The document discusses big data analytics. It begins by defining big data as large datasets that are difficult to capture, store, manage and analyze using traditional database management tools. It notes that big data is characterized by the three V's - volume, variety and velocity. The document then covers topics such as unstructured data, trends in data storage, and examples of big data in industries like digital marketing, finance and healthcare.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
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
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Big data Analytics is a process to extract meaningful insight from big such as hidden patterns, unknown correlations, market trends and customer preferences
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
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
A look back at how the practice of data science has evolved over the years, modern trends, and where it might be headed in the future. Starting from before anyone had the title "data scientist" on their resume, to the dawn of the cloud and big data, and the new tools and companies trying to push the state of the art forward. Finally, some wild speculation on where data science might be headed.
Presentation given to Seattle Data Science Meetup on Friday July 24th 2015.
Big Data Analytics Powerpoint Presentation SlideSlideTeam
If it’s that time to make analysis for the predicament of the management system or simply to present deafening data in front of your qualified team then you have reached the right match. SlideTeam presents you classy and eternally approaching PowerPoint slides for big data analytics. Data analysis agendas and big data plans are shown through captivating icons and subheadings for a precise and interesting approach. This unique PPT slide is useful for studying business and marketing related topics, approaching the correct conclusions and keeping a track on business growth. Make an outstanding presentation for your viewers with this unique PPT slide and deliver your message in an effective manner using Big data analytics Powerpoint Presentation slide and make your pathways more defining. Most of the elements of the slide are highly customizable. The text boxes help you in adding more information about the point mentioned and its associated icon. Every detail in our Big Data Analytics Powerpoint Presentation Slide is doubly cross checked. You can be certain of it's authenticity. https://bit.ly/3fvnRVK
Big Data may well be the Next Big Thing in the IT world. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
A look back at how the practice of data science has evolved over the years, modern trends, and where it might be headed in the future. Starting from before anyone had the title "data scientist" on their resume, to the dawn of the cloud and big data, and the new tools and companies trying to push the state of the art forward. Finally, some wild speculation on where data science might be headed.
Presentation given to Seattle Data Science Meetup on Friday July 24th 2015.
Big Data Analytics Powerpoint Presentation SlideSlideTeam
If it’s that time to make analysis for the predicament of the management system or simply to present deafening data in front of your qualified team then you have reached the right match. SlideTeam presents you classy and eternally approaching PowerPoint slides for big data analytics. Data analysis agendas and big data plans are shown through captivating icons and subheadings for a precise and interesting approach. This unique PPT slide is useful for studying business and marketing related topics, approaching the correct conclusions and keeping a track on business growth. Make an outstanding presentation for your viewers with this unique PPT slide and deliver your message in an effective manner using Big data analytics Powerpoint Presentation slide and make your pathways more defining. Most of the elements of the slide are highly customizable. The text boxes help you in adding more information about the point mentioned and its associated icon. Every detail in our Big Data Analytics Powerpoint Presentation Slide is doubly cross checked. You can be certain of it's authenticity. https://bit.ly/3fvnRVK
Big Data may well be the Next Big Thing in the IT world. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
Data Analytics with R, Contents and Course materials, PPT contents. Developed by K K Singh, RGUKT Nuzvid.
Contents:
Introduction to Data, Information and Data Analytics,
Types of Variables,
Types of Analytics
Life cycle of data analytics.
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
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Global datasphere, Data disruptions, Data mergers and acquisitions, Data marketing, Data advertising, Future of the Data world ... all in one concise slide deck
Big data refers to the vast amount of structured and unstructured data that inundates organizations on a daily basis. This data comes from various sources such as social media, sensors, digital transactions, mobile devices, and more.
Presentation by Luc Delanglez (DataLumen) at the Data Vault Modelling and Dat...Patrick Van Renterghem
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IT industry observers and long-time technology editors James Maguire and Chris Preimesberger share their predictions for enterprise IT in the year 2019. Hear their commentary at: http://bit.ly/2RZFhMA
The 2019 predictions cover artificial intelligence, Big Data, data center, cloud computing, and more.
Who needs Big Data? What benefits can organisations realistically achieve with Big Data? What else required for success? What are the opportunities for players in this space? In this paper, Cartesian explores these questions surrounding Big Data.
www.cartesian.com
With enterprises putting digital at the core of their transformation, our annual Data Science & AI Trends Report explores the key strategic shifts enterprises will make to stay intelligent and agile going into 2019. The year was marked by a series of technological advances, including advances in AI, deep learning, machine learning, hybrid cloud architecture, edge computing (with data moving away to edge data centres), robotic process automation, a spurt of virtual assistants, advancements in autonomous tech and IoT.
