Predictive Analytics & Predictive Modeling on the Cloud

Sam Guiliano
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Predictive Analytics & Predictive Modeling on the Cloud

Certainly cloud computing is growing astronomically, much of it in the public cloud (the standard model). However, a trend has emerged in which businesses are shifting from the public cloud to a managed private cloud (not always necessary, as described below). The reason that is occurring is that the companies desire more predictability, both in costs and performance.

This new trend is not just about reliability and the IT budget but is a part of a broader focus on prediction throughout the business community. The tools made possible by predictive analytics and predictive modeling allow businesses to develop in more sophisticated and conscientious ways than ever before.

Predictive analytics and predictive modeling

Predictive analytics, according to TechTarget, is a form of data mining that helps you predict how your business and your industry will develop. In predictive analytics, you take a predictor – any measurable factor – and analyze it to determine how it will likely change over time. Sample fields in which predictive analytics are used are insurance and finance. Predictors for car insurance companies, such as the driving record and gender of the driver, help insurers quantify risk. The same is true for investment companies looking at predictors to gauge stock market trends.

Predictive analytics become more complex when predictive modeling is used by an organization. A predictive model takes a number of different predictors and analyzes them using a specific model (essentially a formula to generate future data as accurately as possible). As data is fed through the statistical model and predictions are generated, the system is tested and adapted as needed.

Predictive models are not always set up as straightforward formulas. In some cases, software is used to allow a neural network (a network of computers designed to approximate the human brain) to analyze the data in a more refined way. An example of predictive modeling is in the field of spam filtering. Gmail uses an algorithmic model to make an educated guess about whether an incoming message should be marked as spam.

Why predictive analytics are important

Predictive analytics is by no means a new practice (as indicated by the insurance and finance application above), but the ability to use it wisely creates a more significant competitive advantage in the age of big data. As Predictive Analytics Times indicates, the data about your business can tell you what fails and what succeeds so you can move forward with a carefully calculated approach.

You can use it for your entire business. Customer needs can be met more quickly and with less error. You can streamline your operations. You can use it to determine what individuals will do – customers and employees – as well as what organizations, will do – your own, affiliates, and competitors. Overall, you won’t make many mistakes if your business is guided by a predictive model that is strong, versatile, and flexible.

How to use predictive analytics

To take advantage of predictive analytics, you don’t need to create your own software unless you have specialized requirements. SAS, SAP, and IBM all have predictive analytic tools, as do many other software companies. Each of the programs works by assessing all of your data – sales, operational, and even social media numbers – and then plugs it into prebuilt predictive models.

The vendors listed above have robust solutions that can meet the needs of enterprises. However, not every company needs an enterprise solution. Small businesses can take advantage of scaled-down, cost-effective predictive offerings from Emanio and Angoss. Both of those solutions can be run on a PC rather than a server.

Interesting predictive situations

Information Management provides two interesting scenarios in which predictive analytics have been used:

  • The US Special Forces utilized Dean Abbot’s predictive models to garner a better understanding of recruits. An expert in the field of analytics, Abbot says the primary consideration is how much to weigh different factors. Balance is best achieved by looking at the results: look at the data for various successful members of your staff to create a predictive model for hiring new employees.
  • Geolocation of users allows you to develop a better predictive understanding of your customers’ behavior. Social media companies use location data to determine prominence of posts and better tailor advertising.

The predictive cloud

Probably the main reason some businesses have become frustrated with the public cloud is that many cloud hosting providers (CHPs) are not transparent. Bills can seem erratic: it’s difficult to have a sense of what they will be from one month to the next.

Here are a number of features that allow Atlantic.Net to provide our customers with a predictive cloud so that their hosting environment is always consistent:

  • real-time billing information
  • per-second billing
  • no hidden fees or stealth upcharges
  • no charge for inbound traffic
  • 1 TB of free outbound data transfer.

The above parameters apply to all our public cloud hosting plans. Predictive spending comes standard, and you can utilize our managed cloud services for your infrastructure as desired.

Comic words by Kent Roberts & art by Leena Cruz.


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