The massive stress, the high expectation, nation’s passion and panic on the shoulders of an enthusiastic but defective Brazilian team. They trapped under the pressure, but there was more to this pressure, or faulty strategy and player’s selection, or inexpert positional play, or a first class German team filled with efficient passers. First-half goal storm saw Die Mannschaft 5-0 up, Miroslav Klose gave his record-breaking 16^th goal at the finals and the brutality continued into the second period with Schürrle.  Mesut Özil missed to make it 8-0, moments before Oscar scored a goal in the final minute of the first semifinal of World Cup 2014. Germany place up in 4-1-4-1 changing from the 4-3-3 they begun with. David Luiz and Dante saw the ball early in deep areas, but with no one to pass and kept missing. Germans moved with instability, interchanges outside the box and played generously, passing football. Read more about this analysis at: 

http://outsideoftheboot.com/2014/07/09/world-cup-tactical-analysis-brazil-1-7-germany-germany-run-riot-to-trounce-brazil/

Cloud, social, mobile and big data technology have been primarily viewed as the drivers behind Customer Relationship Management (CRM). According to a Gartner report, they are now being joined by an emerging driver- the Internet of Things (IoT). According to Joanne M. Correia “CRM will be the heart of digital initiatives in the coming years”. In the report Gartner views that ioT, where sensors connect devices to the internet, create new services previously never considered. Gartner forecasts CRM market to reach $ 23.9 billion this year, with cloud revenue accounting for 49 percent. IoT joins cloud, social, mobile and big data to spur a critical need for more operational CRM, according to Correia. According to an article in Information Week, Gartner predicts that in 2020, there will be 7.3 billion smart phones, tablets and PCs in use- but about 26 billion IoT devices. IoT is driving CRM investments, because the use of cloud- connected intelligent devices is creating new business opportunities for marketing, sales and support executives. Read more at:

http://www.cio.com/article/2453781/big-data/big-data-as-a-driver-for-crm-investments.html

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

Could business analytics provide a solution to the pressures faced by the American healthcare system? As in the case of Mount Sinai Medical Centre in Miami, business analytics generated a very high payoff because it involves efficiency driven, intensive, statistical exploration of an organization's data. In the above case, it determined that the center was overpaying for the pacemakers. At the HIMSS conference, we witnessed plenty of startups peddling immediate business analytics solutions to healthcare organizations. Eventually healthcare organizations will adopt business analytics, but the process will take several years for the following reasons: Lack of Compatibility between Systems as introduction of business analytics and then integrating it with existing infrastructure is challenging. For business analytics to get to market it needs to undergo trials in one or two hospitals. Organizations require their softwares to be customized. So suppliers require time. Staff members need to be trained on how to use the software correctly. All these are time consuming. Hospitals should approach their management strategy from a higher level, planning systematic solutions. Read more at:

http://www.informationweek.com/healthcare/analytics/business-analytics-will-help-healthcare-just-not-overnight/a/d-id/1279087

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

Today big data is able to change the way real-estate professionals, buyers, sellers and even banks think about transactions involving property. Companies, promoting services that plug consumers into big data real estate information which gives a better education and insight.There are five key ways through which big data is changing the game of real estate business. They are- 1. Big data helps to democratize data for the real-estate customer. Companies such as Zillow combine big data with real estate and offer services like- Mining census information, the results of consumer surveys, listings of homes for sale and rent etc. 2. Big data is not only providing new information to consumers but also new ways of looking at developments and community planning. From that gathered information, real-estate developers can learn what kinds of spaces work best in terms of tenant health, energy efficiency and other points. 3. Institutions like banks are also able to plug into big data resources. 4. As an expert real estate advisor knows recent sales, incentives, and inside secrets to getting the best deal, buyers should have to be careful and should start property searching with the help of big data sites. 5. Finally, Big data let the professionals know what visitors are doing when looking for real estate online and they adjust their paid and organic efforts based on this data daily. Read more at:http://analytics.theiegroup.com/article/53be79af3723a84f1000003f/5-Ways-Big-Data-Is-Changing-Real-Estate

The launch of big data analytics has brought confidentiality concerns as a rising subject for the customers. Obtaining data is important in order to improve its ability to connect their business to the target consumer by analyzing customer behavior. Casting uncertainties from the consumers on how entrepreneurs use their personal information while collecting data endanger the trust that every business enterprise should build. The consumers are aware of the value of their private data. To a reasonable extent the consumers are willing to share. Privacy is more important to the consumers and they expect marketers to give it owing admiration. Businesses that do not respect the privacy of their customers will be likely to mislay their faith. some strategies to be taken to re-establish the trust of customers between business and the customers. Many customers are prepared to share their personal data if they are guaranteed of their privacy. Read more at: 

http://www.socialmediatoday.com/node/216026

The performance metrics for ads shown against brand and generic queries can differ majorly, many advertisers choose to analyze these two categories separately. Many advertisers with paid search campaigns advertise on queries mentioning their brand (e.g., “Motorola smartphone” for Motorola) and also on generic searches (e.g., “smartphone reviews”). A new feature is introduced, that automatically identifies brand-aware paid search clicks tracked in Google Analytics,to make analysis of brand and generic performance easier. It involves a combination of signals (including the click through-rate, text string, domain name and others) to identify query terms showing awareness of the brand. Back in 2012, George Michie from the Rimm-Kaufmann Group, a leading online marketing agency, called analyzing brand and generic paid search together “the cardinal sin of paid search”. To know more go to:http://analytics.blogspot.com/2014/06/segmenting-brand-and-generic-paid.html

