Slides for class session I taught at USC Annenberg on approaching big data for a non-technical audience so that they can learn the project planning skills to work with technical teams. The goal is to teach students the mindset they should when taking in mixed methods and applying to large datasets prior to selecting software packages and methodology. The slides take us through a previous use case and guidance moving forward from a process and cross-functional team perspective.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Slide 2: Etymology: The etymology of the term ‘Big Data’ can be traced back to the mid-1990s, when it was first used by John Mashey to refer to handling and analysis of massive datasets. However, by 2013, ‘Big Data’ was already being declared obsolescent as a meaningful term by some, as it was too wide ranging and vague in definition (e.g. de Goes, 2013).
Side 6: Vagaries: Kitchin argues that it is velocity and these additional key characteristics that set Big Data apart and make them a “disruptive innovation – one that radically changes the nature of data and what can be done with them” (Kitchin, 2014). However, there is no one characteristic profile that all Big Data fit and they can take multiple forms.
Slide 8: Ethics: Several ethical questions have been raised about the scope of data being generated and retained; such as those concerning privacy, informed consent, and protection from harm.
These questions raise wider issues about what kinds of data should be combined and analysed, and the purposes to which the resulting information should be put.
Slide 9: Inequalities: Challenges of inequality have also been posed:
Whose data traces will be analysed? It is likely that only those who are better off will be represented (as they are more likely to use social media, etc.)
Access and use of open data is unlikely to be equally available to everyone due to existing structural inequalities (Eynon, 2013)
Slide 11: What do Big Data actually tell us? Eynon (2013) argues that Big Data is concerned with capturing and examining patterns, and tells us more about what people actually do than about what they say they do. However, this is not sufficient for all kinds of social science research. We need to understand the meanings of behaviours which cannot be inferred simply from tracking specific patterns.
In order that Big Data are used appropriately, we need to ensure understanding of what kinds of research can or cannot be carried out using them. Big Data should not be seen as [a] “technical fix” for research, but should be used to empower, support and facilitate practice and critical research.
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward.
These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.
Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
Slides for class session I taught at USC Annenberg on approaching big data for a non-technical audience so that they can learn the project planning skills to work with technical teams. The goal is to teach students the mindset they should when taking in mixed methods and applying to large datasets prior to selecting software packages and methodology. The slides take us through a previous use case and guidance moving forward from a process and cross-functional team perspective.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
Slide 2: Etymology: The etymology of the term ‘Big Data’ can be traced back to the mid-1990s, when it was first used by John Mashey to refer to handling and analysis of massive datasets. However, by 2013, ‘Big Data’ was already being declared obsolescent as a meaningful term by some, as it was too wide ranging and vague in definition (e.g. de Goes, 2013).
Side 6: Vagaries: Kitchin argues that it is velocity and these additional key characteristics that set Big Data apart and make them a “disruptive innovation – one that radically changes the nature of data and what can be done with them” (Kitchin, 2014). However, there is no one characteristic profile that all Big Data fit and they can take multiple forms.
Slide 8: Ethics: Several ethical questions have been raised about the scope of data being generated and retained; such as those concerning privacy, informed consent, and protection from harm.
These questions raise wider issues about what kinds of data should be combined and analysed, and the purposes to which the resulting information should be put.
Slide 9: Inequalities: Challenges of inequality have also been posed:
Whose data traces will be analysed? It is likely that only those who are better off will be represented (as they are more likely to use social media, etc.)
Access and use of open data is unlikely to be equally available to everyone due to existing structural inequalities (Eynon, 2013)
Slide 11: What do Big Data actually tell us? Eynon (2013) argues that Big Data is concerned with capturing and examining patterns, and tells us more about what people actually do than about what they say they do. However, this is not sufficient for all kinds of social science research. We need to understand the meanings of behaviours which cannot be inferred simply from tracking specific patterns.
In order that Big Data are used appropriately, we need to ensure understanding of what kinds of research can or cannot be carried out using them. Big Data should not be seen as [a] “technical fix” for research, but should be used to empower, support and facilitate practice and critical research.
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward.
These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.
Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
Big data is a phenomenon brought about by rapid data growth, complex, new, and changing data types, and parallel technology advancements; it brings huge possibilities. By optimizing these enormous amounts of structured and unstructured data, CSPs are in a unique position to capture these opportunities and create new revenue streams.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
Big Data has recently gained relevance because companies are realizing what it can do for them and that it is a gold mine for finding competitive advantages. Proximity’s Juan Manuel Ramírez, Director of Strategy and...
