The document discusses how big data analytics is impacting the IT industry and what CIOs must do to incorporate big data analytics. It notes that we are becoming a big data, mobile, and real-time nation. By 2015, big data is predicted to generate millions of new IT jobs in areas like data collection, analysis, mobile technology, social media, and cloud computing. The rise of big data requires CIOs to adapt their approach to information governance and develop strategies to manage growing amounts of unstructured data.
This slide is about real time analytics of Big Data. It explains about Big Data and Analytics. How to deal with them.
see more at - http://bigdataconcept.blogspot.in/2016/03/real-time-analytics-of-big-data.html
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.
Big data is a huge volume of heterogenous data often generated at high speed.Big data cannot be handles with traditional data analytic tools. Hadoop is one of the mostly used big data analytic tool.Map Reduce, hive, hbase are also the tools for analysis in big data.
Tools and Methods for Big Data Analytics by Dahl WintersMelinda Thielbar
Research Triangle Analysts October presentation on Big Data by Dahl Winters (formerly of Research Triangle Institute). Dahl takes her viewers on a whirlwind tour of big data tools such as Hadoop and big data algorithms such as MapReduce, clustering, and deep learning. These slides document the many resources available on the internet, as well as guidelines of when and where to use each.
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
Business executives sometimes ask us, “Isn’t ‘big data’ just another way of saying ‘analytics’?” It’s true that they’re related: The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into business advantage.
This slide is about real time analytics of Big Data. It explains about Big Data and Analytics. How to deal with them.
see more at - http://bigdataconcept.blogspot.in/2016/03/real-time-analytics-of-big-data.html
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.
Big data is a huge volume of heterogenous data often generated at high speed.Big data cannot be handles with traditional data analytic tools. Hadoop is one of the mostly used big data analytic tool.Map Reduce, hive, hbase are also the tools for analysis in big data.
Tools and Methods for Big Data Analytics by Dahl WintersMelinda Thielbar
Research Triangle Analysts October presentation on Big Data by Dahl Winters (formerly of Research Triangle Institute). Dahl takes her viewers on a whirlwind tour of big data tools such as Hadoop and big data algorithms such as MapReduce, clustering, and deep learning. These slides document the many resources available on the internet, as well as guidelines of when and where to use each.
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
Business executives sometimes ask us, “Isn’t ‘big data’ just another way of saying ‘analytics’?” It’s true that they’re related: The big data movement, like analytics before it, seeks to glean intelligence from data and translate that into business advantage.
Introduction to Big Data & Big Data 1.0 SystemPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
Today we will start with a brief introduction to Big Data. We will talk about how Big Data is generated, where we can apply it and also about the first world-wide famous platform of BigData 1.0 System, which is Hadoop.
#CHEDTEB
www.chedteb.eu
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
This presentation shows how Big Data impacts business and technology and asks (and maybe answers) the question: how new is Big Data and the effects it causes... ?
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
Introduction to Big Data & Big Data 1.0 SystemPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
Today we will start with a brief introduction to Big Data. We will talk about how Big Data is generated, where we can apply it and also about the first world-wide famous platform of BigData 1.0 System, which is Hadoop.
#CHEDTEB
www.chedteb.eu
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
This presentation shows how Big Data impacts business and technology and asks (and maybe answers) the question: how new is Big Data and the effects it causes... ?
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include different types such as structured/unstructured and streaming/batch, and different sizes from terabytes to zettabytes. Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.
Analyzing big data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in significantly better and faster decisions.
Big Data Analysis Patterns with Hadoop, Mahout and Solrboorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
View this webcast to learn how you can accelerate your security transformation from traditional SIEM to a unified platform for incident detection, investigation and advanced security analysis. Understand why organizations are moving to a true big data security platform where compliance is a byproduct of security, not the other way around. More via
http://bcove.me/d2e9wpd2
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Great Bigdata eBook giving a perspective of Bigdata Analytics Predictions for 2016. Learn about the milestones, landmarks and futures of this fast growing arena.
Carousel30: Big data for digital marketersCarousel30
Carousel30's white paper that explains the most relevant aspects of big data for digital marketers.
