The document discusses how data has become a central business asset and strategic advantage. It notes that the growth of data from sources like the Internet of Things means that variety, not just volume or velocity, will be important. New business processes will revolve around data, which will become more valuable over the next decade. It also provides examples of how companies like eBay and Groupon have used data for competitive advantages like identifying top sellers.
Big Data Trends - WorldFuture 2015 ConferenceDavid Feinleib
David Feinleib's Big Data Trends presentation from the World Future Society's Annual Conference, WorldFuture 2015, held at the Hilton Union Square, San Francisco, California July 25, 2015.
Big data introduction - Big Data from a Consulting perspective - SogetiEdzo Botjes
Big data introduction - Sogeti - Consulting Services - Business Technology - 20130628 v5
This is a small introduction to the topic Big Data and a small vision on how to enable a (big) company in using big data and embed it into the organisation.
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.
BIG Data & Hadoop Applications in Social MediaSkillspeed
Explore the applications of BIG Data & Hadoop in Social Media via Skillspeed.
BIG Data & Hadoop in Social Media is a key differentiator, especially in terms of generating memorable customer experiences.
Herein, we discuss how leading social networks such as Facebook, Twitter, Pinterest, LinkedIN, Instagram & Stumble Upon utilize Hadoop.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Big Data Trends - WorldFuture 2015 ConferenceDavid Feinleib
David Feinleib's Big Data Trends presentation from the World Future Society's Annual Conference, WorldFuture 2015, held at the Hilton Union Square, San Francisco, California July 25, 2015.
Big data introduction - Big Data from a Consulting perspective - SogetiEdzo Botjes
Big data introduction - Sogeti - Consulting Services - Business Technology - 20130628 v5
This is a small introduction to the topic Big Data and a small vision on how to enable a (big) company in using big data and embed it into the organisation.
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.
BIG Data & Hadoop Applications in Social MediaSkillspeed
Explore the applications of BIG Data & Hadoop in Social Media via Skillspeed.
BIG Data & Hadoop in Social Media is a key differentiator, especially in terms of generating memorable customer experiences.
Herein, we discuss how leading social networks such as Facebook, Twitter, Pinterest, LinkedIN, Instagram & Stumble Upon utilize Hadoop.
To get more details regarding BIG Data & Hadoop, please visit - www.SkillSpeed.com
Big Data and Hadoop Training batch in Pune is scheduled to commence on December 7th, 2013.This batch will be as per a new revamped four day schedule, contents and focus, based on feedback from participants of earlier courses. The training is conducted in a workshop like environment with an effective blend of hands-on practicals and assignments to augment the fundamental theory covered.
About the Faculty:
He is a Doctorate in Engineering and an industry veteran with more than twenty five years experience in launching new technologies, products and businesses. He has been involved in acquiring five patents for the company that he has worked for.
Big Data Analytics – Why?
Data is now generated by more sources and at ever increasing rates. Examples include Social Media sites, GPS based tracking systems, point of sale equipment, etc. The ability to process such data can provide that essential edge required for business success. Demand for Big Data professionals is rapidly increasing. Knowledge of Big Data can provide an advantage leading to faster professional advancement
About this course
This course on Big Data Analytics for Business is a combination of essential fundamentals, practical techniques, hands-on sessions on Hadoop, and case studies to cement all this together.
By completing this course you will be able to …
Understand fundamentals of analytics: Descriptive, Predictive and Prescriptive Analytics
Know what ‘Big Data’, Map Reduce and Hadoop are all about
Get a grip on the structure of Big Data applications
Effectively use Big Data techniques like Map Reduce and tools like Hadoop, Hive, Hbase, Pig
Choose the most appropriate tools to solve Big Data problems
Identify, propose and lead Big Data projects in your organizations
Course Content -
What is Big Data?
Overview of Big Data tools and techniques
In-depth coverage of Map-reduce techniques to manage Big Data
Hadoop - In Depth
HDFS – In Depth
Installing and managing Hadoop – Hands-on
Introduction to Hadoop Clusters
Hands-on session using native installation and Amazon EMR implementation of Hadoop
The Hadoop ecosystem: Pig, HIVE, HBase, Pig, SQOOP and Flume
Analytics: Descriptive, Predictive and Prescriptive
What is Big Data Analytics
Introducing Analytics in the enterprise: Case Studies
Trends in Big Data Analytics
The course takes a "hands-on" approach to ensure that the basics are understood very well and assimilated concepts are applied in practice.
