How to deal with Big Data!? MIT Article...

What are Social Media's larger problems?  One of them may be how to monetize their near 1 trillion member strong networks via strategic advertising by properly harnessing the "insights" from Big Data...  There is no clear road map on how to do this and many people are starting to charter potential routes for various industries.

Below is a great article that proposes means for many IT oriented companies to properly harness big data:

MIT Article

“Big Data for Development,” released by the UN Global Pulse July 10

"looks at how real time big data generated through social media and other sources can be harnessed for the public good"
http://blogs.worldbank.org/voices/node/1421?cid=EXT_TWBN_D_EXT

Massive Shortage of BIG DATA Skills?

Author investigates: "What if I told you that for every 100 open Big Data jobs, there are only two qualified candidates?"
http://www.fastcompany.com/1842928/time-to-build-your-big-data-muscles

Big Data Startups Making for an Easier Commute

Many emerging Big Data start ups are smaller B2B solutions providers that are not in the headlines, and they may never become mainstream names like Splunk.  In the recent Wall Street Journal article, ¨Tapping 'Big Data' to Fill Potholes,¨ several of these smaller startups are mentioned with a theme to help drivers to avoid traffic issues.  Intrix Inc. has turned its data analysis into a viable commercial businesses by generating revenue from the state of New Jersey and has plenty more highways in the world it can potentially expand to.  According to the article, Inrix and, “The New Jersey center offers a glimpse at the power of "big data," a term for techniques to gather reams of computerized information points, analyze them and spit out patterns, often in easy-to-understand visuals like maps or charts.”

In addition to traffic authorities having better information to deal with traffic concerns, Google maps and navigation systems are telling more and more every day to consumers about travel conveniences.  Both mobile phone applications as will as in car services such as OnStar make this possible.  These companies are using both live update information as well as historic traffic pattern data to predict congestion and travel time.

INRIX Inc. is not only getting involved with helping states to improve their traffic situation, they have also recently been selected by BMW to improve navigation and fuel economy efforts. This is a great opportunity for them and we will keep you posted on progress on their partnership.

In addition to Inrix, both RAC & Waze have interesting related stories:

RAC - Over in the UK the RAC uses vehicle data to identify congestion situations.  This insurance based firm has a business model that is designed to utilize navigation and data from vehicles to provide additional value for Breakdown Coverage services.

WAZE - Another startup, called Waze Inc. concentrates on mobile applications catered towards navigation and traffic patterns.  In fact they tell you the optimal times to travel for holiday weekends!  Check it out for your next vacation!

These are just a couple of the business models that are looking to establish commercial businesses of traffic and navigation.   If you are interested in other start ups leveraging Big Data, another great site called Beautiful Data recently came out with a list of Top 10 hot big data start ups that is worth taking a look at!  Let us know if you know of any other interesting Big Data efforts we should continue to keep an eye on!

Big Data Analytics - Techniques and Trends - continued..

Welcome back! So we continue to understand some more techniques and trends to analyze Big Data. Our idea is not for you to become experts in all of these, but hopefully to be able to germinate the seed of inquisitiveness in your mind and simultaneously touch upon the most prevalent concepts.

A couple of more widely used techniques trying to utilize Big Data potential:

Sentiment Analysis:  A technique to identify and extract subjective information from source text material. Key aspects of these analyses include identifying the feature, aspect, or product about which a sentiment is being expressed, and determining the type, “polarity” (i.e., positive, negative, or neutral) and the degree and strength of the sentiment. Examples of applications include companies applying sentiment analysis to analyze social media (e.g., blogs, microblogs, and social networks) to determine how different customer segments and stakeholders are reacting to their products and actions.

Predictive Analysis: A set of techniques in which a mathematical model is created or chosen to best predict the probability of an outcome. It deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. An example of an application in customer relationship management is the use of predictive models to estimate the likelihood that a customer will “churn” (i.e., change providers) or the likelihood that a customer can be cross-sold another product. This is used in conjunction with some earlier described data analyzing techniques like data mining. Following video is sweet and short illustration by a Predictive Analytics company http://goo.gl/9k0sP

Now we look at some buzz words regarding Big Data Analytics as promised before, there are a growing number of technologies used to aggregate, manipulate, manage, and analyze Big Data, most of them are based on Distributed Computing platform, which is:

 - Massive parallel computing where a problem is divided into multiple tasks, each of which is solved by one or more computers working in parallel.

