Is our privacy at risk?- This is the most discussed question in today's world of big data where companies like Google is able to capture data from homes and offices as well as video footage for storage anywhere in cloud, provided by Amazon Web Services. According to Danielle Hughes of Divine Capital, computers are learning to interact with one another and this is raising concerns. Though, it is true that people are living in the post-privacy world today as younger ones have no issues in sharing their personal information in social media. But, Hughes thinks, this is the beginning and in the future machines will start to teach other machines and tell back the information analytically to big companies. She also concludes that it will not lack investments in future, as for example IBM is already projecting $20 billion in revenue from big data in 2015. Read more at:http://analytics.theiegroup.com/article/53ac20613723a8031500002b/Our-Connected-World-Big-Data-Big-Profits-And-No-Privacy

2014 is expected to be the year of cheaper analytics. Along with the announcements of new features from some SaaS providers like Gainsight, gShift and pricier systems from Adobe, IBM, Oracle etc., a firm Rival IQ has recently released the beta version of a new SEO analytics features which is now included in all the company's service tiers and its service includes analytics for websites and social media. It might also provide an easier approach for marketers who are now technically backward. About 1,000 companies are now the users of digital marketing/analytics. One of the officials of HP's cloud computing division has started using Rival IQ and said that they are trying to provide enough data across all the different areas at a very cheap rate so that one can easily grab charts, and see reports from one place and then export everything to PowerPoint, CSV or PDFs. The landscape features of this SEO analytics also allow users to see what their competitors are doing in social and SEO and can be used to research best practices for a whole area like e-commerce. Read more at:http://www.cmswire.com/cms/customer-experience/a-cheaper-alternative-to-marketing-analytics-025687.php

Most shopping websites, regardless as to whether they are auction based like eBay or are just one sprawling marketplace like Amazon, tend to prominently feature a list of recommended products on their homepages. These lists are the results of their product recommendation engine.

They work by taking into user preferences or your preferences for that matter and then correlate it with the products and services available on the site. Needless to say, product recommendation engines naturally enjoy access to the entire product and service database of the website. Information is filtered with your preferences in mind and, afterwards, the product search engine comes up with a list of products and services that it considers likely to appeal to you.

Predictions made by product recommendation engines are not only based on the description of the product and service but also on whatever information it can obtain from your own social environment and previous web history. It first gains access to a pool of users and collects data based on their behavior online, their activities, and their preferences. All the information collected is then filtered and submitted to a platform which categorizes them into products that a group of users may like or dislike. When you visit the site, the first thing it will do is to determine which group of users you belong to. From there, it will provide recommendations on the assumption that your tastes are similar to users it had studied in the past. To read more: http://www.aboutdm.com/2013/01/product-recommendation-by-amazon.html

Big Data is about large volumes of unstructured data along with rapid analysis with insights being noted within seconds. Big Data allows narrower customer segments and help in tailoring precise products and services which will then allow for companies to develop the next-generation products and services. The fact is that Big Data requires more capacity, highly efficient accessibility. It would require scale-out or clustered storage systems - such as scale-out NAS (Network Attached Storage) which can scale out to meet capacity and uses systems which are distributed across many storage devices and can handle billions of files without degradation of performance. Big Data using Hadoop stack has been gaining acceptance widely. Also, organizations which create and store more transactional data in digital form can collect more accurate and detailed performance information on everything. RAID-based storage systems have huge storage capacity but not necessarily what Big Data requires and RAID based systems cannot protect data from loss. Thus, most IT organizations incur additional costs which use RAID for Big Data storage as they need to copy it two or three times to protect it from loss. Read more at:http://www.informationweek.in/informationweek/perspective/296730/environments-object-storage

Apart from external threats that disrupt law and order, public security officers in India also face challenges within an organization. Lack of interoperability and exchange of information leads to weaker response time and fewer positive outcomes. Technology can help to overcome these problems with better integration and management of operational process and data. A single integrated information stores the data from various sources on people, objects, events etc. which in turn leads to efficient resource management. However, law enforcement agencies in India lack a robust IT infrastructure which can help them deal with threats to public security and safety. Ability to capture and mine huge amount of data from diverse sources actually leads to success of a big data project. To analyze and make fast yet accurate decisions, all intelligence sources, law enforcement and other public security agencies gather a large amount of data and employ specialized analysts to make sense of raw intelligence gathered from different sources. Using Big Data, intelligence agencies can analyze and make sense of this huge volume of structured and unstructured data, in real-time speed and it enable agencies to identify relationships and helps to detect, avert & resolve crimes. Read more at:http://www.informationweek.in/informationweek/interviews/296242/help-enhancing-indias-national-security.

