Large Enterprises face huge challenges when dealing with their Big Data.  In this article I am going to outline some of the common challenges with Big Data I see firms dealing with on a day to day basis.  This is a continuation of the discussion that was started in the article titled “The Challenges of Dealing With Big Data”.

In the previous article we discussed how a lot of firms and discussions, in and out of the press, are focused on how to analyze and gain insight from Big Data (whether it be on Twitter or in the traditional Enterprise).  Furthermore, I outlined how, in my personal experience, the root of the true problems with big data are often not in how or what tools we use to analyze the data, but more so in how we capture, or fail to capture it in the first place.  In essence, our failure to capture the data accurately and consistently often renders analysis of it a meaningless exercise due to the Garbage In = Garbage Out (GIGO) principle.  To make this issue more clear, I am going to provide some real world examples of some of the Big Data issues I come across with my clients on a regular basis.  Unfortunately, as I started writing this it was getting more than a bit long so I have broken it into three shorter posts of which this is the first one.

The full article can be found on the Nebility Blog.

As always, you can reach me through the comments, at Nebility, on Twitter, LinkedIn or by using the Ask the Experts form.