Month: May 2018

May 31, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Big Data knows everything  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Oct 12, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Implementing Personalized, Precision Medicine with Artificial Intelligence and Semantic Graph Technology by jelaniharper

>> Discussing #InfoSec with @travturn @hrbrmstr @thebeareconomist @yaxa_io – Playcast – Data Analytics Leadership Playbook Podcast by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Big Data Security Market to Record an Exponential CAGR by 2027 – Find Market Research By Abhishek Budholiya (press release) (blog) Under  Big Data Security

>>
 Money Keeps Flowing Into the US Data Center Market Despite a Rough Year for REITs – Data Center Knowledge Under  Data Center

>>
 VMware reports strong Q4 as AWS partnership pays off in hybrid cloud – ZDNet Under  Hybrid Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

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A practical Python course for beginners with examples and exercises…. more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:Explain what resampling methods are and why they are useful?
A: * repeatedly drawing samples from a training set and refitting a model of interest on each sample in order to obtain additional information about the fitted model
* example: repeatedly draw different samples from training data, fit a linear regression to each new sample, and then examine the extent to which the resulting fit differ
* most common are: cross-validation and the bootstrap
* cross-validation: random sampling with no replacement
* bootstrap: random sampling with replacement
* cross-validation: evaluating model performance, model selection (select the appropriate level of flexibility)
* bootstrap: mostly used to quantify the uncertainty associated with a given estimator or statistical learning method

Source

[ VIDEO OF THE WEEK]

@TimothyChou on World of #IOT & Its #Future Part 2 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 2 #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

Data is the new science. Big Data holds the answers. – Pat Gelsinger

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

Subscribe 

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[ FACT OF THE WEEK]

Distributed computing (performing computing tasks using a network of computers in the cloud) is very real. Google GOOGL -0.53% uses it every day to involve about 1,000 computers in answering a single search query, which takes no more than 0.2 seconds to complete.

Sourced from: Analytics.CLUB #WEB Newsletter

@CyberIgor on #Metric Led Strategic Thinking in #InfoSec #FutureOfData #Podcast

[youtube https://www.youtube.com/watch?v=QGS1BfToCNM]

In this podcast Igor Volovich(@CyberIgor) talks about strategic side of cyber security. He shared some practices that business could adopt to keep their infrastructure safe. Igor sheds some light on some easy ways to measure security for your business and understand the leadership commitment needed to establish a security mindset. Igor also share the need for metric lead strategies to quantify the outcome. This podcast is great for future information security leaders to understand data science and metrics led cyber security strategy.

Igor’s Recommended Read:
How to Measure Anything in Cybersecurity Risk by Douglas W. Hubbard, Richard Seiersen http://amzn.to/2BOoK6D

Podcast Link:
iTunes: http://math.im/itunes
GooglePlay: http://math.im/gplay

Igor’s BIO:
Strategist, advisor, advocate, mentor, author, speaker, and cyber leader. Passionate about the craft of cybersecurity and its role in protecting the computing public, the integrity of global commerce and international trade, and defense of critical national infrastructure.

Internationally experienced cyber security executive and senior advisor with 20 years of service to the world’s largest private and public-sector entities, Fortune 100’s, US legislative and executive branches, and regulatory agencies

About #Podcast:
#FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join?
If you or any you know wants to join in,
Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor?
Email us @ info@analyticsweek.com

Keywords:
#FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Source

May 24, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 Shadow IoT Devices Pose a Growing Problem for Organizations – Dark Reading Under  IOT

>>
 Google invests €500 million in Dutch data center | NL Times – NL Times Under  Data Center

>>
 Veeam to Focus on the ‘Hyper-Available’ Enterprise – Virtualization Review Under  Virtualization

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… more

[ FEATURED READ]

Rise of the Robots: Technology and the Threat of a Jobless Future

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What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:How would you come up with a solution to identify plagiarism?
A: * Vector space model approach
* Represent documents (the suspect and original ones) as vectors of terms
* Terms: n-grams; n=1 to as much we can (detect passage plagiarism)
* Measure the similarity between both documents
* Similarity measure: cosine distance, Jaro-Winkler, Jaccard
* Declare plagiarism at a certain threshold

Source

[ VIDEO OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

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[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. – Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

@BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

 @BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

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[ FACT OF THE WEEK]

94% of Hadoop users perform analytics on large volumes of data not possible before; 88% analyze data in greater detail; while 82% can now retain more of their data.

