Fadak - Big Data - Big Data Analytics: A Management Perspective
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Big Data Analytics: A Management Perspective


  1. 1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     1
  2. 2 What Data Science Means to the Business. . . . . . . . . . . . . . . . . . . . . . .     5
  3. 3 Key Data Challenges to Strategic Business Decisions. . . . . . . . . . . . . .     19
  4. 4 A Chimera Called Data Scientist: Why They Don’t Exist (But They Will in the Future). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     25
  5. 5 Future Data Trends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     31
  6. 6 Where Are We Going? The Path Toward an Artificial Intelligence. . .     35
  7. 7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     37

1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     1

Abstract This introductory chapter will provide big data with a straight practical
definition, bringing to light the reasons why it is such an important topic nowadays.
Some of the most common applications will be listed, and a short literature
review will be discussed. After this short introduction, the reader should be able to
understand what big data and data science are, what has been done so far and what
is the current state of art, as well as having an overview of the book.

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     3

2 What Data Science Means to the Business. . . . . . . . . . . . . . . . . . . . . . .     5

Abstract Big data have been associated with some common misconceptions so
far, and this chapter will help the reader in identify and understand those fallacies.
It is going to be then shown the best data deployment approach, followed by
an ideal internal data management process. A four-stages development structure
will be provided, in order to assess the big data internal advancements, and a data
maturity map will summarize a set of relevant metrics that should be considered
for an efficient big data strategy.

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     17

3 Key Data Challenges to Strategic Business Decisions. . . . . . . . . . . . . .     19

Abstract Many strategic challenges come with formulating a big data strategy:
how to guarantee a secured data access and a constant protection of users’ data;
how to promise a fair data treatment; how to manage data in case of special situation
such as initial public offerings, growth strategies, mergers and acquisitions;
how to handle data in emerging and growing markets. Furthermore, the idea of a
data ecosystem will be sketched out in this chapter.

3.1 Data Security, Ethic, and Ownership. . . . . . . . . . . . . . . . . . . . . . . . .     19
3.2 The Data Ecosystem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     22
3.3 Initial Public Offering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     22
3.4 Growth Strategies: Acquisitions, Mergers, and Takeovers. . . . . . . . .     22
3.5 Emerging Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     23
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     24

4 A Chimera Called Data Scientist: Why They Don’t Exist (But They Will in the Future). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     25

Abstract This chapter discusses the role of the data scientist, what a data scientist
is, and the set of skills needed to become one. The underlying idea proposed is that
the job market is not matured enough yet for this figure to be trained and employed,
but it will be ready in the next few years. In order to help firms to understand
what to look for and how to use resources in the best way, a personality test has
been implemented and different types of data scientists have been classified using
this test.

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     30

5 Future Data Trends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     31

5.1 The Internet of Things (IoT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     32
5.2 The Cloud. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     32
5.3 Application Programming Interfaces (APIs). . . . . . . . . . . . . . . . . . .     33

6 Where Are We Going? The Path Toward an Artificial Intelligence. . .     35

Abstract Big data and data science pushed the technological frontier one
step forward, and as they are an innovation themselves, they also entail the
development of new trends. The Internet of Things is the first trend highly
interconnected with big data that will be discussed. Secondly, an overview of
cloud technologies will be provided. Finally, application-programming interfaces
will be shown to have a huge impact on how data are accessed, protected, used,
and widespread.

7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     37

Big data is a solution, but is not the solution. It has to be wisely understood and managed, and it is not a panacea for the ills, but it can lead any business to the next level. It is a fast-pace environment, and things that work today may not apply tomorrow, so instead on concluding with strong statements written in stone, it is preferable to provide some final thoughts in form of pieces of advice.
First of all, don’t think too much around big data, but rather start practicing it. They represent a new field of exploration for everyone, and a great competitive advantage to not be missed. Hence, even if it will be a low profile or lowincreasing revenues project, start immediately—with small inexpensive pilots and taking care of your “small” data first—and fail, fail, fail. Fail faster, fail better; experiment and fail directly in the open market, because this is where the innovative pioneering ideas and feedbacks come from. Embrace failure as never before, because it teaches something the nothing else can convey, and build your “reputation of failure”—companies will be judged in a future on how they fail and react to failure rather that how they succeed. Create your own solutions because big data applications are not fully transferrable, so be unique in what and how you use big data, and scale up quickly.
Data are definitely a strategic priority and necessity for every firm, but the normal course of the business entails a tradeoff between surviving—doing what your daily job is and what you are good at—and innovating (and risking), but you have to be brave and adopt data science as the new company lifestyle.
Secondly, develop a data sharing discipline and a (non) ownership culture. The next business level is represented by professional ecosystems, ruled by a data democracy, so employ some effort in advancing and cultivating also open-source solutions. Create a data manifest and foster a strong internal communication, which is fundamental for big corporations, since often people do not even know
who may have what, or whether some colleagues is already working on a pilot, on a specific project, or using some particular data or model. Big data will lead to think that everything is possible and at fingertips, and maybe this will be the case in some years. Hence, (i) think holistically and without boundaries, and be a data visionary; (ii) think strategically, state a data governance policy, but do not let you be bounded by either too strict culture, discipline, or formal structures. Be flexible and ready to readapt quickly to what the data suggest you to go for; (iii) think complementary, since big data technologies will pair rather than replace completely your current systems; and (iv) focus on execution, prioritize your activities and set an underlying roadmap to follow. Understand where investing the capitals, what are the perceived and the actual ROI of big data applications, and whether they are enlarging the spectrum of your possibilities, lowering the costs, or reducing the problems.
But above all, embrace the continuous change and adopt flexibility as a mantra: in the big data world, “things keep going working until they do not”, and changing at the same pace of data analytics is the quintessence to succeed in a business context.

Appendices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     39


        

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