This document summarizes the history of big data from 1944 to 2013. It outlines key milestones such as the first use of the term "big data" in 1997, the growth of internet traffic in the late 1990s, Doug Laney coining the three V's of big data in 2001, and the focus of big data professionals shifting from IT to business functions that utilize data in 2013. The document serves to illustrate how data storage and analysis have evolved over time due to technological advances and changing needs.
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.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
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.
Many believe Big Data is a brand new phenomenon. It isn't, it is part of an evolution that reaches far back history. Here are some of the key milestones in this development.
Big Data Tutorial | What Is Big Data | Big Data Hadoop Tutorial For Beginners...Simplilearn
This presentation about Big Data will help you understand how Big Data evolved over the years, what is Big Data, applications of Big Data, a case study on Big Data, 3 important challenges of Big Data and how Hadoop solved those challenges. The case study talks about Google File System (GFS), where you’ll learn how Google solved its problem of storing increasing user data in early 2000. We’ll also look at the history of Hadoop, its ecosystem and a brief introduction to HDFS which is a distributed file system designed to store large volumes of data and MapReduce which allows parallel processing of data. In the end, we’ll run through some basic HDFS commands and see how to perform wordcount using MapReduce. Now, let us get started and understand Big Data in detail.
Below topics are explained in this Big Data presentation for beginners:
1. Evolution of Big Data
2. Why Big Data?
3. What is Big Data?
4. Challenges of Big Data
5. Hadoop as a solution
6. MapReduce algorithm
7. Demo on HDFS and MapReduce
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
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
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.
Big data is a term that describes the large volume of data may be 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.
I've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
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
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
COMEX2017 Smart Talks by Amjid Ali , Muscat, Oman. Covering Introduction to big data, Big Data Definitions, Big Data Revolution, Big Data Timeline, Hadoop and Map Reduce covers importance of storage and DNA, Oceanstore 9000, Microsoft R, Spark,
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
John P. Girard, Ph.D.'s talk at Sales & Marketing Middle East. Everyone is talking about big data. Lots of people of selling big data. Many leaders are wondering about big data. An honest, sans hype, overview of where we are in the big data space.
The talk will cover in broad strokes the building blocks, facilitators and challenges for big data based decision making.
Using examples from two projects from very dissimilar domains (High tech manufacturing and Public Health) Dr. Vinze will present possibilities for Data Science for both practitioners and academic researchers.
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
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.
Big data is a term that describes the large volume of data may be 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.
I've shown you in this ppt, the difference between Data and Big Data. How Big Data is generated, Opportunities with Big Data, Problem occurred in Big Data, solution of that problem, Big Data tools, What is Data Science & how it's related with the Big Data, Data Scientist vs Data Analyst. At last, one Real-life scenario where Big data, data scientists, and data analysts work together.
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
A high level overview of common Cassandra use cases, adoption reasons, BigData trends, DataStax Enterprise and the future of BigData given at the 7th Advanced Computing Conference in Seoul, South Korea
COMEX2017 Smart Talks by Amjid Ali , Muscat, Oman. Covering Introduction to big data, Big Data Definitions, Big Data Revolution, Big Data Timeline, Hadoop and Map Reduce covers importance of storage and DNA, Oceanstore 9000, Microsoft R, Spark,
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
John P. Girard, Ph.D.'s talk at Sales & Marketing Middle East. Everyone is talking about big data. Lots of people of selling big data. Many leaders are wondering about big data. An honest, sans hype, overview of where we are in the big data space.
The talk will cover in broad strokes the building blocks, facilitators and challenges for big data based decision making.
Using examples from two projects from very dissimilar domains (High tech manufacturing and Public Health) Dr. Vinze will present possibilities for Data Science for both practitioners and academic researchers.
SWOT of Bigdata Security Using Machine Learning Techniquesijistjournal
This paper gives complete guidelines on BigData, Different Views of BigData, etc.How the BigData is useful to us and what are the factors affecting BigData all the things are covered under this paper. The paper also contains the BigData Machine learning techniques and how the Hadoop comes into the picture. It also contains the what is importance of BigData security. The paper mostly covers all the main point that affect Big Data and Machine Learning.
Notes from the Observation Deck // A Data Revolution gngeorge
Notes from the Observation Deck will provide you with an examined look at the interesting phenomena and trends taking place around us today. We present them to you with the hope of sparking broader conversations, debates and ideas. Please use this as a resource for knowledge, inspiration and enjoyment.
From AI to Z: How AI is changing the relationship between people and dataiGenius
On the occasion of SMAU Milano 2018, Gabriel Cismondi, COO at iGenius, talks about Artificial Intelligence and how it's changing the relationship between people and data.
Information Governance -- Necessary Evil or a Bridge to the Future?John Mancini
How the world is changing -- Old paradigms are being stretched to the breaking point
How we usually think about governance -- It’s not just about what you keep
How should we respond? -- Building an action plan for the next 2-3 years
Quontra solutions is your premier online IT educational destination in UK. It provides online IT courses like Selenium , Hadoop ,CCNA ,Cloud Computing ,Business Analyst and Many other IT courses. All the courses are designed by experienced instructors and designers. Hadoop is a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment there is an urgent need for IT professional to keep themselves in trend with Hadoop and Big Data technologies
.
