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Data Science and Data Analytics have become very broad terms meaning anything from basic linear regression knowledge to engineering massive data pipelines with layers upon layers of predictive machine learning models. Analytic positions today have even become synonymous with entry-level positions, as "analysis" in the title entices new college graduates. However, true data science is a set of tools that allow for the extraction and prediction of information from historical data.
When I was 19, I moved to Laos to work for a non-profit which functioned under the radar of the federal government, developing hygienic facilities and schools in the most rural and difficult to access regions of the impoverished nation. To some extent, my main purpose in going to Laos was to escape the ridiculousness of American society, a society which I had consistently rebelled against whether it be at school or home. However, my time in Laos taught me just how lucky I truly was to have the education I had taken for granted.
I also learned that almost any problem could be broken into one of three issues: communication, education, or perspective. Communication and education issues are problems, but can both be easily resolved by a little effort from both parties. However, perspective issues are much more difficult to resolve. One cannot simply step into the perspective of another person, as much as one may try. My solution was to dedicate myself to data science, a set of tools which will continue to increase our ability to understand and accommodate different perspectives.
But how does data science help with understanding and accommodating different perspectives?
As we continue to become a more digitally connected society, we are leaving data everywhere. Data that reveals little to nothing by itself, but in combination can provide a much better picture of a person or a set of people. Social platforms like Facebook have the ability to determine your sexual preferences, your entire social network, your political leanings, and your main interests. Many other companies are able to determine all sorts of information about you based on your purchasing habits. For example, Target was able to predict a woman was pregnant before she was aware based on her purchase habits. Thousands of companies around the country give cooperatives like Epsilon, Wiland, and Acxiom all of their data in exchange for customized marketing targeting based on your purchases across different retailers and even industries. China is implementing facial recognition in their countries security system to track social credit. Currently this might sound obscene, but we are heading to a place here in America which is very similar. Likely in the next 5-10 years, most companies will have facial recognition systems built into their retail stores. These systems will keep track of your movements throughout the store to help them better understand what products you are interested in and your shopping habits. They might even sell it as an ease-of-use system, such as mirrors which will allow you to try on products without changing, while little will you know its reading your facial expression and preparing your next customized advertisement. The simple fact is everywhere you go, you are leaving bits and pieces of yourself. In fact, Seagate expects that by 2025 the total size of the global datasphere will be six times greater than it is currently. You won't be able to hide.
This might sound scary and even make you feel like your personal privacy is being invaded, but all that is happening is the implementation of sophisticated Data Science through machine learning to further understand your perspective. Your data is simply being fed into a machine and the machine is figuring out where you are similar or different to everyone else's data. In doing so, the system is effectively figuring out what your perspective is, based on the given information in relation to everyone else's perspective. The system can then figure out how to categorize you and provide the most relevant options for you. Consider the catalog business: 40 years ago companies would send mail to everyone they could in the hope of making sales. Now they have predictive lists of the likelihood of purchase and can select only the best candidates and avoid sending unnecessary mail to people who will never be interested. This increases ROI and reduces junk mail being sent. Its a win-win situation. Data Science simply applies advanced statistical techniques to better make decisions, and better decisions largely help everyone.
While an argument between friends who cannot see each other's perspective will not be solved by Data Science, a great many society issues could.