and arriving on campus for my night class, I was required to swipe my credit card in order to park my car in the parking garage. This data does not even include the so called “passive data” that is collected from my GPS enabled smartphone simply by moving from one place to another while carrying it. And to think that I am just one in billions of people generating a far larger data footprint than I had ever imagined is mystifying. Consequently, as anxieties multiply over the growing forfeiture of privacy that technology users face, especially in the face of public scandals such as the U.S. National Security Agency’s surveillance of electronic communication and Facebook’s emotional manipulation experiment, the perpetual collection and storage of personal data has grown exponentially over the past few years. The volume at which new data is being generated is staggering. We live in an age when the amount of data we expect to be generated in the world is measured in exabytes and zettabyes. By 2025, the forecast is that the Internet will exceed the brain capacity of everyone living on the planet (Webb, 2011). In 2009, the digital universe grew 62 percent or almost 800,000 petabytes, which translates into a stack of DVDs reaching from the Earth to the moon and back (Cukier & Mayer-Schoenberger, 2013). By 2020, it is projected to be 44 times as big as it was in 2009 (those DVDs would be stacked up halfway to Mars) (Cukier & Mayer-Schoenberger, 2013).
But big data is not just about size. It’s about the sheer number of data sources available, its different formats, and the fact that most of it is user generated: 70 percent of thedigital universe is actually generated by all of us through email, Facebook, Twitter, LinkedIn, Flickr, YouTube and other social media sites of the like (Dou, 2011). As of the year 2013 there were: one trillion unique URLs in Google’s index and two billion Google searches every day; 70 million videos available on YouTube; 133 million blogs, more than 29 billion tweets (and three million being added every day; and more than 500 million active Facebook users who spend over 700 billion minutes per month on the site (Cukier & Mayer-Schoenberger, 2013). With all that data being digitally proliferated, maintaining one’s privacy from government or commercial organizations is a difficult, if not impossible, task.
Regardless of its infringement on privacy, big data is persistent and here to stay. Today’s average smartphone offers more computing power than the combined power of every computer NASA used to send humans to the moon in 1969 (Cukier & Mayer-Schoenberger, 2013). Since 2006, The Library of Congress has been archiving all tweets since 2006. And when the Library of Congress archives something, you can count on it being archived for life (Dou 2011). Facebook has tacitly acknowledged that deleting your account does not delete all the data associated with your account (Dou, 2011). Along those same lines, IPhone users were shocked when they learned that they didn’t get to decide whether their locations are tracked via cell towers or not as there is no setting that lets users turn it off nor is there a way to block the logging (Cheng 2011). So, whether or not you're using GPS, if you're using your iPhone as a cell phone, you are being tracked and logged constantly with or without your knowledge or consent. The location data that is logged by any smartphone other than an iPhone is data that is limited to law enforcement, and only obtainable through a court order but iPhone’s track this information and it stays in an unencrypted file on your phone (Cheng 2011). The makes it possible for anyone from a jealous spouse to a private investigator to get a detailed picture of your movements if they are able to get the file stored in yoru phone.
In the mid 90’s Eric Freeman and David Gelernter coined the phrase “lifestream” to describe: “...a time-ordered stream of documents that functions as a diary of your electronic life; every document you create and every document other people send you is stored in your lifestream. The tail of your stream contains documents from the past (starting with your electronic birth certificate). Moving away from the tail and toward the present, your stream contains more recent documents—papers in progress or new electronic mail; other documents (pictures, correspondence, bills, movies, voice mail, software) are stored in between. Moving beyond the present and into the future, the stream contains documents you will need: reminders, calendar items, to-do lists.” The big data revolution has driven massive changes in the ability to process complex events, capture online transactional data, develop products and services for mobile computing, and process many large data events in near real time. These changes have great promise to change the world for the better of humanity. Public, private, and government agencies are using data to improve services to the public and revolutionize the problem solving process to tackle major political and social issues that plague the world. Issues such as crime, poverty, famine, disease, and war. Scientists are using data to predict food shortages ahead of time by studying patterns in drought, migrations, previous regional production, and seasonal variations. Law enforcement use big data to quantifying and locate high crime areas by using data from the time, place, and nature of a crime, thereby making communities safer. With the proper ethical framework in place, big data can put an end to issues that have plagued humanity until the end of time, but without first implementing ethical standards, big data can be abused and misused to do things such as raising insurance premiums based on your internet search history, using social networks for predictive policing, or charging people more for a service by reviewing their online spending history.
As if the issue of ethics and data is not complicated enough, the divided opinions of what even constitutes the ethical issues and constraints, prevent many agencies and organizations from moving forward with their data policies. Without a formal framework for having ethical discussions in business or government environments, people often revert to their own moral code. Which although it’s a great place to start is entirely subjective and complicated by the number of people involved in the discussion. The potential for harm due to unintended consequences can quickly outweigh the value the big-data revolution is able to provide. So, while business innovators and public officials are excited about the potential benefits of products and services based on big-data technologies, the size, variety, and velocity of information available raises an overwhelming amount of issues. Issues that entail the implications of the acquisition, storage, and use of large quantities of data about the public.
While big-data technology offers the ability to connect information and innovate new products and services for both profit and the greater social good, it is, like all technology, ethically neutral. Meaning, it does not come with a built-in perspective on what is right or wrong or what is good or bad in using it. Individuals and corporations, however, do have value systems, and it is only by asking and seeking answers to ethical questions that we can ensure big data is used in a way that aligns with those values. Any organization, be in private, government, or corporate should first ask and seek answers to ethical questions beforehand so that they have a better chance of using their data in a way that aligns with the strategic mission, goal, vision, and values of that organization or agency. In order to do so, four decision points must be decided and agreed upon: inquiry, analysis, articulation, and action (Rayport, 2011). Inquiry is the discovery and discussion of core organizational values and an understanding of what those values actually are (not what we think they are, or more removed, what we think others think they are) (Rayport, 2011). Analysis is the review of current, actual data-handling practices and an assessment of how well they align with core organizational values (Rayport, 2011). Articulation is the explicit, written expression of alignment and gaps between values and practices (Rayport, 2011). Action is the tactical plan that will be used to close alignment gaps that have been identified and to encourage and educate on how to maintain that alignment as conditions change over time (Rayport, 2011). These discussions require each participants personal moral perspective, which is a highly personal and controversial topic. Without these four guiding decision points, conversations about ethics can quickly become polarized and lead to dead ends which impacts the end users substantially.
Americans have been programmed to think that bigger is always better. This just isn’t the case with big data. Just because the database is huge does not mean that insights are going to be better. And big data is practically meaningless without the proper tools to dissect it with. With this in mind, we cannot forget that the data provided to us by the Internet of Things is not always going to be the whole sample of the population you are looking for. There will always be people who will not have a data footprint, either they are not online or have taken intense privacy steps in order to eliminate their data trail. The danger here is that if you forget that you do not really have a full sample, it could lead to erroneous conclusions or missed opportunities. Lastly, as with any new technology, there is always a danger that we will rely too much on the results spit out from the tools and less on critical thinking and thoughtful analysis. Regardless of this danger, turning our backs on big data will not protect us from this reality; it will only seal our failure as the rest of the world jumps on board with this ‘next best thing’.