The document discusses the future of advanced analytics and how increasing data volume, variety, and velocity are impacting businesses, governments, and individuals. It states that the success of organizations is driven by the use of advanced analytics to analyze trends, create predictive models, and optimize business processes. The presentation will examine current and future trends in analytics, with emphasis on embedded analytics and how analytics must adapt to real-time information. Attendees will learn about the future directions of advanced analytics.
Infochimps Survey: What IT Teams Want CIOs to Know About Big Data - Learn the top items that IT team members would like their CIOs to understand concerning their Big Data projects.
The report - CIOs & Big Data: What Your IT Team Wants You to Know - is based on a survey of more than 300 IT department employees, 58% of whom are currently engaged in Big Data projects, and aims to identify pitfalls that implementation teams encounter, and could avoid, if top management had a more complete view.
Information is the principle driver of competitive advantage. How it is collected, analysed and communicated determines our success. No single resource is more critical to organisational survival.
The amount of data in the world is exponentially increasing, to a point where companies capture significant amounts of information about their customers, suppliers, and operations. Millions of networked sensors are being embedded in everything from mobile phones to cars. Social networks and location data from mobile devices will continue to fuel this exponential data growth. These huge data pools are commonly being referred to as "big data".
This talk examines how analytics and big data are exploiting information to drive competitive advantage.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
BIG DATA is having an enormous impact on the profile of workforces around the world. If you've ever seen the technology and experienced the impact it has on the pace of innovation in a business then the predictations made by McKinsey Global Institute will come as no surprise ( and just in case you've been on holiday for around two years, McKinsey is suggesting that by 2018 the US will face a shortfall of close to 200,000 analysts and 1.5 million managers with the right skills. In this presentation I outline the impact of BIG DATA on workforce design. I hope you find it informative and fun to read. Ian.
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
A presentation for the Managing Partners’ Forum. Separating the needs of the individual and those of then organisation has always been an issue for KM and Learning. At times these needs align, sometimes they need to be reconciled and at other times they diverge, particularly when an individual moves to another organisation. The presentation looks specifically at the changing nature of organisations and the emergent power of networks and networking. Personal Knowledge Management (PKM) is a competence we must all learn in order to remain relevant to our organisation. But who ultimately “owns” the ‘corporate’ knowledge that we gather through the workplace networks we nurture and sustain, and do the organisations we work for even recognise the importance of these networks as places for continual learning, knowledge sharing and incubators for innovation?
Infochimps Survey: What IT Teams Want CIOs to Know About Big Data - Learn the top items that IT team members would like their CIOs to understand concerning their Big Data projects.
The report - CIOs & Big Data: What Your IT Team Wants You to Know - is based on a survey of more than 300 IT department employees, 58% of whom are currently engaged in Big Data projects, and aims to identify pitfalls that implementation teams encounter, and could avoid, if top management had a more complete view.
Information is the principle driver of competitive advantage. How it is collected, analysed and communicated determines our success. No single resource is more critical to organisational survival.
The amount of data in the world is exponentially increasing, to a point where companies capture significant amounts of information about their customers, suppliers, and operations. Millions of networked sensors are being embedded in everything from mobile phones to cars. Social networks and location data from mobile devices will continue to fuel this exponential data growth. These huge data pools are commonly being referred to as "big data".
This talk examines how analytics and big data are exploiting information to drive competitive advantage.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
BIG DATA is having an enormous impact on the profile of workforces around the world. If you've ever seen the technology and experienced the impact it has on the pace of innovation in a business then the predictations made by McKinsey Global Institute will come as no surprise ( and just in case you've been on holiday for around two years, McKinsey is suggesting that by 2018 the US will face a shortfall of close to 200,000 analysts and 1.5 million managers with the right skills. In this presentation I outline the impact of BIG DATA on workforce design. I hope you find it informative and fun to read. Ian.
