Big Data Analytics is all about turning volume and variety of data into meaningful insights. When data is refined and combined, new patterns and ideas emerge, and one can take better decisions using these insights. Big Data is being used across almost all the sectors these days. In the Online Apparel Industry where success of next season's collection hinges on selecting the accurate designs, colors, fabrics, shapes, and sizes, Big Data can be a big game changer. Online apparel industry is mostly influenced by predictions that are based on identifying the most popular/liked parametric values (colors, fabric, style and many more) of the apparels. If you predict it right, it may bring a profitable season for you, else it may lead to heaps of discarded inventories. For many years, analysts and fashion reporters have tried to control these drifts. It is a great advantage to recognize customer preferences that will lead into high prospect ratio.

One of the ways to understand customers’ emotions behind the interactions made on social media sites and other forums is sentiment analysis. Sentiment analysis scans tweets, comments, likes, etc., for evidence of positive, negative, or even indifferent impressions to identify the overall trend of sentiments towards any entity. For example, possible positive expression on a personal level would ideally be like – “I like to wear plain cotton clothes in summers…” to an opinion projected in general - “I am looking for some cool blue apparels for my next vacations as it feels comfortable.”

Similarly, negative expressions may go like – “I am fed up of seeing bright yellow apparels all around in summers.” Or an opinion in negative tone may be like - “People look damn horrible in yellow.” A range of tools and methods are available to help determine customers’ sentiments; one of the ways to track is using the Twitter Sentiment APIs.

Big data is also extremely useful in a marketing capacity, using information like customer demographics and spending habits, in terms of how much they spend, on what and where. In addition to these habits, companies that invest in cloud computing studies can monitor how their existing marketing strategies are working - eye scanning data can be analyzed to see the effectiveness of billboards and other visual advertising. Every aspect of the business will change, from what color will be in next season to how to make clothing that fits different body types and how to optimize  supply chains.

Read more at: http://www.gogrid.com/news/2014/07/23/cloud-computing-public-cloud-big-data-how-big-data-will-change-fashion-industry

Big data is the basis for business intelligence, which is about taking all that information and turning it into knowledge to drive better business decisions. Whether its data about retail consumers or homebuyers, it's all the same game.  The business intelligence industry has been analyzing large data sets in corporations for years — decades, really. It’s only now coming to the real estate industry. The amount of data used in the real estate industry isn’t that large. A single major retailer will generate more sales data in a year than the entire real estate industry will in a decade. However, it’s all relative, and the real estate industry is still trying to figure out what data it has, let alone how to use it.

The point is that big data in real estate is about presenting a “whole consumer” picture. It’s about using data to find out who buys what, when, where, why and how. It’s about finding out who will sell a house — when, where, why and how. 

All that data can be used to create tangible insights into consumer behavior using forecasting and modelling software. It’s the analysis that makes the magic happen, that is identifying customers or providing them better services. Analytics is where raw data and the algorithms that crunch it come together. Mining census information, the results of consumer surveys, listings of homes for sale and rent, geographic information systems data and more combine what they draw from numerous databanks with their own proprietary user-generated content. The tools can deliver to consumer’s information about their property's potential value and help them understand home-value trends within a particular milieu, such as a neighborhood or a ZIP code. 

Beyond the consumer and industry-facing aspects of big data, institutions such as banks can plug into big data resources to determine whether a foreclosure or short sale is really worth what a buyer or investor might be offering.

For now, the analysis of big data is likely to stay with those who gather it and companies willing to pay for access, such as the lead generation companies. What real estate agents need to know now is that the data is there and it’s available, in some form or another, to those who are willing to use the right tools.  Read more at: http://mashable.com/2014/07/09/big-data-real-estate/

Big data has the automobile in its sights and the results will be good for both the vehicle and its owner. In the coming years we can expect to see both safer vehicles and car-to-car communications. You'll be advised of a needed repair before a problem and recall notices will be delivered through the car. Ford gathers data from over four million cars with in-car sensors and remote application management software. All data is analyzed in real-time giving engineers valuable information to notice and solve issues in real-time, know how the car responds in different road and weather conditions and any other forces that could affect the car. Ford is also installing numerous sensors in their cars to monitor behavior. They install over 74 sensors in cars including sonar, cameras, radar, accelerometers, temperature sensors and rain sensors. As a result, their Energi line of plug-in hybrid cars generate over 25 gigabytes of data every hour. This data is returned back to the factory for real-time analysis and returned to the driver via a mobile app. The cars in its testing facility even generate up to 250 gigabytes of data per hour from smart cameras and sensors. 

Big data is also used to find out how people wanted their cars to be improved. Nowadays, Ford listens carefully to what their customers are saying online, on social networks or in the blogosphere, and performs sentiment analysis on all sort of content online and uses Google Trends to predict future sales.

Internally, Ford uses big data to optimize its supply chain and to increase its operational efficiency. From the parts before they reach the Ford factory, to the car waiting in the dealer for a customer, big data has infiltrated every part of the supply chain, creating large amounts of data. With so many different parts coming from so many different suppliers, it is vital for Ford to get a complete and detailed overview of all parts within the supply chain at any moment in time. To read more visit: http://www.bigdata-startups.com/BigData-startup/ford-drives-direction-big-data/

In the era of Big Data, the fight for protection has as of now been battled and lost. The personal data is routinely gathered and exchanged and there are few powerful controls over how it is utilized or secured. Data scientists and analysts are now saying that now is the right time for enactment to recover some of that protection and guarantee that any information that is gathered remains secure.

We have become the product and are being productised and sold to anyone. We’re being monetised and mobilized as products with inducement of the services of we use such as Facebook and Twitter. The dilemma that the regulators are facing is how they can regulate the collection, storage and trading of personal data on the on the internet, when all of these activities, and the corporations themselves, operate across multiple continents and jurisdictions.

The task of reclaiming some semblance of privacy is all the more urgent because the rate at which personal data is being collected is accelerating. The buzz around big data is attracting millions of dollars of from investors and brands hoping to turn a profit, while intelligence agencies are also furiously collecting information about our online activities for much different purposes.

And alongside these, there’s also the black market operators that make millions of dollars a year out of things like identity theft and matching disparate data sets across the web to help identify people who might be suitable targets for a scam. 

New privacy principles were recently passed into law which required all businesses earning more than $3m annually to disclose to customers how their information was being stored and used, however the new legislation stopped short of mandating compulsory data breach notifications for businesses who fall victim to security violations.

A bill that would make it illegal to hide security problems was set to pass into law last year, however it failed to make it through both houses of the Senate before the election. And since the Coalition took power, the legislation has stalled. 

Still, there are many privacy challenges ahead, and the problems have by no means been solved. Most methods of anonymizing do not scale well as p or n get large. Either they add so much noise that new analyses become nearly impossible or they weaken the privacy guarantee. Network-like data pose a special challenge for privacy because so much of the information has to do with relationships between individuals. In summary, there appears to be “no free lunch” in the trade-off between privacy and information. To read more: http://www.theguardian.com/technology/2014/jun/20/little-privacy-in-the-age-of-big-data

With more than 120,000 people in need of organ transplants and a shortage of donors, economists, doctors and mathematicians are using data to save lives. On a very basic level, the organ transplant process can be separated into two categories: organs taken from living donors and organs harvested from deceased donors. From living donors, doctors can take one of a person's two kidneys, as well as part of his or her liver. From a deceased donor, doctors are able to extract a cadaver's kidneys, liver, heart, lungs, pancreas, intestines and thymus. Of the organs donated in 2013, roughly 80% came from deceased donors, according to UNOS. While it's preferable to receive a kidney from a living donor, the donors and candidates are incompatible in approximately one-third of potential kidney transplants because of mismatched blood or tissue types. In the case of incompatibility, a candidate is placed on what's commonly referred to by the public as a "waiting list".  UNOS receives information from both the candidate and the deceased donor to establish compatibility such as blood type, body size and thoracic organs, like the heart and lungs, need to be transplanted into a similarly-sized recipient and geography as it seeks to match candidates locally, regionally and then nationally. With that data, UNOS' algorithm rules out the incompatible. It then ranks the remainder based on urgency and geography. For example, a liver made available in Ohio would theoretically go to the closest compatible candidate with the highest MELD score. 

In 2010, UNOS launched its Kidney Paired Donation Program that used Sandholm and his team's algorithm. So far, the program has matches have resulted in 97 transplants, with more than a dozen scheduled in the coming months. To read in detail visit: http://mashable.com/2014/07/23/big-data-organ-transplants/

Convenience store retailers may eventually reach a point of diminishing returns and so they are trying to find out ways to use transactional promotional and loyalty data in a better manner. we could get valuable insight from Big Data by deciding what type of data streams could combine to provide insights. According to Jim Manzi of the analytics firm Applied Predictive Technologies, Arlington, VA, if retailers want to understand how certain business choices affect the bottom line, Customer Data, Transaction Log Data, Weather Information, Area Demographics and Competitor fuel pricing must be prioritized. Full-motion video from all stores, High-volume website clickstreams, and Raw Twitter feeds are less important. According to Manzi, tweeter feeds are not that important for analysis as they cannot help to out the cause and effect on key-metrics. There is a "first law of big data usefulness," said Adrian Bridgwter a contributing editor at Forbes magazine. The first law says, "The degree to which we take the exact depth of big data analytics is directly determined by the corresponding level of insight it produces and where we can still say that we gain 'productive incremental value' from doing so." Businesses like convenience stores gather a lot of information for regulatory purposes, which could ultimately be analyzed as people grow in their technological sophistication, Bridgwater said. Read more at:

http://www.cspnet.com/industry-news-analysis/technology/articles/what-first-law-big-data-usefulness

To monitor, predict, and fight wildfires like the one currently affecting the University of California at San Diego and the University of Maryland, with support from the National Science Foundation (NSF), are in the process of building an end-to-end cyber infrastructure (CI) for that challenge called WIFIRE. It is designed for real-time and data-driven simulation, prediction, and visualization. WIFIRE combines satellite data and real-time remote sensor data with various computational techniques to forecast the rate at which wildfires might spread. Many scientists, engineers, technologists, government policy makers, private companies, and firefighters are a part of the project team involved in architecture and implementation. Some prototypes and pilot applications already are available, although the project is in its first year. The vision for WIFIRE is to put in place a programmable, scalable, and reusable wildfire modeling framework. The project is part of the NSF Hazards SEES program. When fully developed, WIFIRE will be accessible to users via specialized web interfaces and alerts broadcasted to receivers before, during, and after a wildfire. Read more at:

http://www.informationweek.in/informationweek/news-analysis/297321/helping-tackling-wildfire-control

Analytics and big data are on the verge of scoring major points in sports. The following are eight ways data analytics can improve efficiency, accuracy and profitability in sports:

• Better Precision in the Strike Zone- In baseball, Pitchf/x technology from Sportvision has been set up in Baseball Stadiums to have a track on pitches.

• More Resources for Analytics Buffs- Statistic enthusiasts have a series of websites they can visit to see different aspects of specific games and plays.

• Data From Wearable Technologies- Adidas has a system called miCoach that works by attaching a wearable device to the players's jerseys to gather data.

• Live on the Field Data Collection- A Company; SportVU has six cameras in each NBA arena which gather data on the movements of the basketball 25x per second.

• Predictive Insight into Fan Preferences- Analytics can advance the sports fans' experience as teams.

• Career Opportunities for the Blended Sports Fan and Numbers Whiz- Bryan Colangelo, former president of the Toronto Raptors, says "There are mountains of opportunity in analytics now"

• Influence Coaching Decisions- Data analysts could help to transfer the most requisite data sets to coaches for better outcomes on the field.  Read more at:

http://www.cio.com/article/2377954/data-management/8-ways-big-data-and-analytics-will-change-sports.html

Big data analytics has attracted the interest of the security community for its promised ability to correlate and analyze security-related data judiciously and at unprecedented level. New big data applications are beginning to become part of security management software because they can help prepare, clean and query data in incomplete, and noisy formats efficiently. Fraud detection is one of the best uses for big data analytics. One of the main results from big data technologies is that they’re supporting a large variety of industries to build affordable infrastructures for security monitoring. In particular, new big data technologies are enabling the analysis of large-scale, heterogeneous datasets at unprecedented scales and speeds. Now big data tools are improving the information available to security analysts by consolidating, correlating and contextualizing diversified data sources for longer periods of time. Big data tools are also particularly suited to become basic for advanced persistent threat (APT) detection and forensics. Hence big data is changing the landscape of security technologies for network monitoring and forensics.  read more at: 

http://www.infoq.com/articles/bigdata-analytics-for-security

There has been a transformation and a creation of positive impact on the social and economic relationships across the stakeholders. Thus companies today need to treat Big Data and analytical tools as an asset. However data presents inherent challenges in adoption. Companies are grappling on how to contextualize information, and presenting the data in a lucid manner is another challenge. Without making inroads into old processes and investing in the right kind of people to tap into data's hidden potential, a company can never fully realize the scope data offers. According to several research studies, Big Data is a top business priority that can transform processes and organizations. Thus a good data miner always finds a right balance between machine and man. Data visualization is another important tool to help management and reflect how businesses and markets are changing. To know more, please follow:

http://www.informationweek.in/informationweek/perspective/297284/-challenges-opportunities

