BIG DATA
Authors: Niwesh Koirala and Shashank Shreshta
Introduction
The
6th edition of “Data Never Sleeps” by Domo (Domo, 2017) states that "Over 2.5 quintillion bytes of data are created every single day, and it’s
only going to grow from there. By 2020, it’s estimated that 1.7MB of data will
be created every second for every person on earth." It is also surmised
that 90% of the world’s data was only created in the last 2 years. We are
literally generating gigabytes of data every data without realizing it, and
massive internet companies exist to leverage these raw bits into usable
information. Now is the next evolution of information age; it is the time of Big
Data.
In
2005, Roger Mougalas
from O’Reilly Media coined the term Big Data
for the first time, only a year after they created the term Web 2.0 (Rijmenam, 2016) . Big Data refers to
a large set of data that is almost impossible to manage and process using
traditional business intelligence tools. SAS software (SAS , 2019) defines Big Data as
a collective term that describes the large volume of data – both structured and
unstructured – that inundates a business on a day-to-day basis. Rather than
just the collection of Big Data, it is its applications that make it a
cornerstone for modern businesses.
The
key characteristics of Big Data have been the three ‘V’s: Volocity, Volume and
Variety, Those characteristics were first identified by Gartner analyst Doug
Laney (Laney, 2001) .
Big Data usually deals with a large amount of data (volume), differing in
formats (variety) i.e. data is not limited to just text and numbers but can
also draws from images, videos and as such, and finally, the speed at which the
data is collected and processed (velocity). Three more characteristics have
been added to the set lately (Blacksell, 2017) : Veracity, Value and Variability.
Veracity deal with how much ‘noise’ i.e. irrelevant information is in the data
collected, Value attributes to the overall potential of the data collected and
finally Variability deals with the various ways the data can be used.
The
information derived from these massive pools of data allows us to make accurate
market predictions, user segmentation and customization of services. This vast
application of Big Data across a number of fields has made it immensely
appealing as well as raised valid questions on its usage. This paper will try
to understand the business impacts, ethical questions and applicability of Big
Data in the Nepali context and present cases that shows the development of Big
Data.
Brief history and development
An immense volume of Data is created
every single day. Although data has been created since the beginning of time,
we are just realizing the quantum of data that was available from long back. To
begin with, we can decode the history of the Big Data in three eras:
i. 16th – 19th
Century
iii. 20th Century
iv.
21st Century
16th – 19th Century
In 1663, John Graunt was considered
a pioneer when he quested for raising the awareness on the effect of bubonic
plague. He recorded and analyzed information on the rate of mortality in
London. He is also considered one of the first statisticians who used data to
conclude his findings. His book, “Natural and Political observations made upon
the bills of Mortality” does statistical analysis of data. We can technically
observe this event as initial footprints of Big Data. Eventually in 1889,
computing system invented by Herman Hollerith attempted to organize census data
making a huge impact in computational technology history. Unbeknownst to Graunt
and Hollerith, they would be laying the foundations to Big Data –
categorization and analysis of vast volumes of unstructured data.
20th Century:
The era of world war started and
ended which pushed civilization and whole world into information age. IBM was
contracted by then President of United States of America Franklin D.
Roosevelt’s administration for creating track of millions of Americans in 1937.
IBM in response develops a punch card reading system which helped in the
accumulation of data. Later during world war in 1943 British engineer built a
Colossus, a very first data processing machine in order to decipher Nazi codes.
Subsequently, in 1952, National Security Agency (NSA) developed a machine which
independently and automatically collect and process information. The rising
popularity of data and seeing its potential American in 1965 established the
first data center. The purpose of this data center was for storing millions of
tax returns and fingerprints sets. This can be marked as the starting point of
electronic big storage. The revolution in data consumption and availability was
humungous when in 1989 Bernes lee invented World Wide Web. Entering at the end
decade of 20th century, the creation of data grew at an extremely
high rate as more devices gained capacity to access the internet.
