Some strategies haven't succeeded dealing with supply-chain management. The reason is the cost of hiring expert workers. According to researchers the union of data science, predictive analytics and big data likely to alter the way in which supply chain managers lead and supply chains function. They named this as DPB. Companies have used datasets to plan ideas to meet customer demand. But now they combine external data to better estimate future risks .two points to judge analytic skills: 1) Data science and domain expertise are not mutually exclusive: Analytical skills are important for data scientists who focus on Supply Chain Management (SCM).2) that doesn't mean theory doesn't apply: Strong theoretical knowledge is essential in SCM. Use of suitable theory to build models before operating predictive analytics is key to justifying a circulation of false positives. The three links in supply chain: manufacturers, retailers, supply management, shipping management and human capital. Read more at: 

http://sloanreview.mit.edu/article/are-predictive-analytics-transforming-your-supply-chain/

Big data, the huge amount of structured and unstructured information will slow down most traditional approaches to data management. Cloud providers such as Amazon Web Services are contributing powerful, profitable approaches to analyze big data. Analytics can make organizations "self-healing" or "self-optimizing."The AWS services directory has a mixture of SQL and NoSQL database technology. Amazon DynamoDB is a managed NoSQL database service has a single-digit millisecond latency that makes it a fine fit for big data project where fast line with the data is a must.  The first difficulty need to think in big data analytics is data amalgamation. Amazon Relational Database Service (RDS) is a relational database that can sort in the AWS cloud. Amazon Redshift  (Oracle), which is a petabyte-scale database intended for big data analytics and data warehousing. Finally, Amazon Elastic MapReduce is a Hadoop file system structure on Amazon Elastic Compute Cloud decrease queries. Read more at: 

http://searchaws.techtarget.com/tip/Whats-what-in-AWS-big-data-analytics?asrc=EM_ERU_30984098&utm_medium=EM&utm_source=ERU&utm_campaign=20140630_ERU%20Transmission%20for%2006/30/2014%20(UserUniverse:%20932694)_myka-reports@techtarget.com&src=5265609

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

Healthcare industry has changed a lot from what it was five years ago. Thanks to influx of technology, policy reforms and care model innovations. Patients, today, are better informed by instant access to health information sources. According to a recent study by Intel, patients today are ready to see technology play a greater role in their care. Technologies like ingestible monitors can collect valuable health data and also the providers are eagerly using mobile devices and telemedicine tools to better care for their patients which in turn helps to increase operational and administrative efficiencies. Health systems are also dependent on big data to improve and better manage population health. Physician and hospital use of health IT has already more than doubled in 2013, and 2014 is going to see another exponential innovation. Cutting costs and broadening access to quality of care and also emphasizing on patient's experience, providers are refining ways to better serve their patients. Apart from such innovations, use of sensor technology, relying more on big data will increase. Health systems will become more transparent. Read more at:http://www.informationweek.com/healthcare/leadership/healthcare-in-2014-innovate-or-be-left-behind/d/d-id/1204285

Risk analytics is increasingly important for banks as they cope with a complex regulatory and competitive environment. Important technologies and calculation engines are now available that are critically important to the future of banks and the entire industry. At the same time, it is possible to develop an over-reliance on analytics, so a balance needs to be found.

Developing more comprehensive and integrated capabilities is increasingly important. Integrated stress-testing, for example, is an important means by which the science of risk management can be turned into more of an art, such that it can be communicated and appreciated by a wider audience. An effective stress-testing framework encompasses a wider spectrum of macro-economic, social, political and environmental considerations and forecasts and so can help banks avoid the tunnel vision that can prevent them from making good decisions and taking timely action.

Companies are investing in risk analytics and intend to increase those investments, yet the potential return is often stifled by inconsistent or incomplete data. This prevents organizations from generating the insights needed to support a more predictive approach to risk management. To read more: http://www.baselinemag.com/analytics-big-data/banking-on-big-data-and-analytics.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

Business intelligence (BI) is the larger term that depicts the accumulation and investigation of an association's own data, including sales data plus legacy documents. It addresses the inquiry of whether an organization has all the essential assets and techniques to work effectively in a specific business sector. This brainpower is regularly used to control expenses, comprehend operations and execution, and also build profit and adequacy. 

Market intelligence (MI) serves to enhance choice making. The distinction is that BI concentrates on an organization's own particular information and MI concentrates on outside data. MI provides for you a reasonable picture of business sector open doors, dangers, client necessities and the focused scene. Examining this data will help you choose how you can develop the business, addition piece of the overall industry, dispatch new items or enter new markets. 