Nowadays we are witnessing an enormous data explosion which is set to continue and even accelerate. The volume of data is growing at a very high velocity and is rapidly becoming more varied, complex and less structured. As a result, the word Big Data has grown strong on the mind of every business leader who wants to extract critical insights and business benefits from data. Many organizations are planning to implement Big Data related initiatives or have got them already. However most organizations lack an articulated strategy for Big Data execution. Thus there is a strategy gap between high potential and risk about investing in Big Data initiatives. Although, the essential mix of technologies may deliver on the promise of Big Data, what leaders must choose and incorporate for interlocking the set of available data sources and technology is a specific business goal which makes the initiative unique. In response, Big Data providers have prepared a strong ground through the use of cloud computing at the core which address these issues. Organizations can analyze the feasibility and cost of investing. Termed as "Big-data-as-a-service" (BDaaS), it basically refer to services that provide analysis of enormous or complex data sets, typically over the cloud platform as a managed service. The adaptation of Big Data on above grounds precedes with Hadoop which was a major stepping stone, but it still has its own limitations, specifically for Small and Medium Businesses (SMBs) that do not have the resources to create a Hadoop infrastructure in house. Thus, on a conclusive note, It's difficult to predict which Big Data solution businesses will freeze on, but having a majority of Big Data service providers now providing a version of their platform in the cloud, it will emerge a safe bet for SMBs to venture, in wherein cloud will play a major role in building Big Data an integrated part of their business strategy. Read more at:

http://www.informationweek.in/informationweek/perspective/297257/cloud-leveling-playing-field-smbs

While some industries are beginning to see the transformational capacity of big data and predictive analytics, these methods haven't quite panned out for supply-chain managers. The reason is that the largest obstacles happen to be the cost of hiring experienced employees. Researchers Matthew Waller and Stanley Fawcett write in a paper that the convergence of predictive analytics and big data has the capacity to change the way in which supply-chains managers lead. The goal is to increase the understanding of how to utilize big data efficiently and develop a new breed of supply chain leaders that are experienced in using data and analytics judiciously. A recent Wall Street Journal article quoting a survey by The Economist points out that while most companies see the value in using predictive analytics and big data to eliminate increasingly complex issues within their supply chains, they still perceive the cost of deployment as too high.Read more at: 

http://sloanreview.mit.edu/article/are-predictive-analytics-transforming-your-supply-chain/

Over the past two years, one of the seminal issues regarding Big Data was storage, especially with respect to the exponential growth and size of unstructured data that did not fit into databases. Today, however, the competitive landscape is very different. Proper storage is merely a pre-condition to finding the real jewels in Big Data-turning data from massive streams into knowledge, and thereby actionable intelligence in real time as events unfold. The following five steps are imperative to master Big Data and drive business growth:

1. Infer, Infer, Infer- Inferences transform data into knowledge, which results in greater process transparency and improvements.

2. Empower a C-Level Data and Predictive Analytics Champion. - With big data analytics changing rapidly and straining information structures, corporations and governments need “executive horsepower” behind its data initiatives.

3. Assess And Modify Your Supply Chain In A Multidimensional Global Context. - Analysis of supply chain will ultimately include relationships with parties such as customers, manufacturer, etc. 

4. Give Your Data Time-Critical Situational Awareness. - Analytics help a business line identify potential points of improvement.

5.   Rely On a Core Platform That Creates Derivative Intelligence and Knowledge in Real Time -statistical inferences can turn data into actionable intelligence that supports reasoned decisions. Read more at: 

http://www.forbes.com/sites/benkerschberg/2014/01/03/five-steps-to-master-big-data-and-predictive-analytics-in-2014/

Consultants and pharmaceutical companies alike control the market intelligence channels to better understand their target patient population. Big Data offers a lot of opportunities to optimize commercial strategies from helping to identify opportunities for new therapies to assessing the success of current products. Surveyed companies are using Big Data initiatives to better target products or to assess the performance of products already on the market. Pharmacy companies are more likely to focus Big Data initiatives on current products, mainly on the success of drugs on the market. Many surveyed consultant companies use Big Data to assist in developing new therapies compared to the pharmaceutical companies. As data become more useful and the benefits more obvious, the prevalence of Big Data-driven market intelligence initiatives will continue to increase. A decision on regulations guiding social media marketing in particular also help companies decide which market intelligence strategies to attack. From the earliest to latest stages of drug marketing, companies are working quickly to discover the opportunities posed by Big Data. Read more at: 

Big data is very much in rage these days and social media networks are some of the largest sources of big data. There are billions of posts, connections and shares which can be analyzed. Buried in this social data are insights that can help to progress advertising and give the aggressive edge. It can be expensive and take a lot of time to analyze social data.What are the benefits we are looking to achieve- Improving audience, positive feedback and praise, more engagement in conversations, better understanding of our target audience,converting people to customers and doing better than our competitors. Read more at: 

http://www.socialmediatoday.com/content/how-use-social-analytics-improve-your-performance

