We’ve all heard of big data by now, but what exactly it is and what are the characteristics of big data? Big data is a term used to describe high volumes of data that are so large that traditional database systems cannot keep up with the amount of information being collected. While there isn’t one set of characteristics that every piece of big data share, there are five characteristics that most pieces of big data have in common. They are also called 5 V's of Big Data. We will take look at first what is big data and its types then characteristics of big data will be discussed.
Table of Contents
What means Big Data?
Big data refers to huge amounts of data that conventional database management solutions are unable to process. This can include:
- Unstructured data, which means it is not organized in a way that can be used by traditional databases. For example, text documents and images contain information about people and places; however, they do not fit into structured fields like name or address. These types of files are called unstructured because they lack structure—in other words, they're messy!
- Large numbers of records (or "nodes") within each batch may also cause bottlenecks due to the sheer number of connections needed between nodes/files on disk/in memory etc.; this results in slow processing speeds due to excessive network traffic overhead as well as increased CPU consumption per request due to having too many parallel threads performing I/O operations simultaneously."
Types of Big Data
Big data is a broad term that refers to the collection, storage and analysis of large amounts of unstructured or structured data.
- Structured data refers to information stored in a structured format such as databases or XML files. Unstructured data refers to information stored in an unstructured format such as text documents or images.
- Unstructured data often comes from sources like sensors that collect information about the environment around them (e.g., weather reports).
- Semantic Web technology allows us to use machine learning algorithms on such unstructured sources for improved results when dealing with this type of content.
Three broad categories can be used to categorize big data:
- Business Intelligence (BI)- This type of big data consists of structured, transactional and demographic data. Companies use BI software to analyze their sales pipeline, customer demographics and product performance among other things.
- Social Media- This type of big data includes user generated content such as Tweets, Facebook posts and YouTube videos. Companies use social media monitoring software to monitor trends and engage with their customers.
- Machine Learning- This type of big data consists of unstructured data that needs to be analyzed in order to make predictions about future events.
Characteristics of Big Data
The characteristics of big data are not just about size. They include:
Volume
Volume is one of big data's characteristics. Volume refers to the size of data. It’s measured in bytes, and there are several ways to measure volume:
- Bytes per second (bps) – This is how fast your system can process data. For example, a computer with 1 TB hard drive could process 1 terabyte per second (TBps). A slower hard drive would give you less than 1 TBps while a faster one would give you more than that.
- Terabytes per second (tb/s) – This is how much data can be transferred over network at once by using one computer and its network connection(s). If someone uses their laptop as an access point for WiFi at home then they might have an average bandwidth between 15-30mb/sec which means they have around 15-30 mbps available for other devices such as smartphones or tablets connected via WiFi.
Variety
Another important characteristics of big data is variety. Data comes in many different shapes and sizes, with the most common being unstructured, semi-structured and structured.
Unstructured data refers to raw text (e.g., news articles), geographic information systems (GIS), images or video footage. It's hard to find a single definition for this type of data because it can be anything from a single piece of text on an article to a large collection of video clips recorded by someone who was at an event where they saw something happen that you might want to know more about.
Semi-structured refers to databases with rows and columns but no rules around how each row should be represented as fields; there may not even be any real names assigned specifically for each column in such databases—these are simply labels given based on their content meaning rather than any specific definition given by programmers or database experts when creating them originally! This means that if we're dealing with open source software like SQLite3 then there could potentially be multiple versions floating around out there depending upon which developer built whatever program was used initially before being released into public domain where anyone else could use them freely without having any legal rights associated with ownership whatsoever either due process required before taking ownership over such things unless otherwise stated otherwise within contract terms agreed upon beforehand between parties involved during initial negotiations stage."
Velocity
Big data's biggest characteristic is its velocity. Big data flows at a much faster rate than traditional data and can be instantaneous. It takes something like 10,000 seconds to fill up an 8GB memory card with high-definition footage, whereas a minute of high-definition video content can use up about 3GB.
Velocity is the speed at which data is generated, processed and consumed. Velocity is measured in terms of how fast a business can respond to changes in its business environment and how quickly it can take advantage of opportunities that arise.
As an example, let's look at a company that sells security cameras. As the economy expands and shoppers buy more expensive goods like cars or houses, they will need more surveillance systems installed around their properties in order to keep them safe from intruders. This means that this particular retailer must increase its inventory levels (which means hiring more salespeople) as well as buying more equipment—things like video cameras—to meet demand from customers in order to stay competitive against other companies who might be selling similar products at lower prices due to having lower expenses for labor costs associated with manufacturing them themselves rather than outsourcing these tasks onto another entity such as Amazon Prime which has access to sales data that can be used to make strategic decisions about how many products to buy and when.
Veracity
Veracity is also an important characteristic of big data. Veracity refers to the accuracy of data. The data has to be accurate and consistent, so it's important that you use only reliable sources for your information.
The veracity of a dataset depends on several factors:
- The completeness or accuracy of each piece of information in the dataset (that is, whether each piece accurately represents what it purports to represent). For example, if an article about homelessness in New York City claims that there are more than 8 million homeless people in America (which is impossible), then we can't trust those numbers as true because they have an obvious error rate—they're wrong! We need better sources for our information than this one; otherwise we'll end up with inaccurate results from our analysis anyway.
- Whether all pieces of information within each source are relevant or not—for example, if only some parts represent reality while others don't make sense based on other facts from another source.
- Whether these different sources agree with each other when compared side by side.
- What time period applies to each piece; if it doesn't matter how long ago something happened then why bother reporting on it at all? Whether this source is reputable—it's easy to make up a story or tell lies, but we want information that can be verified by other sources and has some kind of proof. Whether there are any conflicts of interest between the writer and their subject. For example, if a newspaper is reporting on something that happened within their own company then it might not be as unbiased as we'd like it to be!
Value
One of the valuable characteristics of big data is its value.
Value refers to how useful or important a piece of information is. If we see something that can be used to make money, or save lives then it's obviously more valuable than something that doesn't do either of those things! We also want to know if this information is new; if everyone already knows about it then why bother reading about it?
The value of big data is Twofold:
- It provides you with information about your customers. This can be used to make decisions, such as what products or services to sell and how much of them to sell.
- It gives you data about your competitors’ products and services so that you can compete effectively against them (and other companies).
Conclusion
While there are many characteristics of big data, the following are the most important. If a business is able to combine those five characteristics into their company-wide approach then they will be one step closer to being able to prepare for any big data situation.
Frequently Asked Questions
Q1. What are the characteristics of big data?
- Volume
- Variety
- Velocity
- Veracity
- Volume
Q2. Types of Big Data
- Structured
- Un-Structured
- Semi-Structured
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