Sourced from: Analytics.CLUB #WEB Newsletter

Hitting the Road to BigData Analytics and Discovery

Hitting the Road to BigData Analytics and Discovery
Hitting the Road to BigData Analytics and Discovery

Businesses trying to get their big data strategy straight are looking for their move from concept to execution. A study from Oxford and IBM found that of the four big-data phases (Educate, Explore, Engage, and Execute), “Twenty-four percent of respondents were in the educate phase and another 47 percent in the explore phase. Only six percent had reached the execute phase.” So, it is not as easy as it seams and yet it is picking so much steam when everyone is talking about having a strategy in place. Getting big-data project in place is one thing, getting the discovery right is another thing.

Almost everyone I spoke to who has been dealing with big-data confirms that it is not a new problem, but something that has been haunting businesses for eons. It is just now that big-data focus has reached mainstream and generating all the heat and whistles for the businesses.

In my past projects we’ve dealt with problem ranging from ensuring a telecom system will be able to test on wide range of stochastic model, ensuring ad-serve on mobile platform works for millions of users, retail chain fixing their customer experience problem by utilizing customer experience touch points and learning from it, to a mobile manufacturer wanting to utilize it’s data to learn how they could master product innovation. These are one of the few that we dealt with and with almost everyone you could see some or the other side of big-data execution.

In my promising career, I have helped many companies get their automation strategy straight, which many times includes automating the discovery of insights as well as making sure their processes will auto magically generate insights that will help them in their longer span of business. In almost all the interactions, initial conversation is painful but as the value became evident, it is a cakewalk. The good part of big-data project is that it is not all smoke and bone but truth lies in data.

The faster we are getting deep into digital age, the relevance of big-data is all the more important. It is not a fluke that big data has gained so much grounds and so fast. If anything, it will keep on going crazy and higher. So if businesses don’t join the bandwagon, they could find themselves in area of dark and discovery less zone. Thanks for open source vendors around various assets of big-data, it has become extremely easy to dive into big-data capabilities and get the science to work at nominal cost.

Large volumes, blobs of data or Big-data is used to solve fundamental business problems. On one hand it is helping businesses find the answer to their most pressing questions and on the other, if done properly, it is helping business find the most pressing questions as well to find the answer to them. So, it depends on which side of savviness lies your business.

Some businesses, despite carrying boat load of data, are still looming in the ocean of ignorance and dissatisfaction. Take for an example some of top airlines carriers, they must be having so much data running under their nose, but they still stay on worst satisfied customers list. They do get the 3Vs of big data but still when it comes to use the data to build a competitive edge and take their airlines to north in every aspect of business, they fail at its execution.

And, we all know big blob of data is not the problem here. If, anything, these airlines must be dipped in data, and more the data, more the ability to get insights, discoveries, opportunities and results. The problem lies in the inability to translate the data to insights that teams could execute on and act in a way that drives transformational results. Thus, resulting in Big Data projects failures.

For example, in working with mobile carriers to accelerate their Big Data initiatives around reducing churn and driving data revenues, I often emphasize that ‘too much data’ is not a good thing – it’s a great thing. In most cases what businesses need is the right cross-discipline push– the push, that is, to become a truly analytics-driven business guided by decisions that are data-informed. So how do you get there?

So, what does it take to get the big-data strategy right and achieve much-needed discoveries to prove the ROI:

– Focus is the key: Get your focus on KPIs that are directly impacting the business and not focus on too many things or too irrelevant things. If right KPIs are picked, insights will come eventually.

– It’s a leadership, not a management problem: Sometime having several competing task forces to attack a problem is countered productive. Get this problem it’s own dedicated leader and invest in their strategy. It is cheaper/faster to do one thing at a time as compared to contesting things that could create loads of cross dependencies.

– Pick right strategy to get the game face on: In many of my previous blogs, I have rooted on the idea of picking the right strategy (Hiring consultants, outsourcing, building in-house teams) for starting your big-data project. So, get the right strategy on what is most cost effective to your business. If you can’t get to strategic insight, finding a strategy consultant is helpful as they could recommend the best possible way.

