Recently I wrote about the "Top 10 Big Data Challenges – A Serious Look at 10 Big Data V’s", which summarizes some of the big issues associated with the deployment of big data projects. The use of the letter V may seem forced and contrived, but it is used primarily as a mnemonic device to label and recall these critical challenges, in much the same way the original 3 V's of Big Data have been invoked many times to identify the three defining characteristics of big data.
The main point of the V-based characterization is to highlight big data's most serious challenges: the capture, cleaning, curation, integration, storage, processing, indexing, search, sharing, transfer, mining, analysis, and visualization of large volumes of fast-moving highly complex data. Many businesses have been excited by the opportunities presented by big data analytics (for marketing innovation, customer engagement, building the bottom line, and many more business goals) but the majority of them have fallen short in achieving meaningful ROI from it. In fact, it has been said that more than 55% of big data projects end in failure. In some sense, the 3 V’s are to blame – the businesses see these enormous challenges and wonder if there is any way to overcome them. The good news is that there are many big data solutions and success solutions to help out. The Big Data Innovation Summit 2014 in Santa Clara provided many excellent examples from vendors and end-users.
Here is my Top 10 List of the V's of Big Data: