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

The clinical data analytics market has shifted its focus on new payment models, electronic health records. As a result many healthcare groups are looking to analytics solutions to reduce the risk of the patient population. According to a recent Frost and Sullivan market analysis, clinical data analytics is poised to grow at a CAGR of 37.9 percent from 10 percent, in 2011 to 50 percent adoption by 2016. Despite strong market growth it is not smooth for data analytics. Managing such complex and extensive data is a difficult task. Such analytics can be very expensive. Claims based analytics is still very much a reality. Many analytics vendors have tried to align their brand with PHM by any means necessary, which has increased competition making it difficult for them to differentiate in the market. Also researchers have found a very immature buyers' market, which has raised the doubt as to how far the capabilities of analytics solutions will bear results. Read more at:

http://www.healthcareitnews.com/news/clinical-data-analytics-get-red-hot?page=0

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

Many companies are discovering that big data can also be used for security by offering a broader view of risk and vulnerabilities. In today's complex network environments, Advanced Persistent Threats (APTs) and other cyber threats can be wiped out by leveraging intelligence from data providers. Six ways for using big data to wipe out cyber threats include DNS feed which can provide lists of newly registered domains, domains commonly used for spamming and newly created domains, which can be incorporated into black and white lists, C2 systems which provide black lists of IP addresses and domains, threat intelligence which may be used to help determine if an address is safe or not, network traffic logs help to log all of the networks traffic  or even just parts of it, honeypots can be effective in identifying malware targeting a particular network and finally data quality is important to call attention to the data feed itself. By utilizing big data organizations can create more robust threat and risk detection programs. Read more at

http://www.informationweek.in/informationweek/news-analysis/296971/tips-wipe-cyberthreats/page/2

 According to a survey of 900 business and IT executives from 70 countries by IBM Institute for Business Value study, Eighty percent of respondents are evaluating the impact of their analytics investments, 60 percent already have predictive analytics capabilities, and "leaders" who were perceived to be proactive in engaging analytics in their organizations were more than two times likely than average to have formal career paths for analytics professionals.The next generation of IT workers will work in highly automated environments that combine both in-sourced and out-sourced technologies that businesses will expect to get immediate value from. “We hire many of our people right out of college," said the CEO of a healthcare cloud analytics vendor. The intuition behind is that the company already possessed the internal analytics expertise to train these recruits, and the added advantage of hiring freshers is that it also gives the company an opportunity a to train these recruits in to customer service that is valuable to the company. To know more about this, go to: http://www.techrepublic.com/blog/big-data-analytics/better-training-in-search-of-next-generation-it-workers/

Today, a smartphone app would tell one what to eat, how much to eat, when to visit a doctor etc. based on analysis of medical research, medical history of that person, family medical records etc. Quality of our health will be increasingly improved by the quality of data and the ability to bring it all together. The growth of big data in the health industry will only take place once privacy concerns are addressed because health data, unlike the marketing data, is lot more personal. Big data analysis is already being used to make diagnoses in some hospitals. In Canada, Toronto Hospital uses big data to detect blood infections in premature babies. It could save the American health care system $300 billion per year and the European public sector €250 billion, according to a 2011 report. Doctors today are using Watson, IBM's supercomputer, to keep up with health research and to leverage the latest breakthroughs. Big data analytics also could be used to follow epidemic outbreaks. For example, Big Data enabled doctors and scientists to learn so much about the Severe Acute Respiratory Syndrome (SARS), and how quickly it spread, within weeks of the World Health Organization's initial warnings. In such case social networks and mobile data are used to ensure the delivery of real-time information. Read more at:http://analytics.theiegroup.com/article/53b6b4c43723a83b82000035/Big-Data-Could-Help-Your-Health-If-You-Let-It

Generally people think that the role of a data scientist is just to examine the relationships between diverse sets of data as well as the disparate systems, processes and locations which store them. But the role is actually mature across certain sectors like retail. With the help of this, Amazon, for e.g., is able to analyze the behavior across multiple accounts, and knows exactly when and why to push a certain product to a customer. But the case is somewhat different in financial services where the role is not properly organized. Though Big Data analytics is used across the retail banking industry from fraud and sanctions management to improving account management processes, analysis of Big Data provides the potential for banks to create new income streams and the sector as a whole is benefitted when it comes to deriving value from vast quantities of information. Thus financial services, in spite of having people with good skills to do modeling and statistical analysis, need people who are able to spot key trends and focuses on looking for the relationships between data across disparate sources. Once these two skills are combined, the financial sector will start to see the rise of data scientists in it like other industries. Read more at:http://www.banktech.com/business-intelligence/piecing-together-the-data-scientist-puzz/240168604