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
A presentation for the Managing Partners’ Forum. Separating the needs of the individual and those of then organisation has always been an issue for KM and Learning. At times these needs align, sometimes they need to be reconciled and at other times they diverge, particularly when an individual moves to another organisation. The presentation looks specifically at the changing nature of organisations and the emergent power of networks and networking. Personal Knowledge Management (PKM) is a competence we must all learn in order to remain relevant to our organisation. But who ultimately “owns” the ‘corporate’ knowledge that we gather through the workplace networks we nurture and sustain, and do the organisations we work for even recognise the importance of these networks as places for continual learning, knowledge sharing and incubators for innovation?
Business unIntelligence - a Whistle Stop TourBarry Devlin
The old world of business intelligence is being transformed into a new biz-tech ecosystem. Analytics is forcing the recombination of operational and informational systems in a consistent and coherent IT environment for all business activities. Big data—despite the hype—introduces two very different types of information that transform how business processes interact with the external world. Together, these directions are driving a new BI, so different to its prior form that I call it “Business unIntelligence”. This session covers:
- Business drivers and results of the biz-tech ecosystem
- Modern conceptual and logical architectures for information, process and people
- Positioning of all forms of business analytic and big data
A sample of my book "Business unIntelligence - Insight and Innovation beyond Analytics and Big Data", published by Technics Publications, 2013.
Chapter 5 shows the evolution of the Data Warehouse architecture and provides a description of some aspects of a modern Information architecture.
The book can be ordered in hard and softcopy formats at http://bit.ly/BunI-TP1
Why Big Data Analytics Needs Business Intelligence Too Barry Devlin
Business and IT are facing the challenge of getting real and urgent value from ever-expanding information sources. Building independent silos of big data analytics is no longer enough. True progress comes only by integrating data from traditional operational and informational sources with the new sources that are becoming available, whether from social media or interconnected machines.
In this April 2014 BrightTALK webinar, Dr. Barry Devlin describes the thinking, architecture, tools and methods needed to achieve a new joined-up, comprehensive data environment.
Democratizing Advanced Analytics Propels Instant Analysis Results to the Ubiq...Dana Gardner
Transcript of a discussion on how HTI Labs in London provides the means and governance with their Schematiq tool to bring critical data to the interface that users want most.
Get Smart: The Present and Future of Data DiscoveryInside Analysis
Hot Technologies of 2013 with Bloor, Fitzgerald & Neutrino BI
Live Webcast July 17, 2013
http://www.insideanalysis.com
Somewhere in your data, discoveries wait to be found. Finding them can be quite a challenge, though, which is why data discovery gets so much attention these days. A whole array of tools is being promoted for data visualization and business discovery. But what are the component parts of this technology? And how can discovery tools be used to sift through vast amounts of data effectively? Register for this episode of Hot Technologies to find out!
Analysts Dr. Robin Bloor of The Bloor Group, and Jaime Fitzgerald of Fitzgerald Analytics will each offer their take on what constitutes a high-quality discovery tool. They'll then take a briefing from Jon Woodward of Neutrino BI, who will tout his company's platform for facilitating data discovery. He'll talk about the value of being able to go "direct to data" during the discovery process. He'll also outline their roadmap for developing a next-generation "smart" discovery platform.
Paper which discusses the notion that Data is NOT the "new Oil". We hear copious amounts said that Data is an asset, it's got to be managed, few people in the business understand it & so on. The phrase "Data is the new Oil" gets used many times, yet is rarely (if ever) justified. This paper is aimed to raise the level of debate from a subliminal nod to a conscious examination of the characteristics of different "assets" (particularly Oil) and to compare them with those of the 'Data asset".
Written by Christopher Bradley, CDMP Fellow, VP Professional Development DAMA International & 38 years Information Management experience, much of it in the Oil & Gas industry.
The Black Box: Interpretability, Reproducibility, and Data Managementmark madsen
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust.
Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models.
Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.
This talk will treat the black boxed of ML the way management perceives them, as black boxes.
There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
Challenges are consistent in Big Data environments; resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. Big Data has grown so large that traditional data analysis and management solutions are too slow, too small and too expensive to handle it. Many companies are in the discovery stage of evaluating the best means of extracting value from it. This Enterprise Tech Journal interview with Kevin Goulet, VP Product Management, CA Technologies, explores the challenges of Big Data, the approach to resolving them. With Big Data environments, the challenges are consistent – resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. For more information visit http://www.ca.com/us/products/detail/business-intelligence-and-big-data-management.aspx?mrm=425887
Big Data initiatives should focus on outcomes first.
