Big data analytics is increasingly playing a role in the fight against crime. Publicly shared information combined with data from local authorities, social services and intelligence gathered by beat officers is helping police forces around the world spot trouble before it starts. It helps the police be much less reactive, and slowly starts to reveal the real trouble spots and troublemakers in a neighborhood, estate or street. When information like that becomes clear, the police can do something about it long before anyone dials 999. And that counts for people as much as it does for pubs or clubs. Law enforcement is finding new ways to use technology and big data against crime. CCTV cameras are no longer impotent. They are commonly used in police cars and carried by officers to create a permanent digital record of everything going on around them. 

 This will make it harder for criminals to commit crimes. In recent years we have witnessed criminality moving off the streets with a huge increase in the amount of credit card and online identity fraud. But even there new big data algorithms are being developed to detect fraudulent behaviors in real time.

Read more at: http://smartdatacollective.com/bernardmarr/199521/big-data-analytics-and-criminals

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

Research shows that fraud against bank deposit accounts cost the industry $1.744 billion in losses in 2012. Debit card fraud accounted for more than half of 2012 losses (54 percent), followed by check fraud (37 percent). According to Prakash Santhana, a director in the Advanced Analytics practice for Deloitte Transactions and Business Analytics LLP, there has been a significant increase in the number of cyber-criminal groups who are trying to get their hands on customer lists, personal identification data, and anything else that could be of economic value. Some strategies for fighting fraud are listed below: Continuous tracking of online and face-to-face transactions to avoid any unauthorized ones. Development of “chip and PIN” technologies. The implementation of additional controls within ERP platforms that require dual approval on all payments to vendors. Read more at: http://deloitte.wsj.com/cio/2014/07/30/fraud-trends-in-the-banking-industry

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

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

With the advancement of measurement and analytics, the pressure on marketers to demonstrate return on investment also advances. According to McKinsey & Company, an integrated marketing approach is essential to uncover meaningful insights and drive growth. Integrated measurement reduces biases in any one measurement method and enables leaders to identify which activities produce the best return.  Standout measurement approaches include: • Marketing mix modeling. Marketing mix models quantify the sales impact of various marketing activities and determine the effectiveness and ROI for each.

• Media measurement. Marketers can measure the reach, cost and quality of components to assess performance — more specifically, the number of target consumers reached, the cost per unique touch and the quality of engagement and/or media placement. 

• Attribution modeling. Attribution modeling, or crediting converting traffic to online touch points, has become increasingly important for media buying and marketing execution.

In order to boost marketing ROI companies use: 

• Marketing mix modeling to track how well each activity generates audiences; 

• Attribution modeling to pinpoint which activities within the marketing mix generate the most conversions (such as search vs. display marketing in digital)

• Media measurement to monitor marketing activities via print, which will help it capture a new audience and generate more revenue.

Although some companies rely on one analytical technique, organizations that use an integrated measurement approach will see the optimal ROI.

Read more at: 

http://www.cyberalert.com/blog/index.php/using-integrated-marketing-measurement-to-make-better-decisions/

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

When one starts a new project that involves analyzing his company's data especially when the data is stored across functional areas, that person is in trouble. The data lake model helps in this case. To get access to data doesn't require an integration effort, because data is already there in the lake and one can apply MapReduce and other algorithms to use it. In the lake some data are unstructured or not structured by us for a given project. To construct a data lake one needs to learn some of the Hadoop stack such as Sqoop, Oozie and Flume. Next a data scientist should be found who understands Hadoop as well as business and the company’s business data in particular. Then one should start with basic cases and use simple and familiar tools like Tableau to make nice charts, graphics, and reports demonstrating that he can do something useful with the data. Next security up front should be considered, as well as who can access what data. Use of core Hadoop platform is beneficial. Apart from this one should keep in mind that lake security may have business unit implications and one should not have a lot of mini lakes i.e. data ponds that are separate and not equal. Read more at:http://www.infoworld.com/d/application-development/how-create-data-lake-fun-and-profit-246874?page=0,0

One can use the big data, available in hand, in a right or a wrong way. Here is the list of top 10 worst big data practices which one should try to avoid. First, though MongoDB has an aggregation platform, it is not good as an analytical system and thus should not be used as big data platform. Second, RDBMS schema is used as files by many which should be avoided too. Third, creating a series of data points. Fourth, failing to develop use cases. Fifth, over-dependence on Hive should be reduced as the whole point of big data is to expand beyond what one could do with one technology. Sixth, it's not right to treat HBase like an RDBMS. Seventh, trying to install Hadoop and all its moving parts on 100 nodes by hands is also a worst practice. Eighth, one should also avoid RAID/LVM/SAN/VM-ing one's data nodes. Ninth, instead of treating HDFS as just a file system one needs to think about how one is going to secure all of this and for whom. Finally, everyone is free but each one should have a plan. Read more at:http://analytics.theiegroup.com/article/53c925453723a81857000073/The-10-Worst-Big-Data-Practices-

Massive amount of climate data waits to be  interpreted by the press, public agencies, and general public and the challenge lies in finding analytics software that is easy to use, and produces understandable results, and can handle the volumes of big data available. However, to get a handle on the sort of governmental data on climate and resources that often resides in generic comma-delimited files, Circle of Blue, who specializes in reports on the global competition for water, food, energy in a changing climate, uses QlikView Business Discovery Platform. Such visual analysis software helps users to view, explore, and interpret the data with little technical training. Circle of Blue hopes to make the public more informed about the vulnerability of water supplies in the era of climate change by merging technology with on-the-ground reporting and online networks. With the use of Qlik View platform, it becomes easier for them to deliver data to a wide range of people. QlikView dashboards work with the large data sets to produce sophisticated, engaging, and state-of-the-art graphics. The data can be scaled to compare local information with national and global trends and that information, in turn, can help in formming public policy discussions.

Read more at:http://tdwi.org/Articles/2014/07/08/Massive-Climate-Data-Awaits-Analysis.aspx?Page=1

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

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

In today's world data is not only growing at a very fast rate but it is also being accessed and processed at unprecedented rate. So now, businesses have to focus on the velocity of Big Data, acting on it with precision for real-time results. This is where Fast Data comes in. Fast Data can provide real-time insights from events as and when they take place and help to make a decision at that place and point in time. Fast Data is complementary to Big Data for managing large quantities of real-time data. There are many examples of why Fast Data is becoming increasingly important. In a telecom industry, a Fast Data approach can help telecoms manage resources more effectively. In the financial services industry, Fast Data is using event correlation to contextualize available financial data. In retail industry, customer service centers are using Fast Data for click-stream analysis and customer experience management. Healthcare is another area where Fast Data holds huge potential. Read more at:

http://www.informationweek.in/informationweek/perspective/296992/speeding-business-transformation-fast

Research Company Gartner suggests that there will be 4.4 million big data jobs available in the next two years. Everything is moving towards data: big data, mobile datasets.  Creating and implementing an analytics program requires four steps: 1) Defining your metrics and developing a plan 2) Collecting the data 3) Developing reporting features and capabilities 4) Ongoing analysis and implementation. Understanding each of these core components enables a company to make the right investments at the right time. Corporate data culture is a spectrum that can often be classified as follows: 1)No or limited data 2)Basic data 3)Deeper data that's soiled or controlled 4)Democratic data access. The idea is that your strategy should take the following points into account: 1) Data collection and reporting. 2) Analyzing the data, articulating the implications for business. Useful data tracking comes down to evaluating: 1) who is coming to your site, and what are those people doing once they get there. 2) What channels are driving buying customers? 3)Who is converting 4)What conversions are deepening relationships 5)What conversions are driving revenue 6)Who is buying multiple times 7)What's your lifetime customer value 8)What are your churn rates. Any solid analytics plan will take your business model into account and develop a set of metrics that maps to your unique needs and buying funnel. Read more at:

http://www.forbes.com/sites/jaysondemers/2014/02/10/2014-is-the-year-of-digital-marketing-analytics-what-it-means-for-your-company/

Today,banks are using analytics to control frauds in electronic payments. Private banks such as HDFC Bank have implemented analytics software. The thing is that the out-of-the way transaction would decline if one fails to respond to a phone call or message immediately after the transaction. There are two kinds of fraud detection in payments- One is during the transaction, and the other is using analytics to identify suspicious transactions based on past behaviour. Banks are now specializing in the analytics part. According to an official of an analytics software company that provides banks with software to detect frauds, the software can be used to personalize services, like ATMs, for customers depending on their preferences. Read more at:http://timesofindia.indiatimes.com/business/india-business/Banks-use-analytics-to-check-fraud-in-online-payments/articleshow/38347410.cms

Successful companies in 2014 are expected to take Big Data Analytics and utilize it for growth and competitive advantage. Experts discussed how to use data strategically for a strong analytics culture at the webinar "Fine-tune your analytics strategy for 2014". According to Stephen Sharpe, Director of Global Strategic Analytics of Johnson & Johnson, companies need to start working on a cloud based system to integrate and broader integration and training across the various sectors to share best practices. According to Larry Seligman, VP, Advanced Consumer Analytics, Inter-Continental Hotels group, companies need a new definition of data integration and they need to learn how to value projects and programs and quantify the things which were previously not quantifiable. According to Mazhar Hussain, Leader Big Data Practices, HP, gives insight on the importance of analytics as a service. Mid-sized and small customers will take great advantage of analytics as a service which is going to take up big stream and it is important to have the right kind of strategy implemented in the company. Read more at:

http://www.modernanalyst.com/Resources/News/tabid/177/ID/2915/6-things-to-focus-on-in-2014-analytics-strategy.aspx

Those in the managerial level want data to back up assumptions. We can’t formulate decisions on a raze sensation, or what we recognize our rivals are doing, or what we believe our consumers desire. A lot of time companies have no perception of their purchase. So to ignore taking bad decisions, data is used to back it up. Analytics is checked to see what public are doing. So what are business analytics and how do we acquire them? At this level of analytics we need to know our rivals, and what their digital outlets are. We require a digital fingerprint of the rivalry, and we should also have a good knowledge of our market. What people are wanting and giving the services we propose. Information of the industry is very important to make a plan to assess the true data, and then make an approach that originates our expedition  . Read more at:

http://www.socialmediatoday.com/content/whats-going-your-industry