A Survey, based on the responses from 111 data scientists in US, found that Hadoop is too slow according to 76% of data scientists as they believe that the open source software framework requires too much effort to program and isn't fast enough to keep up with big data demands. On the other hand almost 91% of the survey respondents claim that they are performing complex analysis of data on the basis of which 39% of overall respondents say that their job is getting tougher. However, Big Data is becoming highly important for all enterprises. According to a research commissioned by Dell and conducted by Competitive Edge Research, a big section of midmarket companies with 2,000 to 5,000 employees are embracing the rise of big data and almost 80% percent of the midmarket thinks they need to better analyze their data, as they believe big data initiatives provide a significant boost to company decision making. Read more at:http://analytics.theiegroup.com/article/53baa9d23723a81e1300007b/Survey-Finds-Hadoop-Is-Too-Slow-Big-Data-Is-Too-Complex

The healthcare analytics market is expected to grow at a CAGR of more than 25% over the forecast period 2014-2019. Increasing healthcare IT adoption, centralized healthcare mandates across the globe, emerging fields of predictive, prescriptive analysis and venture capital are the factors driving the market growth. Digitization of world commerce, the emergence of Big Data and increase in the number of advanced technologies are other growth providing factors. Factors hampering the growth of the healthcare analytics market include lack of skilled labor with analytical skills, data securing and patient data privacy. North America holds the largest share of healthcare analytics market driven by US centralized healthcare mandates such as Meaningful Use and The Patient Protection & Affordable Care Act (PPACA). These initiatives assist to improve the acceptance of Electronic Health Records and Healthcare Information Exchange, thus improving the usage of analytics to influence the generated data. Read more at:

http://www.fortmilltimes.com/2014/07/21/3616557/research-and-markets-healthcare.html

Big Data is a buzzing concept nowadays. When all people around the world are reviewing, commenting, tweeting, blogging, clicking pictures all about the same movie over the Internet, it makes a data worth billions of bytes. This data spread across the Internet is called the Big Data. According to McKinsey a business using Big Data to the full could increase its operating margin by more than 60 percent. Internet has provided businesses with new and profound ways to improve productivity. Companies will benefit from Big Data if they are able to extract unknown patterns from the data and use them in remodeling business activities. According to Weatherhead  University Professor Gray King, there is a Big Data revolution which is the fact that now we can do something with the data. As Gary King said “The importance of Big Data lies in improved statistical and computational methods, not in the exponential growth of storage or even computational capacity”. Read more at:

http://www.informationweek.in/informationweek/perspective/297095/unmasking-gold-internet

In the mid-1980s some of the big companies were trying to bring video telephone technology in the market but it was a big flop with the consumers. The market did not want video phones even though the technology existed. Today's banks have something at their disposal that the telecoms of the 1980s did not: big data and pervasive computing. The financial services industry is trying to create personalized banking so that it would use the right IT solutions and it would allow for robust predictive analytics- in order to use the banking features that will satisfy their customers and improve the bottom-line. The challenge is to understand how to have their data at their disposal into value. Stepping outside traditional platforms will help banks realize that they need to reevaluate self-service and customer engagement in this completely new environment. Banks need to make sure that they have a strategy around all self-service devices. Customers are ready to connect to banks over smart phones and tablets, from any location and at any time. For that to happen banks must use their customer feedbacks. Read more at:

http://www.informationweek.in/informationweek/news-analysis/297141/master-branch-online-platform-transformation

Today, with the rise of mobile devices and simple health trackers, people will soon be able to analyze their own health data themselves. The proliferation of mobile devices has helped liberate the insights from that huge amount of data organizations are collecting. For years, big data and analytics has been the solitary domain of the enterprise and today there is no shortage of people in analytics space, from traditional enterprise players such as Oracle, IBM, SAP Business Objects, to relative newcomers such as Roambi, Tableau, and Pentaho. While businesses are analyzing big data to make decisions, individuals will soon be able to analyze big data to improve their own lives. Consumer can also choose which fitness band to use to check calories, number of steps, activity level, heart rate, sleep patterns, and so on. With this type of data collection, real time biometrics could help in reaching out alerts to doctor so that it can save lives. New innovations will allow individuals to compare their health metrics to others in similar demographics. Thus, analytics along with the interconnection between mobile device, wearable devices and appliances, we will soon have access to greater insights to improving our health.

Read more at:http://analytics.theiegroup.com/article/53a7f76e3723a85c3a0000a1/The-Health-Tech-Revolution-Will-Turn-All-Of-Us-Into-Big-Data-Wonks