The value of Big Data is the potential change in outcomes. Companies should first evaluate which areas of their business and decision making are receptive to change.
Receptiveness to change dictated or directed by models, black box algorithms needs to be accepted by managers, execution staff (i.e. call these prospects and discuss x becuase the model says so).
This is a cultural change, a mind set change and a governance change. Advanced modeling must also bear the responsibility of scenario testing and multiple outcome hypothesis and simulation testing.
Investing in AI: Moving Along the Digital Maturity CurveCognizant
Digitally mature businesses are more likely to consider themselves at an advanced stage of AI adoption, according to our recent study, enabling them to turn data into insights at the scale and precision required today.
The reflections of a successful corporate intrapreneur, change agent and innovation program manager.
What to do,
What not to do
and of course the results achieved, well they're on my LinkedIn profile
Discover what comes next for IBM Watson and the industries particularly suited for Watson solutions, such as healthcare, banking, and the financial sector. All of which deal with massive amounts of unstructured data coming from various sources. Find out how the advanced analytics used in Watson are being put to work in businesses around the world.
Business unIntelligence - a Whistle Stop TourBarry Devlin
The old world of business intelligence is being transformed into a new biz-tech ecosystem. Analytics is forcing the recombination of operational and informational systems in a consistent and coherent IT environment for all business activities. Big data—despite the hype—introduces two very different types of information that transform how business processes interact with the external world. Together, these directions are driving a new BI, so different to its prior form that I call it “Business unIntelligence”. This session covers:
- Business drivers and results of the biz-tech ecosystem
- Modern conceptual and logical architectures for information, process and people
- Positioning of all forms of business analytic and big data
A sample of my book "Business unIntelligence - Insight and Innovation beyond Analytics and Big Data", published by Technics Publications, 2013.
Chapter 5 shows the evolution of the Data Warehouse architecture and provides a description of some aspects of a modern Information architecture.
The book can be ordered in hard and softcopy formats at http://bit.ly/BunI-TP1
Why Big Data Analytics Needs Business Intelligence Too Barry Devlin
Business and IT are facing the challenge of getting real and urgent value from ever-expanding information sources. Building independent silos of big data analytics is no longer enough. True progress comes only by integrating data from traditional operational and informational sources with the new sources that are becoming available, whether from social media or interconnected machines.
In this April 2014 BrightTALK webinar, Dr. Barry Devlin describes the thinking, architecture, tools and methods needed to achieve a new joined-up, comprehensive data environment.
Democratizing Advanced Analytics Propels Instant Analysis Results to the Ubiq...Dana Gardner
Transcript of a discussion on how HTI Labs in London provides the means and governance with their Schematiq tool to bring critical data to the interface that users want most.
Get Smart: The Present and Future of Data DiscoveryInside Analysis
Hot Technologies of 2013 with Bloor, Fitzgerald & Neutrino BI
Live Webcast July 17, 2013
http://www.insideanalysis.com
Somewhere in your data, discoveries wait to be found. Finding them can be quite a challenge, though, which is why data discovery gets so much attention these days. A whole array of tools is being promoted for data visualization and business discovery. But what are the component parts of this technology? And how can discovery tools be used to sift through vast amounts of data effectively? Register for this episode of Hot Technologies to find out!
Analysts Dr. Robin Bloor of The Bloor Group, and Jaime Fitzgerald of Fitzgerald Analytics will each offer their take on what constitutes a high-quality discovery tool. They'll then take a briefing from Jon Woodward of Neutrino BI, who will tout his company's platform for facilitating data discovery. He'll talk about the value of being able to go "direct to data" during the discovery process. He'll also outline their roadmap for developing a next-generation "smart" discovery platform.
Paper which discusses the notion that Data is NOT the "new Oil". We hear copious amounts said that Data is an asset, it's got to be managed, few people in the business understand it & so on. The phrase "Data is the new Oil" gets used many times, yet is rarely (if ever) justified. This paper is aimed to raise the level of debate from a subliminal nod to a conscious examination of the characteristics of different "assets" (particularly Oil) and to compare them with those of the 'Data asset".