The objective of this module is to provide an overview of the basic information on big 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 data
Duration of the module: approximately 1 – 2 hours
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
The success of an organization increasingly depends on their ability to draw conclusions regarding the various types of data available. Staying ahead of competitors requires many times to identify a trend, problem or opportunity microseconds before anyone else. That's why organizations must be able to analyze this information if they want to find insights that will help them to identify new opportunities underlying this phenomenon.
People are spontaneously uploading large amounts of information on the internet and this represents a great opportunity for companies to segment according to their behavior and not only socio-demographic factors. Companies store transactional information from their customers by making them fill in forms but the challenge for brands is to enrich these databases with information describing their customer’s behavior and daily habits. This info can be obtained through the online conversation and can be processed, crossed and enriched with many other types of information through different models based on Big Data. Following this procedure, we can complement the information we already have from our customers without having to ask them directly and therefor providing more value-added proposals to clients from a brand perspective.
Using the same technology with the right platform and the correct tactic, companies can achieve more ambitious goals that provide valuable information for the brand, which in turn could also enrich the customer’s experience, improving the customer journey for all types of clients.
less
Bigger and Better: Employing a Holistic Strategy for Big Data toward a Strong...IT Network marcus evans
Bigger and Better: Employing a Holistic Strategy for Big Data toward a Strong Value-Adding Proposition
by Patrick Hadley, Australian Bureau of Statistics at the Australian CIO Summit 2014
Tijdens de vierde sessie van de vierdelige reeks Master Minds on Data Science hield Eric van Tol een presentatie over businesscases en verdienmodellen.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Digital Tools and AI for Teaching Learning and Research
Smart Data Module 1 introduction to big and smart data
1. 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
2. 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
3. The emerging role of data in VET and enterprise1
V‘s of data2
How does big data become smart data?3
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.
– A Brief History of Data
– What is Big Data?
– Classification of Data
– Sources of Data
– The Importance of Big Data
– Turning Big Data into Value
– Smart Data Applications
– How to Start Smart?
4. A BRIEF HISTORY OF DATA
Smart Data Smart Region | www.smartdata.how
2400 BCE
300 BCE
1663
1926
1989
1997
2005
2015
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.
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.
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.
Nikola Tesla predicted that
humans will be able to access
and analyse huge amounts of
data in the future by using a
pocket friendly device.
Possibly the first use of the term Big
Data in the way it is used today.
Author Erik Larson speculated 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.”
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.
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.
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.
5. 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
6. 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?
Smart Data Smart Region | www.smartdata.how
„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
• High degree of
organization, such as
relational database.
• It represents only 5 to
10% of all data
• Examples: Dates, phone
numbers, customer
names, transaction
information,...
UNSTRUCTURED
DATA
• Information that is
difficult to organize using
traditional mechanisms.
• It represents around 80%
of data
• Examples: Images,
reports, social media,
spreadsheets,
communications,...
SEMI-STRUCTURED
DATA
• Information that doesn’t
reside in a relational
database but that does
have some organizational
properties that make it
easier to analyze
• Examples: Websites, XML,
e-mails,...
CLASSIFICATION OF DATA
To learn more about Big Data and its
importance complete Exercise 1 from
Learners Workbook
7. 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
Semi-structured Data
8. Smart Data Smart Region | www.smartdata.how
SOURCES OF DATA
Big data is often boiled down to a few varieties including social data, machine data, and
transactional data.
Machine data
Transactional data
Social media data
9. 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.
Social Media Data
Complete Exercise 2 from Learners
Workbook to learn how much you are
worth in the social media world
12. Smart Data Smart Region | www.smartdata.how
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
Time Reductions
New Product Development
Understanding the Market Conditions
Control Online Reputation
13. Smart Data Smart Region | www.smartdata.how
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.
14. Smart Data Smart Region | www.smartdata.how
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
15. 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.
Smart Data Smart Region | www.smartdata.how
Find out how a big retailer used the power
of Big Data in Learners Workbook, Exercise 3
16. HOW TO START SMART?
Smart Data Smart Region | www.smartdata.how
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?
17. LACK OF
TALENT
Smart Data Smart Region | www.smartdata.how
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
ACTIONABLE
INSIGHTS
DATA
QUALITY
SECURITY
COST
MANAGEMENT
18. Smart Data Smart Region | www.smartdata.how
BIG DATA PLATFORMS
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. There
are hundreds of big data
tools and services.