There are plenty of predictions circulating about the year ahead, and fortunately, unlike the Mayans, IT industry analysts don't stop their prognostications at 2012. But the best place to turn for data management and analytics perspectives is my pal Jim Kobielus, Forrester Research's data guru.
Jim sees a lot of growth in Big Data, analytics, and “data science” in the year ahead. “Advanced analytics was the hot new frontier of business intelligence in 2011,” he remarked in a recent post.
Here are some of his projections on what will be shaping enterprise data management over the coming year:
More open-source solutions for analytics: The open-source Hadoop project is becoming the de facto platform for Big Data analytics, and with it, growth in the open-source “R” language, which is used to write analytic algorithms. Bottom line: lots of forces are driving down the cost of analytics that used to be reserved for the Fortune 500 crowd.
Predictive analytics will become commonplace: This is a key area for many insurance companies concerned with fraud detection and prevention. “In other words, what we regard today as 'advanced' analytics is rapidly on the way to becoming mainstream analytics,” Jim says. “Even the traditional statistical analysis 'rocket scientist' power tools have become far more user-friendly, visual and wizard-driven over the past several years, facilitating broader adoption of this technology for the new world of ubiquitous big data.
More of a variety of databases, including in-memory analytics platforms: This is significant, because it means there will be more systems that can run advanced analytics involving tons of data at blazing speeds. “Both startups and established vendors are rolling out BI and advanced analytics tools that are either entirely in-memory or persist many terabytes of working data in fast dynamic random access memory,” Jim points out. “As the cost of DRAM continues to decline, all-in-memory analytics will become the predominant architecture for all users, uses, and data. Big data will increasingly occupy huge pools of virtualized memory that spans many servers in the cloud.”
The rise of “data scientists:” Having specialists that can help insurers make sense of all the data coming in – whether they're called “data scientists” or merely “quants.” Jim sees more of these specialists in organizations, handling challenges from building multivariate statistical models to predictive modeling to developing data mining approaches to look for hidden patterns in historical data sets.
Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.
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