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/