Essential pre-requisite for practitioner course: Java programming language.
Note: Basic Java Module for participants those who are new to Java.
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.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
Data is not consistent, sometimes searches or general interest in certain topics, say social media or other types of data experienced peaks and valleys. Data analysis techniques allow the data scientist to mine this type of unstable data and still draw meaningful conclusions from it.
What Is DataOps? When Agile Meets Data AnalyticsBernard Marr
The amount of data enterprise businesses are producing is growing by 40 to 60% a year, and many companies are facing challenges managing, analyzing, and interpreting all that data so they can enable solutions, support their data-focused teams, and glean valuable business insights.
Big Data analytics have increasingly gained prominence in business because it has provided beneficial insights regarding emerging trends, behaviors and preferences. Relying exclusively on analytics to address the vast majority of business uncertainties, however, is detrimental to our ability to solve problems. Madsbjerg and Rasmussen, in a WSJ article, insightfully captures the essence: “By outsourcing our thinking to Big Data, our ability to make sense of the world by careful observation begins to wither, just as you miss the feel and texture of a new city by navigating it only with the help of a GPS.”
If we are to gain a better understanding of our customers and the business itself, we must not miss “the feel and texture.” We need to see problems and opportunities in terms of human experience and capture and interpret data with a human context. We need to examine and understand how people live their lives from their own perspective, rather than from traditional business’ perspective. This applies to markets and products, as much as it applies to corporate culture because humans are complex and difficult to qualify and quantify. By using ethnographic research methods, we can uncover and understand the needs and desires – the whys and the feel and texture - that drive the emotional lives of customers.
This paper argues that business needs to combine analytics with ethnography for richer and even more valuable insights to move ahead in a global market. It also provides some suggestions on how to combine analytics with ethnography.
Lead Author: Matt Artz, Azimuth Labs
Co-author: Dr. Uldarico Rex Dumdum, Marywood University
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
Big Data and Hadoop Training batch in Pune is scheduled to commence on December 7th, 2013.This batch will be as per a new revamped four day schedule, contents and focus, based on feedback from participants of earlier courses. The training is conducted in a workshop like environment with an effective blend of hands-on practicals and assignments to augment the fundamental theory covered.
About the Faculty:
He is a Doctorate in Engineering and an industry veteran with more than twenty five years experience in launching new technologies, products and businesses. He has been involved in acquiring five patents for the company that he has worked for.
Big Data Analytics – Why?
Data is now generated by more sources and at ever increasing rates. Examples include Social Media sites, GPS based tracking systems, point of sale equipment, etc. The ability to process such data can provide that essential edge required for business success. Demand for Big Data professionals is rapidly increasing. Knowledge of Big Data can provide an advantage leading to faster professional advancement
About this course
This course on Big Data Analytics for Business is a combination of essential fundamentals, practical techniques, hands-on sessions on Hadoop, and case studies to cement all this together.
By completing this course you will be able to …
Understand fundamentals of analytics: Descriptive, Predictive and Prescriptive Analytics
Know what ‘Big Data’, Map Reduce and Hadoop are all about
Get a grip on the structure of Big Data applications
Effectively use Big Data techniques like Map Reduce and tools like Hadoop, Hive, Hbase, Pig
Choose the most appropriate tools to solve Big Data problems
Identify, propose and lead Big Data projects in your organizations
Course Content -
What is Big Data?
Overview of Big Data tools and techniques
In-depth coverage of Map-reduce techniques to manage Big Data
Hadoop - In Depth
HDFS – In Depth
Installing and managing Hadoop – Hands-on
Introduction to Hadoop Clusters
Hands-on session using native installation and Amazon EMR implementation of Hadoop
The Hadoop ecosystem: Pig, HIVE, HBase, Pig, SQOOP and Flume
Analytics: Descriptive, Predictive and Prescriptive
What is Big Data Analytics
Introducing Analytics in the enterprise: Case Studies
Trends in Big Data Analytics
The course takes a "hands-on" approach to ensure that the basics are understood very well and assimilated concepts are applied in practice.
Essential pre-requisite for practitioner course: Java programming language.
Note: Basic Java Module for participants those who are new to Java.
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.