Big data gives governments tools to discover more effective and innovative ideas on how to decrease poverty across the world. Proper application of big data and technology advancement can improve the circumstance of people currently impoverished. Before, collecting reliable data from all corners of a country can pose as a hindrance in the complete elimination of poverty but now this problem is solved by the advances in mobile technology. Reportedly, more than 85% people in the world have access to mobile phones, even if some of them are the poorest people which lead to greater data collection on the poor and availability of big data acts as a catalyst to help the poor people prosper. Data collected from cell phone call data records and an individual's bill payment history banks and other financial institutions can effectively evaluate a person's ability to pay back loans and determine their risk attitude. This helps the institutions to give credit to the required people through micro financing. Read more at:

http://www.bigdata-startups.com/5-applications-big-data-in-government/

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

Intelligent processing of data is essential for addressing societal challenges. Data could be used to enhance the sustainability of national health care systems and to tackle environmental problems by, for example, processing energy consumption patterns to improve energy efficiency or of pollution data in traffic management. 

To make the best use of big data now and in the future, government must have the right infrastructure in place. It advocates a data force, based on the successful nudge unit, to access data from different departments and identify where savings could be made. New data science skills will be needed across government, but it is more important is ensuring that public services leaders are confident in combining big data with sound judgment.

Closely linked to government's drive to make better use of big data, is its drive to make data open. Open data, particularly that in the public sector, is often big data – for example, the census, information about healthcare or the weather – and making large government datasets open to the public drives innovation from within government and outside. See more at: http://fcw.com/Articles/2013/09/25/big-data-transform-government.aspx?Page=1

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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/

Enhanced e-commerce is a trending reason nowadays for the uprising online retail shopping. It includes tracking code updates, data model changes, and new end-user reports that address many ecommerce-specific use cases. Together they help online retailers see farther and understand customer behaviors better than ever before. According to reports, online retail grew above 30% in year 2013. Digital data has played an essential role in that growth offering deep insights into online shopping behavior and letting retailers make smarter moves. But needs are rapidly increasing and retailers are requiring more sophisticated and comprehensive analysis tools to understand shoppers and improve product-level performance. We should analyze how far shoppers get in the shopping bag and understand which products are viewed most and which are frequently abandoned in cart. We should also create product lists for onsite merchandising rules, product landing pages to see which lists and products are best at driving customer engagement and analyze how internal promotions impact sales and act immediately on the results. Read more at: 

In the recent past, most companies have recognized the bottom-line impact to be gained through effective pricing. Tapping the full promise of pricing requires an infrastructure to drive real and sustained pricing performance. With such a foundation, a company can establish and strengthen pricing activities by creating deliberate decision processes, a specialized pricing organization, mechanisms that appropriately measure and reward pricing excellence, and vigorous support tools and systems.

A pricing infrastructure can be difficult and costly to create. It requires investing appropriately, empowering the right people, articulating clear targets and goals, and managing risk. Yet the benefits of realizing true pricing excellence are worthwhile: a one-percentage-point improvement in average price of goods and services leads to an 8.7 percent increase in operating profits for the typical Global 1200 company. 

Every company should have a set of pricing metrics that measure the financial and operational health of pricing across the business. These metrics may include simple data, such as the average selling price, discount, and margin for key products; operational data, including the number of pricing exceptions and win/loss percentages; and special measures to track the progress and impact of specific pricing initiatives. While the manager of a single product line may see metrics only for that, the general manager of a business unit sees those same metrics across the operation and can drill down to the level of individual products to understand the root causes of pricing performance.

Without uncovering and acting on the opportunities big data presents, many companies are leaving millions of dollars of profit on the table. The secret to increasing profit margins is to harness big data to find the best price at the product—not category—level, rather than drown in the numbers flood. To read more visit: http://www.mckinsey.com/insights/marketing_sales/using_big_data_to_make_better_pricing_decisions

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

To boost manufacturing and entrepreneurship in the country, Flipkart (Indian e-commerce company)  announced its tie up with Small and Medium Enterprise (SMEs) promotion bodies. Strong data analytics that forms the base of e-marketplaces will help the sellers to improve their products easily and attract more customers. According to the Executive Director of NCDPD (National Center for Design and Product Development), analytics and market intelligence provided by Flipkart will assist NCDPD in improving their products and R&D and also enable the craftsmen to create better saleable products. The objective of this tie-up is to continue helping entrepreneurs to create products according to buyer requirements and grow significantly by expanding their business so that they may become manufacturers not only at a local but also at a national level. Read more at: http://articles.economictimes.indiatimes.com/2014-06-18/news/50678912_1_data-analytics-flipkart-market-data.