Here are some trendy technologies:

MapReduce: A software framework introduced by Google for processing huge data sets on certain kinds of problems on a distributed system. Check out this nice online presentation for a simple understanding http://goo.gl/Qz5PP

Mashup: An application that uses and combines data presentation or functionality from two or more sources to create new services. These applications are often made available on the Web, and frequently use data accessed through open application programming interfaces or from open data sources.

Hadoop: An open source (free) software framework for processing huge data sets on certain kinds of problems on a distributed system. Its development was inspired by Google’s MapReduce and Google File System. It was originally developed at Yahoo! and is now managed as a project of the Apache Software Foundation.

Although the scope of this genre of technologies is very vast and hard to bring under the purview of this post, nevertheless, we tried to make you familiar with the basic concepts. Do let us know your views, see you soon …..

References:

McKinsey report: http://goo.gl/ycvef

TDWI library reports: www.Tdwi.org

Wikipedia

Big Data Analytics - Techniques and Trends …

   Making sense out of BIG DATA

Alright! Now we have got tonnes of information about Big Data. Question is, how do enterprises make sense out of it? So let us explore the various Data Analysis techniques that are either 1) most commonly used by companies across various industries or 2) relatively new but show strong growth potential in the near future.Through a series of posts, we will try to touch upon these techniques. The idea is to get familiarized with the buzzwords around Big Data.

Although there is a buzz around “Advanced Analytics” these days for Big Data analysis, researchers claim that they are mostly built upon the fundamentals of “Business Intelligence” or “BI” techniques, so barring all tweaks, customization and modifications at the moment, let us grasp the basics first.

BI encompasses a set of computer based methodologies that help analyze and report/present large amounts of ‘structured’ or ‘unstructured’ data. Is this something new? Apparently not, it has been used by businesses since long to support various business related activities like decision making, predictions, number crunching etc. Checkout this marketing video by a company called Avitas giving an idea of BI and the prospects: http://goo.gl/blKTe

However, the context in which these techniques are being utilized is changing - that is to analyze Big Data, which is just data after all!

Here are some known techniques under BI:

1. OLAP – Online Analytical Processing:

A data retrieval process used for structured databases more commonly known as Data ware houses. The major focus of this technique is to query or retrieve and effectively combine data from multiple sources or dimensions aggregated in a relational structure. Commonly used are the OLAP cubes, which combine, analyze and present data from 3 different sources. A typical data extraction would read like: - Sales of a company’s product x in region y for a period z which has been extracted from data sets for products (x,y,z), regions (x,y,z), periods (x,y,z).

2. Data Mining:

A methodology used to extract patterns from large datasets by combining methods from statistics and machine learning with database management. Examples of usage might include mining customer data to determine segments most likely to respond to an offer, mining human resources data to identify characteristics of most successful employees, or market basket analysis to model the purchase behavior of customers.

Further drilling into this category, following are certain methods which are used independently or in conjunction with one another to analyze data or in extension ‘Big Data’ -  

- Association rule learning

A technique for discovering interesting relationships, i.e., “association rules,” among variables in large databases based upon a set of algorithms. One application is market basket analysis, in which a retailer can determine which products are frequently bought together and use this information for marketing (a commonly cited example is the discovery that many supermarket shoppers who buy diapers also tend to buy beer. you can refer to the Forbes article about the IBM computing which brought about that discovery here - http://goo.gl/UNIFS

- Cluster analysis

A method for classifying objects from diverse groups into smaller groups of ‘seemingly’ similar objects whose characteristics of similarity are not known in advance. An example of cluster analysis is segmenting consumers into self-similar groups based on collective group behavior for targeted marketing. Example - recommending a customer in a movie which was bought/liked by another customer in the same group. It is almost in contrast to simple ‘classification’, up next!  

- Classification

This method identifies categories in which new data points belong, based on a training set containing data points that have already been categorized based on similar traits. One application is the prediction of segment-specific customer buying behavior where there is a clear hypothesis or objective outcome.

Dear avid readers! Considering the heaviness of the data dose being provided in this post, we have decided to use a common technique in providing the most sought after information effectively – (No it’s not related to Big Data!) It’s simply called providing a 'sequel'. So keep visiting to find the next one soon where we will talk a bit more about some other basic techniques and introduce the latest trends like Hadoop, Mashup, MapReduce in managing BIG DATA .…

Sources and references for detailed report and materials:

McKinsey report: http://goo.gl/ycvef

TDWI library reports on BigData: www.Tdwi.org

The “Bigness” of Big Data.