Data-Ed Webinar: Demystifying Big Data DATAVERSITY
We are in the middle of a data flood and we need to figure out how to tame it without drowning. Most of what has been written about Big Data is focused on selling hardware and services. But what about a Big Data Strategy that guides hardware and software decisions? While virtually every major organization is faced with the challenge of figuring out the approach for and the requirements of this new development, jumping into the fray hastily and unprepared will only reproduce the same dismal IT project results as previously experienced. Join Dr. Peter Aiken as he will debunk a number of misconceptions about Big Data as your un-typical IT project. He will provide guidance on how to establish realistic Big Data management plans and expectations, and help demonstrate the value of such actions to both internal and external decision makers without getting lost in the hype.
Takeaways:
- The means by which Big Data techniques can complement existing data management practices
- The prototyping nature of practicing Big Data techniques
- The distinct ways in which utilizing Big Data can generate business value
- Bigger Data isn’t always Better Data
Looking at what is driving Big Data. Market projections to 2017 plus what is are customer and infrastructure priorities. What drove BD in 2013 and what were barriers. Introduction to Business Analytics, Types, Building Analytics approach and ten steps to build your analytics platform within your company plus key takeaways.
Data is not consistent, sometimes searches or general interest in certain topics, say social media or other types of data experienced peaks and valleys. Data analysis techniques allow the data scientist to mine this type of unstable data and still draw meaningful conclusions from it.
What Is DataOps? When Agile Meets Data AnalyticsBernard Marr
The amount of data enterprise businesses are producing is growing by 40 to 60% a year, and many companies are facing challenges managing, analyzing, and interpreting all that data so they can enable solutions, support their data-focused teams, and glean valuable business insights.
Big Data analytics have increasingly gained prominence in business because it has provided beneficial insights regarding emerging trends, behaviors and preferences. Relying exclusively on analytics to address the vast majority of business uncertainties, however, is detrimental to our ability to solve problems. Madsbjerg and Rasmussen, in a WSJ article, insightfully captures the essence: “By outsourcing our thinking to Big Data, our ability to make sense of the world by careful observation begins to wither, just as you miss the feel and texture of a new city by navigating it only with the help of a GPS.”
If we are to gain a better understanding of our customers and the business itself, we must not miss “the feel and texture.” We need to see problems and opportunities in terms of human experience and capture and interpret data with a human context. We need to examine and understand how people live their lives from their own perspective, rather than from traditional business’ perspective. This applies to markets and products, as much as it applies to corporate culture because humans are complex and difficult to qualify and quantify. By using ethnographic research methods, we can uncover and understand the needs and desires – the whys and the feel and texture - that drive the emotional lives of customers.
This paper argues that business needs to combine analytics with ethnography for richer and even more valuable insights to move ahead in a global market. It also provides some suggestions on how to combine analytics with ethnography.
Lead Author: Matt Artz, Azimuth Labs
Co-author: Dr. Uldarico Rex Dumdum, Marywood University
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
The mountain of Big Data is growing, presenting immense opportunities for businesses ready to summit its peak, but the journey requires careful preparation. Integra helps businesses equip their network infrastructure to handle big requirements for Big Data—with fully-symmetrical Ethernet solutions designed to deliver low-latency, high-bandwidth connectivity between organizational peers, the cloud, and the servers where your data is stored. Our infographic, "Summiting the Mountain of Big Data" will help you understand how big "Big Data" really is; who's producing, consuming, managing and storing all that data; the business advantages you can capture by tapping into its power; and how you can prepare your organization to meet its demands—resulting in Big Gains from Big Data.
Big Data, Trends,opportunities and some case studies( Mahmoud Khosravi)Mahmood Khosravi
Humans have been generating data for thousands of years. More recently we have seen
an amazing progression in the amount of data produced from the advent of mainframes
to client server to ERP and now everything digital. For years the overwhelming amount
of data produced was deemed useless
Keeping pace with technology and big data.pdfClaire D'Costa
How IT companies can bridge the gap between ever-increasing talent needs and ever-changing technology?
In this pdf, you will get to know:
1- The technology's part in the play
2- The widening skills gap
3- Ways to fill up the void
4- Future of Big Data
5- Other useful insights
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
What is AI without Data?
1. 4/16/2018
1
What is AI without Data?
The New Convergence of Data; the
Next Strategic Business Advantage
David Smith
DATA is the central asset of your company. The growth of data has accelerated beyond even
the opportunistic forecast of a few years ago. The new definition of convergence is very
different from even a decade ago.
