Module 1 Introduction to Big and Smart Data- Online

D: DRIVE
How to become Data Driven?
This programme has been funded with
support from the European Commission
Module 1. Introduction to Big and Smart Data

Smart Data Smart Region | www.smartdata.how
This programme has been funded with support from the European Commission. The author is
solely responsible for this publication (communication) and the Commission accepts no
responsibility for any use that may be made of the information contained therein.
The objective of this module is to provide an overview of
the basic information on big and smart data.
Upon completion of this module you will:
- Comprehend the emerging role of big data
- Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big and smart
data
Duration of the module: approximately 1 – 2 hours
Module 1.
Introduction
to Big and Smart
Data

The emerging role of data in VET and enterprise1
V‘s of data2
How does big data become smart data?3
This programme has been funded with support from the European Commission. The author is
solely responsible for this publication (communication) and the Commission accepts no
responsibility for any use that may be made of the information contained therein.
– A Brief History of Data
– What is Big Data?
– Sources of Data
– The Importance of Big Data
– Turning Big Data into Value
– Smart Data Applications
– How to Start Smart?
– Big Data Challenges

1. A Brief History of Data
2. What is Big Data?
3. Classification of Data
4. Sources of Data
5. The Importance of Big Data
THE EMERGING ROLE
OF DATA IN VET AND
ENTERPRISE

A BRIEF HISTORY OF DATA
Long before computers (as we know them today) were
commonplace, the idea that we were creating an ever-
expanding body of knowledge ripe for analysis was popular in
academia. Although it might be easy to forget, our increasing
ability to store and analyze information has been a gradual
evolution – although things certainly sped up at the end of the
last century, with the invention of digital storage and the
internet.
Before we dive into the Module 1 it is neccessary to look at
the long history of thought and innovation which have led us
to the dawn of the data age.

C 18,000 BCE
The earliest examples we have of
humans storing and analyzing data
are the tally sticks. The Ishango Bone
was discovered in 1960 and is
thought to be one of the earliest
pieces of evidence of prehistoric data
storage. Notches were marked into
sticks or bones, to keep track of
trading activity or supplies. They
would compare sticks and notches to
carry out rudimentary calculations,
enabling them to make predictions
such as how long their food supplies
would last.
C 2400 BCE
The abacus – the first dedicated
device constructed specifically for
performing calculations – comes into
use in Babylon. The first libraries also
appeared around this time,
representing our first attempts at
mass data storage.
300 BC – 48 AD
The Library of Alexandria is perhaps
the largest collection of data in the
ancient world, housing up to half a
million scrolls and covering
everything we had learned so far.
Unfortunately it has been destroyed
by the invading Romans in 48AD .
Contrary to common myth, not
everything was lost – significant parts
of the library’s collections were
moved to other buildings in the city,
or stolen and dispersed throughout
the ancient world.
C 100 – 200 AD
The Antikythera Mechanism, the
earliest discovered mechanical
computer, is produced, presumably
by Greek scientists. It’s “CPU”
consists of 30 interlocking bronze
gears and it is thought to have been
designed for astrological purposes
and tracking the cycle of Olympic
Games. Its design suggests it is
probably an evolution of an earlier
device – but these so far remain
undiscovered.
Ancient History of Data

1663
In London, John Graunt carries out
the first recorded experiment in
statistical data analysis. By recording
information about mortality, he
theorized that he can design an early
warning system for the bubonic
plague ravaging Europe.
1865
The term “business intelligence” is
used by Richard Millar Devens in his
Encyclopedia of Commercial and
Business Anecdotes, describing how
the banker Henry Furnese achieved
an advantage over competitors by
collecting and analyzing information
relevant to his business activities in a
structured manner. This is thought to
be the first study of a business
putting data analysis to use for
commercial purposes.
1880
The US Census Bureau has a problem – it estimates that it will take it 8 years to
crunch all the data collected in the 1880 census, and it is predicted that the
data generated by the 1890 census will take over 10 years, meaning it will not
even be ready to look at until it is outdated by the 1900 census. In 1881 a
young engineer employed by the bureau – Herman Hollerith – produces what
will become known as the Hollerith Tabulating Machine. Using punch cards, he
reduces 10 years’ work to three months and achieves his place in history as the
father of modern automated computation. The company he founds will go on
to become known as IBM.
The Emergence of Statistics