Written by Christopher Bradley, CDMP Fellow, VP Professional Development DAMA International & 38 years Information Management experience, much of it in the Oil & Gas industry.
The Black Box: Interpretability, Reproducibility, and Data Managementmark madsen
The growing complexity of data science leads to black box solutions that few people in an organization understand. You often hear about the difficulty of interpretability—explaining how an analytic model works—and that you need it to deploy models. But people use many black boxes without understanding them…if they’re reliable. It’s when the black box becomes unreliable that people lose trust.
Mistrust is more likely to be created by the lack of reliability, and the lack of reliability is often the result of misunderstanding essential elements of analytics infrastructure and practice. The concept of reproducibility—the ability to get the same results given the same information—extends your view to include the environment and the data used to build and execute models.
Mark Madsen examines reproducibility and the areas that underlie production analytics and explores the most frequently ignored and yet most essential capability, data management. The industry needs to consider its practices so that systems are more transparent and reliable, improving trust and increasing the likelihood that your analytic solutions will succeed.
This talk will treat the black boxed of ML the way management perceives them, as black boxes.
There is much work on explainable models, interpretability, etc. that are important to the task of reproducibility. Much of that is relevant to the practitioner, but the practitioner can become too focused on the part they are most familiar with and focused on. Reproducing the results needs more.
Challenges are consistent in Big Data environments; resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. Big Data has grown so large that traditional data analysis and management solutions are too slow, too small and too expensive to handle it. Many companies are in the discovery stage of evaluating the best means of extracting value from it. This Enterprise Tech Journal interview with Kevin Goulet, VP Product Management, CA Technologies, explores the challenges of Big Data, the approach to resolving them. With Big Data environments, the challenges are consistent – resource-intensive processes, unwieldy time commitments, and challenging variations in infrastructure. For more information visit http://www.ca.com/us/products/detail/business-intelligence-and-big-data-management.aspx?mrm=425887
Big Data initiatives should focus on outcomes first.
The value of Big Data is the potential change in outcomes. Companies should first evaluate which areas of their business and decision making are receptive to change.
Receptiveness to change dictated or directed by models, black box algorithms needs to be accepted by managers, execution staff (i.e. call these prospects and discuss x becuase the model says so).
This is a cultural change, a mind set change and a governance change. Advanced modeling must also bear the responsibility of scenario testing and multiple outcome hypothesis and simulation testing.
Investing in AI: Moving Along the Digital Maturity CurveCognizant
Digitally mature businesses are more likely to consider themselves at an advanced stage of AI adoption, according to our recent study, enabling them to turn data into insights at the scale and precision required today.
The reflections of a successful corporate intrapreneur, change agent and innovation program manager.
What to do,
What not to do
and of course the results achieved, well they're on my LinkedIn profile
Discover what comes next for IBM Watson and the industries particularly suited for Watson solutions, such as healthcare, banking, and the financial sector. All of which deal with massive amounts of unstructured data coming from various sources. Find out how the advanced analytics used in Watson are being put to work in businesses around the world.
Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
Whitepaper: Thriving in the Big Data era Manage Data before Data Manages you Intellectyx Inc
Paper Overview -
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Data comes from everywhere and we are generating data more than ever before.
This white paper will explain what Big Data is and provide practical examples, concluding with a message how to put data your data to work.
Analytics 3.0.pdfArtwork Chad Hagen, Nonsensical Infographic .docxSHIVA101531
Analytics 3.0.pdf
Artwork: Chad Hagen, Nonsensical Infographic No. 5, 2009, digital
Those of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case.
Some of us now perceive another shift, fundamental and far-reaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy.
I’ll develop this argument in what follows, making the case that just as the early applications of big data marked a major break from the 1.0 past, the current innovations of a few industry leaders are evidence that a new era is dawning. When a new way of thinking about and applying a strength begins to take hold, managers are challenged to respond in many ways. Change comes fast to every part of a business’s world. New players emerge, competitive positions shift, novel technologies must be mastered, and talent gravitates toward the most exciting new work.