It’s hard to read a blog, pick up a magazine, or have a conversation about business these days without the term “Big Data” coming up in some form or another. What it is exactly and how it relates to you as a digital marketer can be harder to determine. The purpose of this white paper is to talk about Big Data in terms that relate to marketing and advertising, and more specifically that relate to the digital marketer. There is much more information (or data, if you will) on this subject than this white paper allows, but the objective is to encourage further research and discovery on the areas of the subject that are most relevant to you and your current challenges within your organization or company.
Many of the references cited within this white paper provide deeper insights into specific aspects and we recommend reading them in their entirety, especially when they refer to areas of interest to you. We hope that this provides a good introduction to Big Data and is the beginning of a new step in the sophistication of your digital marketing and advertising efforts.
What exactly is big data? What exactly is big data? .pptxTusharSengar6
big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.” Put simply, big data is larger, more complex data sets, especially from new data sources.
Convergence of AI, IoT, Big Data and Blockchain: A Review.
Kefa Rabah .
Mara Research, Nairobi, Kenya .
Abstract
Data is the lifeblood of any business. Today, big data has applications in just about every industry – retail, healthcare,
financial services, government, agriculture, customer service among others. Any organization that can assimilate data
to answer nagging questions about their operations can benefit from big data. In overall, the demand for big data
transcend across all sectors and business. Those who work to understand their customers’ business and their problems
will be able to proactively identify big data solutions appropriate to their needs, and thus gain competitive advantage
over their competitors. Job demand for people with big data skill-set is also in the rise especially professional,
scientific and technical services; information technology; manufacturing; and finance and insurance; and retail.
DevOps is baseless without the cloud. IoT needs cloud to operate efficiently, for computing is required by the cloud
operate efficiently. AI remained only as model up until the advent of big data. Blockchain and related distributed
ledger technologies are disrupting the technology sector as we know it. The confluence of technologies is just
inevitable and often they are beneficial especially today when usher in the 4th industrial revolution (Rabah, 2017a)
and the forth coming machine economy (Rabah, 2018). More-so, data is a key ingredient of approaches to developing
AI and machine learning, which are now being applied to a wide variety of uses, from stock trading to chatbots to
self-driving cars. There is barely a business or human activity today that is not considered as a target for AI in future
years and decades.
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.
EUBrasilCloudFORUM actively participated at Beyon2020 event. Professor Sergio Takeo Kofuji from the University of São Paulo (USP) discussed the importance of Open Data for cities.
The Beyond 2020 event took place at "Centro de Convenções de Pernambuco" from the 27th to the 29th of July, focusing on how urban innovation ecosystems can support citizens and what the future will look like for Cities, Politics, Citizens, Local Development, and Tourism, based on the use of Open data Platforms.
Big data for the next generation of event companiesRaj Anand
Only on rare occasions do we consider the amount of data that our every action produces. It’s pretty overwhelming just to think about every interaction on every app on every device in our bag or pocket, in every environment and every location.
But then there’s more. We also use access cards, transportation passes and gym memberships. We have hobbies, we travel, buy groceries, books and maybe warm beverages on rainy days. We are part of multiple communities. Looking around billions of people are doing the same. Our every action produces data about us. This is big.
We believe taking an interest in this wealth of data will be the key to success for next generation Event Companies.
We are living in a fast changing world, where it’s ever more important to foresee trends and seize opportunities. A global perspective is not a strategic advantage anymore it is a necessity.
Event companies are facilitators , they create common grounds for brands and audiences, by thoughtfully connecting goals and means. Having a deep understanding of customer behaviour, group psychology, digital habits, brand interaction, communication, and awareness through unlocking the power of big data will ensure next generation event companies thrive on strategy.
Big Data in the Fund Industry: From Descriptive to Prescriptive Data AnalyticsBroadridge
NICSA’s Technology Committee, including Dan Cwenar, President, Access Data, Broadridge, offer perspectives on the “state of play” of Big Data in the fund industry:
The history of “ Big Data”
The definition of Big Data in the context of industry applications.