21st Century:
The era of evolution to rapid
informational age was a boon to Big Data concept. There is analogy of data
being created it says that since the beginning of time up-to year 2003 we have
5 exabytes of data stored. But to the surprise, we are creating that volume of
data every 2 days since 2016. In
2005, Roger Mougalas coined the term ‘Big Data’ to signify the massive amounts
of data that we had began generating. The sheer volume of raw data also posited
the question, “How do we turn Big Data into information?” The solution
lied in the same year - 2005, with a discovery which is considered a turning
point for a field of Big Data. This is the year when Yahoo launched the open
source platform Hadoop. Initially created by Yahoo to index the entire web,
Hadoop became the solution for processing the vast ocean of data we had now
begun generating. Today, Hadoop is used by millions of business around the
world to go through the colossal amounts of data. The data analytics since then
have seen the remarkable changes around the world. Till this day, we are
witnessing the march towards yet un-scaled horizon of Big Data and its
Analytics.
Workflow, Benefits and Challenges
A Big Data Ecosystem needs a robust
workflow to properly function. Creating a workflow that suits the business and
its goals is essential in actually reaping the benefits of becoming
Data-driven. According to Harvard Business Review (Randy Bean,
2019) ,
40.3% identify lack of
organization alignment and 24% cite cultural resistance as the leading factors
contributing to the failure to adopt data-driven workflows. Alon Lebenthal
identifies a functional Big-data workflow having the following 4 steps (Lebenthal, 2018) :
·
Ingesting data
·
Storing the data
·
Processing it
·
Making data available for analytics
The ingesting of the
data i.e. data acquisition can be done from a number of sources. In the current
social media age, our cellphones have become hubs for data acquisition for
online companies. Once acquired, the data is then stored and processed for analysis.
Traditionally, large storage units may have been required for this and hence,
been too costly for most companies.
However, hardware and
software costs are reducing and becoming more powerful, companies
can even take advantage of cloud computing services so to do all the data crunching. Data centers can
distribute batches of data for processing over multiple servers, and the number
of servers can be scaled up or down quickly as needed. This scalable
distributed computing is accomplished using innovative tools like Apache
Hadoop, MapReduce and Massively Parallel Processing (MPP). Similarly, NoSQL
databases have been developed as more easily scalable alternatives to
traditional SQL-based database systems.
The challenges
of using Big Data can be identified in two groups – Data Complexity and
Computational Complexity. These topics are briefly discussed as follows:
Data complexity:
· The
emergence of Big Data has provided us with unprecedented large-scale samples
which lead us to face far more complex data objects.
· The
typical characteristics of Big Data are diversified types and patterns,
complicated inter-relationships, and greatly varied data quality. The inherent
complexity of Big Data (including complex types, complex structures, and
complex patterns) makes its perception, representation, understanding and
computation far more challenging and results in sharp increases in the
computational complexity when compared to traditional computing models based on
total data.
· Traditional
data analysis and mining tasks, such as retrieval, topic discovery, semantic
analysis, and sentiment analysis, become extremely difficult when using Big
Data.
· We
lack knowledge regarding the laws of distribution and association relationship
of Big Data.
· We
lack deep understanding on the inherent’s relationship between data complexity
and computational complexity of Big Data, as well as domain-oriented Big Data
processing methods.
· A
fundamental problem is how to formulate or quantitatively describe the
essential characteristics of the complexity of Big Data.
Computational
Complexity:
·
New
approaches will need to break away from assumptions made in traditional
computations
· When
solving problems involving Big Data, we will need to re-examine and investigate
its computability, computational complexity, and algorithms.
· New
approaches for Big Data computing will need to address Big Data-oriented, novel
and highly efficient computing paradigms, provide innovative methods for
processing and analyzing Big Data, and support value-driven applications in
specific domains.
· New
features in Big Data processing, such as insufficient samples, open and
uncertain data relationships, and unbalanced distribution of value density, not
only provide great opportunities, but also pose grand challenges, to studying
the computability of Big Data and the development of new computing paradigms.
· There
is a massive hurdle in terms of ROI and unless the Big Data initiative is tied
to company onjectives and goals, the information obtained will only be
scientific data that is not actionable. The Data driven initiative must be made
with the company and its objectives in mind.
Business
Impact
The literature review was carried
out for the Big Data and Big Data analytics. For our report we have included
research from some of the top journals, conferences, and white papers by
industry around the world. Though enough information was found on Big Data, we
found it little difficult to retrieve data about Big Data Analytics. We found
the most of the research was carried out in academia. Here we present the
benefits in business due to Big Data with relevant literature supporting it.
The Big Data has been changing and transforming the way we live, work and think
(V. Mayer-Schonberger, 2013) . The business impact
Big Data has been able to bring in the world are as follows:
Impact in National development:
Depth analysis and utilization of Big
Data plays an important role in promoting sustained economic growth of
countries and enhance the competitiveness of companies. In the future, Big Data
will eventually become a new point of economic growth. With Big Data, companies
will be able to upgrade and transform to the mode of Analysis as a Service
(AaaS), thereby changing the ecology of the IT and other industries.
At the national level, the capacity
of accumulating, processing, and utilizing vast amounts of data will become a
new landmark of a country’s strength. The data sovereignty of a country in
cyberspace will be another great power-game space besides land, sea, air, and
outer spaces. The Western countries, represented by the United States, are
moving under their national agenda towards a modernization of their national
strength through Big Data research and applications. It is anticipated that
future economic and political competitions among countries will be based on
exploiting the potential of Big Data, among other traditional aspects.
Impact in Industrial upgrades:
Big Data is currently a common
problem faced by many industries. Everyone in the industry hopes to mine from Big
Data extracting the information, knowledge and even intelligence and ultimately
taking full advantage of the big value of Big Data. It has become actually a
key product for getting relevant decision making tools and strategies rather
than being a byproduct. Big Data and its analytics is a new engine to sustain
the high growth of the information industry, but also the new tool for
industries to improve their competitiveness. For example cloud computing
provides the IT infrastructure to Big Data and Big Data is an application of
cloud computing. So the industry based on active decision based on off grid
situation can get benefit from Big Data and its analytics.
Impact to scientific research:
Big Data has caused the scientific
community to re-examine its methodology of scientific research (J. Hey, 2009) and has triggered a
revolution in scientific thinking and methods. It is well-known that the
earliest scientific research in human history was based on experiments. Later
on, theoretical science emerged, which was characterized by the study of
various laws and theorems.
However, because theoretical
analysis is too complex and not feasible for solving practical problems, people
began to seek simulation-based methods, which led to computational science. The
emergence of Big Data has spawned a new research paradigm; that is, with Big
Data, researchers may only need to find or mine from it the required
information, knowledge and intelligence. Turing Award winner, Jim Gray,
believed that the fourth paradigm may be the only systematic way for solving
some of the toughest global challenges we face today. In essence, the fourth
paradigm is not only a change in the way of scientific research, but also a
change in the way that people think (V. Mayer-Schonberger, 2013) .
Impact to multidisciplinary researches:
Big Data technologies and the
corresponding fundamental research have become a research focus in academia. An
emerging interdisciplinary discipline called data science (Data Science, 2014) has been gradually coming into place.
This takes Big Data as its research object and aims at generalizing the
extraction of knowledge from data. It spans across many disciplines, including
information science, mathematics, social science, network science, system science,
psychology, and economics (Loukides, 2011) (C. O'Neil) .. It employs various
techniques and theories from many fields, including signal processing,
probability theory, machine learning, statistical learning, computer
programming, data engineering, pattern recognition, visualization, uncertainty
modeling, data warehousing, and high performance computing.
Many research centers/institutes on Big
Data have been established in recent years in different universities throughout
the world (such as the University of California at Berkeley, Columbia
University, New York University, Tsinghua University, Eindhoven University of
Technology, and Chinese University of Hong Kong). Lots of universities and
research institutes have even set up under-graduate and/or postgraduate courses
on data analytics for cultivating talents, including data scientists and data
engineers
Implementations
According
to a Forrester TechRadar Study of Big Data based technology (Press, 2016) ,
MPP data warehouse, Predictive Analysis, Data Visualization and Distributed
File storage are some the most significantly successful technologies with most
them reaching the next phase of development in the next 3 to 5 years.
In
light of these developments in the technical side of thing, it bears importance
to see how practical applications of Big Data are being implemented in the
business sector and how successful they have been.
Dr.
Pepper Snapple Group (DPSG)
has utilized machine learning and predictive analysis tools in its Big
Data Plaform MyDPS, boosting its productivity and revenue. In a testimonial for
the platform (Symphony Retail, 2018) , John Williams,
Director of Category management, DPSG said, “By using aisle
optimization technology, we have increased our margin dollars by $1.4 million.”
The challenges
DPSG was facing was two-fold, their information flow to their sales route was
voluminous - large binders filled with customer data, sales notes and
promotions, and secondly, with the consumer preferences changing, they needed
to keep up with the market and identify category growth categories. Utilizing
MyDPS and SR Assortment and Space solution, they were able
to consolidate these problems and use the massive amount of data they had into
strategic business insights.
MyDPS was
initially tested in isolated DPSG branches. According to a NetworksAsia case
study (Boulton, 2017) , the sales staff that used the platform
reported a 50 present increase in sales. This motivated Tom Farrah, CIO of
DPSG, to implement the platform company-wide, “Our Sales Route staffs were
glorified order takers. Now, they are becoming intelligent sales people
equipped with information to achieve their goals,” He remarked. The platform is
equipped with machine learning and analytic tools that funnels recommendations
and a daily scorecard to workers showing expected projections, their sakes
track and insights of correcting course if needed.
Rolls-Royce Holdings is another
prominent name that is using Big Data to their competitive advantage. In a
Forbes Report (Marr, 2015) ),
Paul Stein, the company’s chief scientific officer, said:
“We have huge clusters of high-power computing which are used in the design
process.We generate tens of terabytes of data on each simulation of one of our
jet engines.” The chief areas Rolls-Royce is using Big Data in their operations
are: design, manufacture and after-sales support.
The terrabytes of design data is processed into design
visualization and evaluations. This allows them to simulate the design
perfomance in extreme conditions, taking the need to practically test these out
of the equation bring both performance and reduced testing costs. The company’s
manufacturing systems are also networked and communicated with each other in an
Internet of Things environment.
An example of this can be seen in what Rolls Royce refers
to as its Ship Intelligence
initiative (Marr, 2015) .
Developed with the VVT Technical Research Center of Finland, the initiative
automates security processes and gives the commanding crew of the ships with a
digital dashboard. It also enables the craft with sophisticated Big Data-driven
automatic piloting and operating systems. Hazards detected by sensors can be
highlighted to the crew right in front of their eyes by augmented reality (AR)
displays, and the ship can automatically plot a safe path.
So
efficient has Rolls-Royce been in their data driven initiatives that it has
started becoming a product in itself. In 2015, Rolls-Royce inked a 5-year deal
with Singapore Airlines to provide the airline with its TotalCare civil aerospace software (Murphy, 2015) . The software provides fuel consuption,
on-board system monitoring, flighting planning, operations control and
engineering systems and is projected to significantly cut fuel consumption in
aircrafts.
However, not all have been successful in their big-data
initiatives. One of the biggest failures in Big Data initiatives remains with
one of the frontrunners - Google. In 2008, Google launched Google Flu Trends
(GFT) with the aim to predict future disease outbreaks and trends at a margin
of the price such models take. In 2015, the service closed down amidst massive
criticisms regarding its accuracy and privacy issues stemming from its data
aggregation. According to Lazer, Kennedy, King and Vespigini (David Lazer R. K., 2014) , GFT was predicting more than double the proportion of
doctor visits for influenza-like illness (ILI) than the Centers for Disease
Control and Prevention (CDC), which bases its estimates on surveillance reports
from laboratories across the United States. This happened despite the fact that
GFT was built to predict CDC reports. According to a Harvard research paper,
even after Google Flu Trends was updated in 2009, the comparative value of the
algorithm as a stand-alone flu monitor was questionable. A study in 2010
demonstrated that GFT accuracy was not much better than a fairly simple
projection forward using already available (typically on a 2-week lag) CDC data
(4).