Managers will see all these things in the coming months and years. The ones who respond most effectively will be those who have connected the dots and recognized that competing on analytics is being rethought on a large scale. Indeed, the first companies to perceive the general direction of change—those with a sneak peek at Analytics 3.0—will be best positioned to drive that change.
The Evolution of Analytics
My purpose here is not to make abstract observations about the unfolding history of analytics. Still, it is useful to look back at the last big shift and the context in which it occurred. The use of data to make decisions is, of course, not a new idea; it is as old as decision making itself. But the field of business analytics was born in the mid-1950s, with the advent of tools that could produce and capture a larger quantity of information and discern patterns in it far more quickly than the unassisted human mind ever could.
Today it isn’t just online and information firms that can create products and services from analyses of data. It’s every firm in every industry.
Analytics 1.0—the era of “business intelligence.”
What we are here calling Analytics 1.0 was a time of real progress in gaining an objective, deep understanding of important business phenomena and giving managers.
In this document, the five disruptive trends shaping the corporate IT landscape today are layed out. Out of the five, Big Data has the biggest potential to generate new sustainable competitive advantages. But the benefits will remain out of reach of many organizations as they struggle to adopt the technology, develop new capabilities, and manage the cultural change associated with the use of big data. This document offers a pragmatic approach to generating business value.
Palestra sobre conceitos Big data no evento IDETI em SP. Aborda o que é Big data, debate alguns beneficios e desafios. Debate também o papel do CDO- Chief Data Officer.
1. 11/7/2012
The Future of Advance Analytics
David Smith
Chief Executive Officer, HBMG Inc.
dsmith@hbmginc.com
Room: 11
Volume, variety, and velocity are changing us in our companies, government
agencies and at home. How do BI, Social/Business media, mobility, Devices and Big
Data drive business decisions? The success is driven by the use of advance
analytics. Business analytics facilitates realization of business objectives through
reporting of data to analyze trends, creating predictive models for forecasting and
optimizing business processes for enhanced performance. This is important not only
in business but the military and government as well.
This presentation will look at the current and future trends in analytics and how they
will impact each of us. Special emphasis will be given to the new trend of embedded
analytics. As the world moves faster toward real-time the analytics must move as
well. Attendees will leave the session with a understanding of the future directions of
advance analytics.
The Future of
Advance
Analytics
David Smith
CEO HBMGInc.
dsmith@HBMGINC.com
1
2. 11/7/2012
Definition for Advanced Analytics
Analysis is the examination process itself where
analytics is the supporting technology and
associated tools. BI is quite synonymous to
analytics in IT context. Advanced Analytics,
Business Analytics, Data Analytics, Analytics
Software, Analytics Technology are almost
always marketing pleonasms (redundant
always marketing pleonasms (redundant
expressions) and can be safely substituted by
just ‘analytics’
Definition for Advanced Analytics
Analysis is a pretty old, well understood term
and essentially means “breaking down” or
d ti ll “b ki d ”
“decomposition”. More accurately –the
process of decomposing complex entity into
simpler components for easier
comprehension.
2
4. 11/7/2012
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
respondents – whose time is considered the
most valuable in most organizations – report
being frequently interrupted by irrelevant
incoming data.
4
6. 11/7/2012
Business, Knowledge, and Innovation Landscape
• Typically 80% of the key knowledge (and value) is
held by 20% of the people we need to get it to the
held by 20% of the people – we need to get it to the
right people
• Only 20% of the knowledge in an organization is
typically used (the rest being undiscovered or under‐
utilized)
• 80 90% f th
80‐90% of the products and services today will be
d t d i t d ill b
obsolete in 10 years – companies need to innovate &
invent faster
Copyright 2012@ HBMG Inc.