If everybody is talking about Big Data it must be something very cool, don’t you think? Every day the term is mentioned in newspapers, websites, schools, business meetings, conferences… Currently, Big Data is all over, and that is exactly why we are writing about it.

EMC^2’s video which we featured last week helped us a lot in understanding what Big Data is all about, now let us present our interpretation of this in-vogue tech concept.

Massive amounts of data which cannot be handled with conventional tools are Big Data (BD). Imagine analysing all tweets posted in one country in a day, using conventional data base tools, tricky, right? There is so much information available in our world that it is becoming very problematic to use it. Big data applications allow people or companies to solve problems, converting unprocessed data to useful information.

Two of the big players in this market, IBM and EMC^2 identify three main dimensions of Big Data:

·      Size: Big data is certainly big. Data is available in enormous quantities.

·      Speed: Data is generated extremely fast. To be competitive, users need to process and to analyse the data very fast.

·      Variety: Data come in many forms and from many different sources. (Dates, Names, Bank Accounts, Bar Codes, Videos, emails, Tweets, Web Sites, etc.)

Big Data is useful in a wide range of contexts, some of our favourite applications: Electronic Payment for private or public companies. Agile analytics in the Stock Market. Business Intelligence for new ventures. Security: predicting or detecting fraud, And Data Warehouse in Social Networks.

Big Data is a tool which creates competitive advantages in business. Getting updated information from many more sources, and processing data faster will enable companies to understand their markets better, to anticipate crisis, and to make intelligent decisions. Those extracting value of the existing and growing data will be ahead of the competition. That is for us, the “bigness”, of Big Data.

As always, more on this topic in the coming days…

The image used in this post is a piece named Electress by Nick Gentry. He is British artist who recycles tech products like floppy disks to create his paintings. http://www.nickgentry.com/index.html

How big, really is BIG DATA?

Are you still asking yourself: How big, really is BIG DATA?

Listen to Patricia Florissi who explains in an easy way some of the key concepts of BIG DATA on this didactic video.

Patricia is the CTO of EMC^2, one of the largest providers of data storage and management platforms in the world (Cloud Computing).  EMC Website

   

Some key facts to remember:

“…. By the end of the decade (2020) the amount of data generated will be 50 times the amount of data generated today.”

“… One of the not so secret secrets of BIG DATA is that it is fuelled by the very properties of the cloud…”

Watch the video

Comments & Contributions welcome!

Part 2 - Our Review of Big Data’s Impact on the Financial Services Industry

Big Data Brings Transparency, one of Wall Street’s Biggest Fears!

(Part 2 of 2)

What new Big Data has Become Available to Change Things???

Along with better systems to process large amounts of data, the introduction of FINRA TRACE trade data (A Regulatory Function) for many OTC securities is now readily available to the financial community.  What was previously a non-transparent market is now open information to everyone!  In some markets however, Big Data is leading to market inequality, with high-frequency trading many investors, mostly hedge funds, have access to data very rapidly and are equipped to have trading decisions made with algorithms faster than the rest of the market is able to (also referred to as “algo-trading”).  Although competitive regulation around this is in the works in many regions, it is questionable whether high frequency trading has an overall material impact on the markets.

Increased Transparency from Big Data is now REVOLUTIONIZING Traditional Bond Markets

Traditionally, OTC markets have been profitable for the Broker-Dealer community, since due to the lack of relevant trade transparency, they have been able to earn more profits through a higher risk premium and higher bid-ask spread, in some cases up to 2% of market value.  This means a Bond worth USD 2m, a single trade is roughly $20k of revenue for a nearly no-risk execution trade for the broker dealer that can take minutes to execute!  That profitability model certainly does not sound sustainable.

With the availability of Big Data including TRACE pricing and other relevant investment financial data (Bloomberg, Reuters, StatPro, etc), the justification for the Traditional Sales based Broker Dealer OTC market model is waning away for many asset classes.  Because of this, investors become more price sensitive to the fees they are being charged by investment banks and overall fees have dropped tremendously.