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

 A McKinsey study last year stated that companies using Big Data and data analytics effectively show high productivity rates and profitability. But implementation of data analytics is challenged by lack of skilled manpower. Recently many B-schools are stepping up and have emphasized on advanced analytics techniques such as 'Clustering', however very few institutes teach Big Data technologies like 'Hadoop'. The hiring companies which look for data analytics include digital companies, ad agencies, and IT companies and so on. Internationally a vast number of management schools including New York University, University of Texas at Austin, Georgia Tech have had Analytics certificate programs for the last five years or so.Read more at

http://www.business-standard.com/article/management/learning-the-abc-of-big-data-114062900615_1.html

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

According to the Business Technology Innovation Research, analytics is the topmost priority. Three key core areas comprise 2014 analytics research agenda. The first consists of a definite focus on business analytics and methods like discovery and exploratory. The people and process aspects of the research include how governance and controls are being implemented along with these discoveries. The exploratory analytics space comprises business intelligence. Value indexes, mobile business intelligence and business intelligence will provide deep explanations and ranking of vendors in these categories. The area of second agenda is big data and predictive analytics. The first research on this topic will be released as benchmark research on big data analytics which explains vendors of software that helps organizations do real-time analytics against vast data. The third focus area includes information simplification and cloud-based business analytics including business intelligence. Thus, Analytics as a business discipline is getting more importance as we move forward in the 21st century. Read more at: 

http://tonycosentino.ventanaresearch.com/2014/01/23/business-analytics-in-2014-trends-and-possibilities/

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

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/

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/

Cloud computing includes using a network of remote servers hosted on the internet as opposed to a local server. This helps schools and educational systems to manage their content in a simpler way and it helps to cut IT cost. In fact, it is said that within one year, cloud computing in K-12 schools is expected to consume a quarter of the entire IT budget; four years from now, that figure will grow to 35 percent. Benefits of cloud to students and educators:

Storage: The Cloud allows its users to store almost all types of content and data including music, documents, eBooks, applications, photos, and much more

Accessibility: Any data stored in the Cloud can easily be accessed from almost any device including mobile devices such as phones or tablets.

Collaboration: Because the Cloud allows multiple users to work on and edit documents at the same time, it enables effortless sharing and transmission of ideas.

Resource and Time Conscious: With the availability of content online, it is no longer necessary for teachers to spend time and resources printing or copying lengthy documents or lesson plans.

Assignments: Cloud allows teachers to post assignments online. Students are able to access these assignments, complete them, and save them in a folder to be reviewed later.

See more at:http://www.pearsonschoolsystems.com/blog/?p=1507#sthash.Mmt9pOFt.dpbs

In-order to perform better and earn more profits a company should take help of data analysis and CRM analytics to find correlations, patterns, and find out trends that will serve up the type of information to tailor customer experience. According to an article by Marianne Cotter at CRMSearch. According to Forrester analyst Kerry Bodine “Despite its economic power, customer experience remains the most misunderstood element of corporate strategy today,” In a soon to be published book called “Outside In,” Forrester Research argues that customer experience is a fundamental business driver. Five reasons to integrate big data analytics to CRM are:

• Better customer understanding

• Better understanding of the customer-facing operations

• Decision support

• Predictive Modelling

• Benchmarking

To know more about the five reasons to integrate big data analytics to CRM, go to:http://spotfire.tibco.com/blog/?p=12660

While CRM applications officially gather terabytes of helpful client data for organizations, significant deeper insights are also en route because of a creating new pattern of predictive analytics capabilities being integrated into CRM. The huge draw is that organizations will have the capacity to utilize existing CRM information to tremendously enhance basic one-on-one associations with clients. An alternate key profit is that it will help organizations create extra deals when clients reach them by breaking down approaching client information progressively. 

It's the same thought with CRM that incorporates add-on or implicit predictive analytics when a potential client arrives at your company's Web webpage to make a purchase. In the event that a client is offered this item at this cost at this point, would they say they are likely to purchase it? One can make a targeted offer to a client focused around what they are looking for. The probability that they acknowledge that offer will figure out whether you can augment client maintenance, deals and benefits. 

As these sorts of predictive analytics features are presented, organizations will need to evaluate their methodologies to joining the right parts into their own particular foundations. That will take research, detailed inquiries and discussions with teams from marketing, IT and other departments, as well as market research and more. It's not something one will be able to jump into with little thought. One ought to know his objectives before he make the first strides so he can attain sufficient payback from his investments of time and resources. To read the full article visit: http://www.cio.com/article/2371968/customer-relationship-management/how-predictive-analytics-will-improve-crm.html