What -IoT
Why - IoT
Why - IoT
IoT is enabling Technology to ML, DL, AI and Data Science
Applications
IoT Product Development – Entrepreneurs
Research Gap
Online Tools
Linux commands working with file contents:
head, tail, cat, tac, more, less and strings, more file
attributes: hard links, symbolic links, fins, umask
and inodes The Linux file tree: the root directory, binary
directories, configuration directories, data
directories, Commands and arguments: $PATH,
echo, ls, env
Discussed on
Introduction to Linux: Linux history,
distributions, licensing, Linux commands: man
pages, commands working with directories,
absolute and relative paths
commands working with files: file, touch, rm, cp,
mv and rename, general purpose utilities: cal,
date, script, who, tty, pwd, ps, uname
Discussed about
An overview – Object basics – Object state and properties – Behavior – Methods – Messages –
Information hiding – Class hierarchy – Relationships – Associations – Aggregations- Identity –
Dynamic binding – Persistence – Metaclasses – Object oriented system development life cycle.
Discussed about:
A Short History of Business Models
The Business Model Canvas
Who is the Business Model for
Models
Funding an IoT Start-up
Lean Start-ups
Discussed about the following topics: A Web Security Forensic Lesson
Web Languages
Introduction to different web attacks
overview of n-tier web applications
Web servers
Apache
IIS
Database Servers
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
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How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Model Attribute Check Company Auto PropertyCeline George
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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.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
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2. Big Data Analytics
Vikram Neerugatti
Sri Venkateswara University, Tirupati.
vikramneerugatti@gmail.com
vikram@smartnutsandbolts.com
www.vikramneerugatti.com
www.smartnutsandbolts.com
Vikram Neerugatti
Vikram Nandini
3. Content
What is Big Data
Varieties of Data
Unstructured Data
Trends in Data Storage
Industry Examples of Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti3
4. What is Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti4
Two men operating a mainframe computer, circa 1960. It’s amazing how
today’s smartphone holds so much more data than this huge 1960’s
relic. (Photo by Pictorial Parade/Archive Photos)
5. What is Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti5
Big Data is the next generation of data
warehousing.
twenty-first century, when the Age of Big Data
Analytics was in its infancy.
It ’s not an overnight phenomenon.
Reasons for now
Computing perfect storm
Data perfect storm
Convergence perfect storm
6. Computing perfect storm
25-07-2019Big Data Analytics by Vikram Neerugatti6
Big Data analytics are the natural result of four
major global trends:
1. Moore ’s Law (which basically says that
technology always gets cheaper),
2. mobile computing (that smart phone or mobile
tablet in your hand),
3. social networking (Facebook, Foursquare,
Pinterest, etc.),
4. and cloud computing (you don ’t even have to own
hardware or software anymore; you can rent or
lease someone else ’s).
7. Data Perfect Storm
25-07-2019Big Data Analytics by Vikram Neerugatti7
Volumes of transactional data have been around
for decades for most big firms, but the flood gates
have now opened
with more volume , and the velocity and variety—
the three Vs—of data that has arrived in
unprecedented ways.
This perfect storm of the three Vs makes it
extremely complex and cumbersome
with the current data management and
analytics technology and practices.
8. Convergence perfect storm
25-07-2019Big Data Analytics by Vikram Neerugatti8
Traditional data management and analytics
software and hardware
technologies, open-source technology, and
commodity hardware
are merging to create new alternatives for IT and
business executives
to address Big Data analytics.
9. What is Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti9
People are able to store that much data now and
more than they ever before.
We have reached this tipping point where they
don ’t have to make decisions about which half to
keep or how much history to keep.
It ’s now economically feasible to keep all of your
history and all of your variables and go back later
when you have a new question and start looking
for an answer.
That hadn ’t been practical up until just recently.
10. What is Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti10
Certainly the advances in blade technology and
the idea
that Google brought to market of you take lots
and lots of small Intel
servers and you gang them together and use
their potential in aggregate.
That is the super computer of the future.
11. Evolution of data systems
25-07-2019Big Data Analytics by Vikram Neerugatti11
Dependent (Early Days).