 “Corporate and sovereign-bond deals around the world generated a total of $13.6 billion in fees for bankers, down from $14.9 billion in 2010, according to data compiled for Bloomberg Markets’ ranking of the best-paid investment banks.”  BusinessWeek - SOURCE

In fact, many major global banks have closed shop on their OTC trading desks because they were simply not profitable.  For example, UBS closed the majority of theirUS desk in 2011.

How Banks Are Reacting to the Transparency of Big Data… 2 Financial Titans React with Bold Moves

Similar to the traditional stock exchange demise in the US, just last week, Goldman Sachs announced they were launching an online broker dealer interface that would swiftly undercut the fees of the traditional OTC street norms.  Roughly 2 weeks ago, another financial titan, Blackrock, announced they were launching a similar system under their Aladdin platform.  This means that in the last month alone, 2 of the largest and most powerful players in the financial services industry are realizing openly and committing time and resources to launching platforms that may announce the beginning of the end of the OTC nature of these markets.  These systems will match buyers and sellers and help alleviate the unnecessary costs of maintaining a costly sales force.  Other major banks on the street are expected to follow soon with competing products.

Big Data is making markets cheaper to trade, more efficient, more liquid and more transparent!  Net Impact is less transaction fees for the 99% to pay for

These are just initial efforts to re-work the OTC system and adoption by the investment community is still up for grabs and market share in this new market will bring out some fierce competition among banks.  Utilizing Big Data properly has made this possible and ultimately the end consumer (The 99%!) will reap the benefits of this by their investments having drastically lower transaction costs. 

More Reasons to Love Big Data to Come, Please check back!

Part 1 - Our Review of Big Data’s Impact on the Financial Services Industry

Big Data Brings Transparency, one of Wall Street’s Biggest Fears

(Part 1 of 2)

So last week we were a bit critical of Big Data, today we are going to share one of the many reasons why we love it.  Big Data is shaking up many industries and perhaps banking more than most!  When referring to the 99% regarding the Occupy Wall Street movement we personally think about failed banking oversight, government bailout packages, recession catalysts and excessive banking compensation.   Clearly there is room for improvement in banking fairness in the eyes of the 99%.  We think availability as well as proper utilization of Big Data in the Finance industry will overall help the 99% and we will tell you why in 1 word… TRANSPARENCY!  In many cases, Wall Street thrives on lack of transparency in order to hoard the little financial information available, and uses that info to profit against the 99%.

7 years ago, Big Data’s first major impact on Wall Street began, when Stock exchanges began to migrate towards electronic trading.  Average transaction fees went down, many specialist trading firms closed or consolidated, and the online brokerage model was able to thrive.  Big Data made this possible because it made information rapidly available to near automate the market making process.  These drastic improvements made possible by Big Data regarding transparency were only the beginning…

The “Subprime” Financial Crisis created an even Greater Demand for Big Data!

After the Subprime blow-up, increased regulatory standards were rolled out worldwide.   Risk management and process controls around financial services became a priority, and demanded better utilization of data.  Now, major investment banks have bulked up their risk management systems to a point where regulators are somewhat content as the regulation evolves.  The Big Data within major banks was not being utilized effectively and regulators needed to step in to put a universal risk framework in place to do so.  According to the notorious McKinseyBig Data Study that we love to refer to, Securities Trading and Financial Services have now generated and manage some of the largest numerical/text based databases in the world!

Big Data’s Next Major Impacts on Banking & Investments

“Together with a new breed of cloud-based portfolio analytics and performance management solutions, data aggregators are providing an IT solution to a client service problem. Key to these alliances is the ability to turn Big Data insights into a beautiful, dashboard-like view of portfolio performance so that both portfolio managers and their clients get a complete view of a portfolio’s performance and have total confidence in the completeness of the information that underlies the big picture.”  SOURCE  -Andrew Peddar, CEO StatPro America

This is Transparency, exactly what legitimate value investors love and banks fear!  As Big Data in a useable format becomes quickly and cheaply available to investors and financial markets, the OTC (“Over-the-Counter”) business model is being constantly questioned for more standardized asset classes.  These OTC securities are traded outside of a formal stock exchange because the products are specialized and often need sales people to interact with buyers.  Some asset classes that still remain OTC are corporate bonds, municipal bonds and many of the troublesome mortgage bonds that were at the heart of the credit bubble. 

