In the past year, the word big data has been trending. Clearly, those up to pace with the latest trends already know what this is and what it means to everyone in whatever industry today. But, others do not really understand what big data is all about. Some do but do not really understand the details. This article will dig deep into what big data really is. Keep reading.
Put into context
Let’s put this into context. Today’s clients are wiser than ever and demand more than before for value for money. For that reason, manufacture, producer, or even provider cannot risk offering standards goods. But, how do you tell exactly what your clients want? You have to do proper analysis to establish the particulars your buyers prefer.
Today, data is available everywhere and anytime. Research has established big data has enough information to give you a better picture—an analogy you can to establish and predict the future. To get the full of it, you have to assemble this data. But, once data becomes enormous, handling it is not a walk in the park. You have to have the machines, brains, and time to disseminate the data into decision-making conclusions.
Big data helps determine patterns. From the pattern, you can tell the current trend and almost accurately predict the future. Once you have details of what the future holds, you can make strategic decisions to be more competitive and grow your organization.
Human behavior is what managers try to understand. By establishing this, you can intelligently make decisions that will ensure you stay in business and expand. When big data is mentioned, IT is involved. This mass of information has to be broken down into simple bits using special data processing software.
What is Big Data?
Have you ever been on a jet? It’s alright if you haven’t, but do you know its engine can generate more than ten terabytes of data for only thirty mins of flying? Amazing, isn’t it. Now think about how many flights take off every day. That is the petabytes of information every day. Do you use Facebook? Media uploads, messages, and comments on this social media platform create more than five-hundred terabytes of new data every day. That’s a massive amount of data. That’s what is known as Big Data.
These attributes make up the three Vs. of Big Data:
- Volume: The huge amounts of data being stored.
- Velocity: The lightning speed at which data streams must be processed and analyzed.
- Variety: The different sources and forms from which data is collected, such as numbers, text, video, images, audio, and text.
Moreover, there are more than three now because the concept behind Big Data has evolved a lot. Let’s go through a brief history of Big Data to understand what it really is. Data storage has got very cheaper with time, due to which it has become a lot easier and less expensive to store more data. But, why would anyone want to store data? Well, I can give you hundreds of reasons, but I guess the following will be enough:
- present this data to your customers,
- use it to create new products and functionalities,
- make business decisions,
- and so forth
The term Big Data is a pretty old term, but what we were calling Big Data a few years ago was far less data than it is now. It all began in the 1960s when the first part of data warehouses was opened. Decades later, companies saw how many datasets could be gathered through sites, apps, and any product and service users interact with. All this resulted in Hadoop’s popularity spike, NoSQL, and other Big Data services, which made storing and analyzing Big Data easier and cheaper.
Today we live in the age of IoT (aka Internet of Things). Millions upon millions of devices are connected to the internet, gathering data on users’ usage patterns and product performance. And then someone said, “Why not use all that data to have machines learn by themselves?” – so machine learning was created, and this started generating data, too.
So, to put it simply, Big Data is a larger and complex data sets. Now, these data sets cannot be managed by traditional software primarily because they are too big. That’s why a new set of tools and software were created.
Big Data Tools
There are many tools out there that can be used to manage Big Data, and the good news is that a big part of them are open-source ones. Different organizations opt for different tools depending upon their needs. An open-source framework for storing and processing large sets of data, Apache Hadoop is the most established one among all the Big Data tools.
Another solution is the Apache Spark, and a rising star following are its main advantages:
- It can store a big part of the memory’s processing data and on the disk, which can be much faster.
- It can run on a single local machine, thus making working with it very much easier.
Written in Scala and Java, Apache Kafka is another Big Data tool. Kafka’s main task is to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Other big data tools are:
- Apache Lucene
- Apache Zeppelin
As they say, the only thing constant in life is change. The same is true for Big Data as well. As time passes, it will continue to grow and change, and the same will happen to the tools as well. I think it’s time we should go through different types of Big Data.
Types of Big Data
So, there are three types of Big Data:
- Structured data
- Semi-structured data
- Unstructured data
Let’s review each type in detail.
- Structured data conform to a data model, has a well-defined structure, follows a consistent order, and can be easily accessed and used by humans or a computer program. This data type is usually stored in well-defined schemas such as SQL databases, data lakes, and data warehouses.
- Unstructured data is not organized in a predefined manner or does not have a predefined data model. Thus it is not a good fit for a mainstream relational database. For instance, it includes data gathered from social media sources, and it can be put into text document files held in Hadoop like clusters or NoSQL systems.
