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How Architecture Impacts Real-Time Data

How Architecture Impacts Real-Time Data

The field of analytics has taken on new layers of depth and urgency thanks mostly to the need for real-time results.

Analytics: The Need for Real-Time ResultsAnalytics: The Need for Real-Time ResultsThe field of analytics has taken on new layers of depth and urgency thanks mostly to the need for real-time results.

Ultimately, every business investment happens in the pursuit of solving a problem. Whether it’s in the pursuit of more sales, fewer support issues, higher product quality, or lower downtime, achieving these goals requires data, and the better the analysis of that data, the better the odds of addressing those problems.

As you might expect, the larger the business and the more complex the problem, the more data will be required. This leads to IT infrastructure demands for both high storage capacity as well as plenty of compute horsepower to crunch the algorithms necessary in order to extract useful patterns and information from the mass of data. This sort of data mining has been around for many years. Portfolio analysis is one famous example of how database analytics are employed when assessing risk and value on a wide range of possible investments. In this instance, analytics serves to find patterns in the past that are likely to help in predicting the future.

However, recently the field of analytics has taken on new layers of depth and urgency. Foremost among these factors is the need for real-time results.

“What we used to do was build a model, and once a year we’d plow through all the data in our databases as a big batch job,” says Tom Joyce, vice president of marketing, Strategy and Operations division with HP Storage. “We’d calculate scores and figure out who to send mailings to once a month. Now we’re talking about when the person is interacting with us. It might be in an online gaming system, on a Web site, or whatever. We’re taking the context of what’s going on at that moment and modifying how we treat the person. We’re getting much more personal.”

No better-known example of this exists than Google’s AdWords, which can pull keywords from a Gmail message and use them almost instantly to customize banner ads appearing in subsequent Web pages or Google Search results. Timeliness of analytics here is obviously paramount. If a customer is emailing thoughts about “summer,” “vacation,” and “Puerto Vallarta,” it’s a fair bet that the time to show that customer travel information and airfare discount deals pertinent to the Mexican destination is immediately.

Analytics, particularly Web analytics, are about building better models and deploying those models at the right touch points. It should also be noted that these models don’t necessarily need to involve sales processes or even online customers. For example, analytics might be employed on an oil rig in which sensors are monitoring thousands of characteristics, from flow pressure to wire tension. In the face of rapidly changing conditions, the sixty seconds it might take for a system to display the relevant information to a human operator who, in turn, must make a decision, could be far too long. Effective analytics able to convert live data into real-time decisions can avert disaster in a myriad of circumstances.

William Van WinkleWilliam Van Winkle

William Van Winkle has been a full-time tech writer and author since 1998. He specializes in a wide range of coverage areas, including unified communications, virtualization, Cloud Computing, storage solutions and more. William lives in Hillsboro, Oregon with his wife and 2.4 kids, and—when not scrambling to meet article deadlines—he enjoys reading, travel, and writing fiction. See here for all of William's Tom's IT Pro articles.

(Shutterstock image credit: Analytics)

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