Quontra Specialties :
***All the courses are designed by Experienced Instructors and Designers.
***. Trainers are not limited to the syllabus, they explain off –the-shelf content also.
*** 24X7 technical support team .
***Unlimited access to all recorded sessions ,available after every live class.
***Syllabus built based on professional standards and employer insights.
***Trainers are Certified Experts in their corresponding field and they bring years of industry experience in to the training classes
John Girard's keynote talk at KM Singapore "Big Data: Friend, Phantom or Foe?" Asking and answering some of the tough questions leaders have about Big Data.
What is Santa has a data audit log application? What would he do with it? Check out Realise Data System's fun holiday slideshow that imagines what it would be like if Santa had a data tracking application to help him access when children were naughty or nice throughout the year, their addresses, their wish list, and more. Think of all the possibilities! Happy Holidays from Realise Data Systems!
How to Lose Data, Customers, and Fail a Government AuditGadi Eichhorn
Realise Data Systems brings you the sequel to "How to Be A Terrible Field Service Organization, a spoof on what happens to companies that don't invest in data tracking software to help with data governance and compliance.
What If Fireworks Displays Used Scheduling Software Gadi Eichhorn
Would scheduling software (field service management software) reduce the number of injuries and mistakes caused by lighting off fireworks displays every year? Realise Data Systems reflects...
The Power of Social Media for Field ServiceGadi Eichhorn
Field service organizations should be using social media to provide the best customer service to their clients. Here is what social media customer service looks like these days.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
3. 1967
The “information explosion”
noted in recent years makes it
essential that storage
requirements for all information
be kept to a minimum. A fully
automatic and rapid three-part
compressor which can be used
with “any” body of information to
greatly reduce slow external
storage requirements and to
increase the rate of information
transmission through a
computer is described in this
paper.
Automatic
Data
Compression
published by
B. A. Marron &
Paul de Maine
from the Abstract
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
4. 1980
“I believe that large
amounts of data are
being retained because
users have no way of
identifying obsolete
data; the penalties for
storing obsolete data
are less apparent than
are the penalties for
discarding potentially
useful data.”
I.A. Tjomsland gives
the talk titled
“Where Do We Go
From Here?”
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
5. 1996
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
Digital storage
becomes more
cost-effective
for storing
data than
paper
VS
6. 1997
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
The term big data is used for
the first time in publication
“Application-controlled demand paging for out-of-
core visualization”
7. 1998
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
400%
1997 1998 1999 2000
GROWTH RATE OF INTERNET
200%
0%
Data Traffic
Voice Traffic
by
2002
“The Size and Growth
Rate of the Internet.”
8. 1999
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
≈ 1.5
Study finds that in 1999
the world produced
exabytes of unique
information
X 250
exabytes of unique
information
For every man, woman, and child
9. 2001
Volume
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
Velocity
Variety
Doug
Laney, an
analyst with
the Meta
Group, coins
the 3 V’s“3D Data Management:
Controlling Data
Volume, Velocity, and
Variety.”
10. 2002
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
In 2002, digital
information storage
surpassed non-digital
for the first time
11. Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
Database management
is a core competency
of Web 2.0
companies, so much
so that we have
sometimes referred to
these applications as
‘infoware’ rather than
merely software.”
Tim O’Reilly -
“What is Web 2.0”
12. 2011
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
1986 2007
+ 25% per year
“The World’s Technological Capacity to
Store, Communicate, and Compute Information”
99.2% of all
storage capacity
was analog
94% of storage
capacity was
digital
VS
13. 2012
Facts taken from A Very Short History Of Big Data by Gil Press – Forbes.com
Big Data is defined in “Critical Questions for Big Data” as
a cultural, technological, and scholarly phenomenon that
rests on the interplay of:
1. Technology: maximizing computation power and
algorithmic accuracy to gather, analyze, link, and
compare large data sets
2. Analysis: drawing on large data sets to identify
patterns in order to make economic, social, technical,
and legal claims.
3. Mythology: the widespread belief that large data sets
offer a higher form of intelligence and knowledge that
can generate insights that were previously impossible,
with the aura of truth, objectivity, and accuracy.
14. 2013
Facts taken from TATA Consultancy Services
SALES
MARKETING
CUSTOMER SERVICE
R&D
IT
MANUFACTURING
FINANCE
LOGISTICS
HR
15.2%
15%
13.3%
11.3%
11.1%
8.3%
7.7%
6.7%
5%
Where Are Companies
Focusing Big Data
Professionals Who
Analyze Big Data
In an IT Function
In Business Functions That
Use the Data
In a Separate Big Data Group
15. 2013
Introducing Observato™
Independent Data Archive
Complete Transaction Record
Multi-system Data Tracking/History
Fully Compliant
Data Reporting
Easy to Navigate UI
Helping businesses manage their big
data, in a big way.
16. This SlideShare is a visual presentation of the article “A
Very Short History of Big Data” by Gil Press, taken from
Forbes.com.
Additional sources are cited within the text.
Realise Data Systems is a business solution technology
provider that specializes in workforce management system
integrations and offers a one-of-a-kind data tracking
application called Observato. Our mission is to transform
service organizations worldwide with
independent, professional, and trustworthy
implementation, consulting, and enterprise auditing services
that will improve efficiency and help to deliver first-class
customer service.
Please visit www.realisedatasystems.com/observato
for more information.