For the very first time in this computing era, it is made possible for machines to learn from experience and penetrate through the complexity of the data and identify associations between them, collectively known as cognitive analytics. This innovation works in a similar manner as of human brains. It processes information, draws conclusion and codifies behaviour and experience into learning. Cognitive analytics has the ability to process and understand exploding volumes of data in real time including data that may contain wide variations of format, structure, and quality. Instead of depending on predefined rules and structured queries to mine answers, cognitive analytics relies on systems that draw from a wide variety of potentially relevant information and connections to generate hypotheses. This process differs from traditional analysis in the way that more data is fed into a machine learning system, the system learns, which results in higher-quality insights and more accurate hypotheses. Read more at:http://deloitte.wsj.com/cio/2014/05/13/human-brain-inspires-new-cognitive-analytics/?KEYWORDS=analytics

Today, companies have the option of collecting information about consumers which was never available in the past. This information is collected through internal sources, such as company websites and sales records, and external ones, such as social media, smartphones and tablets. This vast amount of information on consumers is increasingly referred to as big data. When a consumer visits a website for the first time, cookies are sometimes uploaded on his browser containing a unique ID, making it possible for the company to identify him during his next visits. Once identified, it will be possible to link the consumer to all information the company stored about his profile, which makes personalized marketing possible. Today, because of prescriptive analytics models embedded into their operational systems, websites and apps can analyze consumer information in real time in order to offer personalized travel options instantly. In the next few years, we will witness a gradual move to 1-to-1 marketing in the online travel category, with each consumer treated in a different way in terms of the whole marketing mix. To know more about this visit:

http://blog.euromonitor.com/2014/05/big-data-unique-ids-and-prescriptive-analytics-revolutionising-online-travel-marketing-part-1.html  .

Today's big data and analytics efforts bring welcome relief to banks, insurance companies, healthcare agencies, non-profits, and other organizations that have habitually struggled with finding the most profitable customers and then selling to them. A new set of analytics reports can move these companies forward in connecting with their best customers. According to a research by Fractal Analytics, banking users are struggling in an industry where 40% of cardholders are inactive and 60% are unprofitable. Banks want to increase spending in its existing credit cardholder base, so that it can implement customer analytics framework that was once targeted at improving first-hand understanding of these customers' needs. Once the bank understood who its most profitable customers were, it developed a "genomic" understanding of how these customers spent their money and found that insurance and food expenses were among the leading "spend" categories. This enabled banks to plan and target promotions built around these major spend areas. By doing so, banks increased its value per customer while decreasing expenses on marketing campaigns, likely because the campaigns were better targeted. To know more about this go to: http://www.techrepublic.com/article/genomic-analytics-build-sales-by-finding-your-most-profitable-customers/ .

Mobile phones serve a dual purpose in the context of Big Data. Each mobile phone, non-smart phones inclusive, creates numerous types of data every day. These include call detail records, SMS data, and geo-location data. In case of smartphones, such devices also generate log data via the use of mobile applications, financial transaction data associated with mobile banking and shopping, and social media data from updates to Facebook, Twitter and other social networks. The volume of portable information and the velocity at which it is made is just going to build as both the worldwide population and cell phone infiltration rates ascent, and the utilization of online networking expands. When investigated viably, this information can give knowledge on client opinion, conduct and even physical development designs. Because of the sheer number of cell phones being used, Big Data specialists can tap versatile Big Data examination to better see such patterns cross over large population and sub-portions of clients to enhance engagement strategies and improve the conveyance of administrations. It gets to be especially valuable for examination purposes when joined together with outside information sources, for example, climate information and investment information, which permit experts to relate macro-level patterns to focused on sub-portions of clients. To read more: http://wikibon.org/blog/the-dual-role-of-mobile-devices-for-big-data/

Big models and big data have long been a feature of weather and climate modelling. Computer-generated global weather forecasts are initialized from millions of diverse observations from satellites, weather balloons, surface weather stations, ships and buoys. Data assimilation, the procedure of ideally mixing these perceptions into the estimate model, is the most computationally difficult part of making a worldwide conjecture, and is a basic component of forecast skill. The international climate modelling community has evolved interesting infrastructure and social institutions that enable a diverse community of interested users to obtain standardized results from leading climate models developed around the world, to capture aspects of climate modelling certainty and uncertainty and help inform decision-makers and the interested public.

