This is the age of Big Data and the amount of data surrounding us is actually huge. The rate at which new data is created almost doubles every month. Some examples to show the trend of data driven decisions in almost every sphere are as follows. The big business of sports has led the charge. We're using our smart phones, watches, and other wearable devices to gather data about ourselves to better understand fitness, nutrition, health, and behavioral tendencies. Local and national governments are contributing too with significant movements towards transparent publication of data on websites. The approaching Internet of things -- as governed by new devices such as the Nest Thermostat, Quirky devices, or even the Waze service that uses consumers' GPS-enabled smart phones to gather information --have such companies as GE and Google making substantial investments based on their potential to both generate and find value in big data. Though there exists so much of data some companies still face problem in dealing with it due to existence of some challenges. Read more at: http://tdwi.org/articles/2014/07/08/turning-big-data-into-smart-data-1.aspx

The use of the statistical software R in healthcare analytics is growing and has become quite widespread. Some reasons for appreciating R as the statistical tool are: It is an open-source software. There are several graphical user interfaces like R Studio. The R user community is very large and always there to answer any conceivable question. The availability of numerous packages that add capabilities ranging from machine-learning to Six Sigma quality improvement; if you need it done, chances are that somebody’s built a package that does it. The capabilities and features of R are to expand and has future scope due to its active user base. To know more the use of R in healthcare analytics, follow the link: healthcareanalytics.info/2014/05/get-up-to-date-with-r/#.U95q6OOSyBk

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

License plate scanning technology has been around for decades - the British police originally adopted it in the 1970s to track the Irish Republican Army members, but it only came into wide use in the last decade as cheaper but highly effective models became available. These scanners use high-speed cameras and optical character recognition technology to capture up to 1,800 plates per minute, even at high rates of speed and in difficult driving conditions. The scanner also records the date, time, and GPS location of each scan. The explosive growth of license plate readers and large police-compiled databases that store information about  for arbitrary periods of time probably going up to indefinitely period of time. One of the problems with this practice is that different states have different policies on how such data can be used and shared — some states put strict controls on the use of this information, while others have what amounts to an open door policy on driver information.

License plate readers have become vastly more popular in recent years thanks to falling prices, federal funds, and an aggressive marketing campaign from device manufacturers. In theory, they’re a great way to find stolen property, track fleeing criminals, or keep an eye on felons with a high risk of re-offense. At present, however, there are virtually no limits on data retention, usage, or who has access to the information. As it is getting become more popular, an increasing number of police departments are deploying them on patrol cars as well as at fixed locations.

A license plate can be tagged to a particular vehicle, registered to a specific person. There are two problems with this argument. First, the police aren’t just using these readers to track known criminals — they’re building associative databases of people who have never been charged with any crime on the grounds that such information might be useful in the future. Not only does this have a known chilling effect on people’s actions, it opens the door to profiling groups of people based on the erroneous belief that doing so will help identify future criminals. 

As things stand right now, most of these databases are open to anyone who wants a look at them. Sure, your boss can’t technically fire you for your political affiliation, but he can check and see where your car was when Obama came to town last time, or whether it was picked up outside a polling station on the day of election results. Then, come next performance review, you’re out of a job with no idea why. To read more visit: http://www.extremetech.com/extreme/161604-police-departments-and-data-mining-companies-team-up-to-track-license-plates

For healthcare Analytics one of the greatest contributors of data is mobile phones. Not only doctors but even patients access their Smart phones or tabs to track healthy behaviours. But the problem is the risk increasing simultaneously. Although Short Message Service (SMS) can be a quick and effective way to communicate, there are definite drawbacks to the use of SMS. First of all delivery of a Short Message service is not guaranteed. Also, the Joint Commission has, in essence, banned physicians from using SMS for any communications that would result in the transmission of ePHI [electronic protected health information] data or orders for a patient to a healthcare organization (such as hospital or other service). So a safer alternative like Secure Messaging should be used instead. It enables secure and protected transmission of healthcare information that employs bidirectional encryption of point-to-point delivery of messages, stores information on a secured network server, and ensures delivery of the message to a single known receiving entity. Read more at: : http://healthcareanalytics.info/

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

Nowadays powerful connected devices are enabling everything from aircrafts to cars, to industrial machines, providing better user experience and saving time and costs through improved operations. The key to this machine intelligence lies in advanced analytics. The Internet of Things (IoT) connects new places such as manufacturing floors, energy grids, healthcare facilities and transportations systems to the Internet. This ensures more data gathered from more places and more opportunities to leverage that data through real-time predictive analytics to improve business outcomes. In the automotive industry connected devices are bringing new levels of intelligence to ensure safety as well as convenience. New ecosystems of connected machines have the potential to increase efficiency with insights to make smarter decisions. Other emerging areas are also witnessing rapid growth of connected devices. These developments are leading to improved safety, security and loss prevention. The three steps companies can take to embrace IoT are- capture and extract data, leverage an analytical engine and give back the insight. Read more at:

http://www.informationweek.in/informationweek/perspective/297298/companies-unlock-value-generated-connected-machines

Traditionally business have relied on graphs and charts to analyse crucial information. But these basic visualizations have a propensity to miss two of the most important aspects of a retailer’s data — where things are located and what is happening around them. Imagine being able to better understand where customers live, what they buy, what they do and why they do. Location analytics is a game changer. It helps organisations see where data is, not just what it is. Location analytics brings together dynamic, interactive mapping; sophisticated spatial analytics; and rich, complementary data to enhance the overall picture of business operations. Best of all, it is available from within already-established analytics software, so there is no need to say goodbye to familiar business tools or workflows.

