The United States spends more on health care than any other country in the world, at an estimated 17.7 percent of Gross Domestic Product (GDP) in 2013. U.S. health care spending is outperforming revenue as a percentage of GDP, and is projected to grow by an average of 4.9 percent a year in 2014-18, to 17.9 percent of GDP by 2018. (more…)
Predictive analytics can easily mine and turn a large volume of data into valuable business insights. This requires organizations to build statistical predictive modeling systems that demands significant time and resources with niche skill sets. It’s just a matter of time that organizations start realizing and moving their predictive and statistical analytic systems onto the cloud.
Drivers for cloud are not just dealing with big data, niche skills, and time it takes to build the system, but also the volume of consumer’s behavioral information that is available online, which can help in building a full proof predictive modeling system.
When you plan to build and deploy a predictive model, one of the major bottle necks would be to convert your data into a format that facilitates building and deploying predictive models, The transformation of data however is often a series of database operations (group by, join, where clauses), Numerical transformations (binning, ratios, log transforms, etc.), and text processing (stemming, grouping/binning). Except for some database operations, these operations are inherently parallelizable, lending themselves nicely to cloud solutions.
Lastly, a major driver for moving predictive analytics stack to cloud would be mobility and accessibility of the solution if the solution is on cloud; Decision makers are often traveling and having access to their business data on cloud can be a major driver for organizations to move their predictive analytics to cloud.