The movement from descriptive towards prescriptive analytics in driving decisions
Common misconceptions about the use of predictive analytics.
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
June 2015 (142) MIS Quarterly Executive 67The Big Dat.docxcroysierkathey
June 2015 (14:2) | MIS Quarterly Executive 67
The Big Data Industry1 2
Big Data receives a lot of press and attention—and rightly so. Big Data, the combination of
greater size and complexity of data with advanced analytics,3 has been effective in improving
national security, making marketing more effective, reducing credit risk, improving medical
research and facilitating urban planning. In leveraging easily observable characteristics and
events, Big Data combines information from diverse sources in new ways to create knowledge,
make better predictions or tailor services. Governments serve their citizens better, hospitals
are safer, firms extend credit to those previously excluded from the market, law enforcers catch
more criminals and nations are safer.
Yet Big Data (also known in academic circles as “data analytics”) has also been criticized as a
breach of privacy, as potentially discriminatory, as distorting the power relationship and as just
“creepy.”4 In generating large, complex data sets and using new predictions and generalizations,
firms making use of Big Data have targeted individuals for products they did not know they
needed, ignored citizens when repairing streets, informed friends and family that someone
is pregnant or engaged, and charged consumers more based on their computer type. Table 1
summarizes examples of the beneficial and questionable uses of Big Data and illustrates the
1 Dorothy Leidner is the accepting senior editor for this article.
2 This work has been funded by National Science Foundation Grant #1311823 supporting a three-year study of privacy online. I
wish to thank the participants at the American Statistical Association annual meeting (2014), American Association of Public Opin-
ion Researchers (2014) and the Philosophy of Management conference (2014), as well as Mary Culnan, Chris Hoofnagle and Katie
Shilton for their thoughtful comments on an earlier version of this article.
3 Both the size of the data set, due to the volume, variety and velocity of the data, as well as the advanced analytics, combine to
create Big Data. Key to definitions of Big Data are that the amount of data and the software used to analyze it have changed and
combine to support new insights and new uses. See also Ohm, P. “Fourth Amendment in a World without Privacy,” Mississippi.
Law Journal (81), 2011, pp. 1309-1356; Boyd, D. and Crawford, K. “Critical Questions for Big Data: Provocations for a Cultural,
Technological, and Scholarly Phenomenon,” Information, Communication & Society (15:5), 2012, pp. 662-679; Rubinstein, I. S.
“Big Data: The End of Privacy or a New Beginning?,” International Data Privacy Law (3:2), 2012, pp. 74-87; and Hartzog, W. and
Selinger, E. “Big Data in Small Hands,” Stanford Law Review Online (66), 2013, pp. 81-87.
4 Ur, B. et al. “Smart, Useful, Scary, Creepy: Perceptions of Online Behavioral Advertising,” presented at the Symposium On
Usable Privacy and Security, July 11-13, 2 ...
Another great content/horrendous stock photo "presentation" from IT Business Edge about Big Data. (http://www.itbusinessedge.com/slideshows/big-data-eight-facts-and-eight-fictions.html)
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
2. BIG DATA ANALYTICS
Home
‘Big data’ analytics,
number-crunching
nation
Big data era requires
new approach to
information governance strategy
W
e are a big data analytics, mobile app, real-
time nation. Gartner predicts that by
2015, 4.4 million IT jobs will be generated
to support big data, generating 1.9 million
jobs in the United States. With that point, CIO’s must change their
thought process when it comes to big data. This e-guide, from
SearchCIO.com, explains how big data is impacting today’s IT industry and what CIO’s must do to incorporate big data analytics in
their overall business strategy.
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3. BIG DATA ANALYTICS
'BIG DATA' ANALYTICS, NUMBER-CRUNCHING NATION
Home
‘Big data’ analytics,
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Big data era requires
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information governance strategy
Not all "big data" analytics are created equal. Not all real-time data is real or
really matters. Not all killer mobile apps kill. That much can be surmised from
even a cursory review of the digital weapons deployed in this presidential
election, already dubbed the nerdiest in American history. But there can be no
doubt that politics, like everything else, is entering the big data analytics world.