– Bring the talent and equip them: Through whichever ways, throw people at the problem and equip them so that they could work through it. This will get the momentum going to fix the problem.

– Get the conversation going: It is all about learning, sharing and optimizing; insight is power and sharing is the only way to move things forward.

Whether you’re a mobile carrier, a brick and mortar retailer, or a high-tech software firm, there are always new ways to connect the data dots to make new discoveries. At the end of the day, it’s the brains behind those technologies that will make the real difference.

So, when you look at your business next, see who is driving the discovery? Are they focused solely on finding the right answer to a question, or are they continually seeking the right questions to ask? It is the iteration between the two is where lies the real insights and discoveries.

Originally Posted at: Hitting the Road to BigData Analytics and Discovery

Focus on success, not perfection: Look at this data science algorithm for inspiration

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The quest for perfection often gets in the way of success. For example, Coca-Cola wasted millions on New Coke when the Old Coke was just fine. It might surprise you to learn that a frequently used data science technique can serve as a good reminder for leaders to strive for success, not perfection.

Let Naive Bayes be imperfect

Naive Bayes is a machine learning technique that’s primarily used for classifying text. For instance, Naive Bayes may be used to identify spam in email. The algorithm takes your email text as an input, does its magic, and then determines — or classifies — your email into one of two categories: spam or no-spam.

This is unremarkable for a machine-learning algorithm; however, if you look under the hood, the algorithm makes bold assumptions that would ostensibly render it ineffective. For instance, word order or context is not considered, so the phrases “monkeys drive crazy cars” and “cars drive monkeys crazy” are treated the same. What is remarkable is that, notwithstanding these seemingly reckless assumptions, it works.

Naive Bayes illustrates that real life isn’t always logical or formulaic. The fact that Naive Bayes works irritates many data scientists. A common slang used for Naive Bayes is idiot Bayes. Some data scientists feel Naive Bayes needs to be improved because, even though it works, they know there’s something wrong with how it works.

The trouble with trying to perfect Naive Bayes is that you quickly run into a problem solving mess. Trying to account for all the semantics and contextual nuances of the English language is a fool’s errand. And for what? The algorithm already works well, so what are they hoping to improve? This is the classic Achilles heel of our dear data scientists: Their incessant pursuit of perfection when success is already in-house. We’ll give them allowances because that’s their nature, but leaders and managers, you don’t get the same pass.

When ignorance is bliss for leaders

Naive Bayes teaches us to focus on results, not methods. Methods do matter, but let’s get straight on the means and the ends.

I once heard a comedian tell a joke about the game show, “Who Wants To Be A Millionaire?” The contestant is presented with four answers to choose from, and when they seem stuck, the host (originally Regis Philbin) encourages them to talk through the answer. The joke was that the contestant would talk it through with clearly fallacious and erroneous reasoning — and then select the right answer. Something like, “Well…I know it was an African-American community leader around the turn of the 20th century, so I’ll go with…Jimmy Carter.” As funny as that sounds, as long as Jimmy Carter is the correct answer, the contestant wins the money.

We need to take this naive approach with leadership. Sometimes we get too hung up with our methodologies and forget about why we studied them in the first place. Kotter’s eight-step process for leading change is great to know, but if you can bring about change in the first three steps, you don’t need to finish the other five. If someone achieves self-actualization with low self-esteem, we don’t need to investigate why Maslow’s Hierarchy of Needs isn’t working.

We can use Naive Bayes for practical, everyday purposes even though we know it shouldn’t work as well as it does. Let the data scientists focus on the methods — that’s what they do. But as a leader, you’d be remiss if you joined them on the path to perfection. Stay on the road to success and keep your eyes on the outcome.

Final thoughts

Learning from your environment is the hallmark of a great leader. If your strategy incorporates data science, you probably have a great lesson that’s right under your nose.

Your data scientists are using Naive Bayes for its pragmatics, not its elegance. As a leader, you should be naive in this respect as well. If you’re lucky enough to stumble upon something that works, don’t question it — just appreciate what you have and focus on the next challenge.

There’s nothing classic about Classic Coke — that was just Coca-Cola’s idea of undoing a bad mistake. They should’ve just called it Naive Coke and left it alone.

