Getting to the Bottom of Big Data

Man and woman Viewing a Large Screen of Information

If you ask five different people to explain big data, you’ll probably get five very different answers. Clearly, “big data” is referring to information, but what sets big data apart from any other information that isn’t given this catchy label? Big data, also called enterprise data, is one of those topics that seems like it should be far simpler than it is. If you’re pursuing a GCU’s DBA with an Emphasis in Data Analytics, you’ll explore the intricacies of big data, and how it can be used to support a company’s competitive advantages.

Understanding Big Data

Even if a universal definition of big data remains elusive, experts generally agree that it involves the following elements:

  • Substantial volume of data
  • Both structured and unstructured data
  • Sources from digital and traditional sources
  • For ongoing data analysis and discovery

When data analysts are talking about structured vs. unstructured data, they’re simply referring to data that can fit neatly into a spreadsheet or chart compared to data that cannot.

For example, let’s say you’re putting together a spreadsheet of movie data. You’ll likely have columns for titles, genres and directors. This is structured data that can easily be categorized and labeled. Unstructured data is a little messier. It comes from tweets, Facebook updates, pictures, webinars and emails. Unstructured data is trickier to categorize, but big data analytics can still glean intelligence from it.

Performing Big Data Analysis

Big data analytic tools must be capable of handling an unimaginably huge volume of information. To put this into perspective, consider these measurements:

  • 1 gigabyte equals 1,024 megabytes
  • 1 terabyte equals 1,024 gigabytes
  • 1 petabyte equals 1,024 terabytes
  • 1 exabyte equals 1,024 petabytes

One exabyte equals one quintillion bytes. This could consist of trillions of records. Clearly, big data analysis can’t be performed with human hands alone. Experts can put technology to work sorting through the data compiled by other technologies. Here’s a look at some of the technologies and techniques used to analyze big data:

  • Data mining: Identifies relationships and patterns
  • Natural language processing: Analyzes free form speech
  • Statistical algorithms: Builds models and considers possible outcomes
  • Machine learning: Adapts and enhances models to manage new data

Generally, the purpose of any given big data analytics tool could fall under one of these categories:

  • Descriptive (explains what happened)
  • Diagnostic (explores why it happened)
  • Predictive (explores what will happen next)
  • Prescriptive (recommends actions to produce a desired result)

Optimizing the Use of Big Data

The analysis of big data, and the application of its findings, can nurture business growth. It can optimize supply chains and cut waste. It can improve customer and client engagement. Big data analytics can even help companies gain the competitive edge by figuring out how to advertise in a way that attracts new customers and how to price products to optimize profits.

Are you passionate about data analysis? Consider applying to the College of Doctoral Studies at Grand Canyon University, where you can earn your Doctor of Business Administration with an Emphasis in Data Analytics.

The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University. Any sources cited were accurate as of the publish date.