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

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

Today companies have vast amounts of informations available to carry business moves. The General Mills and IBMs of the world now are using complicated-and expensive-Business Intelligence (BI) systems that can combine data together from a multiple operating systems and generate best reports that highlight not only what has occured and what is happening, but also what will probably happen. Can Small Business Benefit From Business Intelligence Software?

• It can help a small business compete with larger competitors or enhance market share.

• Vendors are getting experienced at making software that’s affordable.

• BI vendors are starting to educate a younger crowd. 

• The cloud now puts non-IT users in the driver’s seat. 

• Before investing in BI tools, one must know what answers they are looking for.

• Forget about using BI software if one don’t have access to best operational data.

• BI is increasingly going mobile. 

 Read more at:

http://www.inc.com/articles/201109/business-intelligence-software-for-small-business.html

Master Data Management (MDM) programs often start with the analysis of the available technology and products but not the business problem they tend to solve. Three primary categories of MDM benefits are operational efficiency, better business intelligence, and regulatory compliance. The following are ten things one can do now to prepare the organization for  MDM initiative:

• Set goals and success criteria.

• Prepare a business case.

• Build a governance process.

• Choose the first subject areas to attack.

• Carry a data inventory.

• Recognize executive sponsorship.

• Identify business champions.

• Set up an educational forum.

• Educate yourself about products and approaches.

• Develop a business model of the data to be handled.

Read more about this article at: 

http://esj.com/Articles/2008/06/25/Ten-Tips-for-Master-Data-Management-Success.aspx?Page=1

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

20-30% of the business decisions really need the use of advanced techniques like predictive analytics.   70-80% of marketing decisions can be judiciously carried with simple analytics techniques. A CMO broadly expects 3 key outcomes for his business:

• Bring more “future” customers in the most cost-effective manner.

• Convert those who come to the door into customers.

• Maintain the current customers “buying.”

Predictive Analytics need advanced skills and constant maintenance. A product manager or an operations manager equipped with the right “Data to Decisions” framework and easy access to data can optimize 80% of their daily workflow on their own, without having to depend on little and costly analytics resources. For 20% of decisions, where the potential ROI justifies the use of advanced techniques, they can work with their analytics counterpart. In summary, a smart CMO knows that a marketing team equipped with a “Data to Decisions” framework and easy access to data without the company of a data science team would emerge much better than a marketing team lacking data skills supported by a large data science team.  Read more at: 

http://www.forbes.com/sites/piyankajain/2013/05/26/the-8020-rule-of-analytics-every-cmo-should-know/

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

There is a blending of the system administrator and network administrator roles as a result of emergence of cloud computing and software defined infrastructures. Historically, the demarcation of the two, happened at the RJ-45 socket on the NIC. Anything that involved the cable, switches, routers etc. was not the concern of the sysadmins. Likewise, if the light was blinking on the NIC, anything happening inside the box was not the problem of network admins. Now, sysadmins, particularly application administrators, must now be cognizant of network technologies and operations, network admins who want to keep networks in top shape must now have an awareness of what application traffic is flowing across the network and how to design and implement networks to support those needs. Thus the roles are merging and in the near future, they both can be together known as "cloud administrators". Read more at: http:

http://www.informationweek.in/informationweek/news-analysis/297170/rise-cloud-administrator

Social media is an ever changing scenario where social media marketers are increasingly using predictive analysis to ensure longevity. Various brands are using predictive analysis technologies to trawl through social media chatter to identify upcoming trends. It will also ensure that your brand be one of the first few to take advantage of the trend and gain maximum exposure. Your social media campaigns will also be much more refined compared to those of brands that don’t use predictive analysis. With predictive analysis, your brand will be able to pick out the right news, items, etc. that could become the next big thing on social media landscape, giving you ample time to prepare. Now smart brands are realizing that predictive analysis can be used in social media marketing to understand what consumers are looking for. Predictive analysis tools ensure that brands understand consumer behavior on social media.

Read more about this article at:

http://www.simafore.com/blog/bid/205332/How-Predictive-Analytics-Can-Boost-Your-Social-Media-Campaigns

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/