Big Data: Ethics and Governance in Business Analytics

With the ever-increasing focus on artificial intelligence, there is an
exponential growth of data generation from government, private
and public. There is increase in the number of data generating
devices as well, such as smart devices, wearable devices, and the
everyday used and seen sensor devices around us. It is essential
to understand the governance related to the data and its privacy
along with the ethical issues to surrounding the usage of data.
Data breach amongst the individuals and organizations is a major
challenge in today’s business world. The data which is a powerful
resource for decision making and to scale the businesses is to be
validated amongst the challenges of collecting storage, usage
and the rights associated. The field of data ethics explores these
questions and offers guiding principles for business professionals
who handle data, there exists a significant risk of data exploitation
for both individuals and companies. Business intelligence, big data
and business analytics is the buzz in this new era of business data.
Big data analytics and business analytics carry a lot of similarities:
they both bite off some data, chew it up and spit it out as some
new form of cohesive, useful information supporting the decision
making of an organization. In the same vein, business analytics is
very human-focused, while big data analytics requires too much
processing and focused approach to be adopted to analyse the data. This study revolves around the 5 principles of data ethics provided
by Harvard business school(online) and the governance of big data
by Kate Prochorik , data intelligence researcher. The five Harvard
data ethics are: Ownership, transparency, privacy, Intention and
outcomes.9 Even when intentions are good, the outcome of data
analysis can cause inadvertent harm to individuals or groups of
people. Unfortunately, instead of reaping instant benefits, business
users have realized that in spite of data governance frameworks
which exists they have to execute an extended set of data analytics to
solve challenges connected with multiple data formats and security.
There are 4 big data governance components: people, polices
procedures standards, technology and infrastructure and support
and optimization. According to Kate there are three data governance
levels: strictly governed, loosely governed and nongoverned data
which we aim to delve deep into in this research. The study aims to
provide a grasp of the state of big data ethics and its governance in
large organizations and corporations. This study aims to uncover the
loop holes in the existing frame works in data privacy, security and
transparency. This study will give an overview of the co-operation of
different functions of an organization, the current systems in place
for ethics violation and steps involved in whole data lifecycle used
for data analytics. In the end, the study offers the state of ethics
and governance of big data in business analytics and the policies in
place and provide suggestions if any