Sampling error can cause problems if they are not taken care of. Errors in judgment about sample size can be fixed easily and sample sizes must be considered seriously if big data is being used for predictive analysis. A leader trying to use big data in predictive analysis should always consult the data scientist. The way to understand whether enough data has been collected or not for the purpose of prediction involves understanding the tolerance of the risk associated to accept the assumptions drawn from the sample size characteristics. There are two types of risk: the risk that you're going to take some action when you shouldn't and the risk that you are not going to take some action when you should. Also enough information should be available about the sample variation and precision of measurement to know whether enough data has been collected to make prediction. To know more about importance of sample size in predictive analytics, go to John Weathington (President and CEO of Excellent Management Systems, Inc.)'s link: http://www.techrepublic.com/blog/big-data-analytics/why-samples-sizes-are-key-to-predictive-data-analytics/ 

New technologies are emerging and along with that various analytics tools are also developing. Unlike previously, where building predictive models would take as long as twelve months, it now takes only a day or a week, provided you have the right information in your hands.

 But, wait. Your task does not finish here. You have just built a predictive model. The main thing is how you implement it. Properly deploying your model must be made in a systematic approach, as otherwise, can take months. You have to keep in mind that you have to implement your model in the real world.

How to go about it? Read these tips: http://www.banktech.com/business-intelligence/5-tips-for-operationalizing-analytics/240168237 .

For the long term purpose, Predictive analytics will be very significant for healthcare organizations and providers. With the advancement in technology and complexity in healthcare sector, predictive analytics must be implemented as it will make the work easier and reliable for the providers.
In order to avoid risk and uncertainty and for taking quick decision,
predictive analytics will be of a great substitute to a traditional business intelligence practices.

Please go through the link for more details: http://www.predictiveanalyticsworld.com/patimes/what-can-predictive-analytics-do-for-healthcare-reform/

Data complexity is a major challenge faced by the healthcare industry. Now healthcare industry is going through a transformation phase. So companies like General Electric, 3M CO, Siemens and Xerox Corporation are taking initiatives to gain insights for making healthcare processes more efficient by the use of predictive analytics.

Read more at: http://www.nasdaq.com/article/analytics-a-shot-in-the-arm-for-healthcare-analyst-blog-cm337055

Eric Siegel, Ph.D., founder of Predictive Analytics World and Text Analytics World and Executive Editor of the Predictive Analytics Times, talks about how businesses can incorporate predictive analytics to upgrade their process.  A predictive model will help to know which new customers are likely to return and which are probably one-timers. He also mentioned about various benefits of retaining new customers. Moreover, by adopting this model, the first time customer can become a loyal customer.

For more information follow  http://www.predictiveanalyticsworld.com/customer_retention.php

In the recent years, social media has become a place where emotional roller coaster of the stock markets can be captured. How the Twitter and Facebook users are reacting to different business news can foretell where the index is heading. Knowing this information can help the companies in predicting their market price, top line, etc.

To know more on how social media can help in predicting in stock prices read http://www.investopedia.com/articles/markets/031814/can-tweets-and-facebook-posts-predict-stock-behavior-and-rt-if-you-think-so.asp .

 HR predictive analytics empowers a company to anticipate and prepare for the future. There is a continuous growth in the use of predictive analytics for the last few years. Studies have shown that predictive analytics act as a decision support system, enabling managers to take best decisions, resulting in an average of 5-6% higher gains than competitor companies.

Predictive Analytics 

For more information follow

http://resources.rpoassociation.org/blog/bid/327375/The-Potential-Value-of-Predictive-Analytics-in-Human-Resources                

 “The future is like a corridor into which we can see only by the light coming from behind”, well said by Edward Weyer Jr..  Like that, to analyze anything, the first step is to collect relevant data and develop predictive score for each element, the second step is to assess these predictive score and then implement it into a model. Finally prediction and conclusion can be made accordingly.  The book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel explains how predictive and other insights associated with predictive analysis work.

Please go through the link for more details:

http://www.amazon.com/Predictive-Analytics-Power-Predict-Click/dp/1118356853

Susanne Sclafane, senior editor at Wells Media Group, talks about the extensive use of predictive analytics in insurance sector. The most prevailing uses of predictive modelling are rating, underwriting, marketing and forecasting. It also conveys some of the threats faced by the insurance industry.

For more information follow http://www.insurancejournal.com/news/national/2013/11/07/310601.htm

A recent webinar by INDUSTRYWEEK explains the different uses of predictive analysis in auto industry and how it is changing the competitive atmosphere.

For More Information :  http://www.industryweek.com/webinar/predictive-analytics-transforming

Machines are important in any manufacturing industry. As wear and tear of the machines are obvious, a preventive maintenance program is vital to i) reduce machine downtime, ii) get the overall view of the health of the machine and iii) estimate the cost of the maintenance.

The preventive maintenance can be done with the help of a Predictive Maintenance program, also known as PdM which includes the techniques of Predictive Analytics. Different sensors installed in the machine provide inputs to the Analytical Software from which the degradation process can be understood. This will help in predicting different failure modes of the machine.

To know its benefits and how PdM improves machine & equipment maintenance, please go to http://www.lselectric.com/4-ways-predictive-analytics-improves-equipment-maintenance/

Imagine, in this IT enabled world, different departments of a bank are operating on a standalone framework, managing millions of customer data. So it’s obvious that they will not have a clear picture about customers’ demographic pattern, their preferences and their account usage patterns.

Thanks to Analytics, these problems are now being solved. Omar Sohail, Principal, Deloitte Consulting LLP and Banking & Securities, Business Analytics & Information Management Leader, writes how structured, unstructured and semi-structured data can help the banks in reaping benefits by getting more insights about customer data.

For detailed information please go to http://www.baselinemag.com/analytics-big-data/banking-on-big-data-and-analytics.html .

With the advancement of technology, business analytics helps to analyze huge number of data and create predictive models. It also helps in the areas like data mining, statistical sampling and forecasting. The main objective of business analytics is to cluster data from different systems into a common storehouse. Using Business Analytics, smart enterprise set up can be built that benefit businesses to combat the volatile demand.

For more information please follow 

http://dreamfireinteractive.com/industry_insights/accelerating-business-analytics

Now Analytics can also predict weather for as long as 40 days before.

EarthRisk Technologies has come up with an analytical model that can predict extreme weather events. This model is designed in such a way that it can identify different patterns of weather which involves intensive calculations. With the help of historically available data, it compares with the present conditions and uses predictive analytics to forecast the weather 40 days in advance.

Knowing advance weather forecasts will certainly help industries in the utility, energy and agricultural sectors as they heavily depend on weather information. With the help of this model, proper timely decisions can be taken.

To gain more insights on how Analytics are helping in predicting weather you can go to http://venturebeat.com/2013/05/10/big-data-can-predict-weather-up-to-40-days-into-the-future/ .

Data Analytics is used in an ample number of ways to achieve a competitive advantage over other businesses. The various areas are - Big Data and Information Optimization helps in determining an organisation on how they can apply the present analytical access patterns and skill sets though it is still critical to audit the interaction between the analytical assets and core database system. Visual Analytics and Data Discovery gives guidance in finding a trend in the data set by developing various hypotheses and visualizing the complexity of the diverse data set. Companies need to inspect on different patterns and accord in the data for future benefit using Predictive Analytics. Right Time and Real Time Analytics are used to grasp on how customers are handling data analytics for managing industry. 

For more Information follow 

http://tonycosentino.ventanaresearch.com/2013/03/11/the-four-pillars-of-big-data-analytics/