The new trends of Big Data, Data Science, Cloud, A I, Mobility and IoT are changing how
organizations are using data. It is now a critical business asset.
New business processes will revolve around the data and it will soon become even more
intensive through massive streaming data coming from ubiquitous sensors in the Internet of
Things. Variety, not volume or velocity will drive the investments. During this session you
will see how the data has become a strategic business advantage and its value will only
increase in the next decade.
David Smith
CEO
david@strategicpathways.com
linkedin.com/in/davidsmithaustin
What is AI without
Data?
The New Convergence
of Data; the Next
Strategic Business
Advantage
Copyright 2018 All Rights reserved May not be distributed without permission David Smith
Copyright 2018 David Smith All Rights Reserved
2. 4/16/2018
2
Why bother with the future?
"If you think that you can run an
organization in the next 10 years as
you've run it in the past 10 years you're
out of your mind.“
CEO,
Coca Cola
The Age of Data
In the last two years we have generated more data than in
the history of mankind
Data is expected to double in size every two years
through 2020, exceeding 40 zettabytes (40 trillion
gigabytes)
2020
2012 - 2014
The Beginning –
2011 The Economist:
digital information increases10
times/5 years!
2016 - 2017
Copyright 2018 David Smith All Rights Reserved
3. 4/16/2018
3
Forecast of Data Growth
zettabytes (ZB) – 1 of which accounts for 1 billion terabytes (TB)
Copyright 2018 David Smith All Rights Reserved
4. 4/16/2018
4
Business Problem
More than half of business and IT
executives, 56 percent, report they feel
overwhelmed by the amount of data their
company manages. Many report they are
often delayed in making important
decisions as a result of too much
information. Surprisingly, 62 percent of C-
level respondents – whose time is
considered the most valuable in most
organizations – report being frequently
interrupted by irrelevant incoming data.
Copyright 2018 David Smith All Rights Reserved
5. 4/16/2018
5
Entering the Age of Data
Data is THE central business asset:
– “Data are an organization’s sole, non-depletable, non-
degrading, durable asset. Engineered right, data’s value
increases over time because the added dimensions of
time, geography, and precision.” (Peter Aitken)
Data generation has changed forever
– Instrumentation of All businesses, people, machines
Data is born digitally and flows constantly
– “All things are flowing..” (Heraclitus, 500 BC)
DATA
Copyright 2018 David Smith All Rights Reserved
7. 4/16/2018
7
Today most data is retrospective,
there is a need for real-time and
predictive
Retrospective
Real-time
Predictive
Today's Cycle
Where is Real Time?
Copyright 2018 David Smith All Rights Reserved
8. 4/16/2018
8
Volume
Variety
Velocity
………..
Volume
Volume is increasing at incredible
rates. With more people using
high speed internet connections
than ever, plus the growth of IoT
and always on devices these are
causing this tremendous increase
in Volume.
Copyright 2018 David Smith All Rights Reserved
9. 4/16/2018
9
Variety
Next in breaking down Data into easily digestible
bite-size chunks is the concept of Variety. Take
your personal experience and think about how
much information you create and contribute in
your daily routine. Your voicemails, your e-mails,
your file shares, your TV viewing habits, your
Facebook updates, your LinkedIn activity, your
credit card transactions, etc.
Whether you consciously think about it or not the
Variety of information you personally create on a
daily basis which is being collected and analyzed
is simply overwhelming.
Variety
•FB generates 10TB daily
•Twitter generates 7TB of data
Daily
•IBM claims 90% of today’s
stored data was generated
in just the last two years.
Copyright 2018 David Smith All Rights Reserved
10. 4/16/2018
10
Variety
Big Data isn't just numbers, dates, and strings.
Big Data is also geospatial data, 3D data,
audio and video, and unstructured text,
including log files and social media.
Traditional database systems were designed to
address smaller volumes of structured data,
fewer updates or a predictable, consistent
data structure.
Streaming data and real-time analysis includes
different types of data
Velocity
The speed at which data enters organizations these
days is absolutely amazing. With mega internet
bandwidth nearly being common place anymore in
conjunction with the proliferation of mobile devices,
this simply gives people more opportunity than ever
to contribute content to storage systems.
