SigmaWay Blog

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

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