Big data is a field that treats ways to analyze, systematically extract information from, or otherwise, deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Sounds too techie and complicated, does it not? Worry not, in this article we shall break down what Big Data is in simpler, easier terms that could be easily understood for none-techies.
First off, you have to understand what data is. It might sound a bit weird seeing as you use that word so many times in the space of 24 hours, but you’ll be surprised. Also, if you defined data as information, guess what! You got it wrong.
Data and information are in fact not the same as there is a fine line between the two.
What Is Data?
Data is an individual unit that contains raw materials which do not carry any logical meaning. The symbol ‘!’ Is an example of data, but you see on its own, it carries no meaning.
What Is Information?
Information is a group of data that collectively carries a logical meaning. What’s more? Data doesn’t depend on information, but information depends on data.
Remember, we’ll break it down into very simple terms and leave out the overly complex parts.
So, data is unorganized and unrefined facts. Information comprises processed, organized data presented in a meaningful and logical context. So it’s safe to say that without data, there will be no information, but without information we can still have data, which is the raw material.
Think about it like raw material and finished products.
Let’s take Wood and Paper for instance. Wood from trees is the raw material (data) and paper gotten from the processed wood is the finished product (information). You see, the paper (information) depends heavily on the raw material, wood (data) in order to exist. Basically, that’s the relationship between data and information.
Big data is simply a collection of data that is too huge and yet continues to grow with time. It is data that is so large and complex that none of the typical data management tools can store it or process it properly.
Do not be confused, Big data is also data but with huge size.
Now that we know what Big Data is, the next question would be “Why is Big Data important”? You want to know what the big deal about big data is.
Firms use big data analysis to improve operations, provide better customer service, create personalized marketing strategies and take other actions that can increase revenue and profits. More so, businesses that use it effectively have a potential competitive advantage over those that don’t see as they’re able to make faster and more informed business decisions based on the data analysis.
Simply put, companies that use big data have access to more vital information and can make smarter business moves compared to companies that don’t use big data.
Big Data isn’t only used in the financial sector, it is used in a variety of fields such as medicine, energy industries, manufacturing and transportation services, and other government uses which include emergency response and crime prevention.
There are three types of Big Data and they’re:
- Structured big data
- Unstructured big data
- Semi-structured big data
1. Structured Data
Structured data, just as the name implies, is a set of data that is organized. It is quite easy to work with because all its dimensions are defined by set parameters. A typical example is what you see on a spreadsheet but on a much larger scale.
So, essentially any data that can be stored, accessed, and processed in a fixed format is called structured data.
2. Unstructured Data
Unlike structured data, this type of big data is not organized and sorted with instructions on how to use it. There is a general consensus that only 20% of all data is structured. So, if only 20% of all data is structured, does that mean the remaining 80% of data is unstructured? The answer to that question is yes.
Nearly everything you do with your computers and phones generates unstructured data. A typical example of unstructured data is a data source containing simple text files, images, and videos.
3. Semi-Structured Data
Semi-structured data is like a hybrid of structured and unstructured data, it toes the line between the two types of data. So, semi-structured data can contain both forms of data.
Characteristics of Big Data
Big data has four characteristics:
Just like the name ‘big data’, one of its primary characteristics is volume. Big data is primarily defined by its sheer size. The size of data plays an important role in determining the value out of data. Also, the volume of data is what ultimately determines if a data set is big data or not.
During earlier days, spreadsheets and databases were the only sources of data considered by most of the applications. Nowadays, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. are also considered in the data-processing applications. This variety of unstructured data poses certain issues for storage, mining, and analyzing data.
Velocity here, just like in our Physics classes simply implies the speed with which data is generated. The speed with which the data is generated and processed shows real potential in the data – the greater the speed, the better.
This has to do with the inconsistency which is portrayed by the data sometimes. This inconsistency hinders the ability to handle and manage the data efficiently.
There are a couple of big data management technologies out there, here are a few:
- Cloudera Data Platform
- Google Cloud Dataproc
- Amazon EMR
- Microsoft Azure HDInsight
Now that you know what big data is all about, you will not be taken aback next time you hear your colleagues talking about it. Big data is something we can see all around us, especially in this day and age where everything is digital. Furthermore, the bigger the digital footprint we make, the bigger the size of data. That is big data analysis is so crucial. Most of the top apps that you use on a daily basis to help make your day better and easier all rely on big data analysis.
See? The article was easy to read and digest, wasn’t it?
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