A blend of BI and MI can reveal to you whether your inner assets are ideally adjusted to outside business sector potential. Using both MI and BI can give effective experiences – yet the test is that business knowledge and business sector sagacity information come in distinctive structures and configurations. This influences how the accessible information is gotten to, consolidated, dissected and utilized. 

All these mirror the difficulties with Big Data, implying that we frequently find our information sets are excessively expansive and intricate to control or research with standard strategies or device. To read more: http://www.globalintelligence.com/insights/world-class-market-intelligence/articles/trending-now-integrating-business-intelligence-data-with-market-intelligence-insights

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/

It is found that big data and healthcare analytics help patients to stick to their treatment plans following doctor's orders. According to a New England Journal of medicine report, despite having treatable ailments, about 125,000 people die in United States every year just because they don't take their medication properly advised by the doctors. According to a doctor, to overcome the problems, they needed to identify the gaps in scores and ratings which includes Healthcare Effectiveness Data and Information set (HEDIS), Consumer Assessment of Health Providers and Systems (CAHPS) etc. As non-compliance hurts healthcare provider's goal of improving care and reducing costs, many organizations are handling the matter by growing their insights built from big data and analytics to deploy human resources like managers and social workers in better ways. Medicare, Medicaid, dual-eligible, and commercial plan members on care management strategies incorporate technologies such as big data, analytics and integration to improve health outcomes, enhance satisfaction and compliance, also increases income and lower cost. For populations who often have chronic conditions and don't want to comply with doctor's orders because of homelessness, financial instability, joblessness and emotional issues, one can use big data to solve the problem in a logistic-driven way to help those patients comply on their care plan. Read more at: http://www.informationweek.com/healthcare/analytics/analytics-help-patients-follow-doctors-orders/d/d-id/1278717

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

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

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/.

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

The healthcare industry historically has generated large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care. While most data is stored in hard copy form, the current trend is toward rapid digitization of these large amounts of data. Driven by mandatory requirements and the potential to improve the quality of healthcare delivery meanwhile reducing the costs, these big data hold the promise of supporting a wide range of medical and healthcare functions, including among others clinical decision support, disease surveillance, and population health management. Big data in healthcare refers to electronic health data sets so large and complex that they are difficult to manage with traditional software or hardware; nor can they be easily managed with traditional or common data management tools and methods. Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed. The totality of data related to patient healthcare and well-being make up “big data” in the healthcare industry. To read more about the advancement of big data in Healthcare visit: http://www.mckinsey.com/insights/health_systems_and_services/the_big-data_revolution_in_us_health_care.

Big Data has joined the crowd of millions with the fever of 2014 FIFA World Cup on. Much like the social media, marketing, advertising- big data has become important. With a whooping figure of 3.2 billion people who watched the last World Cup, this year the figure is expected to explode even further. Ronaldo's Nike shoes ad is one of the example to illustrate the growth and potential of online videos. It was seen online by 78 million people before it appeared on TV. 12.2 million tweets about the 'first-match' all during the hours match was played, depicts the social captivity of the universe. An event of this magnitude is likely to generate Big Data. The 2014 World Cup is so big that it is expected to have a negative impact on the economy of Brazil, as "major Brazilian cities have the power to grant local public holidays on the dates that World Cup matches are played". Read more at: http://www.infinitive.com/category/big-data/

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/

Big Data does not necessarily mean good data. And that, as increasing number of experts are saying more insistently, Big Data does not automatically yield good analytics. As everyone realizes, bad data equates to bad intelligence, which equates to bad decision-making and thus equates to bad things happening in your business. If the data is incomplete, out of context or otherwise contaminated, it can lead to decisions that could undermine the competitiveness of an enterprise or damage the personal lives of individuals. So how do we detect that? Firstly it's important to understand where your data originates. Has it been captured by your own work force? What measures were put in place to ensure that the very best job has been done and that the data being captured lives up to expectations? What are the requirements for the data your business needs and uses daily? Do you enhance your data from other sources (external or internal)? An example of how out of context data can lead to distorted conclusions comes from Harvard University professor Gary King, director of the Institute for Quantitative Social Science who was attempting to use Twitter feeds and other social media posts to predict the U.S. unemployment rate, by monitoring key words like "jobs," "unemployment," and "classifieds." To read more about the episode visit: http://www.infoworld.com/d/business-intelligence/big-data-without-good-analytics-can-lead-bad-decisions-225608.

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/ .