The combined solution joins key business intelligence (BI) data with spatial location, resulting in improved store performance driven by better marketing decisions. It covers all stores operated by the group to guide expansion and development strategy, optimize direct marketing actions such as distribution of weekly circulars, monitor store performance, and gain a better understanding of the sales territory. Moreover, it helps in viewing and analysing data, including traditional retail information such as trade and mailing areas, competition analysis, customer locations, and advertising hoardings. Geographic data used includes Bing Maps, Nokia data, and aerial and satellite images. A BI map service’s bi-directional link provides a unique and dynamic integration solution between the mapping and BI systems.

The geo-marketing application is used for many strategic activities such as guiding expansion and development strategy of the company and optimizing direct-marketing actions including distributing weekly circulars store performance can be monitored and a better understanding of territories can be provided. All this information feeds one database and can be shared across the enterprise. Location analytics is enabling a refined and deeper understanding of how to improve marketing and other store-level operations. It enriches data for a more intimate understanding of customer relationships, behavior, and need. See more at: http://blogs.hbr.org/2014/03/how-location-analytics-will-transform-retail/

Billions of dollars are lost by gamblers every year along the Vegas Strip, but some casino operators are taking strides to soften the blow of serious gambling losses and leveraging big data to keep customers coming back, according to one executive.  "They could win a lot or they lose a lot or they could have something in the middle. So we do try to make sure that people don't have really unfortunate visits," said Caesars Entertainment Chairman and CEO Gary Loveman on Big Data Download.  Caesars and other casino operators offer loyalty programs. As gamblers spend, companies gather data on those spending trends. Customers also receive tailored incentives for gambling and spending. 

"We give you very tangible and immediate benefits for doing so. So we give you meals, and hotel rooms and limousines and show tickets. You share with us information on what you've been doing, what sorts of transactions you've made," said Loveman, whose company is the biggest U.S. casino operator.

Caesars in particular employs about 200 data experts at its Flamingo Hotel alone. They scour through data on the types of games customers have played, what hotel they've stayed at and where they've been dining. So the next time when you visit a casino, expect a suddenly friendlier slot machine after you are on a losing streak.

Read the complete report here:  http://www.cnbc.com/id/101027330

Social media now holds a place alongside print and broadcast as a major, essential marketing channel for businesses. As such, social media now should be held to the same standard as those channels: your social media ROI needs to contribute to your bottom line. To prove that your social media investment is truly warranted, you need to track how social is influencing every interaction you have with your clients.

The first step involves setting social media goals that complement existing business and departmental goals. If you have set a specific number of leads you’re trying to attain this quarter, set the number of leads you want to specifically be driven by social media. If one of your goals is to increase landing page conversion by say 10%, ensure that you’re tracking the conversion rate of people who land on the page through social channels. Audit your existing social media performance to establish baseline targets, and then set appropriate goals for improvement.

Once you’ve established your social media goals, you’ll need to identify and implement the tools and processes required to measure the ROI on your social media. This may involve adding tracking codes to URLs, building custom landing pages, and more. There are a variety of social media analytics tools which service to track the diverse metrics you are after.

Once you’ve identified what works and what doesn’t work on social, it’s time to adjust your strategy. The point of tracking your social media ROI isn’t just to prove your social campaigns are valuable, it’s to increase their value over time.

Due to the short lifecycle of social media campaigns, a failing campaign should be changed and improved as soon as possible. Social media is never static. To meet your social media ROI goals, you’ll need to constantly update and adapt your strategy taking into account the analytics data you’re tracking. To read the full article visit:http://blog.crazyegg.com/2014/02/10/social-media-roi

Cloud Computing connects large pool of resources through private or public network and this technology makes infrastructure planning easier. Cloud Computing provides dynamically scalable infrastructure for cloud based applications, data, and file storage. Businesses can choose to deploy applications on Public, Private, Hybrid clouds or the newer Community Cloud. Public clouds are provided to the public by a service provider who hosts the cloud infrastructure. Public cloud providers like Amazon AWS, Microsoft and Google own and operate the infrastructure and offer access over the Internet. With this model, customers have no visibility or control over where the infrastructure is located.All customers on public clouds share the same infrastructure pool with limited configuration, security protections and availability variances. Private cloud is cloud infrastructure dedicated to a particular organization. Private clouds allow businesses to host applications in the cloud, ensuring data security and control, which is not ensured inn a public cloud environment. Hybrid Clouds are a composition of two or more clouds (private, community or public) that remain unique entities but are bound together offering the advantages of multiple deployment models. A community cloud is a multi-tenant cloud service model that is shared among several organizations and governed, managed and secured commonly by all the participating organizations or a third party managed service provider. To know more go to: https://blog.zopim.com/2013/11/20/3-ways-analytics-transforming-customer-service/

The year 2014 tends to be an important year where technology discovery will further build a future in which companies make data-driven decisions. The Top 5 data analytics trends that companies believe are going to rule the industry are:

• Data Visualization Goes Mainstream-Visual analytics allows business users to ask interactive questions regarding their prepared data sets which makes the whole process engaging.

• Mobile Data Marches to the Top- The top priorities for companies will be defining mobile metrics that matter, understanding mobile technology and collecting and analyzing mobile data.

• Analytics in the Cloud Grows Up- Innovations like cloud data warehouse platform from Amazon will gain importance  enabling fast and secure solution at very cheap prices.

• Predictive Analytics Takes Center Stage- the increasing demand for business users to examine data for decision making, provide the base for predictive analytics to gain significant ground in 2014.

• Internet of Things -- It’s everywhere! - Companies that are doing a great effort in product design and development will emerge as the first winners as they adopt through innovative marketing.

Read more at: 

http://tdwi.org/Articles/2014/01/28/5-Data-Analytics-Trends-2014.aspx?Page=1</a