Manufacturing industry, in the past 20 years, have been able to reduce the quantity of wastage and the variability in the production process and improve their product quality after implementing Lean and Six Sigma programs. However, extreme variations are found in certain processing environments. Thus manufacturers need a better approach that would remove such flaws and advanced analytics helps in this way.  In manufacturing, managers use advanced analytics to identify patterns of data, relationships among discrete process steps and inputs and then optimize the factors that greatly affect the yields. Advanced analytics also helps to increase yield. Manufacturers that want to use advanced analytics to improve yield, consider how much data the company has at its disposal, as their first step. Some companies have too little data to be statistically meaningful and the challenge for these companies lies in taking a long-term focus and investing in systems and practices to collect more data. Advanced analytics and big data forms a critical tool to realize improvements in yield. Process complexity, process variability, and capacity restraints are present in such manufacturing environment. Read more at:  http://www.mckinsey.com/insights/operations/how_big_data_can_improve_manufacturing

A recent survey by The Economist Intelligence Unit found that those who were polled believe that customer service and marketing is expected to gain most from analytics, but customer service is unprepared to deal with big data. Proper analytics of the brand will enable business houses to understand how their business is performing, whether they need any improvement or not.  An October 2013 Gartner report, Market Trends: Leveraging Analytics in Vertical Industries, identified four major categories of analytics: Descriptive, Diagnostic, Prescriptive, Predictive. Three ways in which analytics is transforming customer service: Analytics is encouraging organizations to break down barriers

Analytics is changing perceptions of important measurements

Analytics is allowing brands to get ahead

To know more about the three ways in which analytics is transforming customer service, go to:https://blog.zopim.com/2013/11/20/3-ways-analytics-transforming-customer-service/

CRM (customer relationship management) analytics comprises all programming that analyzes data about an enterprise's customers and presents it so that better and quicker business decisions can be made. CRM analytics can be considered a form of online analytical processing (OLAP) and may employ data mining. As web sites have added a new and often faster way to interact with customers, the opportunity and the need to turn data collected about customers into useful information has become generally apparent. As a result, a number of software companies have developed products that do customer data analysis. According to an article in InfoWorld CRM can provide customer segmentation groupings, profitability analysis, personalization, event monitoring, what-if scenarios and predictive modelling. One of the major challenges implicit in CRM analytics is how to integrate the analytical software with existing legacy systems as well as with other new systems. To know about CRM analytics go to: http:http://searchcrm.techtarget.com/definition/CRM-analytics

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Three ways analytics can change customer service: (1)Barriers to the organizations are to be broken down as encouraged by Analytics: The multi-channel analytics is causing many brands to call their customer service technologies and processes frequently to break down data vaults and multi-channel support and reaction is centralized to avoid repetition of customer information. say of the customer, a 360-degree vision of the customer, makes analytics more convenient and insightful. (2) Changing awareness of chief Measurements by Analytics: Organizations are deviating from old methods such as rapidity to answer and quantity of calls handled per hour, and moving towards more important methods such as overall client pleasure, first call decision. (3) Allowance of analytics to   move ahead: Once the right analytical reports are collected organizations use them to improve their services and support. Using analytics an organizations can estimates what customers are wanting and expecting from their services. Read more at:

http://www.parature.com/customerservice-analytics/

The two important ways to redirect a user agent client-side are meta refresh and JavaScript redirect. Either method of client-side redirection has potential for causing significant issues with both analytics and SEO in spite of having flexibility in choosing whether or not to redirect after the page is rendered and portability in having code. Essentially there are two issues of client-side redirection with Analytics: 1) page view bloat 2) unclean referrer. The first issue occurs when the client-side redirect fires after the analytics code has already discovered the page view. The second issue, unclean referrer, refers that the final result has an unclear referrer. Regarding SEO, the problems are more extreme. There are at least two key issues: 1) page rank devaluation 2) content mismatch. The use of a meta in short explains that most crawlers support a meta refresh of 0 and treat it as a redirection of server-side, and a JavaScript redirect is simply ignored as crawlers don't read JavaScript.Read more at: 

http://thesimplesynthesis.com/post/effect-of-client-side-redirects-on-analytics-and-seo-meta-refresh-and-javascript

