Any implementation if not monitored and managed correctly can turn into a complete disaster. This is especially true of Big Data. When you're dealing with large volumes of sales data, customer data, even confidential data, it makes it that much more important to ensure that you mitigate risk at every level of the implementation process. Having prior experience with successful implementations in the big data space will give you a distinct advantage.
A hiring manager wants to hear about the challenges you had with past big data implementations. Managers know that implementations don't always go as planned, so they will be listening carefully to see if you own up to your mistakes or blame others for things that go wrong. It goes without saying, but you never badmouth your company, subordinates or colleagues. A good way to turn this question around is to give an example of a big data implementation that didn't go as planned, but that you were able to get it back on track by re-evaluating the process, the requirements and your team's ability and experience to get this implementation completed successfully.
"I have used Business Intelligence tools like ETL, Informatica, Tableau, QLIK, and Power BI. These tools have helped me shape my knowledge base and career path over the years. I enjoy working with data because it's fun to work with, and I get enjoyment out of it. It has been my experience that big data doesn't always work as advertised. I did have some set-backs on a couple of projects that I managed, and the way I was able to resolve some of the impending issues was I had to re-assess the overall situation, and after doing that, was able to figure out that there was miscommunication between team members and the understanding of the final delivery of the implementation. The issue was the data wasn't being analyzed thoroughly enough to use it as accurate data for the business."
"A lot of technologies are used simultaneously in the implementation process of a big data project. One area that can't be overlooked is storage. My experience with implementations is that if you have the right infrastructure in place like software-defined storage, compression, duplication, and tiering; it can reduce the amount of space and costs associated with big data implementations. If you don't have these in place before you start the implementation, then you're setting yourself up for failure. I was involved in a project that was delayed for a few reasons. One was that the data wasn't validated, another reason was we were working with disparate data sources, and lastly, we came across organizational resistance (Insufficient alignment and lack of middle management understanding)."