Results show that over 75 percent of the organizations have gained big payoffs with the application of big data and analytics in their organization. Also the Return on Investment (ROI) has increased within six months of application. Certainly executive support as well as their involvement in analytics is vital to value creation since in organizations with low levels of executive support, analytics implementations are hampered by lack of funding, resources and follow through. Besides, strong governance and security are important in instilling confidence in the data, and trust is necessary. Also the direct factor which has implication on organization's value is the trust between people within an organization. This is not trust in the quality of the data but the old fashioned trust that is earned by getting to know someone's character and what they are capable of delivering. The level of trust - a belief that others will do a competent job, deliver on promises and support the organization's best interest - among executives, analysts and data managers significantly impacts the willingness to share data, rely on insights and work together seamlessly to deliver value. Read more at: http://www.informationweek.in/informationweek/perspective/286293/roi-about-trust

Big data is a key infrastructure in the Internet of Things (IoT), but it's far from the only piece of the fabric. In the coming global order, every element of the natural world, and even every physical person can conceivably be networked. Everything will be capable of being instrumented. If you think that the world of driverless cars, robots carrying out maintenance in hazardous locations like oilrigs, or advertising that reads and responds to individuals' unique facial expressions sound like science fiction. As these trends come to fruition, each of us will evolve into a walking, talking, living beneficiary of the Internet of Things. These are all developments happening today and they're prompting a new exciting phase in analytics that needs to be addressed now. Those that embrace data will be more likely to be surfing on top of the wave of creative destruction, instead of having it crash down on top of them.

Read more at: http://blogs.computerworld.com/business-intelligenceanalytics/23447/internet-things-what-it-and-what-does-it-mean-analytics

Data is more than just power. Organisations across all industry verticals are upgrading their data management systems, investing in new resources, and using their rich databases to streamline the practices of their departments. Indeed the message from experts is clear: organisations that fail to adapt and evolve to meet the emergence of big data, face the prospect of falling behind. As with any phenomenon, however, there are lessons to be learnt. 

The way data is being used in sports is a poignant example. Moneyball, the popular book inspired by Oakland Athletics manager Billy Beane, explained the core philosophy of the manager’s vision for the baseball team: using statistical analysis to maximise player acquisition and performance with a low budget. 

The Moneyball philosophy had huge ramifications for the sporting world. People started adopting variants of it in all sports – from soccer to basketball to football. Arguably the most noticeable application of Beane’s philosophy was by Andy Flower, the former England cricket coach. Flowers was known for his admiration of Beane’s work, and he too would use statistical analysis to not only determine who would be on the field but also what decisions players should make once they were selected and enjoyed notable victories also. Both of them have stood by data analytics and the benefits it can bring. Yet, what is often untold is that data was both a virtue and a vice for both men.

His 5-0 defeat at the Ashes last year was one of England’s most disappointing performances to date. As commentators suggested, it was a classic case of overreliance on data, replacing intuition with numbers, and allowing data to dictate rather than inform. Flower ultimately got the balance between trusting people and numbers wrong. He was in good company, those who thrive will not be those who use data most—but those who use it most smartly. But data is emphatically not a substitute for intuition and flair - either in the office or on the cricket field.

These instances of sports analytics are particularly relevant for organisations looking to add big data analytics to their existing operations. The example of Beane and Flower show how data does not have all the answers, and relying too heavily on it can have devastating effects.

Read more at: http://www.espncricinfo.com/magazine/content/story/724435.html

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/

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

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

In spite of being important, big data analytics is yet to be deployed successfully by most of the organizations. Many companies are struggling with how to maximize big data, and properly incorporate the results into something substantial. Results of the survey showed that the investment in analytics was growing rapidly. 64.4 percent of those surveyed said that their firm is investing more in analytics. However, just 12.6 percent of respondents said their company has completed several big data projects. One reason that prevents organizations from moving forward despite understanding the benefits of big data analytics, is the shortage of expertise in the field and with such lack of big data skills organizations are reluctant to take the plunge. It is also a major concern to keep sensitive information from the gathered big data, secured. On the basis of Big data analytics businesses should conduct their own research and see what options best fit their needs. However, technological innovation should be pursued to make big data analytics accessible to ordinary business users as without such innovation business could be left behind. Read more at:http://analytics.theiegroup.com/article/53a04cf93723a81d72000021/Is-Big-Data-Just-A-Big-Problem

The impact of big data in health care is tremendous and it has potential impact on every person as well. It helps in the advancement of disease diagnosis and treatment. Big Data is able to determine whether men need to undergo prostate cancer surgery or not, also can assess the risk of heart disease later in life, based on our health status as teenagers. Genomics, the genetic information, aims to discover the basis of heritable traits and understand how genes work to prevent disease and we may soon be able to see Web-based patient profiles that aggregate genomic data with other types of Big Data and produce "risk map" mobile apps that people can download to a smartphone. If it is about the hospital treatment, then also comes the importance of big data which requires the integration of information including admissions, records, nursing, diagnostic imaging, rehabilitation and home care. Researchers around the world are investigating ways to access, analyze and apply Big Data in healthcare. Corporations are looking for ways to use it to support their product development. Moreover, regardless of whether it's how patients are treated in the hospital or how they keep themselves healthy at home, they are learning about, interacting with and embracing Big Data .

