Your perspective on data depends on the type of task you want to accomplish. They could be broadly specified as: Analytics : Helps you explore what happened and why.

Monitoring : Looking at things as they occur to find abnormalities.

Prediction : To predict what might happen in future.

Some of the most popular algorithms that can be applied to a predict future trends are :

The Ensemble Model : It uses multiple model output to arrive at a decision , however, one has to understand how to pick correct models and what problem does one want to solve.  

Unsupervised Clustering Algorithms : These algorithms help to group similar people and objects together.

Regression Algorithms:  These are used to predict future values of a product/service

There is no ideal formula to find the best suitable method for predictive analytics. A strong level of business expertise is required to master ‘art’ of predictive modelling. Read more at: http://www.analyticbridge.com/profiles/blogs/the-ultimate-guide-for-choosing-algorithms-for-predictive

Predictive analytics tools and techniques are becoming a rage nowadays because of their immense application in a variety of sectors. Collecting data and predicting future trends have been so easy. More and more business are vying for these tools as they help with predictive modelling of customer behavior. Even bigger potentials are lurking in the near future and organizations are investing in the development of these software keeping that in mind. Companies which have still not adopted these software are falling behind in competition with other firms. Thus there has been a surge in the usage of predictive analytics as every firm is craving for more information. Read more at: http://www.dataversity.net/how-your-business-can-benefit-from-predictive-analytics/

Let’s say you work for a publishing company deciding upon what New Release Newsletter to send to a particular customer. One simple way would be to just ask your customers as to what genre of books they would prefer to read- Preference Modeling-and the decide accordingly what newsletter to send to whom. Alternatively you can try to predict an individual’s buying propensity for a particular genre and send the newsletters accordingly – Cluster Modeling.
Which of the above should you chose to yield more accurate results?
A survey was conducted with a New York based publisher revealed that deciding which genre newsletter to send to whom based on Clusters had a 2-time better open rate, a 4-time better and 7-time higher click-through-rate, than those based on the consumer’s self-stated preference.
The conclusion: It is equally, if not more, important to observe what people do as compared to what they say.
Read more at: http://www.agilone.com/clustering-beats-preferences

When it comes to risk management, it is often observed that the companies either implement blanket management programs applying the same strategies to all employees, or use the "squeaky wheel approach" focusing primarily on at-risk employees. However, both the approaches result in inefficiency. Thus, a strategic employee-specific management program can be adopted to identify the at-risk employees. Such a program monitors the employees for subtle and almost undetectable changes that are indicative of risky behavior and this is where predictive analytics model is of immense help. Predictive modeling enables the manager to identify not only the high-risk employees, but also the cause behind a particular incident.  Predictive modeling is fast becoming an indispensable tool for mitigating risk, retaining top talent, and building long-lasting relationship with the employees. Read More:- http://www.natlawreview.com/article/mitigating-risk-predictive-modeling

Content marketers are delving more into analytics to give interesting insights about their past content. With some careful analysis of this content they are able to come up with new content to meet the needs of their customers. This exercise of tracking the past performance data is important for the content marketers in many ways. Even without analytics, most rigorous analysis and tracking of the data can give information about what to do next. The usage of analytics is giving better pay offs and is the new trend among Content marketers. With the new technologies emerging the usage of analytics tools is expected to make valuable recommendations to help identify the right strategies. 

Read more at: http://www.acrolinx.com/blog/predictive-analytics-content-marketers/

There are variety of languages and modeling packages available at the dispense of any predictive modeler. In most surveys capturing the trends and usage of various packages and software tools, R and Python occupy the top positions. Surprisingly these are both command line languages. The reasons for this are many. But what are advantages of using command line languages like R and Python or GUI based packages? Which user interface is useful to what kind of programmers? What is the market share of usage of these packages? Which one among them is most useful for a job aspirant? To know answers read

http://www.predictiveanalyticsworld.com/patimes/what-programming-do-predictive-modelers-need-to-know-0408152-2/

Predictive analytics is a “business game changer” that will separate the winners from the losers, according to Forrester. The better a company is at predicting what will happen in the future, the better positioned they are to do something about it. While data scientists will do the heaviest analytic-related lifting at big enterprises, the improvements that have been made to predictive analytic (also called advanced analytic) applications enables regular business people and developers to partake of the predictive bounty. With so many companies coming into the foray of analytics services, today the users have more options to choose from keeping the cost-benefit & need-value trade-offs in mind.  RapidMiner offers a “rock solid” enterprise solution with more than 1,500 methods that address all stages of the analytics lifecycle and has among the tightest integration with the cloud, Forrester says. There are also other options like SAS, SPSS, KNIME, sap, oracle to name a few.

To read more, visit:

http://www.datanami.com/2015/04/07/predictive-analytics-now-in-reach-of-the-average-enterprise-forrester-says/

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

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

At this point when individuals consider saving rare species, they consider remote jungles, researchers and individuals anchoring themselves to trees. The stereotyped thought is that creatures in the wild are extremely hard to track and that the main way that individuals do this is through a basic following framework with a little specimen making presumptions for the more extensive group. Big Data and the complexities of data analysis could not be further from this, with the collection of massive data sets combined with complex predictive models and algorithms creating insights. The idea that enough data could even be collected to make a useful analysis is hard to imagine.  However, this has changed as of late as HP have collaborated with Conservation International (CI) to make Earth Insights. This system has been intended to give an early cautioning framework for creature numbers amongst jeopardized species over the world. Through the utilization of cameras and atmosphere sensors, the framework can gather information from around 1000 of these gadgets and use it to group data on population numbers. To know more about this aspect go through Dan Worth (news editor of V3)’s article link:http://www.v3.co.uk/v3-uk/news/2318103/hp-big-data-tools-help-wildlife-charity-save-the-planet 

The world’s music habits were once relatively private. Record companies were aware about which radio station played their songs and where their CDs were popular, but that information painted an incomplete picture at best in reality. Who knew what music people were sharing on tapes and CDs burnt in the privacy of their own bedrooms? The sales figure notifies companies about the number of records or CDs sold, but no information can be tracked after that. With the time, the music industry is changing, thinking is changing, market is changing. The explosion of data from sources like torrents, music streaming sites and social media platforms has offered the music industry a huge opportunity to track sales, monitor post selling behaviour, understand their fans and spot upcoming artists like never before. Music analytics is now worth an estimated £ 1.8 billion per year. While internet is taking power away from record labels, it is also giving them the ability to predict future hits. Universal Music UK’s director of digital, Paul Smernicki, thinks that ultimately the music industry will always be focused on content, no matter what analytical tools are available. According to him,“It’s important to remember that it's just a set of tools to help inform us. The data doesn't make the decisions, that's an un-replicable part of what we do.” But with robots replacing humans in every sphere of work, who knows how long it will be before music- the most successful talent scout in the industry is defined by an algorithm!

To read more, visit: 

http://www.theguardian.com/technology/2014/apr/09/music-analytics-is-helping-the-music-industry-see-into-the-future