Tapping into the Data
• Data Storage
• Reporting Utilized data
• Analytics
• Advanced
Analytics
– Computing with Unutilized data
big datasets is a
g that can be
available t
il bl to
fundamentally business
different challenge
than doing “big
compute” over a
small dataset
6
7. 11/7/2012
Innovation:
‘Innovation =
‘The real voyage of discovery consists creative idea and
not in seeing new lands, implementation’
but in seeing with new eyes’ (Source: Glossary of Electronics)
(Source: Marcel Proust)
‘Innovation: change that
creates a new dimension of
‘A new method, idea,
performance’
product, etc’ (Source: Peter Drucker)
(Source: Oxford English
Dictionary)
‘Value innovators look for
‘An innovation to be effective what customers value in
has to be simple and it has common’
to be focused’ (Source: Kim & Mauborgne)
(Source: Peter Drucker)
‘Firms need to manage steady state ‘Innovation is the process by which new products or
innovation and radical change because methods of production are introduced, including all
continuous improvement is no longer the steps from the inventor’s idea to bringing the
enough’ new item to market’
(Source: Tom Peters) (Source: Baumol, Economics: Principles & Policy)
“Big Data” and it’s close relatives “Cloud
Computing”, “Social Media” and
"Mobile"
are the new frontier of innovation.
Driven by Advance Analytics
7
8. 11/7/2012
Big Data and It’s Brothers
Volume
Variety
Velocity
………..
Volume
Volume is increasing at incredible rates.
With more people using high speed
h l h h d
internet connections than ever, plus
these people becoming more proficient
at creating content and just more
people in general contributing
information are combined forces that
information are combined forces that
are causing this tremendous increase in
Volume.
8
9. 11/7/2012
Variety
Next in breaking down Big Data into easily digestible bite‐
size chunks is the concept of Variety. Take your personal
experience and think about how much information you
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
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.
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.
contribute content to storage systems.
9
10. 11/7/2012
VELOCITY
Worldwide digital content will
double in 18 months, and
every 18 months thereafter.
IDC
Mobile
Inventory
CRM Data
GPS
Emails
Planning Demand
Tweets
Instant Messages
Opportunities
Speed
VOLUME Customer
Velocity
VARIETY
In 2005, humankind Things
80% of enterprise data
created 150 exabytes of
Service Calls
will be unstructured,
information. In 2011, Sales Orders
spanning traditional and
over 1,200 exabytes was Transactions
non traditional sources.
created. Gartner
The Economist
But I Believe there are Four V4
10
11. 11/7/2012
• Volume:Gigabyte(109), Terabyte(1012),
Petabyte(1015), Exabyte(1018), Zettabytes(1021)
• Variety: Structured,semi‐structured, unstructured;
Text, image, audio, video, record
• Velocity(Dynamic, sometimes time‐varying)
• BUT needs to add and create Value!
BUT needs to add and create Value!
Trends driving data management
– The volume of data has never been greater and is
growing exponentially
– The value of data has never been better understood
The value of data has never been better understood
– The capabilities for processing data have never been
better
• Higher processor performance and density are enabling
advanced processing on commodity hardware
• Software enhancements designed to make best use of
processing performance and scalable architecture
i f d l bl hit t
• Advanced and in‐database analytics bring processing to the 22
data, reducing latency and improving efficiency
– The data deluge problem is also a big data opportunity
11
13. 11/7/2012
Advance Analytics as a strategic asset
“The future belongs to companies and people
that turn data into products.”
Mike Loukides, O’Reilly
25
Advance Analytics 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 26
behavioral data, enabled identification of top sellers, driving
increased revenue from those sellers
13
14. 11/7/2012
Advance Analytics 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
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
H
Harnessing naturally occurring data streams
i t ll i d t t
Like harnessing raw energy to be converted into usable energy 27
Conversion of raw data into usable data
Facebook
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15. 11/7/2012
BIG DATA
REAL TIME
PREDICTIVE
ENABLED BY
ADVANCE ANALYTICS
As the world gets smarter, infrastructure
demands will grow
Smart Intelligent oil Smart Smart
Smart
traffic field food energy Smart retail
healthcare
systems technologies systems grids
Smart
Smart water Smart Smart Smart Smart
supply
management countries weather regions cities
chains
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20. 11/7/2012
Growth at the Edge of the Network
4,000
3,500
• Mobile
• Device to Device
3,000
Petabytes/Day Global
• Sensors
• Entertainment
2,500 • Smart Home
• Distributed Industrial
2,000 • Autos/Trucks
• Smart Toys
1,500
Converged
1,000 Content
500 Traditional
Computation
0
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Year
Copyright 2012@ HBMG Inc.
