As organizations look to stay competitive by expanding their use of real-time analytics, implementation becomes a challenge. Finding options to effectively serve your company over the long term is often more difficult than it appears. We’ve identified 12 common obstacles you’ll want to avoid as your company pursues real-time analytics.
What does “real time” analytics mean to you and your business?
IT departments have been bombarded with requests for real-time analytics capabilities for years, although not everyone who asks for it actually needs it or even knows what it means. Is the high cost of moving to real-time analytics justified? Although the prices of memory, storage, and bandwidth all continue to fall, there are technology integration issues, process issues, and cultural issues to be considered. Adding to the confusion, there is no standardized definition of “real time.” Depending on who you asks, “real time” can be measured in anything from sub-seconds to a span of more than 24 hours.
Technology innovations such as in-memory computing, Hadoop, and Spark are all designed to address the insatiable need for speed. Yet, a number of persistent issues keep companies from moving as fast as business leaders might desire.[ What’s hot in data science? Read 10 Programming Languages And Tools Data Scientists Use Now. ]
“IT organizations often do not have the platforms in place to collect, manage, and respond to real-time data. This means any attempt at real-time [action] will be flawed and even dangerous,” said Graham Clark, head of digital services for global IT solutions service provider NIIT Technologies, in an interview.