Copyright 2018 David Smith All Rights Reserved
11. 4/16/2018
11
Velocity
• Clickstreams and ad impressions capture user
behavior at millions of events per second
• High-frequency stock trading algorithms reflect
market changes within microseconds
• Machine to machine processes exchange data
between billions of devices
• Infrastructure and sensors generate massive log
data in real-time
• On-line gaming systems support millions of
concurrent users, each producing multiple inputs
per second.
But I Believe These are the Real Four
Copyright 2018 David Smith All Rights Reserved
12. 4/16/2018
12
The Structure of Data
Structured
• Most traditional data
sources
Semi-structured
• Many sources of big data
Unstructured
• Video data, audio data
23
Historical Development of Database
Technology
Early Database Applications: The Hierarchical and
Network Models were introduced in mid 1960’s
and dominated during the seventies. A bulk of
the worldwide database processing still occurs
using these models.
Relational Model based Systems: The model that
was originally introduced in 1970 was heavily
researched and experimented with in IBM and the
universities. Relational DBMS Products emerged
in the 1980’s.
Copyright 2018 David Smith All Rights Reserved
13. 4/16/2018
13
Historical Development of Database
Technology
Object-oriented applications: OODBMSs were
introduced in late 1980’s and early 1990’s to
cater to the need of complex data
processing in CAD and other applications.
Data on the Web and E-commerce
Applications: Web contains data in HTML
(Hypertext markup language) with links
among pages. This has given rise to a new
set of applications and E-commerce is using
new standards like XML (eXtended Markup
Language).
Extending Database Capabilities
New functionality is being added to DBMSs in
the following areas:
– Scientific Applications
– Image Storage and Management
– Audio and Video data management
– Data Mining
– Spatial data management
– Time Series and Historical Data Management
– IoT
– Streaming
The above gives rise to new research and development in
incorporating new data types, complex data structures, new
operations and storage and indexing schemes in database
systems.
Copyright 2018 David Smith All Rights Reserved
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Top10 Time Series Databases
• DalmatinerDB
• InfluxDB
• Prometheus
• Riak TS
• OpenTSDB
• KairosDB
• Elasticsearch
• Druid
• Blueflood
• Graphite (Whisper)
Copyright 2018 David Smith All Rights Reserved
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The Intelligence is in the Connections
Connections between people
ConnectionsbetweenInformation
Email
Social Networking
Groupware
Javascrip
t Weblogs
Databases
File Systems
HTTP
Keyword Search
USENET
Wikis
Websites
Directory Portals
2010 -
2020
Web 1.0
2000 - 2010
1990 - 2000
PC Era
1980 - 1990
RSS
Widgets
PC’s
2020 - 2030
Office 2.0
XML
RDF
SPARQLAJAX
FTP IRC
SOA
P
Mashups
File Servers
Social Media Sharing
Lightweight Collaboration
ATOM
Web 3.0
Web 4.0
Semantic Search
Semantic Databases
Distributed Search
Intelligent personal agents
Java
SaaS
Web 2.0Flash
OWL
HTML
SGML
SQL
Gopher
P2P
The Web
The PC
Windows
MacOS
SWRL
OpenID
BBS
MMO’s
VR
Semantic Web
Intelligent Web
The Internet
Social Web
Web OS
Source: Gartner, Cisco, DSmith
Big Challenge
24/7 Streaming Data
It seems that everything in 2018 will have a
sensor that sends information back to the
mothership.
Copyright 2018 David Smith All Rights Reserved
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The Ubiquity of Data Opportunities
With vast amounts of data now available, companies in
almost every industry are focused on exploiting data for
competitive advantage.
In the past, firms could employ teams of statisticians,
modelers, and analysts to explore datasets manually, but
the volume and variety of data have far outstripped the
capacity of manual analysis.
At the same time, computers have become far more
powerful, networking has become ubiquitous, and
algorithms have been developed that can connect
datasets to enable broader and deeper analyses than
previously possible.
The convergence of these phenomena has given rise to the
increasing widespread business application of data
science principles and data mining techniques.
33
Data Science as a strategic asset
“85% of eBay’s analytic workload is new and
unknown. We are architected for the
unknown.”
Oliver Ratzesberger, eBay
Data exploration – data as the new oil
The exploration for data, rather than the exploration of data
Uncovering pockets of untapped data
Processing the whole data set, without sampling
eBay’s Singularity platform combines transactional data with
behavioral data, enabled identification of top sellers, driving
increased revenue from those sellers 34
Copyright 2018 David Smith All Rights Reserved
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18
Data as a strategic asset
“Groupon will not be the first or last organization
to compete and win on the power of data. It’s
happening everywhere.”