Read more at:http://analytics.theiegroup.com/article/53a31b043723a81ea9000096/Making-It-Personal-Why-Everyone-Should-Care-About-Big-Data

With the advent of web and social media the speed of the evolution of language has increased dramatically. There are many contributing factors to language that affect the changes. Big data takes linguistics to the next level and the technology like Hadoop helps in assisting interested parties in gaining deeper and clearer insights into linguistics. The reasons why Linguistics should be understand are that- Firstly, to benefit from the insights into linguistics provided by big data whether it may be vocabulary or grammar or something else. Secondly, today's technology continues to develop and improve, the use of voice commands for phones, TV's and game systems is going to increase and it's more important that developers understand the language people will be speaking to their devices in order to ensure the responsiveness. Big data will greatly enhance their ability to provide such speech oriented aspects. Thirdly, in case of learning a language and the way it is learned, understanding of linguistics matters a lot. Finally, to understand the past and looking to the future, it is again important to understand linguistics. With big data technology, the huge amount of data and information can be gathered and used to provide better insights into the past and future of language. Read more at:http://analytics.theiegroup.com/article/53bd6b6d3723a864d8000023/The-Impact-Of-Big-Data-On-Linguistics

Despite having the technological advancements which makes the process of job finding easier and user friendly, the whole thing is not so easy and in many cases it's more complicated. Here comes the role of Big Data which is now making its way into the field of recruitment and helps recruiters to find best people for the right positions. Some companies receive thousands of resumes for a single post and with such a huge number of resumes, companies are engaging in people analytics, applying big data analytics practices to a field of prospective job seekers. Many businesses build their own resumes of candidates by identifying details from people's social media profiles on Facebook, LinkedIn, Twitter, and other sites. From these profiles, companies use big data to identify patterns of behavior, interests, skills and attitudes that they are qualifying factors for current and future job openings. So, job seekers need to manage their profiles to get a job. But, as a negative impact, there are concerns over relying on it too much and as a matter of fact a heavy use of big data also takes factors like race, gender, and religion out of the equation. However, despite the drawbacks big data is of vital importance and job seekers should put themselves in a position to take advantage of big data and utilize it to get noticed.

Read more at:http://analytics.theiegroup.com/article/53b522e83723a80d7e000065/The-Modern-Job-Hunt-How-to-Beat-the-Big-Data-System

Insurers are today trawling big data about customers’ finances and other sources. The development shows how access to big data is revolutionizing the insurance industry and companies are accessing a wide range of information on everything from financial probity to shopping habits of the customers to determine the risk premium for individual customer. One official of AA insurance company said that the winners in the insurance market will be the ones that have got the data insights that others don’t have. It could be supermarkets, banks or social media companies. However, ultimately the big data trend means some policyholders will pay more than others not for the climate change only but also for insurers to know more about the risks posed by particular properties. But, as with the sensitive data some companies also go far beyond what customers would expect them to do with it, customers should be given the option to opt in to such analysis than allowing them to opt out. Read more at:http://analytics.theiegroup.com/article/53c7a5c03723a8014f0000cd/Insurers-Trawl-Big-Data-For-Clues

The desire to be mobile and make a mark is not new to us. Today we use GPS for wherever we go and communicate on a variety of devices. Big data is the phenomenon which has helped in generating and sharing information. The difference between data in ancient times and now is that before, only humans created and collected data whereas now with the rise of sensors and other technology that creates and collects data. However, the big thing about big data is the self-organization i.e. without human intervention and awareness, data is organizing itself.  But, this leads to a big question that-Are we playing with fire? With big data revolutionizing, there comes a new responsibility, because the purpose of managing data is not to predict the future but to shape it which is a huge responsibility. However, revolution hasn't stopped. Changes took place slowly in the evolutionary manner. Using technology that provides insight into data, today's business leaders have a unique opportunity to make thoughtful decisions that will have long-lasting impacts. But along with all this, disruptive changes are happening in every industry around the world which increasingly making us concern about whether today's leaders rise to the challenge of shaping the future in a responsible way or not. Read more at:http://analytics.theiegroup.com/article/53c3f6413723a87216000156/Shaping-The-Future-With-Big-Data-Are-We-Playing-With-Fire