Past the thriving information administrations industry, weather has huge monetary and well-being ramifications. Weather Analytics, an organization that gives atmosphere information, evaluates that climate affects more than 33% of overall GDP, influencing the farming, tourism, angling, amusement, and air transport commercial enterprises, to name simply a few. Dubious climate conditions likewise affect little entrepreneurs. Moreover, public safety is of vital concern when officials aim to understand the impact of extreme weather events such as hurricanes, tsunamis, or wildfires. To know more about this aspect go through Per Nyberg (Senior Director of Business Development at Cray)’s article link: http://www.informationweek.com/big-data/big-data-analytics/3-ways-big-data-supercomputing-change-weather-forecasting/a/d-id/1269439

At this point when individuals consider saving rare species, they consider remote jungles, researchers and individuals anchoring themselves to trees. The stereotyped thought is that creatures in the wild are extremely hard to track and that the main way that individuals do this is through a basic following framework with a little specimen making presumptions for the more extensive group. Big Data and the complexities of data analysis could not be further from this, with the collection of massive data sets combined with complex predictive models and algorithms creating insights. The idea that enough data could even be collected to make a useful analysis is hard to imagine.  However, this has changed as of late as HP have collaborated with Conservation International (CI) to make Earth Insights. This system has been intended to give an early cautioning framework for creature numbers amongst jeopardized species over the world. Through the utilization of cameras and atmosphere sensors, the framework can gather information from around 1000 of these gadgets and use it to group data on population numbers. To know more about this aspect go through Dan Worth (news editor of V3)’s article link:http://www.v3.co.uk/v3-uk/news/2318103/hp-big-data-tools-help-wildlife-charity-save-the-planet 

With the advent of healthcare industry, the flow of data has increased by leaps and bounds. Now with the presence of analytics, huge number of unstructured data can be easily analysed to find out patterns and behaviours which in turn helps the companies associated with healthcare to take more sound and logical decisions.

To know more kindly visit:-

http://www.healthcareitnews.com/news/healthcare-data-goes-big-great

Recently, Tibco made an announcement to acquire Jaspersoft for some $185 million and D&B announced that it has acquired small analytics startup Indicee. This shows that companies in order to be more focused and remain valuable to customers, it is very important to provide more value through insights from big data. Please go through the link for more details: http://www.forbes.com/sites/benkepes/2014/04/30/suddenly-everyone-wants-to-be-in-cloud-analytics/

Many companies are trying to figure out how to make the best use of big data and which skills are most needed when trying to get useful business insights out of unstructured data. With hard technical skills, managers are also looking for some more unexpected skills too. Creativity, developer background, business intelligence expertise, data science expertise are some of the important skills which a manager looks for. To know more on this, go through the article by Colin Barker, senior reporter for ZDNet.

http://www.zdnet.com/what-skills-do-companies-really-want-on-their-big-data-team-7000025945/

When talking of Big Data, most of the times we mean business. But now, Big Data can improve your health too. Yes, it’s true.

Big Data is increasingly playing a role to modernize biomedical researches. Scientists, researchers and clinicians are leveraging the power of Big Data to generate aggregate of digital data through DNA sequencing, biomedical imaging and transforming a patient’s health record electronically.

With the help of Big Data, linkages can be found out between various genes, diseases and traits. Given that there is a huge database of genetic variants associated with diseases, like that of VARIMED, a list of gene-disease pairs can be established. This can be tracked down to the traits by developing disease – trait associations, depending on which a prediction can be made whether an individual will develop a particular disease.

To know more on how Big Data can play a role in improving our health, read at http://www.govexec.com/excellence/promising-practices/2014/05/how-big-data-can-improve-your-health/84904/?oref=voices-top

For emerging markets, exploring data to predict trends is not an easy option, mostly because of less availability of data in new fields. However, organizations in the emerging markets have the option of gradual progress. They have to start with analysing small data and then there will be scope to move into big data in near future. Problems for companies trying to do business in these areas are limited access to information and appropriate analytic modelling techniques. Gartner reports that companies are predicting 40 to 60% of their growth in revenues will come from emerging markets over the next decade, and McKinsey & Company predicts that in 12 years, 57% of the nearly one billion households with earnings greater than $20,000 a year will live in the developing world. "Companies need analytics to cope with these new markets," said Richard Howells, vice president of Go to Market for SAP. To know about this aspect, please visit the following link: http://www.techrepublic.com/article/start-with-small-data-in-emerging-markets-to-get-to-big-data-analytics/#. 

