Monday 26 May 2014

Learn from your mistakes, analytically


As discussed in the previous blog, every business has a risk and needs to find how to decrease the risks. Each negative outcome that occurs presents an opportunity from which to learn new things. In a small scale business, these decisions could be easy and more mind driven being controlled by a set of managers only. As the businesses grow beyond a size, predicting the future course of action and avoiding the past mistakes becomes important. 
The enterprise integrates the predictive model’s scores in order to act upon what has been learned. At each step, the predictive scores foresee where a “blunder” may incur unnecessary risk, thereby guiding the organization to avoid it. In this way, predictive analytics delivers a complete data-driven system for risk management. 
Predictive modeling capabilities are scientifically proven and have benefited from decades of advancement. With a tenured track record of success, predictive analytics boasts mature software solutions that deliver this technology to – and integrate it with – the modern enterprise.

Wednesday 21 May 2014

Predictive Analysis used by Insurance Companies


All businesses are run at a risk. Risk is the way business is managed. Every decision an organization takes impacts the risks an enterprise can withstand like the risk of customer defecting, of not responding to an expensive glossy mailer or offering a huge retention discount to a customer who was not leaving even otherwise and in turn missing out on a critical customer who leaves.
The data driven means to compute risk of any type of negative outcome in general is predictive analysis. Insurance companies have used this very well. Insurance companies are augmenting their practices by integrating predictive analysis in order to improve pricing and selection decisions. 
The actuarial methods that enable an insurance company to conduct its core business perform the very same function as predictive models: Rating customers by chance of positive or negative outcomes. Predictive modeling improves on standard actuarial methods by incorporating additional analytical automation, and by generalizing to a broader set

Monday 19 May 2014

Predictive Analysis and Big Data


Predictive analytics is an enabler of big data: Businesses collect vast amounts of real-time customer data and predictive analytics uses this historical data, combined with customer insight, to predict future events. Predictive analytics enable organizations to use big data (both stored and real-time) to move from a historical view to a forward-looking perspective of the customer.
For example, historical preferences of consumer can be analyzed from their usage patterns and promotional offers can be planned accordingly. The historical data can help predict which promotional offer would be most useful. 
There are a few solutions available that help achieve this. They combine the capabilities of data mining solutions and predictive analysis to provide a single solution for predictive analysis. Vendors may offer proprietary solutions or solutions based on open source technologies. Predictive analytics software can be deployed on-premises for enterprise users or in the cloud for small businesses or for project or team based initiatives.