Big data contains consumer information including transaction data, demographic information, buying patterns, CRM data etc., collected across multiple platforms. The data gives an insight to the customers' preferences of support options, desired communication mode, future buying patterns, impactful promotions, etc. Big data provides better customer service in several ways. Using predictive analysis tools organizations can now predict a customers' next move also. Big data using these tools helps the organization to predict customers' present and future preferences, drive real-time decisions, increase customer retention and increase profitability. More than 77 percent marketers agree that individualized messages and offers are more effective than mass messages and offers, which can drive engagement, boost sales and increase conversions. Usage of Omni channel marketing strategy increases client retention rates and bring superior financial results. Using data to create a cross-platform customer engagement strategy ensures highest customer service. A multichannel shopping experience shapes a brand's story generating revenue and customer loyalty. In two years smartphone users will be more than basic phone users, mobile service increases at the rate of 7 percent annually. Thus best customer experience is delivered through mobile channels for high performance organizations. Unable to ignore the potentiality of social media big brands register tens of thousands of social media interactions every day. There are wide range of options available. Communication through online communities reduces call center costs. Read more at:
http://it.toolbox.com/blogs/insidecrm/5-ways-big-data-can-enhance-customer-service-62054

Importance of Big data and Talent Analytics is a much discussed topic for the last couple of years. One just has to know how to use talent analytics as it can do a whole lot of difficult task like quantifying different behaviors, skills, intelligence, and mindsets of a HR. Talent analytics uses data in management decisions like talent acquisition, retention, placement, promotion, compensation, and succession planning. By analyzing the skills and attributes of high performers in the present, it enables organizations to build a template for future hires. Advanced software algorithms can identify talent and match it to an organization's needs. Some intangible aspects like social skills, flexibility, emotional intelligence, initiative, attitudes are now measurable- thanks to talent analytics. Along with these, new mobile apps also make talent searches a matter of anytime and anywhere. However, as it's still growing, Gartner predicts that the market for Big Data and analytics will generate $3.7 trillion in products and services and generate 4.4 million new jobs by 2015. Read more at:http://analytics.theiegroup.com/article/53b2b9c13723a81923000046/Talent-Analytics-A-Crystal-Ball-For-Your-Workforce

Today big data is able to change the way real-estate professionals, buyers, sellers and even banks think about transactions involving property. Companies, promoting services that plug consumers into big data real estate information which gives a better education and insight.There are five key ways through which big data is changing the game of real estate business. They are- 1. Big data helps to democratize data for the real-estate customer. Companies such as Zillow combine big data with real estate and offer services like- Mining census information, the results of consumer surveys, listings of homes for sale and rent etc. 2. Big data is not only providing new information to consumers but also new ways of looking at developments and community planning. From that gathered information, real-estate developers can learn what kinds of spaces work best in terms of tenant health, energy efficiency and other points. 3. Institutions like banks are also able to plug into big data resources. 4. As an expert real estate advisor knows recent sales, incentives, and inside secrets to getting the best deal, buyers should have to be careful and should start property searching with the help of big data sites. 5. Finally, Big data let the professionals know what visitors are doing when looking for real estate online and they adjust their paid and organic efforts based on this data daily. Read more at:http://analytics.theiegroup.com/article/53be79af3723a84f1000003f/5-Ways-Big-Data-Is-Changing-Real-Estate

Today, a smartphone app would tell one what to eat, how much to eat, when to visit a doctor etc. based on analysis of medical research, medical history of that person, family medical records etc. Quality of our health will be increasingly improved by the quality of data and the ability to bring it all together. The growth of big data in the health industry will only take place once privacy concerns are addressed because health data, unlike the marketing data, is lot more personal. Big data analysis is already being used to make diagnoses in some hospitals. In Canada, Toronto Hospital uses big data to detect blood infections in premature babies. It could save the American health care system $300 billion per year and the European public sector €250 billion, according to a 2011 report. Doctors today are using Watson, IBM's supercomputer, to keep up with health research and to leverage the latest breakthroughs. Big data analytics also could be used to follow epidemic outbreaks. For example, Big Data enabled doctors and scientists to learn so much about the Severe Acute Respiratory Syndrome (SARS), and how quickly it spread, within weeks of the World Health Organization's initial warnings. In such case social networks and mobile data are used to ensure the delivery of real-time information. Read more at:http://analytics.theiegroup.com/article/53b6b4c43723a83b82000035/Big-Data-Could-Help-Your-Health-If-You-Let-It