Originally posted at: http://www.techrepublic.com/article/focus-on-success-not-perfection-look-at-this-data-science-algorithm-for-inspiration/

Source by analyticsweekpick

May 17, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Implementing Personalized, Precision Medicine with Artificial Intelligence and Semantic Graph Technology by jelaniharper

>> US Army enterprise apps must move to core data centers by analyticsweekpick

>> Lack of big data talent hampers corporate analytics by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 What is Santiment Net (SAN)? | Beginner’s Guide – CoinCentral Under  Sentiment Analysis

>>
 Adopt The Right Cyber Posture For Your Hybrid Cloud Environment – Forbes Under  Hybrid Cloud

>>
 The Monday Stack: In and Out of the Cloud – DMN Under  Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… more

[ FEATURED READ]

The Industries of the Future

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The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what’s next for the world and how to navigate the changes the future will bring…. more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:What is the Central Limit Theorem? Explain it. Why is it important?
A: The CLT states that the arithmetic mean of a sufficiently large number of iterates of independent random variables will be approximately normally distributed regardless of the underlying distribution. i.e: the sampling distribution of the sample mean is normally distributed.
– Used in hypothesis testing
– Used for confidence intervals
– Random variables must be iid: independent and identically distributed
– Finite variance

Source

[ VIDEO OF THE WEEK]

Surviving Internet of Things

 Surviving Internet of Things

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[ QUOTE OF THE WEEK]

Data really powers everything that we do. – Jeff Weiner

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

Subscribe 

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[ FACT OF THE WEEK]

As recently as 2009 there were only a handful of big data projects and total industry revenues were under $100 million. By the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some big data initiatives under way.

Sourced from: Analytics.CLUB #WEB Newsletter

CMOs’ Journey from Big Data to Big Profits (Infographic)

CMOs’ Journey from Big Data to Big Profits (Infographic)
CMOs’ Journey from Big Data to Big Profits (Infographic)

Since the consumer purchase funnel is generating great amounts of data, it has become extremely difficult to track and make sense of the data, as consumers add social media and mobile channels to their decision-making. This is fueling the ever mounting pressure on CMOs to show how their budget delivers incremental business value.

Better data management is turning out to be a strong competitive edge and great value generation tools for organizations. So, well managed marketing organization will make adequate use of data.

This has pushed many marketers to stand overwhelmingly towards better big data analytics, as analytics will become a major component of their business over the next several years—according to the Teradata Data Driven Marketing Survey 2013 released by Teradata earlier this year, 71 percent or marketers say they plan to implement big data analytics within the next two years.

Marketers already rely on a number of common and easily accessible forms of data to drive their marketing initiatives—customer service data, customer satisfaction data, digital interaction data and demographic data. But true data-driven marketing takes it to the next level: Marketers need to collect and analyze massive amounts of complicated, unstructured data that combines the traditional data their companies have collected with interaction data (e.g., data pulled from social media), integrating both online and offline data sources to create a single view of their customer.

Visually and McKinsey & Company co published this infographic to illustrate the pressures that CMOs find themselves under and respective potential benefit in leveraging big data.

CMOs’ Journey from Big Data to Big Profits (Infographic)
CMOs’ Journey from Big Data to Big Profits (Infographic)

Source: CMOs’ Journey from Big Data to Big Profits (Infographic) by v1shal

May 10, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Convincing  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The Big Data Problem in Customer Experience Management: Understanding Sampling Error by bobehayes

>> How to Use Social Media to Find Customers (Infographic) by v1shal

>> October 24, 2016 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ NEWS BYTES]

>>
 Artificial intelligence will put a premium on physicians’ knowledge and decision-making skills – STAT Under  Artificial Intelligence

>>
 Google Cloud, FogHorn partner to boost the Industrial Internet of … – ZDNet Under  Internet Of Things

>>
 Biological Polymer Coatings Sales Market by Preventive Techniques Report – Business Analytics Under  Business Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Pattern Discovery in Data Mining

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Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:What is star schema? Lookup tables?
A: The star schema is a traditional database schema with a central (fact) table (the “observations”, with database “keys” for joining with satellite tables, and with several fields encoded as ID’s). Satellite tables map ID’s to physical name or description and can be “joined” to the central fact table using the ID fields; these tables are known as lookup tables, and are particularly useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve multiple layers of summarization (summary tables, from granular to less granular) to retrieve information faster.

