Big data seems to be on the lips of every business and industry out there. It has become the go-to conversation piece, the subject everyone “in the know” talks about. As the common perception goes, if you want your business to be successful, you’re going to need to adopt big data analytics. If you’re in the position of investigating big data to see if it’s right for you, no doubt you’ve come across a number of words and terms you don’t understand. These are the big data buzzwords, and they can be very confusing to those just dipping a toe into the big data waters. To clear up any confusion and help you become familiar with big data terminology, here are some of the most common buzzwords along with what they mean.
Let’s begin with the very words you’re taking a closer look at: big data. What exactly is big data in the first place? While there’s no official definition, big data can be best described as the massive amount of data that organizations can use to improve their businesses. A more concrete definition usually comes in the form of what are commonly called the 3 Vs: volume (the massive amount of information), velocity (data that needs quick action) and variety (the different types of data). Big data is an evolving concept, but this is a good starting point.
Predictive analytics is one of the ways you can use big data within your company. It involves the use of statistical modeling in order to predict future trends and results based on past data. Predictive analytics usually involves algorithms that take relevant collected data and formulate possible outcomes from that information. Some techniques of predictive analytics include decision trees, neural networks and regression models.
There are many different types of data terms, and dark data is one that features prominently. Data that gets little or no attention or use is usually referred to as dark data. Sometimes, this can be a case where organizations are aware of the data, but they simply don’t know how to access it or use it properly. This often can be a problem when you’re focused so much on gathering data that you fail to account for how to put it to good use.
Another type of data is called fast data. This type refers to data that is best used quickly as its value tends to decrease the longer you go without using it. Fast data is data that should be acted upon almost immediately after being collected and analyzed. Data collection and analysis processes are best used with fast data in real time. Social media feeds and streaming data often fall under the label of fast data.
One of the most widely used big data tools out there, Hadoop is an open source platform designed specifically for big data storage and processing. Due to its easy scalability, versatility, scalability and free price tag, many companies use it for their big data needs. While not all cases of big data call for Hadoop, many analytics projects do; so having employees on staff who know how to use it can be extremely valuable.
Machine learning can cover a wider area than something like predictive analytics. Machine learning techniques also use big data but can be applied to decision-making processes. The algorithms involved in machine learning have a goal in mind and figure out on their own the best ways to reach it. This doesn’t require specific coding. So the more data machine learning algorithms have, the more efficient their work will be. It’s a continually improving process, one that is gaining greater popularity.
Like Hadoop, Apache Spark is an open source tool that is quickly growing in popularity. Spark is also a big data framework but serves different purposes to Hadoop. Unlike Hadoop, Apache Spark doesn’t distribute storage, instead operating on distributed data collections. That means it can process data from all sorts of data repositories, which includes Amazon Simple Storage Service. Spark can also improve big data analytics applications’ performance thanks to its support for in-memory processing. It’s generally faster than other frameworks and features built-in resiliency.
Converged infrastructure is a specific strategy organizations can deploy to manage their data centers. Sometimes compatibility issues arise between storage systems, networks, IT software and servers, and converged infrastructure tackles those issues directly. It works by combining all of those IT components into a single, easy-to-use package that can be installed into an organization’s system. By pooling these computing, networking and storage resources, multiple applications can draw from them. This leads to reduced costs for things like energy and cabling and can even result in less physical space needed for IT infrastructure.
There are many buzzwords surrounding the growing field of big data. The ones above are a good place to start getting familiar with the terminology. Before long, they’ll be second nature to you, and you’ll be able to talk about analytics easily, knowing precisely what others mean as you discuss how best to take your company to the next level.