The 4 V’s in Big Data

ibm-big-data

(IBM, 2011)

I think we all can agree that there are benefits to big data, but it is fairly new and in its infancy so we are not sure yet about all its advantageous and disadvantages and are looking at its usage, accuracy, limitations, as well as its philosophical and ethical dimensions. But, there are even more basics issues that arise and must be addressed when working with big data and that is the mere collection, storage and networking of all this data.

I came across this article by Tanya Roscorla (2014), titled, “4 Big Data Challenges that Universities Face”, that discusses the various logistic dilemmas that arise due to big data.  “University presidents grapple with how to advance research in an era where big data and big science place increasing demands on networks.”

“Ten years ago, IT leaders weren’t always sure what researchers wanted, so McRobbie decided that his campus would find out. Brad Wheeler, Indiana University’s CIO and vice president of IT, pulled together a community of about 15 people and asked what they wanted. Ultimately the group was looking for the ability to store and preserve data. So that’s what IT gave them.

“It is absolutely essential to ask and continually ask the researchers what it is that they want,” McRobbie said.

As university leaders support their campuses’ missions, they face four major challenges on the road to unlocking the potential of big data and science:

  • Volume: The sheer amount of data coming out of big research projects is staggering.
  • Velocity: The sheer amount of data coming out of big research projects is staggering.
  • Variety: Along with volume and velocity, a variety of data from numerous, if not unlimited, sources and geographic locations poses a research challenge.
  • Veracity: By bringing together data from different sources, researchers now have to determine which information to trust and use. “

In light of all these issues, we have to concentrate on some of these basics before launching optimistic and maybe idealistic initiatives for its use especially in education where a lot sensitive data is being collected. Furthermore, we need to look at how to organize all the information—including perhaps third-party data from commercial outlets and social media channels. Also, determining what data amid the volume of “noise” could be useful in understanding and responding to educational strategies, effectiveness and /or trends.  This sure is definitely one immense challenge facing any industry wishing to use big data.

References

IBM. (2011). IBM Big Data and Information Management. Retrieved from https://www-01.ibm.com/software/data/bigdata/.

Roscorla, T. (2014). 4 Big Data Challenges that Universities Face. Retrieved from http://www.centerdigitaled.com/news/4-Big-Data-Challenges.html.

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