1926
Interviewed by Colliers magazine,
inventor Nikola Tesla states that
when wireless technology is
“perfectly applied the whole Earth
will be converted into a huge brain,
which in fact it is, all things being
particles of a real and rhythmic
whole … and the instruments
through which we shall be able to do
this will be amazingly simple
compared to our present telephone.
A man will be able to carry one in his
vest pocket.”
1928
Fritz Pfleumer, a German-Austrian
engineer, invents a method of storing
information magnetically on tape.
The principles he develops are still in
use today, with the vast majority of
digital data being stored magnetically
on computer hard disks.
1944
Fremont Rider, librarian at Wesleyan University, Connecticut, US, published a
paper titled The Scholar and the Future of the Research Library.
In one of the earliest attempts to quantify the amount of information being
produced, he observes that in order to store all the academic and popular
works of value being produced, American libraries would have to double their
capacity every 16 years. This led him to speculate that the Yale Library, by
2040, will contain 200 million books spread over 6,000 miles of shelves.
The Early Days of Modern Data Storage

1958
IBM researcher Hans
Peter Luhn defines
Business Intelligence
as “the ability to
apprehend the
interrelationships of
presented facts in
such a way as to
guide action towards
a desired goal.”
1962
The first steps are
taken towards speech
recognition, when IBM
engineer William C
Dersch presents the
Shoebox Machine at
the 1962 World Fair. It
can interpret numbers
and sixteen words
spoken in the English
language into digital
information.
1964
An article in the
New Statesman
refers to the
difficulty in
managing the
increasing
amount of
information
becoming
available.
The Beginnings of Business Intelligence The Start of Large Data Centers
1970
IBM mathematician Edgar
F Codd presents his
framework for a
“relational database”. The
model provides the
framework that many
modern data services use
today, to store information
in a hierarchical format,
which can be accessed by
anyone who knows what
they are looking for.
1976
Material
Requirements
Planning (MRP)
systems are
becoming more
commonly used
across the business
world, representing
one of the first
mainstream
commercial uses of
computers to speed
up everyday
processes and make
efficiencies.
1989
Possibly the first use of the
term Big Data in the way it is
used today. International
best-selling author Erik
Larson pens an article for
Harpers Magazine
speculating on the origin of
the junk mail he receives. He
writes: “The keepers of big
data say they are doing it for
the consumer’s benefit. But
data have a way of being
used for purposes other
originally intended.”

1991
Computer scientist Tim
Berners-Lee announced the
birth of what would become
the Internet as we know it
today. In a post in the
Usenet group
alt.hypertext he sets out the
specifications for a
worldwide, interconnected
web of data, accessible to
anyone from anywhere.
1996
According to R J T
Morris and B J
Truskowski in their
2003 book The
Evolution of Storage
Systems, this is the
point where digital
storage became
more cost effective
than paper.
1997
Michael Lesk publishes his paper How Much
Information is there in the World? Theorizing
that the existence of 12,000 petabytes is
“perhaps not an unreasonable guess”. He
also points out that even at this early point in
its development, the web is increasing in size
10-fold each year. Much of this data, he
points out, will never be seen by anyone and
therefore yield no insight.
Google Search also debuts this year – and for
the next 20 years (at least) its name will
become shorthand for searching the internet
for data.
The Emergence of the Internet Early Ideas of Big Data
1999
A couple of years later and the term Big Data
appears in Visually Exploring Gigabyte Datasets in
Real Time, published by the Association for
Computing Machinery. Again the propensity for
storing large amounts of data with no way of
adequately analyzing it is lamented. The paper goes
on to quote computing pioneer Richard W Hamming
as saying: “The purpose of computing is insight, not
numbers.”
Also possibly first use of the term “Internet of
Things”, to describe the growing number of devices
online and the potential for them to communicate
with each other, often without a human “middle
man”.

2000
In How Much Information? Peter
Lyman and Hal Varian (now chief
economist at Google) attempted
to quantify the amount of digital
information in the world, and its
rate of growth, for the first time.
They concluded: “The world’s total
yearly production of print, film,
optical and magnetic content
would require roughly 1.5 billion
gigabytes of storage. This is the
equivalent of 250 megabytes per
person for each man, woman and
child on Earth.”
Early Ideas of Big Data Web 2.0 Increases Data Volumes
2005
Commentators announce that we are witnessing the birth of “Web 2.0” – the
user-generated web where the majority of content will be provided by users
of services, rather than the service providers themselves. This is achieved
through integration of traditional HTML-style web pages with vast back-end
databases built on SQL. 5.5 million people are already using Facebook,
launched a year earlier, to upload and share their own data with friends.
This year also sees the creation of Hadoop – the open source framework
created specifically for storage and analysis of Big Data sets. Its flexibility
makes it particularly useful for managing the unstructured data (voice, video,
raw text etc) which we are increasingly generating and collecting.
2001
In his paper 3D Data Management:
Controlling Data Volume, Velocity and
Variety Doug Laney, analyst at Gartner,
defines three of what will come to be
the commonly-accepted characteristics
of Big Data.
This year also see the first use of the
term “software as a service” – a
concept fundamental to many of the
cloud-based applications which are
industry-standard today – in the article
Strategic Backgrounder: Software as a
Service by the Software and
Information Industry Association.

2008
The world’s servers process
9.57 zettabytes (9.57 trillion
gigabytes) of information –
equivalent to 12 gigabytes of
information per person, per
day), according to the How
Much Information? 2010
report. In International
Production and
Dissemination of
Information, it is estimated
that 14.7 exabyte's of new
information are produced
this year.
Today’s Use of the Term ‘Big Data’ Emerges
2010
Eric Schmidt,
executive chairman
of Google, tells a
conference that as
much data is now
being created
every two days, as
was created from
the beginning of
human civilization
to the year 2003.
2009
The average US company
with over 1,000
employees is storing
more than 200 terabytes
of data according to the
report Big Data: The Next
Frontier for Innovation,
Competition and
Productivity by McKinsey
Global Institute.
2011
The McKinsey report
states that by 2018 the
US will face a shortfall of
between 140,000 and
190,000 professional
data scientists, and
states that issues
including privacy,
security and intellectual
property will have to be
resolved before the full
value of Big Data will be
realized.
2015
Google is the largest big data company in the
world that stores 10 billion gigabytes of data
and processes approximately 3.5 billion
requests every day.
Amazon is the company with the most
number of servers-the 1,000,000,000
gigabytes of big data produced by Amazon
from its 152 million customers is stored on
more than 1,400,000 servers in various data
centres.

Big Data is the
foundation of all of the
megatrends that are
happening today, from
social to mobile to the
cloud to gaming.
Chris Lynch

There are some things that are so big, that they have
implications for everyone, whether we want it or not. Big
data is one of those things, and it is completely
transforming the way we do business and is impacting
most other parts of our lives.
The basic idea behind the phrase „Big Data“ is that
everything we do is increasingly leaving a digital trace,
which we can use and analyse. Big Data therefore refers to
our ability to make use of the everincreasing volumes of
data.
WHAT IS BIG DATA?
„Data of a very large size, typically to
the extent that its manipulation and
management present significant
logistical challenges.“
Oxford English Dictionary, 2013

STRUCTURED
DATA
UNSTRUCTURED
DATA
“Data” is defined as ‘the quantities, characters, or symbols on which operations are performed by a computer, which may be stored
and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media’, as a quick
google search would show.
The concept of Big Data is nothing complex; as the name suggests, “Big Data” refers to copious amounts of data which are too
large to be processed and analyzed by traditional tools, and the data is not stored or managed efficiently. Since the amount of Big
Data increases exponentially- more than 500 terabytes of data are uploaded to Face book alone, in a single day- it represents a real
problem in terms of analysis.
However, there is also huge potential in the analysis of Big Data. The proper management and study of this data can help
companies make better decisions based on usage statistics and user interests, thereby helping their growth. Some companies have
even come up with new products and services, based on feedback received from Big Data analysis opportunities.
Classification is essential for the study of any subject. So Big Data is widely classified into three main types, which are:
SEMI-
STRUCTURED
DATA
CLASSIFICATION OF DATA
1 2 3

STRUCTURED
DATA
1
Structured Data is used to refer to the data which is already stored in databases, in an ordered manner. It accounts for
about 20% of the total existing data, and is used the most in programming and computer-related activities.
There are two sources of structured data- machines and humans. All the data received from sensors, web logs and
financial systems are classified under machine-generated data. These include medical devices, GPS data, data of usage
statistics captured by servers and applications and the huge amount of data that usually move through trading platforms,
to name a few.
Human-generated structured data mainly includes all the data a human input into a computer, such as his name and other
personal details. When a person clicks a link on the internet, or even makes a move in a game, data is created- this can be
used by companies to figure out their customer behavior and make the appropriate decisions and modifications.
Example of Structured Data
An 'Employee' table in a database is an
example of Structured Data.
Employee_ID Employee_Name Gender Department Salary_In_Euros
2365 Rajesh Kulkarni Male Finance 65000
3398 Pratibha Joshi Female Admin 65000
7465 Shushil Roy Male Admin 50000
7500 Shubhojit Das Male Finance 50000
7699 Priya Sane Female Finance 55000

UNSTRUCTURED
DATA
2 While structured data resides in the traditional row-column databases, unstructured data is the opposite- they have no
clear format in storage. The rest of the data created, about 80% of the total account for unstructured big data. Most of the
data a person encounters belongs to this category- and until recently, there was not much to do to it except storing it or
analyzing it manually.
Unstructured data is also classified based on its source, into machine-generated or human-generated. Machine-generated
data accounts for all the satellite images, the scientific data from various experiments and radar data captured by various
facets of technology.
Human-generated unstructured data is found in abundance across the internet, since it includes social media data, mobile
data and website content. This means that the pictures we upload to out Facebook or Instagram handles, the videos we
watch on YouTube and even the text messages we send all contribute to the gigantic heap that is unstructured data.
Example of Unstructured Data
Output returned by 'Google Search‚.

SEMI-
STRUCTURED
DATA
3 The line between unstructured data and semi-structured data has always been unclear, since most of the semi-structured
data appear to be unstructured at a glance. Information that is not in the traditional database format as structured data,
but contain some organizational properties which make it easier to process, are included in semi-structured data. For
example, NoSQL documents are considered to be semi-structured, since they contain keywords that can be used to
process the document easily.
An email message is one example of semi-structured data. It includes well-defined data fields in the header such as
sender, recipient, and so on, while the actual body of The Path to Hyperconverged Infrastructure for the Enterprise the
message is unstructured. If you wanted to find out who is emailing whom and when (information contained in the
header), a relational database might be a good choice. But if you’re more interested in the message content, big data
tools, such as natural language processing, will be a better ft.
Example of Semi-structured Data
Personal data stored in a XML file.

SOURCES OF DATA
Big data is often boiled down to a few varieties including social data, machine data, and
transactional data.
Machine data consists of information generated from industrial equipment, real-time
data from sensors that track parts and monitor machinery (often also called the Internet of
Things), and even web logs that track user behavior online. At arcplan client CERN, the largest
particle physics research center in the world, the Large Hadron Collider (LHC) generates 40
terabytes of data every second during experiments.
Regarding transactional data, large retailers and even B2B companies can generate
multitudes of data on a regular basis considering that their transactions consist of one or many
items, product IDs, prices, payment information, manufacturer and distributor data, and much
more.
Social media data is providing remarkable insights to companies on consumer
behavior and sentiment that can be integrated with CRM data for analysis, with 230 million
tweets posted on Twitter per day, 2.7 billion Likes and comments added to Facebook every day,
and 60 hours of video uploaded to YouTube every minute (this is what we mean by velocity of
data).

Social Media Data
Social Networking and
Media
Big data is all the huge unstructured data generated from social networks from a wide array of sources ranging from Social media
‘Likes’, ‘Tweets, blog posts, comments, videos and forum messages etc. Just to give you some information, Google in any given day
processes about 24 petabytes of data. For your information, most of the data is not arranged in rows and columns. Big data also
takes into account real time information from RFIDs and all kinds of sensors. The social intelligence that can be gleaned from data
of this magnitude is tremendous. Professionals and technologists from organizations in the enterprise and consumer social
networks have begun to realize the potential value of the data that is generated through social conversations. Sometimes Big data is
also often known as ‘bound’ data and ‘Unbound’ data.
Social networks have a geometric growth pattern. Big data technologies and applications should have the ability to scale and
analyze this large unstructured data. It should have the capability to analyze in real time as it happens. Social media conversations
generate lot of context to the information. Such context is an invaluable resource for knowledge and expertise sharing. Context in
conversations is key to a social network’s success. It is not an easy task to analyze millions of conversational messages every
day. This is where traditional analytics can help mainstream Big data analysis and both need to go hand in hand.

WhatsApp users
share
347,222
photos.
EVERY
MINUTE OF
EVERY DAY
E-mail users send
204,000,000
messages
YouTube users
upload
4,320
minutes of new
videos.
Google recieves over
4,000,000
search queries.Facebook users
share
2,460,000
pieces of content.
Twitter users tweet
277,000
times.
Amazon makes
83,000$
in online sales.
Instagram users
post
216,000
new photos.
Skype users
connect for
23,300
hours.
SM Examples

Machine Data
Social Networking and
Media
Machine data is everywhere. It is created by everything from planes and elevators to traffic lights and fitness-monitoring devices. It
intersects with and improves human lives in countless ways every day. Such data became more prevalent as technologies such
as radio frequency identification (RFID) and telematics advanced. More recently, machine data has gained further attention as use of
the Internet of Things, Hadoop and other big data management technologies has grown.
Application, server and business process logs, call detail records and sensor data are prime examples of machine data. Internet
clickstream data and website activity logs also factor into discussions of machine data.
Combining machine data with other enterprise data types for analysis is expected to provide new views and insight on business
activities and operations. For example, some large industrial manufacturers are analyzing machine data on the performance of field
equipment in near-real-time, together with historical performance data, to better understand service problems and to try to predict
equipment maintenance issues before machines break down.
Machine-generated data is the lifeblood of the Internet of Things (IoT).

Transactional Data
Transactional data are information directly derived as a result of transactions. Unlike other sorts of data, transactional data contains
a time dimension which means that there is timeliness to it and over time, it becomes less relevant.
Rather than being the object of transactions like the product being purchased or the identity of the customer, it is more of a
reference data describing the time, place, prices, payment methods, discount values, and quantities related to that particular
transaction, usually at the point of sale.
Purchases Returns Invoices Payments Credits
Donations Trades Dividends Contracts Interest
Payroll Lending Reservations Signups Subscriptions
Examples of transactional data

THE IMPORTANCE OF BIG DATA
The importance of big data does not revolve around how much data a company has but how a company utilizes the
collected data. Every company uses data in its own way; the more efficiently a company uses its data, the more
potential it has to grow. The company can take data from any source and analyze it to find answers which will enable:
Cost Savings
Some tools of Big Data can bring cost advantages to business when large amounts of data
are to be stored and these tools also help in identifying more efficient ways of doing
business.
Time Reductions
The high speed of tools and in-memory analytics can easily identify new sources of data
which helps businesses analyzing data immediately and make quick decisions based on the
learnings.
New Product
Development
By knowing the trends of customer needs and satisfaction through analytics you can create
products according to the wants of customers.
Understanding
the Market
Conditions
By analyzing big data you can get a better understanding of current market conditions. For
example, by analyzing customers’ purchasing behaviors, a company can find out the
products that are sold the most and produce products according to this trend.
Control Online
Reputation
Big data tools can do sentiment analysis. Therefore, you can get feedback about who is
saying what about your company. If you want to monitor and improve the online presence
of your business, then, big data tools can help in all this.

5 V‘s OF DATA
Volume
Velocity
Variety
Veracity
Value
The magnitude of
the data being
generated.
The speed at which
data is being
generated and
aggregated.
The different types of
data.
The trustworthiness
of the data in terms of
accuracy in quality.
The economic value
of the data.
90% of the data in the world
today has been created in the
last 2 years alone.
Literally the speed of light!
Data doubles every 40 months.
Structured, semi-structured
and unstructured data.
Because of the anonimity of
the Internet or possibly false
identities, the reliability of data
is often in question.
Having access to big data is no
good unless we can turn it into
value.
Big Data does a pretty good job of telling us what happened, but not why it happened or what to do about it. The 5 V‘s represent
specific characteristics and properties that can help us understand both the challenges and advantages of big data initiatives.

5 V‘s OF DATA
Volume
The magnitude of the data being generated.
Velocity
The speed at which data is being generated and aggregated.
Variety
The different types of data.
Veracity
The trustworthiness of the data in terms of accuracy in quality.
Value
The economic value of the data.
Big Data does a pretty good job
of telling us what happened,
but not why it happened or
what to do about it. The 5 V‘s
represent specific
characteristics and properties
that can help us understand
both the challenges and
advantages of big data
initiatives.

VOLUME
Volume refers to the vast amounts of data generated every
second. Just think of all the emails, twitter messages,
photos, video clips, sensor data etc. we produce and share
every second. We are not talking Terabytes but Zettabytes
or Brontobytes. On Facebook alone we send 10 billion
messages per day, click the "like' button 4.5 billion times
and upload 350 million new pictures each and every day. If
we take all the data generated in the world between the
beginning of time and 2008, the same amount of data will
soon be generated every minute! This increasingly makes
data sets too large to store and analyse using traditional
database technology. With big data technology we can
now store and use these data sets with the help of
distributed systems, where parts of the data is stored in
different locations and brought together by software.

VELOCITY
Velocity refers to the speed at which new data is
generated and the speed at which data moves around. Just
think of social media messages going viral in seconds, the
speed at which credit card transactions are checked for
fraudulent activities, or the milliseconds it takes trading
systems to analyse social media networks to pick up
signals that trigger decisions to buy or sell shares. Big data
technology allows us now to analyse the data while it is
being generated, without ever putting it into databases.

VARIETY
Variety refers to the different types of data we can now
use. In the past we focused on structured data that neatly
fits into tables or relational databases, such as financial
data (e.g. sales by product or region). In fact, 80% of the
world’s data is now unstructured, and therefore can’t
easily be put into tables (think of photos, video sequences
or social media updates). With big data technology we can
now harness differed types of data (structured and
unstructured) including messages, social media
conversations, photos, sensor data, video or voice
recordings and bring them together with more traditional,
structured data.

VERACITY
Veracity refers to the messiness or trustworthiness of the
data. With many forms of big data, quality and accuracy
are less controllable (just think of Twitter posts with hash
tags, abbreviations, typos and colloquial speech as well as
the reliability and accuracy of content) but big data and
analytics technology now allows us to work with these
type of data. The volumes often make up for the lack of
quality or accuracy.

VALUE
Value: Then there is another V to take into account when
looking at Big Data: Value! It is all well and good having
access to big data but unless we can turn it into value it is
useless. So you can safely argue that 'value' is the most
important V of Big Data. It is important that businesses
make a business case for any attempt to collect and
leverage big data. It is so easy to fall into the buzz trap and
embark on big data initiatives without a clear
understanding of costs and benefits.
Big data can deliver value in almost any area of business or society:
 It helps companies to better understand and serve customers: Examples
include the recommendations made by Amazon or Netflix.
 It allows companies to optimize their processes: Uber is able to predict
demand, dynamically price journeys and send the closest driver to the
customers.
 It improves our health care: Government agencies can now predict flu
outbreaks and track them in real time and pharmaceutical companies are
able to use big data analytics to fast-track drug development.
 It helps us to improve security: Government and law enforcement agencies
use big data to foil terrorist attacks and detect cyber crime.
 It allows sport stars to boost their performance: Sensors in balls, cameras on
the pitch and GPS trackers on their clothes allow athletes to analyze and
improve upon what they do.

1. Turning Big Data into Value
2. Smart Data Applications
3. How to Start Smart?
4. Big Data Challenges
HOW DOES BIG DATA
BECOME SMART
DATA

TURNING BIG DATA INTO VALUE
Smart data describes data that
has valid, well-defined,
meaningful information that
can expedite information
processing.
The „Datafication“
of our World:
• Activities
• Conversations
• Words
• Voice
• Social Media
• Browser logs
• Photos
• Videos
• Sensors
• Etc.
Analysing Big
Data:
• Text analytics
• Sentitment
analysis
• Face recognition
• Voice analytics
• Etc.
VOLUME
VELOCITY
VARIETY
VERACITY
The „Datafication“ of our world gives us unprecedeted amounts of data in terms of volume, velocity,
variety and veracity. The latest technology such as cloud computing and distributed systems together
with the latest software and analysis approaches allow us to leverage all types of data to gain insights
and add value.
VALUE
SMART
DATA

SMART DATA APPLICATIONS
• Fraud
detection/Prevention
• Brand sentiment
analysis
• Real time pricing
• Product placement
• Micro-targeted
advertising
• Monitor patient visits
• Patient care and
safety
• Reduce readmittance
rates
• Smart meter-stream
analysis
• Proactive equipment
repair
• Power and
consuption matching
• Cell tower diagnostics
• Bandwidth allocation
• Proactive
maintenance
• Decreasing time to
market
• Supply planning
• Increasing product
quality
• Network intrusion
detection and
prevention
• Disease outbreak
detection
• Unsafe driving
detection and
monitoring
• Route and time
planning for public
transport
FINANCIAL SERVICES RETAIL TELECOM MANUFACTURING
HEALTHCARE UTILITIES, OIL & GAS PUBLIC SECTOR TRANSPORTATION
Every business in the world needs data to thrive. Data is what tells you who your
customers are and how they operate, and it’s what can guide you to new insights
and new innovations. Any business can benefit from using big data to learn more
about their strategic position and development potential, but in order of not
„drowning“ in big data it is neccessary to find the right area of interest first.

HOW TO START SMART?
Even though data analysis and visualization tools have come a long way in the past decade, big data analysis still relies on human
intervention and coordination to be successful. You need to know how to ask the right questions, how to eliminate your own bias,
and how to form actionable insights rather than basic conclusions.
1. Review your
data.
• What data do you
have?
• How is it used?
• Do you have the
expertise to manage
your data?
2. Ask the right
questions.
• What data do you
have and how is it
used?
• Are you being specific
enough?
3. Draw the
conclusions.
• Could an expert help
to sense-check your
results?
• Can you validate your
hypotheses?
• What further data do
you need?

LACK OF TALENT
To successfully implement
a big data project requires
a sophisticated team of
developers, data scientists
and analysts who also have
a sufficient amount of
domain knowledge to
identify valuable insights.
BIG DATA CHALLENGES
It‘s easy to get caught up in the hype and opportunity of big data. However, one of the reasons big data is so
underutilized is because big data and big data technologies also present many challenges. One survey found that
55% of big data projects are never completed. So what‘s the problem with big data?
SCALABILITY
Many organizations fail
to take into account
how quickly a big data
project can grow and
evolve. Big data
workloads also tend to
be bursty, making it
difficult to allocate
capacity for resources.
ACTIONABLE
INSIGHTS
A key challenge for
data science teams is
to identify a clear
business objective and
the appropriate data
sources to collect and
analyze to meet that
objective.
DATA
QUALITY
Common causes of
dirty data include:
user imput errors,
duplicate data and
incorrect data
linking.
SECURITY
Specific challenges include:
- User authentication for every
team and team member
accessing the data
- Restricting access based on a
user‘s need
- Recording data access
histories and meeting other
comliance regulations
- proper use of encryprion on
data in-transit and at rest
COST
MANAGEMENT
Businesses pursuing big
data projects must
remember the cost of
training, maintenance
and expansion

BIG DATA PLATFORMS
There are several things you notice almost immediately when you start dealing with big
data. One thing that is decidedly different is the amount of open source software in wide
use. Many of the tools you hear the most about, such as Hadoop, are open source. Open
source is a good way to foster rapid innovation in a rapidly evolving field. As a corollary to
that you’ll notice that there are a vast number of big data tools from which to choose.
In addition to strictly open source offerings, well-known systems vendors such as IBM, HP,
Oracle, and Dell have products for the big data market. Many of these combine open
source tools and proprietary solutions. Major cloud providers including Amazon and
Google provide big data services in the cloud—often using versions of open source tools.
There are hundreds of big data tools and services, next slide will
show you just a peek into them.
Big data platform generally
consists of big data storage,
servers, database, big data
management, business
intelligence and other big
data management utilities.
It also supports custom
development, querying and
integration with other
systems.

www.smartdata.howwww.facebook.com/smartdatasr

Module 1 Introduction to Big and Smart Data- Online

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Module 1 Introduction to Big and Smart Data- Online