Have you ever had a file cabinet that you kept shoving papers into until it was too full to close?



Digital data overruns work the same way. In both instances, the overflow doesn’t occur overnight, although it can often seem that way.


Managing Digital  Too Much Information



Data growth happens gradually over time until one day somebody discovers it and has to deal with it. This is how big data actually happened. Company files and databases were manageable at first, but then kept getting bigger and bigger. But it wasn’t until recently that companies purposely sought to grow data, making the problem even worse.



In reality, data analysis has been happening all along. Companies had tools to analyze data – such as database administrators, math geniuses, code writers, statisticians and functional algorithms. But at some point the sheer volume of data simply became too big to fit into data center storage.



Break It Down into Manageable Pieces



In response, some companies began running an analysis on select data samples, rather than the full set. This sometimes worked, but most people agreed it wasn’t the answer. When big data tools were developed, they were welcomed with open arms as a cheap and effective way to analyze huge amounts of data. Now companies are looking for ways to make big data tools cheaper, faster, more efficient and easier to use.



Real-time analytics is one of the most widely hailed advances of our time. It provides on-the-fly analysis of large data sets for the first time, so companies can see exactly what is going on right now and respond accordingly in real time.



But like all tech applications, real-time analytics isn’t a cure-all. There are times when they shouldn’t be used. The answer to when they should be used needs to be made on an individual basis based on an organization’s specific overall strategy, market, and customers.

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