During a recent webinar on big data, several listeners wanted to know what the biggest stumbling blocks and reasons for failure were when it comes to big data projects, and what they could do to avoid them. Given the amount of resonance, in particular the top issue I cited, I thought I’d share it in this blog post. Please let me have your views and comments.
There are clearly many reasons why projects struggle or fail, and big data projects are no exception. What can put big data initiatives in a league of their own, though, is the level of (typically unrealistic) expectations often associated with “big data” technologies. Based on many conversations with clients, consultants, and conference delegates over the past couple of years, I find three key issues are being mentioned time and again. These are:
Not starting the project with a question
Underestimating the technical skills and expertise required