Big Data is starting to Revolutionize the traditionally profitable OTC space for major Banks...Stop back to find out why in Part 2 of our Financial Review in the next couple days!

I Always Feel Like, Somebody's Watching Me...

As we already know, the technology to handle the “Big Data” is already here, or at least – it’s getting there.  The business potential is HUGE.  Today, we are going to play “devils advocate”, and talk about the “other, darker” side to big data. A side that is very easily overlooked, in the Big Data craze, which is sweeping the Internet and IT industry.

When we go online, we start leaving behind data, and this is where the Big Data technology comes in.  As we read our e-mails, surf our social networks, read articles, blog, share photos, shop online, and basically – click away our day, we are like Henzel and Gretel, “leaving behind breadcrumbs of information” – as Gary Kovacs – CEO of Mozilla Corporation stated in a Ted Talk that was published earlier this week.  Mr. Kovacs talked about the vast amount of information, which is being collected about each one of us. With every click of the mouse, more websites begin to follow us, only a small fraction of them being websites we actually visit, and the number of the websites following us grow exponentially as our day wears on and we expand our internet based activities.

Let’s compare this to a “real” world situation.  In most countries, lawyer solicitation is illegal.  If one were to get injured in a car accident, and the day after being released from the hospital, a lawyer would call him offering him legal representation, normally our response would be: “how did he get my number, did the hospital give it to him?”   In the real world we do not fail to see the extreme invasion of privacy and the in-adherence to a certain moral code – and in the real world, in most countries, such a lawyer would actually get disbarred for taking such action.

Now let us conduct a small experiment – go into your Gmail account, and draft an email to yourself.  Title the e-mail “car accident”, and in the body of the mail type in the sentence “I was involved in a car accident”. Now this varies from one user to another, but we are willing to bet that when reading the self-sent mail, you will see on the right hand side of your screen, ads promoting different aspects of the automotive industry, in our case, we got mostly ads for car repair services.   6-7 years ago we all thought this was pretty cool, well it doesn’t seem so cool now…  And again we would like to ask, why is this behavior, which is so intolerable in the “real” world, so accepted in the virtual space?

But surfing habit tracking is only a small part of it.  As businesses utilize Big Data tools more and more to analyze market behavior, and aid their human counterparts in making pricing decisions, more ethical issues are addressed.  Derrick Harris gives the example of landlords utilizing Big Data analysis in order to analyze rental market behavior and maximize rental prices and profit, while totally disregarding the human side of the equation.

Now don’t get us wrong, we love Big Data! We think its business and social implications are huge, and as we mentioned in the beginning of this post, we are only playing “devils advocate”, giving you something to think about over the weekend.

See you next week…

Big Data, Big Business, is the market ready?

Source: The Wall Street Journal

Last April 18^th perhaps the first Big Data specific initial public offering was made by Splunk Inc., a company that developed a software which helps companies to analyze data, but not any data, it’s ‘machine data’ as they call it. At the IPO, the company was valued at $3.28 billion, selling at a revenue multiplier of 28! For instance, Google trades at a revenue multiplier of 5 which goes to show how excited the market is about this Big Data technology. Splunk raised $229.5 million, and at the NYSE close, its stock price jumped 109%. The software that companies such as Splunk offer, help businesses to manage their increasing amounts of data and in this way avoid data inflation. Apparently, the market is getting very excited about this new IT trend, but are companies really prepared to handle these loads of information?

It seems that what firms are requiring nowadays are data scientists. It’s not only about having the engineering skills to build complex mathematical models to process the data, but also about being able to shape the data in order to get a story from it. Once these skills are fulfilled, a deep understanding of the business is also needed in order to be able to ask the right questions from which to make the data work for them. Big Data will give decision makers more information on which they can rely on, but just having more information may overwhelm them and it will not mean they will be able to use it in the right way towards the business objectives.

It is clear now that there will be a rising demand for these data scientists, however the supply side does not seem to be moving at the same rate. Universities still do not teach courses which prepare people for this, and they can hardly be found at recruitment agencies. Nevertheless, just as 30 years ago IBM started a generation of Cobol programmers, it seems as though market forces will lead to a generation of data scientists to cope with the massive amount of everyday data that is being accumulated.

More about this can be found in: 

http://on.wsj.com/Jq8okx

http://bitly.com/I506fo

What actually is Big Data?

New technology and innovation often bring about new terminologies. With Big Data, this is exactly the case. But what does Big Data really mean?