Data systems were fairly new and users didn't
know quite know what they wanted. IT assumed
that “Build it and they shall come.”
Independent (Recent Years).
Users understood what an analytical platform was
and worked together with IT to define the business
needs and approach for deriving insights for their fi
rm.
Interdependent (Big Data Era).
Interactional stage between various companies,
creating more social collaboration beyond your
firm’s walls.
12. Big data
25-07-2019Big Data Analytics by Vikram Neerugatti12
Here is how the McKinsey study defi nes Big
Data:
Big data refers to datasets whose size is beyond
the ability of typical
database software tools to capture, store,
manage, and analyze.
big data in many sectors today will range from a
few
dozen terabytes to multiple petabytes (thousands
of terabytes). 2
13. Big Data Analytics
25-07-2019Big Data Analytics by Vikram Neerugatti13
The real challenge is identifying or developing
most cost-effective and reliable methods for
extracting value from all the terabytes and
petabytes of data now available.
That ’s where Big Data analytics become
necessary.
Comparing traditional analytics to Big Data
analytics is like comparing a cart to a tractor
The differences in speed, scale, and complexity
are tremendous
15. Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti15
The industry has an evolving definition around
Big Data that is currently defined by three
dimensions:
1. Volume
2. Variety
3. Velocity
16. Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti16
Volume
Data variety is the assortment of data.
Traditionally data, especially operational data, is
“structured” as it is put into a database based on the type
of data (i.e., character, numeric, floating point, etc.).
Over the past couple of decades, data has increasingly
become “unstructured” as the sources of data have
proliferated beyond operational applications.
Oftentimes, text, audio, video, image, geospatial, and
Internet data (including click streams and log files) are
considered unstructured data .
Semi-structured data
customer name + date of call + complaint
Velocity
17. Varieties of Data
25-07-2019Big Data Analytics by Vikram Neerugatti17
The variety of data sources continues to increase.
Internet data (i.e., clickstream, social media, social
networking links)
Primary research (i.e., surveys, experiments,
observations)
Secondary research (i.e., competitive and
marketplace data, industry reports, consumer data,
business data)
Location data (i.e., mobile device data, geospatial
data)
Image data (i.e., video, satellite image, surveillance)
Supply chain data (i.e., EDI, vendor catalogs and
pricing, quality information)
Device data (i.e., sensors, PLCs, RF devices, LIMs,
18. Varieties of Data
25-07-2019Big Data Analytics by Vikram Neerugatti18
The wide variety of data leads to complexities in
ingesting the data into data storage.
The variety of data also complicates the
transformation (or the changing of data into a
form
that can be used in analytics processing) and
analytic computation of the processing of the
data.
19. Unstructured Data
25-07-2019Big Data Analytics by Vikram Neerugatti19
structured data (the kind that is easy to define,
store, and analyze)
Unstructured data (the kind that tends to defy
easy definition, takes up lots of storage capacity,
and is typically more difficult to analyze).
Unstructured data is basically information that
either does not have a
predefined data model and/or does not fi t well
into a relational database.
Unstructured information is typically text heavy,
but may contain data such as dates, numbers,
and facts as well.
20. Unstructured Data
25-07-2019Big Data Analytics by Vikram Neerugatti20
The term semi-structured data is used to describe
structured data that doesn't ’t fit into a formal
structure of data models.
However, semi-structured data does contain tags
that separate semantic elements, which includes
the capability to enforce hierarchies within the
data.
21. Unstructured Data
25-07-2019Big Data Analytics by Vikram Neerugatti21
but here are the main takeaways that we would like to
share with you:
The amount of data (all data, everywhere) is doubling every
two years.
Our world is becoming more transparent. We, in turn, are
beginning to accept this as we become more comfortable with
parting with data that we used to consider sacred and private.
Most new data is unstructured. Specifically, unstructured data
represents almost 95 percent of new data, while structured
data represents only 5 percent.
Unstructured data tends to grow exponentially, unlike
structured data, which tends to grow in a more linear fashion.
Unstructured data is vastly underutilized. Imagine huge
deposits of oil or other natural resources that are just sitting
there, waiting to be used. That ’s the current state of
unstructured data as of today. Tomorrow will be a different
story because there ’s a lot of money to be made for smart
individuals and companies that can mine unstructured data
22. Is Big Data analytics worth the effort?
25-07-2019Big Data Analytics by Vikram Neerugatti22
Yes, without a doubt Big Data analytics is worth
the effort.