Whether the much-touted mobile app used by 34,000 Romney volunteers
to relay real-time voter counts at the polls turned out to be "nothing short of a
failure," as has been reported and denied, or was a match for the Mobile Pollwatcher app used by the Democratic party to rustle up voters to the polls, I'll
leave to the politico-technocrats to judge. The Election Day mobile apps were
designed to interact with the massive databases and big data analytics tools
relied on by the dueling campaigns. And there's no doubt the data mining tools
and predictive analytics used by Project Orca, the Romney campaign's effort
to turn big data into meaningful action, will be compared to death with those
used by Narwhal, the Obama campaign's massive IT system used to segment
and target voters.
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4. BIG DATA ANALYTICS
Home
‘Big data’ analytics,
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nation
Big data era requires
new approach to
information governance strategy
On the face of it, Orca didn't get its Narwhal. But technology? Technology,
its history-making promise and terrible limitations on full display, loomed
larger than ever on the political battlefields, big data in particular. The power of
leveraging big data -- which requires being able to collect it and analyze it with
the right data model -- was breathtaking in this election, as demonstrated by
the polling done by such pollster nerds as Nate Silver, author of the FiveThirtyEight blog in The New York Times, and Princeton neuroscientist Sam Wang.
Aggregating all the polls' results (which were calculated by collecting and analyzing varying amounts of big data), Silver and Wang and their ilk predicted the
outcome of a political process nearly perfectly. Gut feelings, political persuasion, the timbre of one's voice -- those had nothing to do with the predictions.
The pundit class ignored these big data conclusions at their peril, as one after
another confessed how wrong they were -- a day late. "Politics as usual" is done
with.
Politics, of course, is just realizing what savvy merchants have known for
a long time: Aggregating statistics from many people makes human behavior
predictable. Big data analytics overcomes the uncertainty of the variations
among individuals.
As the power of big data to make accurate political predictions sunk in, I
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5. BIG DATA ANALYTICS
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‘Big data’ analytics,
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was reminded of nothing so much as Glik's, a family-owned business I covered
as a retail reporter in St. Louis years ago. The Gliks have been merchants since
1897. In the modern era, their business had become a chain of 50-some fashion
stores that offered national name-brand clothes in small towns in the Midwest,
some with populations as small as 7,000. Come time for the obligatory story on
Christmas sales and whether merchants would make their numbers that year,
the big retailers would put out their hot picks and predictions for the season.
This was the heyday of the "genius merchant": legends -- like media darling Millard "Mickey" Drexler, then of Gap -- who operated on instinct, who
knew in their gut the right cut of coat or color for the season. When I needed a
bead on the season, I made sure to call Jeff Glik, the son who was running the
Glik's chain at the time. Between trips to New York, Jeff would be poring over
weather data, surveying customers, shopping the competition, on the phone
with managers from Missouri to Michigan, crunching the numbers to get just
the right selection for each of the chain's stores. Way before the rise of the big
data-driven retailer, he was doing his own version of Moneyball.
Politics -- along with everything else -- is catching up with retailers. We
are a big data analytics, mobile app, real-time nation. Don't take my word for
it. Gartner Inc. stirred headlines last month with its prediction that by 2015,
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6. BIG DATA ANALYTICS
Home
‘Big data’ analytics,
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nation
Big data era requires
new approach to
information governance strategy
4.4 million IT jobs will be generated to support big data, generating 1.9 million
jobs in the United States. Every big data-related job here will spawn three jobs
outside of IT, for a total of 6 million U.S. jobs over the next four years. That's
how much stock employers put in big data and in the analytics required for
making information into something that matters. It's a job that will lash together mobile technology, social media and cloud computing. Gartner analyst
Peter Sondergaard tried out the slogan "Nexus of Forces" to describe the "next
age of computing." Everything that rises must converge.
The computers that caught your attention many years ago are having profound impacts on civilization, let alone on your role as CIO in this tornado of
big-data mobile computing. If the presidential election proved nothing else
(and for the record, I believe it proved a great deal), it drives home the point to
CIOs that their job can't be about "technology as usual."