Reid Hoffman and James Slavet
Greylock Partners
Data harnessing – data as renewable energy
Harnessing naturally occurring data streams
Like harnessing raw energy to be converted into usable
energy
Conversion of raw data into usable data
35
Emergence of a Fourth Research
Paradigm: Data Science
Thousand years ago –
– Experimental Science
Description of natural phenomena
Last few hundred years –
– Theoretical Science
Newton’s Laws, Maxwell’s Equations…
Last few decades –
– Computational Science
Simulation of complex phenomena
Today –
– Data-Intensive Science
Scientists overwhelmed with data!
Copyright 2018 David Smith All Rights Reserved
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Key to Creating Artificial Intelligence:
Increasing Computational Power
NNow =
• Beating a
mouse brain
• About a
thousandth of
a human
Copyright 2018 David Smith All Rights Reserved
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20
Information and Communication
Trends
• Seamless Interoperability Between
Heterogeneous Networks
• Mobility for All – Devices for All Things
• User Centered Content-Based Information
Access
• Agents Take Over Routine Work
• “E”- Processes for Business and Private Life
• Human Computer Interaction is Turning Into
Human Computer Cooperation
• Human is not part of most computer and data
interaction
The “Fat Pipe”
Copyright 2018 David Smith All Rights Reserved
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What is direction of DATA
Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its
user base.
• Decoding the human genome originally took
10years to process; now it can be achieved
in one week.
Copyright 2018 David Smith All Rights Reserved
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22
“The market for enterprise AI systems will increase from $202.5 million
in 2015 to $11.1 billion by 2024.”
- Tractica
Internet of Things:
The Next Frontier
Copyright 2018 David Smith All Rights Reserved
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24
IoT is generating massive volumes of structured and
unstructured data, and an increasing share of this data is
being deployed on cloud services. The data is often
heterogeneous and lives across multiple relational and
non-relational systems.
When these smart devices are connected to intelligent
applications such as Siri, Alexa ,Cortana or Google Home,
the possibilities become endless. Conversational AI will
enable high-level conversations with these intelligent
applications These bots, per Microsoft CEO Satya Nadella,
will be the next apps. 2018 will see the convergence of
these intelligent applications with many IoT devices.
Copyright 2018 David Smith All Rights Reserved
25. 4/16/2018
25
As the world gets smarter,
infrastructure demands will grow
Smart
traffic
systems
Smart water
management
Smart
energy
grids
Smart
healthcare
Smart
food
systems
Smart oil
field
technologies
Smart
regions
Smart
weather
Smart
countries
Smart
supply
chains
Smart
cities
Smart retail
Copyright 2018 David Smith All Rights Reserved
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27
Will technological breakthroughs be developed in time to boost economic productivity and
solve the problems caused by a growing world population, rapid urbanization, and climate
change?
Game Changer - Impact of New Technologies
• The Internet of Things
• Not just Big Data, but a zettaflood
• Much D to D
• Wisdom of the Data Science
• The next 'Net’
• Move from physical to virtual
• The world gets Bio
• Regenerative Medicine
Copyright 2018 David Smith All Rights Reserved
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Conclusion
The Age of Data is here
Data is the central business asset
Data generation has changed forever
• The World is moving to Real Time
• Data Science is the Key
Your legacy analytic software WILL fail in the Age of
Data
Crisis of software that scales to meet demand
Streaming data changes the concept of data
Think about where the data comes from
Attempt to capture and analyze any data that might be
relevant, regardless of where it resides
Data Science is changing how data is:
– Collected, discovered, analyzed, used, acted upon …
In Parting: Be Paranoid
“Sooner or later, something
fundamental in your business
world will change.”
Andrew S. Grove, Founder, Intel
“Only the Paranoid Survive”
Copyright 2018 David Smith All Rights Reserved
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29
Thank You
David Smith
david@strategicpathways.com
9 global GIS data sets that you can download for
free.
1 Natural Earth Data.
2 Esri Open Data.
3 USGS Earth Explorer.
4 OpenStreetMap.
5 NASA's Socioeconomic Data and Applications Center
(SEDAC)
6 Open Topography.
7 UNEP Environmental Data Explorer.
9 NASA Earth Observations (NEO)
Copyright 2018 David Smith All Rights Reserved