DOD Example
DOD Example
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21. 11/7/2012
Operation Trends
Counterinsurgency operations are complex increased emphasis
on:
Information and analysis at lowest levels
Shortened decision making time-scales
g
Wider array of information sources
Continued growth in volume of data, especially informal information Source: TTI Vanguard Conference - Psydex
with limited structure must transform disparate info to knowledge
Processing power and storage capacity increasing faster than
communications capacity must smartly position data and services
within networks
Increased use of commercial cellular networks hybrid networks
that exploit and interoperate with commercial wireless comms is key
Enhancing coalition decision making depends on secure
communications and information networks must address end-to-
end problem of data-to-decision (coalition)
Info in War Revolution
Technology—Information—Organization
“Recce” P-38 “Recce” P-38
RF-101 Voodoo
I ?
I
ISR
S R S R 0-2 Bird Dog
B-17 Spotter
Corps
B-52 0-2 Bird Dog
<10 Minutes
14 Days
75 Days
Number f Weapons
N b of W Number of Sensors
N b fS
Required to Target Required to Target
1943 2009
Evolution of Technology, Information, and Culture Enabled Move from
Segregation of Ops and Intel to Integration of Ops and Intel…
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Today’s Driving Forces
Few Intel Dependencies ‐ Past Many Intel Dependencies ‐ Today
Scan Schedules Based on Single Integrated Intel/Information
Current Threat Data Database at Squadron Level
ISR Analysts Combined Intel/Ops Center to Maintain
Signature Data
g Database/Sensor Engineer Data
/ g
GI&S
Datum
Models DPPDB
MIDB
Intel Data Provided Intel Data
to Operator; Little MEPED Integrated Into
to No Integration Weapon System
Threat Assessments Threat Assessments High‐fidelity ELINT Parametric Data
Countermeasures Low Fidelity ELINT Data
Low‐Fidelity ELINT Data Order of Battle
Od f B ttl Emitter‐to‐Platform Fit Data
E itt t Pl tf Fit D t
Pre‐mission folders Datum Models Countermeasure Techniques Characteristic & Performance Data
Paper Charts Platforms Feed Intel Specific Emitter Identification Data
Increasing Intelligence Needs and Integration
Low‐Tech Smart
Platforms/Weapons Platforms/Weapons
Intel
Time
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21st Century Challenges:
Precision and Information Synergy
Strategic
Strategic Strategic
CYBE
CYBE
SPAC
SPAC
Strategic
AIR
National Operational Operational
Operational p
Operational
R
CE
CE
ER
ER
Tactical Tactical Tactical Tactical
Tactical
DESERT 1999 2001 2009 INFO AGE
STORM ALLIED ENDURING AF/PAK & Iraq WARFARE
1991 & Prior FORCE FREEDOM TODAY TOMORROW
Segregated Ops … I + S&R ISR GLOBAL
Intel & Ops + Intel INTEGRATED
ISR
OPS Kandahar
Runway
NTI Multi‐Domain
Real‐Time Fusion
INTEL Pod Recce Fusion
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Dimensions of ISR…
“More of Everything”
More Collectors
Better Sensors
More Data
• More Storage
• More Comms Better Intel
• More Tools
• More Analysts
• More Linguists
…All on an Operationally Responsive Timeline
Sensor Data Volume
How do we handle all this data?
“Rebalancing Collection & PED may be Necessary”
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25. 11/7/2012
Advance Analytics
• Advanced Analytics and Big Data are two of the
most active areas of innovation in the Tech
sector
• legacy infrastructures and government policies are
increasingly at odds with the realities of the analytic
landscape
• Certain forms of analysis is no longer possible within
an encrypted environment. Rules that require data
to be encrypted, both while in transit and at rest,
also introduce performance penalties that make it
difficult if not impossible to process large datasets in
an acceptable timeframe
Today's Cycle
Where is Real Time?
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26. 11/7/2012
Advance Analytics
• The time to use the output is increasingly getting
shorter – Real Time is becoming very common
• Li it d
Limited available human resources, and performance is
il bl h d f i
often unreliable due to human fatigue and distraction.
