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These are just a couple of the questions that have been plaguing industries and enterprises worldwide since the “Big Data” phenomenon surfaced. By now, most of us have heard this buzzword/phrase that has been penetrating the minds of IT and analytics professionals alike. However, many organizations are still unsure how to effectively analyze and gain new insights from it. Luckily, there are expert specialists in this field who are eager to join and guide them through their journey.
What is “Big Data?”
I’ll spare you the formal definition and put it simply: “Big Data” is everything, and it’s everywhere. “Big Data” is defined by (at least) three ‘Vs’: Volume, Velocity and Variety. And you might even hear about a fourth ‘V’ depending on which “Big Data” solution provider you’re talking to.
- zettabytes = as much information as there are grains of sands on all the world’s beaches
- Veracity (IBM) — Accurate, truthful and trustworthy data
- Variability (SAS) — Data flows that may be unpredictable, inconsistent and anomalous
Now that we have a better grasp of what exactly “Big Data” is, I’d like to explore some of the complexities and challenges companies face because of it, as well, as the opportunities it presents.
Challenges & Complexities
The size, requirements, boundaries and resources of an organization, as well as the industry it’s in, can dictate the adoption of “Big Data” in addition to which obstacles will prevent them from extracting high-value impact and gaining new business insights that were previously unattainable.
However, there are a few common challenges despite the nature of the business:
- An abundance and variety of data sources and the information collected
- Inherent complexity in processing, management and aggregation
I intentionally left out a fundamental part of the “Big Data” definition when I talked about the three or four ‘Vs’ of this concept, but this is a perfect place to sneak it in.
IDC’s definition of “Big Data” embraces the hardware, services and software that integrate, organize, manage, analyze and present the data that is characterized by the ‘Vs’ discussed at the beginning of this post.
This is why new technologies and architectures, advanced tools and platforms are needed and are continuing to be developed. These appliances will allow enterprises to leverage “Big Data” and (you guessed it) analytics.
- Technical: Data scientists with an unparalleled level of skill to understand the interactions of a new class of technologies
- Analytics: Data mining; statistics; business analytics; problem solving; creativity
Although there are some hindrances to enterprises fully embracing this new era of “Big Data” and analytics, there are evolving approaches to conquer them. For example, the Google Analytics Premium and BigQuery integration that will be taking place toward the end of this year was just announced at the Google I/O a couple weeks ago. If you’re a GA Premium user, I’ll venture to guess that this made you smile — even if you’re not 100% sure what it’s going to mean for your business.
Check back next week when I’ll discuss what value, advantages, opportunities and possible use cases can arise from utilizing more advanced technologies, solutions, and analytics strategies such as the “Big Data” movement. Stay tuned!