Google Flu Trends symbolizes the biggest problem
researcher have stated about Big Data, most Big Data trends that have received
popular attention are not the output of instruments designed to produce valid
and reliable data amenable for scientific analysis. Google Flu Trends also did not share its data with
others, as reported by Wired (David Lazer R. K., 2015) , “while Google’s
efforts in projecting the flu were well meaning, they were remarkably opaque in
terms of method and data—making it dangerous to rely on Google Flu Trends for
any decision-making.”
Google Flu Trends closing makes it a cautionary tale (David Lazer
R. K., 2014) ,
sparking the term “Big Data Hubris.” The value of the data held by entities
like Google is almost limitless, which also means those holding these data have
a responsibility to use it in the public’s best interest. Being both opaque and
not being able to forecast the data accurately spelled the end for Google flu
trends turning it, as Wired stated, “from the poster child of Big Data into the
poster child of the foibles of Big Data.”
Practicalities
and Potential in Nepal
The use of Big Data in Nepal is
still in its nascent stage. According to “Nepal’s emerging data revolution” by
Development Initiatives (Rana, 2017) , Nepal’s 27
ministries have digitised their day-to-day operations, and about half of
Nepal’s 7,000 government offices are now reported to be computerised, paper
based systems of data collection and management are still common. “The problem
with Big Data in Nepal, as with many technologies, is that it is still in the
hype phase,” said Prabin Joshi, CTO of Rooster Logic, a Kathmandu-based data
research firm, “The next problem is data collection, there is not enough
collected and what is there, is not in proper structured format. It will still
take a while to properly digitize and structure the data to make anything of
it.”
In 2014 a research project
coordinated by the Open Data in Developing Countries programme set out to
explore the emerging impacts of open data in Nepal. The general lack of open data was quickly
discovered (Rana, 2017) .
The project also stated that data that meets the needs of decision-makers and
accountability actors is not available: data is not disaggregated, there are
significant data gaps, it is not timely and different datasets lack
interoperability due to lack of standards (Rana, 2017) .
Despite the lack of proper
applications, the potential sectors for use remain promising. United Nations
has recently stressed on the use of Data for development and the achievement of
the Sustainable Development Goals:
“Big Data analysis techniques could be
adopted to gain real-time insights into people’s wellbeing and to target aid
interventions to vulnerable groups. New sources of data, new technologies, and
new analytical approaches, if applied responsibly, can enable more agile,
efficient and evidence-based decision-making and can better measure progress on
the Sustainable Development Goals (SDGs) in a way that is both inclusive and fair.” (United Nations, 2017)
Ajay Ohri of the IBM Big Data Initiative & Analysis
Hub (Ohri, 2015)
recognized
financial services, Agriculture, Education, Healthcare, Corruption reduction
and Carbon consumption optimization as major areas where Big Data technologies
can have a significant impact. Similarly, Pratima Pradhan and Subarna Shakya
discuss the possiblity of using Big Data in e-governance akin to India:
“In
India the “Adhaar" card [3, 5, 10] was introduced as a unique identifier
for transparent citizen bene ts. This card could hold the key to verification
for multiple purposes. Nepal could benefit for passport, taxation, and license
and citizen benefit distribution” (Pratima Pradhan, 2018)
The
paper additionally identifies disaster relief as a key area that Big Data
technologies can help. Taking the 2015 earthquake as an example, the use of
identification cards akin to the Indian “Adhaar” card can help consolidate
relief clusters, rescue strategies and rolling out of support materials and
capital. In fact, Kathmandu Open Labs was
rcognized internationally (Sinha, 2015)
for aiding in the initial days of disaster relief during the 2015 Earthquake
through its open source mapping services.
The outsourcing market
has seen immense potential in Nepal however. Dovan Rai (Rai, 2017)
recognizes the following companies in her report on Big Data:
●
YoungInnovations creates automated data tools and data
platforms
●
CraftData Labs works with business data along with
open-data for governance a
●
GrowByData specializes on Big Data for e-commerce.