According to Tali Yahalom, consumers want to be treated indivually and not like any other consumer you deal with every day. Companies should realize that changing rules once in a while is less costly than losing a customer. Moreover, social mediums like Facebook, Twitter, and Yelp have become extremely useful in tracking one's reputation online. One should be considerate towards a customer's emotional state of frustration and listen calmly to him/her instead of becoming defensive. Nowadays, companies have tools for collecting consumer feedbacks and suggestions which are extremely important. A company should be optimistic towards feedbacks and try and learn from it. To know more, follow this link: http://www.inc.com/guides/2010/12/the-new-rules-of-handling-customer-complaints.html

Enterprises hire  lots of people, but in a world where change happens fast and often, they can’t anticipate every need. One solution is to hire contractors for new or temporary projects. But that involves recruiters finding people—but they won’t already know the company’s systems and culture. A better way is to find someone in-house, but in a company with hundreds of employees, that can be difficult—unless you let big data do the heavy lifting.

Progressively, organizations are doing simply that: Big Data is helping them match positions to existing representatives' profiles. Organizations are utilizing HR programs that change the representative profile from ignored comfort into an effective instrument giving them a chance to discover abilities that don't match a specialist's set of expectations or even their delineation toward oneself. They work by scouring social-media profiles, forums, blogs and comments across the Internet, to unearth talent that’s under their own roof—but they just didn’t know it. For a detailed article on this topic visit: http://www.theatlantic.com/magazine/archive/2013/12/theyre-watching-you-at-work/354681/.

In India, banking industry plays an important role in ensuring the sustainable growth through more credit flows and reaching out to more people by financial inclusion. Thus, the retail banking systems has to handle several issues like customer identity, managing credit risk, fraud detection and prevention, customer relationship maintaining etc. Data analytics helps in this way by ensuring organizations in achieving their growth objectives. It also enables them to manage and automate large volumes of day-to-day decisions. Data analytics draws inferences from large amount of data and use these inferences within banking processes transparently. Data analytics (DA) has an effective use in the following stages of customer lifecycle- Customer targeting -DA helps to develop right offer to right customer by understanding their behavior. Customer Acquisition- DA benefits banks by acquiring profitable customers at low cost and also helps to understand the affordability status of customers. Customer Management- DA helps banks to ensure responsible lending and to undertake effective risk management measures which in turn provide better customer services. Collection- Banks can use DA to reduce delinquency, manage cost of collections, and reduce wasted time by knowing the right value of the customers according to their worth retaining for the future. Read more at:http://www.informationweek.in/informationweek/perspective/287791/indian-banks-improve-customer-lifecycle-analytics?utm_source=rss&utm_medium=rss&utm_campaign=how-indian-banks-can-improve-the-customer-lifecycle-by-using-data-analytics

Big data is a popular term used to describe the exponential growth of structured and unstructured data. Despite of such an understanding by business organizations, according to a survey, second annual analytic usage, trends, and future initiatives report, 75 percent of businesses have yet to reach big data production. However, just 12.6 percent of respondents said their company has completed several big data projects that are now in production. Drew Rockwell (Lavastorm CEO)said “It’s organizations that go the next level by removing complexities from the analytics process and empowering others in the organization, namely business analysts, who are going to be able to turn data insights into actionable business enhancements for long-term success.” According to another survey, one concern for executives who are experimenting with big data is a shortage of expertise in that field. With a lack of big data skills, organizations are reluctant to take the plunge since a clear ROI immediately available. Without being open to a little disruption, organizations will have a much more difficult time adapting to changing consumer mindsets. Businesses should conduct their own research and see what options best fit their needs. Read more at: 

http://www.pymnts.com/in-depth/2014/is-big-data-just-a-big-problem/#.U6KBPZS1Yn7

An investment banking firm  has developed a ‘stochastic model’ its predictions to generate distribution of results for the each of the 64 matches to be played during WC.The predictive model is part of the the World Cup and Economics 2014. Stochastic modeling is used to forecast outcomes, even in the field of sports. This mathematical model predicts the probability of outcomes. According to the model, predicting the outcome of the WC is based on predictions for each match on “regression analysis”. This analysis used 14,000 observations to approximate the coefficients of its model. The dependent variable is the number of goals attained by each team during a match. Based on this analysis Brazil fares as the winning team. The next are Argentina and Germany, but these two teams have inferior probability. Read more at:

http://blog.arekibo.com/world-cup-2014-the-use-of-analytics-to-predict-the-winning-team/

Rapid Miner had an existing partnership with Radoop - an analytics company that optimizes the big data platform known as Hadoop. Now, after successfully acquiring Radoop, Rapid Miner will be able to provide access to many other Hadoop features to its customers which will in turn build a larger presence in the Hadoop ecosystem for RapidMiner. The acquisition also brings partnerships with Hadoop platforms Cloudera and Hortonworks, and adds 20 new clients to RapidMiner’s customer base. The powerful combination of RapidMiner and Radoop will allow applications of advanced analytics to big data. Apart from providing scripting and advanced predictive analytics for experts, it will also help non-technical people to access, analyze, and visualize big data.

To read more, Visit the following link:

http://betaboston.com/news/2014/06/17/rapidminer-acquires-big-data-analytics-company-radoop/

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