Business intelligence is certainly a good area to be in right now. It is expanding faster than any other area of the enterprise application landscape. According to Gartner (an American information technology research and advisory firm), the business intelligence market (including data warehouses and CRM analytics) is growing nine percent per year. While it was worth $57 billion at the end of 2010, it will surge to $81 billion by 2014 and as high as $136 billion by 2020. Because analytics is the "combustion engine of business," organizations invest in business intelligence even when times are tough. Gartner predicts the next big phase for business intelligence will be a move toward more simulation and extrapolation. Gartner surveys show most users still focus on measurement of the past, with only 13 percent of users making extensive use of predictive analytics. Less than 3 percent use prescriptive capabilities such as decision/mathematical modelling, simulation and optimization. "This trend is changing as organizations express an interest in increasing their use of advanced styles of analytics," said Sallam (analyst at Gartner).To know more go to:

http://www.enterpriseappstoday.com/business-intelligence/gartner-taps-predictive-analytics-as-next-big-business-intelligence-trend.html

Government today is suspected to be unworthy by most of us. And it is totally justified because of the increasing corruption and selfishness creeping in the society. US president Barack Obama has agreed to keep full transparency between him and the people and prove his credibility. He has signed an 'Open Data Policy' to create an open government. The idea behind is simple. All the government departments are required to update their expenses on the website USASpending.gov. People have full access to this website and can mine the data available for finding any expense that is unnecessary or excessive. Only some areas covering the expenditure of military and covert operations are unlikely to be revealed. Read more at: http://analytics.theiegroup.com/article/538f58943723a83b6f000205/Obamas-Open-Data-Policy

The biggest fallout of the big data revolution -- where every type of business gathers and analyzes data -- is a massive human resources shortage. Across the globe, thousands of data analytics jobs are not filled up because of a shortage of qualified manpower. Data analytics is not coding work but thinking work, described Dinesh Kumar, a professor of quantitative methods and information systems at the Indian Institute of Management in Bangalore. "The potential is huge, but we are behind in creating a talent pool," he said. Quality is a worry, and companies are finding it difficult to recruit top-class people, Kumar said. Data analytics as a job discipline became mainstream almost a decade ago, and the demand for trained professionals has been growing steadily since. Given India's reputation for the availability of professionals in varied disciplines at reasonable costs, global banks and financial services firms were the first to migrate their analytics work to India, followed by pharma and life sciences companies. Global retailers, consumer firms, logistics firms, consultancies, and engineering firms have all begun routing their data analytics work to IT services providers and specialized analytics service providers in India. In India, which has long been a hub for outsourced technology services work, the scarcity of analytics talent is particularly acute, as global companies send increasing numbers of data-related tasks to the country. To know more about this go to:

http://www.techrepublic.com/article/indias-high-demand-for-big-data-workers-contrasts-with-scarcity-of-skilled-talent/ .

There are significant opportunities to make use of big data techniques. Unlike technology and consumer retail sectors in which advanced analytics has been implemented, it can also be used in other industries like insurance, health care, banking and public sector. In insurance, data can be aggregated from public sources and specialist data providers, allowing companies to better target customers and frame policies accordingly. Banks are increasingly using big data to generate a much deeper view of their customers, combining the information collected from all of customer's interactions with bank with selective third-party data like paying patterns for mobile phone bills, tracking trends on social media platforms such as Twitter. Read more about this aspect in Dominic Barton (global managing director at McKinsey & Co.)'s article link:http://blogs.wsj.com/experts/2014/03/28/sectors-where-big-data-could-make-an-impact/?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/ .

The supply chain is a great place to use analytical tools to look for competitive advantage, because of its complex nature and also because of the prominent role supply chain plays in a company's cost structure and profitability. Data analytics is the science of examining raw data and drawing conclusions about information. It is used by many business houses to facilitate better business decisions and verify or disprove existing models or theories. Relying on traditional supply chain execution systems is becoming increasingly more difficult, with a mix of global operating systems, pricing pressure and increasing customer expectations. There are also recent economic impacts such as rising fuel costs, global recession, supplier bases that have shrunk or moved off shore, as well as increased competition from low cost outsourcers. All these challenges potentially create waste in the supply chain that is where data analytics steps in. To know more about the role of data analytics in supply chain visit:http://www.industryweek.com/blog/supply-chain-analytics-what-it-and-why-it-so-important .