Big data is a commonly used term within the contemporary online marketplace, and refers to collections of data too large, complex, or varied to be categorized by traditional data processing software.
These collections of data often come with their own problems and challenges, especially with regards to categorizing and sorting them into manageable collections.
So how exactly are they categorized, and what other challenges do they pose?
The Three V’s Of Big Data
As mentioned, along with being somewhat nebulous in size, big data can also be incredibly complex and varied.
However, there are particular characteristics that all big data tends to share, making them somewhat easier to categorize.
These characteristics are known as the ‘three v’s’, and include volume, variety, and veracity.
Volume
This is the quantity of the data stored and generated. The overall size of a collection of data determines the level of insight you can actually attain from analyzing it.
The larger the collection of data, the more frequent the chance for false discoveries, anomalies, and incorrect findings.
Variety
This relates to the type and nature of the big data.
While earlier technologies were more than capable of analyzing and storing large amounts of uniform, tightly regimented data, with the ever expanding and growing nature of the internet has come a change in the types of data we are seeing.
These new types of data are generally more unstructured, non uniform, and are more prone to being anomalous, less detailed, and full of inaccuracies, thus making them unsuitable for processing on traditional forms of technologies – such as RDBMSs (or ‘relational database management systems’).
Veracity
When we talk about veracity, we are talking about the reliability, or the ‘truthfulness’ of the data. This is directly related to the quality of the data, and the value it has to wider analyses and study.
For big data to be valuable, it must not only be large in size, but also veracious, otherwise it cannot be used in any useful way of analysis.
Unfortunately, the captured data, and the quality therein, is more riddled with mistakes and inaccuracies than ever before, making it incredibly difficult to sort and use.
Other Characteristics Of Big Data
While the three v’s are the most commonly used means of characterization, there are several other factors that can be taken into account when discussing and sorting big data.
Velocity
This is the speed at which the data is generated and processed to meet the demands of the intended purpose.
Big data can often be made available in real time, meaning that when compared to small data, big data is produced more consistently (and continually).
When referring to big data, the two main kinds of velocity used are a) the frequency of data generation, and b) the frequency of the recording, handling, and publishing of said data.
Value
When we talk about value in relation to big data, we are referring to the worth in processing large data sets.
This worth is classified in terms of the three v’s (and other qualities), as well as any financial gain that analyzing large data sets might have to the analyzing body.
Variability
This refers directly to the changes that big data continues to undergo in terms of structure, formats, and the sources where the information comes from.
Big data can now include structured, unstructured, or combinations of the two – a series of variations that can make processing an arduous task. This is why decisions need to be made as to the value and overall worth or undertaking these tasks.
Exhaustive
This is whether the entire system has been captured and recorded or not. Big data, by its very nature, may not include a complete set of data from its original sources, thus adding to its variability, and often unreliable status.
Fine Grained & Uniquely Lexical
Fine grained refers to the specific data of each element, per element collected. Uniquely lexical on the other hand refers to data where the element, and its characteristics, are properly identified.
Relational
This refers to collections of data that appear relatable to one another, thus warranting further combined analysis and pairing.
Extensional
This is if new fields within the data element can be added or changed easily.
Scalability
This refers to instances where the size of the data can expand rapidly.
What Are The Applications Of Big Data?
Despite the unpredictability of big data, there are several applications for it within modern society.
Government
One major governmental entity that makes the most advances and use of big data is the NSA (National Security Administration).
The NSA constantly monitors internet activity in the hopes of picking up, recording, and collating suspicious activity, dangerous occurrences, or worrying data trends that could point to national security issues in the future.
Another source of big data for governments is the civil registration & vital statistics (CRVS), which provides various forms of information pertaining to people from their birth date to their death.
International Development
Another potential application of big data is in international development.
Big data allows for groundbreaking advancements to be made quickly, particularly with regards to things like healthcare, employment, economic productivity, crime, security, and natural disaster resource management.
However, it has also been shown to worsen already existing issues of privacy, poor methodology, and interoperability issues, made more prominent due to the implementation of this technology into developing countries with poor or dated technological infrastructures.
Final Thoughts
And there we have it, everything you need to know about big data, the three v’s, and the applications for it within global society.
Big data might seem like a confusing concept, but it has a number of important uses. And despite justified concerns posed by critics, many of these applications and uses remain paramount to the way we live our lives today.
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