By Kushagra Jajoo, 22nd august, 2020
What comes to mind when you hear the words big data and analytics? Something technical or boring or maybe something even interesting. Whatever it is that you may feel about it, one thing for sure is that the world is moving in a direction that is heavily going to depend on big data and analytics. So what is big data and analytics? Well, as the name suggests, big data is a large amount of versatile data and analytics is the innumerable permutations and combinations that can be applied to it. I say versatile because the data can be in various forms like organized, unorganized, structured, or unstructured. This shows how BIG, big data is. And the innumerable permutations and combinations that can be applied to it are the various ways in which the data can be processed, analyzed, studied, researched upon to glean insights and interesting facts or patterns which could directly or indirectly change the way an organization functions.
In simple layman terms, big data is the grain stores in granaries and analytics is the factory and machinery required to process it for usage and consumption. This is where the main obstacles in what is big data and analytics come in. Firstly, as said before, the stored data is versatile which is stored in many different places and even more different forms. Just the huge scale of the data would make one dizzy. Secondly, the tools required to process this data need to be of a certain scale and advanced level as well. This means that the amount of grain stored in many different places and forms is huge and the machinery required to process this huge data even more so. The sheer complexity of the data cannot be solved with the traditional systems and methods available.
Why big data and analytics?
As the number of people using the internet by day increases, so does the data produced by them. This only contributes more to the amount of data out there and the need of companies to better cater to their audiences makes them want to try to decipher this massive data to see what the people want and like and what they do not. Let’s be honest, the internet is not going anywhere anytime soon and we aren’t going to stop using it either. So the only option left is to use it to scale the ever-increasing mountains of data to unearth beautiful gems and ore veins.
Though the above is just a simple brief of why we use and need big data and analytics. Some very beneficial tangible pros come with big data and analytics. Firstly, data is like a never-ending stream and that means there will always be new types of jobs coming up in this field. Secondly, data comes from everywhere. Be it climate or weather information, social media, politics, sensors, audios, videos, images, digital maps, etcetera. It is a never-ending mine and mining it increases efficiency, reduces cost, makes operations smoother and more seamless, reduces risk, helps in providing better customer service which leads to better customer satisfaction which in turn results in better customer retention, helps in decision making, etcetera. This proves that big data and analytics is going to play a very critical role in the upcoming future. This can be seen even now as the google searches you make or the things you see on your devices are all data which is used to suggest content similar to your tastes. While this does paint a daunting scenario of the machine knowing you better than you, that is a debate for another time.
Mostly, people categorize big data analytics into four different types. While some might classify big data analytics into more than four or less than four different types, the main goal here is to make it easier for the people to understand what it is and how it works. As data accumulation is an ongoing process, the categories might increase or decrease in the future as the data being processed increases and the method’s processing said data get better.
As the name suggests, predictive is a way to predict or foresee, estimate, and guess the future based on previous trends and what has happened.
Prescriptive on the other hand prescribe or recommend a series of moves that should be made like medicine that should be taken after a sickness. The only difference being that this prescription comes before the sickness based on previous trends to remain ahead while actual medicine comes after the sickness.
What descriptive does, is describe or depict/ report what is going on now based on the immediate data that’s coming in.
Diagnostic diagnoses or determines what happened previously and why it happened the way it did.
Some tools being used in big data analytics:
The above are just a few of the tools out there to help you out in big data analytics. These range over a vast variety of functions and learning how to use them could make your life easier in big data and analytics.
Now the big question. How to use it?
The following will be a simple step by step guide that outlines and provides a basic layout on the functioning of big data and analytics.
Interested in learning the skills that go behind big data and analytics? We know just the places you can get started.
As online is the direction the world is moving towards, there are classes that you can take online on DataCamp, YouTube, Udemy, and the like to gain the necessary knowledge of the field you are interested in. Even so, I won’t waste your time telling you about which classes to take. I think that can be left to personal choice. What I will tell you are the subjects you need to delve in and the skills you need to learn.
To be able to synthesize something valuable from raw data will prove to be very crucial in this field of work.
- How well do you know your numbers?
This is important because the level of your relationship with your numbers and your understanding of each other can put you in a better position to grow.
Or simply coding. The data won’t process itself and the algorithms needed to process it or to understand the data itself will require computer knowledge.
Yes. As groaning as it made you sound, this is an important part of big data analytics as mathematics and specifically, statistics have a major role to play in it.
- Business and market knowledge
If you don’t know the market or business trends or how markets and businesses function, then how will you apply your data to it?
- Can you play with data?
If you can convert data from one form to another, analyze it, visualize it, make sense of it, then you are well on your way. Enough practice and you will develop a certain intuition towards data that will allow you to communicate with it and make quick judgments.
- Artificial Intelligence & Machine Learning
Firstly, they are not the same thing. Artificial Intelligence is the womb that cradles machine learning. Gaining a deeper understanding of these will help you with analyzing your data better.
While degrees are not everything and they are losing their value by the day, they still hold some importance in the world out there. The following is a list of a few big data and analytics certifications from around the world.
- Certified Analytics Professional
- Certification of Professional Achievement in Data Sciences
- IBM Data Science Professional Certificate
- Open Certified Data Scientist
- Cloudera Certified Associate Data Analyst
- Microsoft Certified Azure Data Scientist
- Microsoft Certified Solutions Expert: Data management and Analytics
The big question we started with was – what is big data and analytics -and no doubt using big data and analytics can prove to be advantageous as can be seen through live examples in the companies and governments around the world. And if this is not BIG enough to blow your mind, then consider this. A stack of DVDs circling the Earth not once, not twice but a whole 222 times. Inconceivable right? Well, that’s what you get when storing 175 zettabytes of data on DVDs. And that is the predicted amount of data the world will produce by 2025 & also just the tip of the iceberg. This only proves that big data and analytics are going to get more and more important in the future and to hop onto the train right now would be a wise decision to make.