“Big Data” is
coined from collective research findings, projects, social activity, health
ratings, and historically within mobile use. Every action that is set within
the parameters of the technological field incorporates data generation. The
generation of data amounts to “big data”—Data that is so huge for processing
capability! It comes to my mind as information overload. Most data generated is
not always helpful data, but rather it is fluff within our spaces. As more data
is collected everyday regarding citizens, there is in an alarming stance
towards personal privacy and abuse of the use of collective data. Who really
owns data? This may sound like an easy answer, but really it is a two-fold
system. Ideally, it does belong with the consumer’s control, but once a
consumer agrees to “privacy terms and conditions” usually found embedded within
a contract so-to-speak, the entity collecting the data has a right to uphold
consumer protection even further.
Take for instance
Facebook or even the Pandora Application. Both are great tools for social
experience both visually and mentally (some could argue differently). Facebook
allows users to keep up with friends, engage in conversations, and even play
games. Pandora music application allows users to listen to a variety of genre
from classical rock to hip-hop to old school country. Facebook is generally
seen by most as a great way to “be connected”, whether professionally with
co-workers allowing for networking opportunities (LinkedIn would be my better
choice for this, but to each his own) or personally with long-ago friends or
even long-distance family members. Pandora is convenient for long car rides
without boring commercials and can even help within the office setting to make
the work day better. Either way, both are resources for consumers, but both
entities are capturing real time data analysis on end users. The data analysis
can show behavioral and social patterns of activity, likes and dislikes, clues
about one’s current state of mind, matter, and place, and engage with
consumers. The tricky business to the generation and collection of big data analysis
lies within informed end users and their correlation with trust to the entity.
It could be predicted that most of us do not think twice about the
long-drafted, never-ending it seems like contractual agreement that every
application requires end users to sign, which is usually the better known “privacy
agreement”, but it should be noted that careful consideration should be taken
by the end user by carefully proofing the agreement. Even then, be mindful of
posts, engagement, and functionality of the resources being used—not everything
is as what is appears to be.
Of particular interest,
there are endless technological advances within such applications as navigational
tools (Garmin, Google Maps, and Yelp), social resources (Facebook, LinkedIn,
Twitter, and Snapchat), fitness arenas (Fit Bit and Apple watch), financial
advice (Banking, Credit, and Stock apps), and even personal life (Weather
Channel and others).With such an advancement of applications, there seems to be
one striking to mind that (to my knowledge) does not currently exist. A big
data project of analyzing professional job interviews and creating a system of
allowing end users access for review, practice, and even feedback. Critical to
this project would be anonymous identification of both entity and consumer.
For my big data
project, the collection of job interviews across public entities will be
examined through an app. Since the examination of public companies will be
used, in theory and of my approach, citizens have a designated right to the data
collected. Most citizens struggle with the “right and wrong” answers, how to
dress properly, and even how to professionally engage with a potential
employer. It can be stated that business culture plays a role into shaping most
of the “correct/incorrect” doings, but overall for most professional industries,
the norm is standard across borders. Successful completion of such a project
will require examination of introduction, process and production, and privacy.
All three areas are crucial for implementation.
1.
Introduction
An application designed to capture real-time data regarding employer feeds
of job interviews. Data generated will come from potential employee Q/A,
attire, presentation, and overall ratings of hire ability. The application will
serve not only as a personal assessment but professional growth as trends can
be recorded and observed. This production may be tested and/or used within the
United States and abroad. Privacy issues concerning abroad should be analyzed
carefully to ensure proper measures against consumer protection. It may best be
tested and used first in America to ensure profitability for research.
2.
Process and Production
Firstly, employers will begin interviews by launching a desktop or
hand-held app. designed for this project. A consent form must be used to ensure
consumer privacy and entity ethical considerations. The company’s formal HR
operation procedures for interviews should be used. Questions may be altered or
pre-entered as needed dependent upon the company desires. Answers will be
manually recorded. Key attention to interviewee body language cues of behavioral
associations, and potential conflicts should be noted. In addition, attire,
presentation, and response assessment should be added, as well. The application
will be designed in such a way to allow for ranking and additional comments.
Any potential uneventful findings should be included further. Once the
interview is conducted, the interviewer will carefully review ratings and
submit a final upload. The company will have access to the data, as well as the
application for further research.
Secondly, the application will produce generalized results. These generalized
results will be sorted upon classical application tabs to ensure that if there
is a specific section such as “professional attire” or “best responses” for job
interviews that a consumer is looking for within the app., then it can easily
be found. The consumer will pay for the download of the app., and the monies
generated will further back the research credibility and marketability. An end
user will find the information helpful depending upon which professional
industry is completing an interview within, whether it is higher education, healthcare,
or banking.
3.
Privacy
A privacy agreement form should be electronically signed by the corporation
and end user for the application use. The application must uphold all privacy
standards to ensure that both the corporation name and end user identification
remain anonymous. Details of state, city, industry type, and rankings of findings
will be generated; as such will be attractable to many end users. End users may
cross collaborate results, but attention to personal identification will be
omitted. The application is expected to generate mega data, but for
functionality, it will help serve populations of all race, gender, and age types.
It may be advised for testing first in U.S. to ensure growth and use, but it
could very easily serve other countries. Country set privacy issues would come
into play and should be abided.
