If you’re a novice to the concept, Big Data might sound like a solution that’s only fit for the top 100 companies in the world. But Big Data spans across more usage models than many may think. In fact, Big Data is catching on and is likely to become plausible for more and more companies over the next several years.
What is Big Data
One of the paramount challenges of Big Data right now is the lack of understanding concerning the details of the technology. For a lot of people, Big Data is a term that’s shrouded in a cloud of uncertainty. However, understanding Big Data is becoming imperative for those who can benefit from its technologies. After all, Big Data isn’t going to go away. It’s only going to get bigger.
Thankfully, Big Data isn’t really that difficult to understand. The MetaGroup has come up with a helpful and widely-accepted definition to explain the technology in a generic and universally-applicable way. According to their definition, Big Data is “a volume, velocity, and variety of data that exceeds an organization’s storage, compute, and management capacity for accurate and timely decision-making.” To put it simply, Big Data is merely taking unstructured data and turning it into something that is sensible and analytical. It’s going beyond just storing, managing, and computing data; it’s sorting data in way you can study it and make business decisions off of it.
There is structured data, and there is unstructured data. For example, if you have a SQL database, you can run a report against it and it will come back with details and business intelligence about the data in there; this is structured data. However, if we don’t put our information into a structured database, it becomes unstructured data. As an example, spreadsheets are unstructured data, emails are unstructured data, call logs, documents, and videos are unstructured data. And unstructured data is unmanageable. However, if we are able to gather all that data (spreadsheets, emails, documents) and analyze it as one logical stream, we would have better business intelligence.
Naturally, this produces another challenge in and of itself. When any business is considering Big Data, it has to first identify its data sources and what it wants to learn from each. For example, at VLCM, we have a financial database, we have multiple customer databases, we have vendor databases, and we would need to determine what we want to know about each one.
The Advantage of Big Data
Without Big Data, if we have an analytical question to answer, we could only go to one database at a time and ask ourselves something like: who is our best customer? With disconnected data sources, we may be able to determine with whom we gross the most revenue, but then that doesn’t settle the matter of loyalty, necessarily. We could go to another source and determine with what customer we communicate the most, but that doesn’t tell us anything about the nature of those communications. If we have a financial database that isn’t tied to our phone system, then we can’t see who is calling us but never buying. These are the sort of dilemmas that Big Data aims to solve.
In any type of business where data is very key, Big Data solutions are of considerable importance. But what many are beginning to realize is that Big Data can be feasible for many smaller applications as well—and by smaller I don’t mean sources that haven’t had any use for it thus far, because there are certainly some cases where it wouldn’t make much of a difference. But for every other business out there, Big Data is a technology that is progressive and helpful.
Data is an invaluable resource to businesses. A lot of work goes into gathering it and securing it, but using it to a company’s full advantage is where it gets tricky. Without proper data usage, companies can end up wasting time that could or would have been more effectively used elsewhere. This can come about in several different ways, whether through unclear prioritizing, lack of flexibility and adaptability, or from the inability to effectively assess best practices as it relates to real measureable growth. These kinds of bottle necks are the very thing Big Data works to eliminate.
Big Data can be seen, in a most basic sense, as three stages to data usage. The first stage is the gathering of data. There are many resources employed for this, and they aren’t usually a part of a Big Data solution. The second stage is in storing and organizing the data. Likewise, data storage, in this sense, is a huge solution in and of itself, but with these two stages in place, a company has only what we have always had before Big Data came into the picture. This networking of data is what so many of us depend on right now. But the difference in Big Data is all about the third stage—the analysis and visualization of the data that has been gathered and stored.
Of all the data that a company stores, on an average and under a general model, only about 10% of it is structured. Structure data isn’t the problem though; it’s the 90% of unstructured data that can’t be utilized without the proper solution; this is what creates a void. Big Data solutions are used to add structure to unstructured data through means such as tags, metadata additions, and XML extensions. With these structures in place, the analysis and visualization of data is made possible.
Analyzing, in a sensible fashion, the vast amounts of data a company has is something that would benefit any organization, because it allows a team of individuals to identify trends that can help the company advance the efficiency of their business. And finding out whether a Big Data solution is a sensible option is the first step.
This is where VLCM comes into the picture—helping our customers identify such needs and deploy the infrastructure necessary to implement something like Big Data. Though presently, there are only so many companies justifying the investment in Big Data, as the solutions progress and become more affordable and common, there’s certain to be an increase in Big Data implementation in the coming years.
About Josh Linton
Josh Linton is the Vice President of Technology at VLCM, which is celebrating its 30-year anniversary in 2013. In this role, Josh manages the company’s technical team that provides tech support and services to its clients. He is also responsible for evaluating and recommending new products and services to customers.
Josh graduated from Brigham Young University (BYU) with a Bachelors of Science degree in Business Management with an emphasis on Information Systems.