In a recent study it was found that more than 50% of revenue for small businesses comes from repeat customers. It is important for small businesses to retain their customers rather than acquiring new ones. Customer loyalty program is a good tool for small organizations to retain customers. Successful customer loyalty program requires authentic customer information. A digitally run and managed program will efficiently explore the data available and will in turn provide deeper insight about customers. For more details please visit the following link: http://www.businessnewsdaily.com/6310-big-data-customer-loyalty.html

Big data can improve sales and services by analyzing. Hence it is important for an organization to find big data and analyze the data properly. Big data can be collected through archives, documents, media, business apps, social media, public web, data storage, machine log data, and sensor data. In an article Verónica Maria Jarski discussed how Kapow software) helps an organization to find various sources of Big Data. Read more at: http://www.marketingprofs.com/chirp/2014/24904/intelligence-by-variety-where-to-find-and-access-big-data-infographic

The term big data refers to large amount of customer information gathered from social media, which helps companies to improve sales and services by analyzing those data. But unfortunately small businesses have limitations while analyzing big data. Also the huge volume of data may be confusing for small organization and some time they don't know where to begin. Social platform gathers information which can be important for organisations. Hence tools provided by Twitter, Facebook, LinkedIn, are a good start as they offer low start-up investment as per Evan Greenberg, CEO of marketing and communications firm Allscope Media. To read more about how big data can help businesses to think outside the box, follow Nicole Fallon's article in this link: http://www.businessnewsdaily.com/6190-smb-big-data-tips.html

When it comes to businesses and increasing their profitability, it is of no doubt that Big Data has brought in revolutionary changes. With proper handling of Big Data and data management, companies are trying to increase customer satisfaction and increase customer retention. Now, let's take a different look. 

Consider trading - capital market is one industry, where millions and millions of data float around. Regulatory bodies, banks, financial institutions store and analyze all these data that range over a large time horizon. They also need to study risk management practices and create new trading strategies. In capital market where focus has shifted from high frequency trading to discovering patterns and insights from financial data that can be used as case studies, big data can be made to its use. Financial services organizations are needed to invest in data management software that can harness the power of big data technology and extract hidden information.

How can they do so? Read them at: http://www.bankingtech.com/221932/big-data-and-data-management-in-capital-markets/ .

In a blog written by Irv Shapiro (CEO and head coach ifbyphone), we come to know the basic differences between proactive call optimization and reactive call tracking analytics. In his blog he explains how a VBMA suite has state of art technologies to deal with many impending problems that today's business organizations face with respect to call tracking etc. VBMA is used to route the call to a proper agent. The technology should be easy to use and not complex in its handling.

For more information please visit:-

In a blog written by Dr Jodie Monger, a pioneer in customer satisfaction research for the contact center industry, explains how problem arises in collecting multiple customer comments from post call IVR survey. He also explains what the problems are and how to solve those problems. 

For more information please visit:-

Human element plays an important role in successful application of big data.  Companies mainly focus on the tools (like Hadoop), using the R language, MongoDB and a few others.  But, it is also very important to understand what kind of data is available, what kind of data is needed and what role an expertise in computer science and applications, modeling, statistics, analytics play. Very often single source of big data leads to incorrect conclusion. So, human power is very much needed. To know more on this topic, go through the article link by Daniel Kusnetzky (a reformed software engineer and product manager):

http://www.zdnet.com/the-human-element-is-critical-in-applying-big-data-7000028983/

Red Hat has gathered a selection of open source software to create a Big Data development and deployment environment. It announced a software stack which includes Red Hat Enterprise Linux, Red Hat Storage, a Hadoop plug-in allowing Hadoop to process data stored using Red Hat Storage, Red Hat Enterprise Virtualization etc. The company has thrown its hat into the ring to compete with and cooperate with many in the Hadoop community. To know more on this, go through the article by Daniel Kusnetzky, a reformed software engineer and product manager.

http://www.zdnet.com/red-hat-throws-its-hat-into-the-big-data-ring-7000011680/