Therefore, automated real‐time sensor processing
techniques are required to reliably detect and
discriminate targets of interest
• Limited automated processing and tagging tools
• – Still NOT enough
Advance Analytics
• The time to use the output is increasingly getting shorter – Real
Time is becoming very common
• Limited available human resources, and performance is often
p
unreliable due to human fatigue and distraction. Therefore,
automated real‐time sensor processing techniques are required to
reliably detect and discriminate targets of interest – Still NOT
enough
• Need to move to the
sensor/collector
• Needs to be embedded in the
the sensor
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27. 11/7/2012
Autonomous Systems
Agents dynamically adapt Agents coordinate
to and learn about and negotiate to achieve
their environment common goals
Social
Intelligent Adaptive Cooperative Personality Information
Agents Agents
Autonomous Mobile Interoperate
Agents are goal directed Agents interoperate
Agents move
and act on their with humans, other,
to where they
own performing legacy systems, and
are needed
tasks on your behalf information sources
HBMG Inc. Copyright 2012
Autonomic Networks
Self-configuring : Adapt Self-healing:
automatically to the Discover, diagnose,
dynamically changing and react to
environments of link and disruptions from
node failures. Self- Self-
Self- Self- catastrophes and
attacks.
Configuring Healing
Self-optimizing: Monitor Self- Self-
Self- Self- Self-protecting:
and tune resources Anticipate, detect,
automatically during an
Optimizing Protecting identify, and protect
attack to minimize its against attacks from
attack during and in the anywhere (safety )
(safety.)
aftermath.
HBMG Inc. Copyright 2012
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Numbers
• How many data in the world?
– 800 Terabytes, 2000
– 160 Exabytes, 2006
– 500 Exabytes(Internet), 2009
– 2.7 Zettabytes, 2012
– 35 Zettabytes by 2020
• How many data generated ONE day?
– 7 TB, Twitter Big data: The next frontier for innovation, competition, and productivity
McKinsey Global Institute 2011
– 10 TB, Facebook
1 million
illi
transactions during this presentation
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31. 11/7/2012
2012 Business Intelligence, Analytics and Information
Management Survey from InformationWeek Reports
A few insights from the report:
•58% of those surveyed are “very interested” in advanced analytics
y y y
•Advanced analytics is the No. 1 leading-edge technology
•Cloud analytics systems are hot because they are easier on the
pocketbook; yet 63% of users have privacy concerns
•Data pros just can’t get good data – data quality still ranks as the top
barrier to adopting BI products throughout the company
•25% of those surveyed are mobilizing their data analytics with
dashboards and data visualizations
•40% of d t pros are struggling t stay above th bi d t wave
40% f data t li to t b the big data
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32. 11/7/2012
Conclusion
Data is one the major factors driving infrastructure computing
The growing volume of data is a problem, but it is also an opportunity
Don’t worry about ‘big data,’ worry about your data
y g , y y
Take a Total Data approach to data management
• Think pragmatically about data storage and analysis
• Attempt to capture and analyze any data that might be relevant,
regardless of where it resides
‘Datastructure’ will become increasingly valuable, not only as a source of
data but also as a source of intelligence
The rise of the ‘data cloud’ and the PaaS data layer will encourage a
more flexible approach to data management and analytics
The companies that win will be those that think about data as a strategic
asset and implement the technology to monetize it
Conclusion
The World is moving to Real Time
Advanced Analytics is the Key
y y
Advanced Analytics Must be embedded in the
collectors and sensors
• Think about where the data comes from
• Attempt to capture and analyze any data that
might be relevant, regardless of where it resides
• Realize collaboration is the key in Advance
Analytics just as it is in Business
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33. 11/7/2012
If we don’t change our
don t
direction, we’ll end up exactly
where we are headed.
—Ancient Chinese Proverb
In Parting: Be Paranoid
•“Sooner or later, something
fundamental in your business world
fundamental in your business world
will change.”
• Andrew S. Grove, Founder, Intel
“Only the Paranoid Survive”
Only the Paranoid Survive
Copyright @2008 HBMG Inc.
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