●
Grepsr provides data scraping solutions.
●
Fusemachines creates automated sales platform using Big
Data.
●
Deerwalk provides Big Data solutions to
healthcare industry.
●
LeapFrog Technology offers healthcare data solutions as one
of their technology services.
●
Javra Software works with Big Data to create business
intelligence tools.
This is,
by no means, an exhaustive list of all the companies working on outsourced Big
Data solutions. It does stand to notice that Nepali Companies has stepped up to
the plate of being trained in and learning the architecture of Big Data
technologies. “There is, of course,
potential in Nepal, but so much of the collected data is in offline
format and that is the hurdle,” said Aakar Anil, member of CloudFactory. A data
processing company with a focus on Natural Language Processing, CloudFactory
has converted the Big Data process workflow by creating a workforce that can
analyse tons of information they receive from their clients. “Our workers work
online and they analyse the data provided as per the client specifications.
Some information requires Human observation and our platform uses our
cloudworkers (online workers) to process the data.” CloudFactory currently
employs more than 8,000 workers to process the data needs of more than 150
A.I., NLP and automation projects for global companies like Microsoft,
Drive.ai, Ibotta and nuTonomy (CloudFactory, 2016) .
Currently,
the cellphone/telecommunication penetration of Nepal is high; with Nepal
Telecommunications estimating about 60% of the population has access to a
cellphone (Nepal Telecom, 2019) . OnlineKhabar states
that the actual ownership data of cellphones actually states that the number of
Cellphone users in Nepal is 34% more than the actual population (OnlineKhabar, 2018) obviously caused due
to a single user owning multiple cellphones. Leveraging off of the penetration
of telecommunications, the opportunities for Big Data driven initiatives are
plentiful. “The biggest platforms I see are Health, Education and Agriculture,”
said Pravin Joshi of Rooster Logic, “Nepal needs infrastructure and educational
reforms and data driven analytics can elevate both development and commercial
organizations in the sector.” The potential is there, but the what must happen
is a long-term vision on how to access the potential and not merely jump aboard
the hype train.
Conclusions
As mentioned above, it was found
that Big Data analytics can provide vast horizons of opportunities in various
applications and areas, such as customer intelligence, fraud detection, and
supply chain management. Additionally, its benefits can serve different sectors
and industries, such as healthcare, retail, telecom, manufacturing, etc.
However, Big Data is also very difficult to deal with. It requires proper
storage, management, integration, federation, cleansing, processing, and analyzing.
All the problems we face with traditional data management, Big Data
exponentially increases these difficulties due to additional volumes,
velocities, and varieties of data and sources which have to be dealt with.
We saw that Big Data analytics is of
great significance in this era of data surplus, and can provide unforeseen
insights and benefits to decision makers in various areas. If properly harnessed
and applied, Big Data analytics has the potential to provide a basis for
advancements, on the scientific, technological, and humanitarian levels. Industries
are already applying the Big Data for its advantages in huge degree. According
to the figure Alibaba disclosed in March 2014, their data center has, so far, stored
more than 100 PB of processed data, which amounts to 100 million
high-resolution movies. During the just past “Singles’ Day” (also known as
“Double 11 Day”), Alibaba pulled in around 278 million orders. For this annual
shopping event, Alibaba developed a real-time data processing platform called
Galaxy, which could handle 5 million transactions per second. The total amount
of data that Galaxy can process every day is about 2 PB. Industry is more
successful in this respect because it has two essential driving forces: they
really need to possess Big Data in real time and they have the requirements on
making better use of the data collected.
However, Big Data requires more
clarity of one’s own business and also some ethics driving its use. As seen
from Cambridge Analytica’s case, Big Data makes it easy to manipulate one’s
perspectives and such unethical profiling is not marketing or business strategy
but fraud of the highest order. Data driven approach to business or even
development can yeild massive benefits but it must be focused, tailored to
one’s objective and used in an ethical manner.
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