Lookup tables:
– Array that replace runtime computations with a simpler array indexing operation

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The goal is to turn data into information, and information into insight. – Carly Fiorina

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

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[ FACT OF THE WEEK]

Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.

Sourced from: Analytics.CLUB #WEB Newsletter

Using Big Data In A Crisis: Nepal Earthquake

As I write this, hundreds of emergency services, charities, disaster relief agencies and volunteers are doing their best to help people affected by the terrible Nepalese earthquake which struck during the weekend. And Big Data is playing its part, too – with crowdsourced, data-driven efforts to connect people outside the country with their missing loved ones, and assist in getting aid to where it is needed.

Big Data is the name given to our ever-increasing ability to collect more data from a multitude of sources, and analyze it for insights using advanced computer algorithms. Patterns humans can’t see provide a better understanding of situations and solutions to problems. Disasters are big, messy and noisy situations, and exactly the sort of conditions in which Big Data can help to make sense of the chaos. The massive amounts of data that we are generating with mobile phones, satellites and social media can all play a part in providing clues to the best way to respond to a situation.

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Much of the work on developing Big Data systems to help with disaster relief began in the wake of the 2010 Haiti earthquake and the 2011 Tohuku, Japan earthquake and tsunami. Japan and the US instigated a joint research program to find workable methods of using data to ease the toll of natural disasters which kill thousands each year, and cost the global economy billions. Last year, the US National Science Foundation and Japanese Science and Technology Agency offered $2 million in funding to groups working on data-driven solutions to disaster management problems.
At the other end of the scale, crowdsourced, data-led initiatives have also started off at a grassroots level, with community members coming together to collate data to assist others. This happened following Hurricane Sandy in the US, when high school students collaborated to create an online map of the New York and New Jersey area showing where gas was available. Following Typhoon Haiyan in the Philippines, the international Red Cross collaborated with volunteers around the world to map the effects on the region and its people.One spokesman said “Online volunteering platforms scale very well … before there would be one or two people pouring over satellite imagery. Now there’s 700 volunteers”.
The four key elements of disaster management are prevention, preparation, response and recovery. Big Data has potential to help with all of them.

While not much can be done to prevent natural disasters, sophisticated Big Data systems such as those developed by Palantir are being used to crack down on man-made disasters such as those caused by terrorism. But when it comes to “acts of God”, of course the focus will be on preparation, response and recovery.

 Companies such as Terra Seismic carry out real-time monitoring of satellite data and environmental factors which they say allow them to predict earthquakes anywhere in the world with 90% accuracy. Being pre-warned will make carrying out the next two stages of disaster-management – response and recovery – far simpler.
With response, help can be sought from the huge amount of data captured by satellites, smartphones, UAVs and social media. This data is called Big Crisis Data by those working with it, and it is used to producecrisis maps to direct relief organizations to people and area most in need. The US Marine Corps was one of the first agencies on the ground following the 2010 Haiti earthquake which killed over 100,000 people, and they stated that an interactive, crowd-sourced map produced by Ushahidi was hugely helpful.
The big internet companies – which have more people data than anyone else – are also doing their bit to help.Google GOOGL -0.06% and Facebook have both launched systems designed to help track or trace missing loved ones following disasters, and both are being used to assist those affected by the Nepal earthquake today. Google’s People Finder was launched after the 2010 Haiti earthquake and allows anyone to update live information about the condition or whereabouts of missing people. It was updated 5,300 times in the first two days following the Nepal quake. Facebook’s Safety Check service automatically sends messages to people whose GPS data shows they are within disaster zones, and suggests they provide information which could help anyone who is looking for them. It also lets users provide information on people they are worried about, similarly to Google’s service.
It’s unlikely that Big Data will ever be able to prevent tragedy on the scale that we are witnessing today in Nepal and elsewhere around the world. But if it can cut the loss of life in post-disaster situations, then any amount of investment has to be worthwhile. Solutions being found today show that there are workable applications for it in disaster preparation, response and recovery which we can all be thankful for. The race is now on to put them to use to save lives and ease suffering as best we can.

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Originally Posted at: Using Big Data In A Crisis: Nepal Earthquake by analyticsweekpick