It will be a competitive advantage, and it ’s likely
to play a key role in sorting winners from losers in
our ultracompetitive global economy.
23. From a business perspective, you
’ll need to learn how to:
25-07-2019Big Data Analytics by Vikram Neerugatti23
Use Big Data analytics to drive value for your
enterprise that aligns with your core competencies
and creates a competitive advantage for your
enterprise
Capitalize on new technology capabilities and
leverage your existing technology assets
Enable the appropriate organizational change to
move towards fact based decisions, adoption of new
technologies, and uniting people from multiple
disciplines into a single multidisciplinary team
Deliver faster and superior results by embracing and
capitalizing on the ever-increasing rate of change that
is occurring in the global market place
24. Big Data analytics uses a wide
variety of advanced analytics
25-07-2019Big Data Analytics by Vikram Neerugatti24
26. Big Data Business Models
25-07-2019Big Data Analytics by Vikram Neerugatti26
27. Enabling Big Data Analytic
Applications
25-07-2019Big Data Analytics by Vikram Neerugatti27
28. The key to success for organizations seeking to
take advantage of this opportunity is:
25-07-2019Big Data Analytics by Vikram Neerugatti28
Leverage all your current data and enrich it with
new data sources
Enforce data quality policies and leverage today’
s best technology and people to support the
policies
Relentlessly seek opportunities to imbue your
enterprise with fact based decision making
Embed your analytic insights throughout your
organization
29. Trends in Data Storage
25-07-2019Big Data Analytics by Vikram Neerugatti29
Following are differing types of storage systems:
Distributed File Systems
NoSQL Databases
NewSQL Databases
Big Data Querying Platforms
30. Trends in Data Storage
25-07-2019Big Data Analytics by Vikram Neerugatti30
31. Trends in Data Storage
25-07-2019Big Data Analytics by Vikram Neerugatti31
Big Data Querying Platforms:
Technologies that provide query facades infront of
big data stores such as distributed file systems or
NoSQL databases.
The main concern is providing a high-level
interface, e.g. via SQL3 like query languages and
achieving low query latencies.
32. Industry Examples of Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti32
collected from thought leaders in subjects and
industries such as
Digital Marketing
Financial Services
Advertising
and Healthcare.
33. Digital Marketing and the Non-line
World
25-07-2019Big Data Analytics by Vikram Neerugatti33
Don ’t Abdicate Relationships
Is IT Losing Control of Web Analytics?
34. Database Marketers, Pioneers of
Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti34
Database marketing is really concerned with
building databases containing information about
individuals, using that information to better
understand those individuals, and communicating
effectively with some of those individuals to drive
business value.
35. Big Data and the New School of
Marketing
25-07-2019Big Data Analytics by Vikram Neerugatti35
New School marketers deliver what today ’s
consumers want: relevant interactive
communication across the digital power channels:
email, mobile, social, display and the web.
Consumers Have Changed. So Must
Marketers
The Right Approach: Cross-Channel Lifecycle
Marketing
It really starts with the capture of customer
permission, contact information, and preferences for
multiple channels.
36. Lifecycle Marketing approach: conversion, repurchase,
stickiness, win-back, and re-permission
25-07-2019Big Data Analytics by Vikram Neerugatti36
38. Fraud and Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti38
One of the most common forms of fraudulent
activity is credit card fraud.
The credit card fraud rate in United States and
other countries is increasing.
In order to prevent the fraud, credit card
transactions are monitored and checked in near
real time.
If the checks identify pattern inconsistencies and
suspicious activity, the transaction is identified for
review and scalation.
39. Fraud and Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti39
Big Data technologies provide an optimal
technology solution based on the following three
Vs:
High volume. Years of customer records and
transactions (150 billion1 records per year)
High velocity. Dynamic transactions and social
media information
High variety. Social media plus other unstructured
data such as customer emails, call centre
conversations, as well as transactional structured
data
41. Risk and Big Data
25-07-2019Big Data Analytics by Vikram Neerugatti41
The two most common types of risk management
are credit risk management and market risk
management.
A third type of risk, operational risk management,
isn’t as common as credit and market risk.
Credit risk analytics focus on past credit
behaviors to predict the likelihood that a borrower
will.
Market risk analytics focus on understanding the
likelihood that the value of a portfolio will
decrease due to the change in stock prices,
interest rates, foreign exchange rates, and
commodity prices.