SigmaWay Blog

The investment banks are now looking at how they can use big data to do what they do better, faster and more efficiently. Senior executives at the banks want to enhance how they use data to raise profitability, map out markets and company-wide exposures, and ultimately win more deals. Big data is also a fundamental element of risk-profiling for the banks, enabling data analysts to immediately assess the impact of the escalation in geopolitical risk on portfolios and their exposure to specific markets and asset classes. Specifically, banks have now built systems that will map out market-shaping past events in order to identify future patterns. There lies the requirement of big data talent!

The banks are actively recruiting big data and analytics specialists to fill two main, but significantly different roles: big data engineers and data scientists. Data Scientists are responsible for bridging the gap between data analytics and business decision-making, capable of translating complex data into key strategy insight, while Data Engineers typically come from a strong IT development or coding background and are responsible for designing data platforms and applications. The competition between banks and fund managers to hire big data specialists is heating up. Data scientists are expected to have sharp technical and quantitative skills. They are in highest demand and this is where the biggest skill shortage exists.

To read more, visit the link given below:

http://www.computerweekly.com/opinion/Investment-banks-recruit-for-the-rise-of-big-data-analytics

Cricket and Analytics are hot topics today. With the help of technology Analytics, the interest quotient of cricket has gone up exponentially. High definition telecasts, data visualisation have really made cricket exciting to watch nowadays. Vijay Sethi, VP and CIO at Hero MotoCorp Ltd, speaks about how he sees his favourite game today that comes with a generous dose of analytics. To read more, visit the following link:

http://ibnlive.in.com/news/analytics-in-cricket-is-a-technological-marvel-hero-motocorp-cio-vijay-sethi/536201-3.html

Google Analytics is one of the best tools one can use for the purpose of analyzing the audience. With the right insights, one can modify the approach, anticipate customer needs, and create a superior user experience. It is also possible to measure a tremendous diversity of data on your web users, and using that data, you’ll be able to make meaningful change to your branding and marketing strategies. The acquisition insights and behavioural insights will help you explore more details and trend understanding. This information is especially useful in determining which of your marketing strategies is the most effective, but it’s also useful for determining what types of people are visiting your site and why. For example, if you find that the majority of your users are finding your site through content you’ve syndicated on social media, you could double your content writing and syndication efforts to attract an even greater number of users.

To read more, please visit the following link:

http://www.forbes.com/sites/jaysondemers/2015/03/19/how-to-analyze-your-audience-in-google-analytics/

Analytics has now become an integral part of cricket. Before the match begins, there is a series of analysis about the pitch, weather conditions, weather predictions, history of two teams against each other, history of the teams on the particular ground, in the particular country, among others . Similarly, each player of the teams gets analyzed in great details, in terms of his records, his performance against the opponent, on the pitch, his strengths and weaknesses. Each player is also analyzed as a fielder in terms of the history of his catches taken or dropped. Strategies of captain get predicted and its possible impacts are discussed. The list is just endless. Cricket is no longer only the game to be played on the ground. Much of it happens off the ground, using analytical tools.

Satish Pendse, president of Highbar Technologies Ltd, an HCC group company, explains how he sees cricket today with technology impacting the viewing experience. To know more, click on the link below

http://ibnlive.in.com/news/cricketnext/real-cricket-action-happens-off-the-ground-with-analytics-highbar-tech-cio-satish-pendse/533130-78.html 

Information available about consumers on the web is enough to scare marketers. There are large rewards for those who confront and harness big data. Big data can create problem for brands. Information collected from large, complex data sets weblogs, social media, smartphone analytics and even medical records is difficult for brands to manage and process within traditional database systems. According to a report from the McKinsey Global Institute, Big Data is the next big thing for innovation, competitive advantage and productivity. But, some companies are missing this opportunity because of a lack of data management expertise. Retail is one industry that has a great potential for big data. Customer transactions (on & offline), conversations and intentions can all be brought together so that brands can get ideas how to reach potential consumers. Read more at: http://analytics.theiegroup.com/article/53a2f6693723a807f3000029/Big-Data-Embracing-The-Elephant-In-The-Room

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/

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

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

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

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

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

Analytics for Small Medium Businesses (SMB) today, is a much discussed topic. SMBs face the challenges of effectively using analytical tools to gain precious business insights from data generated. Today markets are able to provide solutions to SMBs which were costly before and this was made possible by the advent of Qlikview, Tableau etc. in Analytics sector. SMBs are realizing that analytics can help them understand customer preferences, expand their market share, cut down cost, increase efficiency and give them a competitive advantage even against the big players. Moreover, with the advent of new technologies like cloud, social media and open source platforms like Pentaho and Hadoop, the requirement for big infrastructural set-up and capital cost have been reduced considerably. The success of Analytics tools depends to a large extent on collecting and managing data and in such case ERP and CRM tools are a must for success. For successful implementation of analytics tool, SMBs need to assess the external market as well as their internal systems and processes. However, SMBs will soon be able to adapt their systems to bring in the external big data from social media like any other big enterprises and hence make their analytics more robust. Read more at:http://www.informationweek.in/informationweek/perspective/296888/value-analytics-businesses