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BIG DATA ERA REQUIRES NEW APPROACH TO INFORMATION GOVERNANCE STRATEGY
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Many organizations have struggled with managing unstructured data -- the often text-heavy, unorganized information that, left unattended, can cause huge
risks and unnecessary storage costs.
As the big data era continues, companies will have to reexamine and adapt
their information governance strategy. By 2018, 25% of progressive organizations will manage all their unstructured data using information governance
and storage management policies, up from less than 1% today, predicts Stamford, Conn.-based consultancy Gartner Inc.
"Once it's created, it's around forever," Gartner Research Director Alan
Dayley said of company data at the Gartner Security and Risk Management
Summit in National Harbor, Md., in June. "We need to do something with it,
and we need to start governing it."
The trouble is, many modern organizations struggle with data governance.
The amount of data floating around the average organization -- much of it
trivial -- makes determining who owns specific data, how long to keep that
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data and who is responsible for managing it a difficult proposition. First and
foremost, organizations must understand exactly what data they have, and the
value of it, Dayley said.
Another cause is data confusion, especially from a regulatory compliance
standpoint.
"We're not clear on regulatory and compliance issues," Dayley said. "We
don't understand what we're supposed to keep, so we keep everything."
Information governance strategy implementation and deployment requires input from across the organization, Dayley said during his Gartner
Summit presentation:
Compliance officers should be consulted to interpret regulatory compli
ance requirements and how long information must be retained according to these regulations. They can also help determine audit schedules.
he legal team has responsibility for assessing information risk and
T
determining a defensible deletion policy.
Business users must understand the current and historical value of data
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and need to be included during the information management policy
development.
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The primary goal of an information governance strategy is to make sure
data supports business priorities effectively and efficiently. Any data that presents very little value, such as transient user communications, early working
copies of files and old data from legacy applications, should be deleted, Dayley
added. Over time, the value goes way down, whereas the cost to continue to
manage it goes way up, he said. You just can't keep keeping everything forever. It's costly on storage; it's costly just trying to filter through all of it and
understand it.
INCORPORATE BIG DATA ANALYTICS
If used strategically, analyzing this big data produces huge benefits, said Vice
President and Gartner Fellow Neil MacDonald during the summit. MacDonald
said that when it comes to information security, organizations often establish
baselines of normal data behavior and look for meaningful deviations.
Giving information more context through big data analytics allows organizations to establish a better understanding of this normal behavior and
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determine meaningful deviations from the baselines.
You have to have a very good idea what normal looks like, then look for
meaningful variations from that to infer malicious intent, MacDonald said.
How do you find a needle in a haystack if you don't know what the needle looks
like?
Difficulties arise when organizations are required to govern content that
they did not create and do not own but may be responsible for -- or find value in.
For example, employee-generated social media data creates potential privacy
risks but can also be very useful to the business from a marketing standpoint.
The cloud is another concern, because it's part of a trend wherein IT has
less and less direct control of the organizational infrastructure, MacDonald
said. As more elements of IT infrastructure go mobile, the tech department
must offset these security concerns with detailed auditing, logging and monitoring of big data activities.
You want visibility to compensate for the lack of direct controls, he said.
If properly managed, breaking down and analyzing big data provides huge
business benefits, he added.
You've got the data; why not leverage it? MacDonald said. Focus on your
objective, which is risk prioritized, actionable insight telling you what to do
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and what to focus on so that you can have the most impact on protecting the
assets of your company.
This won't be easy. The volume of information in the big data era, combined
with new file system technologies, repository formats and nascent programming interfaces, mean that more sophisticated and mature archiving, e-discovery and compliance technologies are not yet available, Dayley said.
As a result, organizations will be forced to manage and govern some of this
content using manual policies and practices versus automated software while
they wait for vendors to catch up, he added. Dayley predicts progressive companies will incorporate policies and products to assist them with automatically
governing their unstructured data.
What these tools do is give you a good visualization of the data and help
companies understand what it is, Dayley said.
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