- Semi-structured data has not been classified under a particular repository (database) but still contains vital information or tags that segregate individual elements within the data. You can store them in the relation database (this may be very hard for some semi-structured data), but Semi-structured exist to ease space. Example: XML data.
How does Big Data work?
The more you know about anything, the more you can gain insights and make an informed decision. This is the main idea behind Big Data. With time, the tools have become so advanced that this process is completely automated, apart from a few cases. These tools can run millions of simulations to give us the best possible outcome. Achieving all this automation with analytics tools, machine learning, or even AI is not easy. You need to know how Big Data works and set up everything correctly.
A very stable and well-structured infrastructure is required to handle these huge volumes and different types of data. All this data can easily overload a single server or cluster; thus, it can potentially demand hundreds or thousands of servers for larger companies. Moreover, when you add in all the tools you will need… this can start to get very pricey. Therefore, you need to know how Big Data works and the three main actions behind it so you can plan your budget and build the best system possible.
Big Data is always collected from a plethora of different sources. As we speak for huge volumes of information (in some cases, petabytes of information), it will be a big challenge to integrate such enormous loads of information into your system. Once you receive it, you will have to process and format the data in the right form, according to your business needs.
Another thing that you will need for such enormous loads of information is a place where you can store it. You can opt for either cloud, on-premises, or both.
Okay, so you have received, integrated, and stored the data; the next step is to analyze it so you can use it. Explore your data and use it to make important business decisions, such as knowing which feature are your customers using the most and then further improve it and so on. Do whatever you want and need with it – put it to work because you made big investments to have this infrastructure set up, so take full advantage of it.
Big Data Interpretations
There are basics to big data interpretations. When a mass of data has an increased number of rows, the impression given is a hike in the statistical power of the mass. Well, that means better decision-making by the management. When the data has increased complexity, then it will be harder to get details, although you will have a bigger picture of how things might look like in the future.
The bigger the bulk, the higher chances of making false conclusions. It might be misleading as well as beneficial. That is why you need experienced data handlers. People who can break down information into bits and make solid conclusions that will change the organization’s fortunes for now and for good.
Challenges facing Big Data
Big data is not so new a concept, but not many have the right experience in dealing with big data. Lack of proper experience causes wastage of time and making decisions that may be experimental. In the long run, that may cause the organization money or even reputation. Before time passes, you realize you have lost your dominance as an organization.
Big data analyzes already captured information. There’s little effort in finding out whether this information is valid or tampered with. That means if the data was wrongly keyed, then the big data analyst will be relying on the wrong information. Well, that spells doom for both the analyst and the organization. Data should be captured correctly. Therefore, whether the analysis will be as accurate as expected or not depends on the officer’s accuracy in the capturing or the machine to be used.
There are different data capture methods. Different organizations have varying data capture technique which the employees work hard to implement. The accuracy of this information is subject to methods employed to receive and save the information. Besides, this information is not always true.
For instance, I’ve dealt with clients who chose to lie their age while registering them for the first time to our institution. Well, later on, I found out she had altered her age by a year. When you have nothing to prove against some things, you have to accept them as they are. If we were to sample individuals of that age group, it would reflect an extra one. That would be slightly misleading. The analyst might give the correct advice but for the wrong thing on the ground.
The medium of storage has and will always be a great challenge. Whether to use the clouds, papers, or soft copies for storage remains a huge decision to take. Storage in hard copies requires a lot of filing. With time, the files pile up and may cause the massive unnecessary need for shelves. If not destroyed or gotten rid of, the files can strain the office space.
Soft copy is a better alternative to hard copy storage. It is less expensive, easier, convenient, and saves office space. The only challenge is the risk of losing information if the storage device is messed with. Altering soft copy information is also possible and easy. That makes the information untrustworthy. Cloud offers one of the storage options at the moment.
Big data somehow takes away the issue of privacy. Information loses confidentiality, and that marks the dawn of problems. Client information should be private and confidential. During analysis, this information is shared among people who are human and many times leak the same. In the end, the information lands the wrong people problems start there.
Big data has a great future ahead. Organizations will be running to data analytics to determine which decisions to make. While storage and privacy continue to be a challenge, the accuracy of captured information will always be challenged. Big data is gaining popularity by the day, and management is realizing, you can make better decisions by relying on big data. The cost of using this analytical method is not low. The best thing about it is the productivity and value for money. Much as you’ll spend a lot on investing in big data, you can be sure it will yield results.
So, that’s it for now. I hope you loved the article. Thank you for reading.