It appears that so far there is no standard definition for the term Big Data. A search reveals that various explanations have evolved over time.

■       In 2009, Adam Jacobs described Big Data as “Data whose size forces us to look beyond the tried-and-true methods that are prevalent at that time” in his interesting article “The pathologies of Big Data” (http://queue.acm.org/detail.cfm?id=1563874) Jacobs argues that getting stuff into databases is easy, but getting it out (in a useful form) is hard; the bottleneck lies in the analysis rather than the raw data manipulation.

■       In 2011, IBM, which has the Big already in its nickname "Big Blue" in turn focuses on the three V’s on its definition of Big Data

• Volume – Big Data comes in one size: large. Enterprises are awash with data, easily amassing terabytes and even petabytes of information.
• Velocity – Often times-sensitive, Big Data must be used as it is streaming into the enterprise in order to maximize its value to the business.
• Variety – Big Data extends beyond structured data, including unstructured data of all varieties: text, audio, video, click streams, log files and more. (http://www-01.ibm.com/software/data/bigdata/)

IBM is one of the pioneers of bringing Big Data analyses to their customers. I highly recommend taking a look at their eBook titled “Understanding Big Data”

(https://www14.software.ibm.com/webapp/iwm/web/signup.do?source=sw-infomgt&S_PKG=500016891&S_CMP=is_bdebook1_bdmicro)

■       Recently, the McKinsey Global Institute, the research arm of McKinsey and Company pointed out that no specific threshold can be set for amounts of data to be accounted for as Big Data by saying: “Big Data” refers to data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. This definition is intentionally subjective and incorporates a moving definition of how big a data sets needs to be in order to be considered as Big Data - i.e., we don’t define Big Data in terms of being larger than a certain number of terabytes (thousands of gigabytes). We assume that, as technology advances over time, the size of data sets that qualify as Big Data will also increase. Also note that the definition can vary by sector, depending on what kinds of software tools are commonly available and what sizes of data sets are common in a particular industry. With those caveats, Big Data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes). The consultancy also provides insides into the financial opportunities associated with the topic. Check out their report.

(http://www.mckinsey.com/Insights/MGI/Research/Technology_and_Innovation/Big_data_The_next_frontier_for_innovation) 

What do all these definitions have in common? They highlight that existing approaches to collecting, handling and analyzing data no longer help companies to gain a competitive advantage. In contrast, new approaches are needed to take into account the exponential speed of change. It seems that Big Data calls for

     a) Radical thinking

         and

     b) Willingness to deal with uncertainty

We will investigate these points further and keep you posted!

BIG DATA - Overview

This blog is a project for INFORMATION TECHNOLOGY & INNOVATION with Professor Kiron Ravindran at IE Business School.  It has been created by the following IMBA (April '12) student group members (Nationality): Beatriz (Colombia), Matan (Israel), Jaime (Peru), Tom (USA), Denny (Germany) & Vikas (India).

With this project we want to communicate about "BIG DATA", an emerging technology and its application in business. In weekly blog posts we will compile articles about Big Data and give our opinions about this IT topic. The Blog will also be a way of sharing information about Big Data with other users interested in the subject. It will be updated weekly from today 12/04/2012 to the end of June 2012. 

So stay tuned and we look forward to reading your comments!

"LIKES!" That’s what we expect anxiously whenever we post a new picture on our Facebook accounts. Ever wonder how many such likes, comments or tags, on how many pictures or videos are saved and available for us and our friends to see every second, minute, day, month or even a year? And now think of all those 800 or so million users on Facebook thinking exactly the same way as you are right now. Well that’s Big data we are talking about, isn’t it?

When we, the mundane people are having a hard time comprehending the logic behind storing such massive amounts of data, it is worthwhile sparing a thought for establishments who need to do so in order to remain in business. Now, also imagine, what if the top modelling agencies of the world were willing to spend millions of dollars in order to scout the next Tyra Bank or Gisele Bundchen from the mediocre photographs on such social networking sites? What? Now it’s totally worth it?

Through this blog, we set out to explore the virtual worlds where our lives are stored in - Big Data. The terabytes, exabytes or even zettabytes of data that’s collected, stored and consumed. In the upcoming posts, we will also try to touch upon the various aspects of these consequential and/or random yet potentially highly useful information (Big Data) being collected from all walks of life.