What is Big Data, and How Can You Benefit from It?

“Big Data” is a term that should be familiar to everyone in the modern world of digital technology. It is an ever-evolving concept which continues to reshape the way the average person thinks about—and generates—digital data. From how businesses build their IT infrastructure to how we go about our daily lives, big data is intrinsically connected with every person on this planet.《Glossary: What is IT?》

You might be thinking: I have heard of big data, but how does it affect me personally? Why are enterprises clamoring to get on the big data train? Are there any drawbacks to using big data? In the following “Tech Guide”, GIGABYTE Technology, an industry leader which provides high-performance server solutions for a wide range of vertical sectors, goes into detail about what big data is; we also explain how you can benefit from using the right GIGABYTE products.

As the name implies, “big data” is a massive set of data, and it is being added to every time someone uses a computer or browses the internet. Since this happens every second of every day in the modern world, this enormous collection of data is growing exponentially. How many views a YouTube video gets; who buys what on an ecommerce website; what the online transactions of a banking system are; who is interacting with whom on social media; how much time is spent reading a webpage—all these random tidbits of information contribute to the massive entity we call “big data”.

Given the explanation above, you might be tempted to think all data is “big data”, and so there is in fact no distinction between the two terms. But that is not so. Not only does the modern definition of “big data” specifically refer to data points that are too massive and complex for traditional processors to handle, it also points to the fact that enormous data centers and server farms, comprised of hundreds, if not thousands of servers, have been built to organize and extract value from big data. One popular way of conceptualizing big data that is often cited is the “3 V’s”, proposed by Gartner analyst Doug Laney in 2001:

Glossary:
《What is Data Center?》
《What is Server Farm?》

● Volume
It should come as no surprise that the first rule of “big data” is that the volume must be “big”. Depending on the time scale, something as innocuous as browser history may take up terabytes (TB), petabytes (PB), or even exabytes (EB) of storage.

● Variety
Not only is “big data” big, it is invariably complex. Besides the “structured input” that is being added to the database at all times, there is also “unstructured input”, which is becoming more common as technology advances and the definition of data changes. Needless to say, “unstructured data” will need to be dissected and organized before it can become a valuable part of big data.

● Velocity
Despite its massive size, big data comes and goes at the speed of light. Modern computers must receive, process, and transmit data fast enough for it to qualify as “big data”. In other words, big data may intersect with what is known as “real-time” data.

Over the years, the “3 V’s” have undergone some changes. As big data has gotten progressively bigger, more fragmentary, more complex, and more various, the data’s reliability and usability have also come under the spotlight. Modern experts tend to add two more V’s—“veracity” (whether the data is accurate) and “value” (whether is data is useful)—to the traditional “3 V’s”. If you remember the “5 V’s”, you will have a pretty good sense of how big data is not like other data. However, there’s no telling how many new “V’s” (or other letters) people might come up with as technology continues to advance.

In the past, if we wanted to learn about the behavior of a certain demographic (how middle-aged women responded to shampoo ads, for example), the scientific way would be to study quantitative survey data and statistics. Big data turns the old method on its head by tracking and observing ALL data. Since more and more aspects of our lives are being digitized and going online (just consider how far we’ve come, from connecting to the World Wide Web to shopping online to building the Internet of Things), it is not inconceivable that big data will soon become a replica (or a digital twin, if you will) of all layers of human behavior.

Glossary:
《What is Internet of Things (IoT)?》
《What is Digital Twin?》

Let’s use ecommerce as an example. What the customer looks at and what the customer buys—that is information that is readily available to the seller. Big data can be used to analyze user behavior and give the shopper what they want before they even know it. Precision marketing and accurate ad placements can not only improve the user’s overall experience and close more deals, they can also reduce ineffective marketing and operating costs. In this way, big data can increase revenue while cutting down on expenditure.

Big data also presents a more accurate depiction—the “big picture”, if you will—of reality. Getting your hands on such a treasure trove of information means that you can help your company mitigate risks and eliminate errors. But there are pitfalls to avoid. We come naturally to our next question: how can you utilize big data effectively?

Just about everybody is using big data: from global conglomerates to ecommerce giants, from government institutes to academic institutions. They are relying on big data to gain new insights into a situation or solve complicated problems. But, in order to effectively use big data, the three fundamental steps must be observed; these are “organization”, “integration”, and “application”.

Big data is grow exponentially; according to the IDC, the size of big data is expected to reach 175 zettabytes (or 175 trillion gigabytes) by 2025. Contrast this to its size in 2018, when it was only 33ZB; or in 2016, when it was below 20ZB. How to amass, organize, and store all this data is obviously the first hurdle to overcome when adopting a big data-centric approach. Cloud storage, a component of cloud computing, and distributed computing are just some of the ways an IT expert might bring some semblance of order to this massive collection of information.

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Once you have gone through the trouble of organizing data, you must integrate it into your business strategy, your public policy, your academic theory—what have you. The data you’ve collected may comprise of unprocessed raw data, and they may come in many forms; besides clearly defined, structured input, there may also be a lot of unstructured or semi-structured input, in the form of texts, pictures, audio or video files. Before all the data can be coherently integrated, you may need to do a lot of compiling, analyzing, and processing. However, the benefit of big data is that new information is coming in all the time, so the strategy can be tested and fine-tuned in real time.

Processing power and computational methods are key in the integration phase. Rather than asking a single computer to do all the heavy lifting, businesses will often employ a host of computers and servers—preferably in the form of a computing cluster—to utilize parallel computing and high performance computing (HPC) for greater efficiency. A graphical user interface (GUI) may be used to quickly pinpoint problems and arrive at a solution. A powerful set of hardware and software tools will be necessary during the integration phase of big data.

Glossary:
《What is Computing Cluster?》
《What is Parallel Computing?》
《What is High Performance Computing (HPC)?》

If you’ve been following closely thus far, you might already be wondering—wouldn’t data security be a problem? The answer is—yes, it absolutely is. During the integration process, you may find a lot of personal information mixed into the big data. Data de-identification is one way to protect privacy, but that is just the tip of the iceberg. It is absolutely necessary to observe personal data-related laws and regulations to make sure you are utilizing big data in a legal, responsible way.

Not to wax philosophical, but big data is a lot like that old quote about love: it must be made and remade all the time, made new. This is because the intrinsic value of big data is how accurately it depicts the real world. Therefore, any business strategy (or public policy, etc.) concocted with big data must be used in real-world applications, so it can be tested and improved upon continuously. Below are a few examples of big data put into action.

● Machine Learning and Artificial Intelligence
A lot of modern machine learning and artificial intelligence (AI) is built upon a sea of big data, which can be used to help train the computer model. Whether you are talking about using computer vision to steer self-driving cars or NLP (natural language processing) to help AI comprehend human communication, big data can act as the store of knowledge that the computer can draw from, so it can break the mold and teach itself, make predictions, and automatically execute actions. Predictive maintenance in hardware or recommender systems in software are examples of this type of big data application.

Learn More:
《Glossary: What is Machine Learning?》
《Glossary: What is Artificial Intelligence (AI)?》
《Glossary: What is Computer Vision?》
《Glossary: What is Natural Language Processing (NLP)?》
《Success Case: Constructing the Brain of a Self-Driving Car with GIGABYTE》
《Success Case: GIGABYTE Helps NCKU Train Award-Winning Supercomputing Team》

● Anticipatory Business Model
As previously stated, big data is currently being used to formulate a lot of business and marketing strategies. Whether you are dipping a toe in an entirely new market, tracking the behavior of existing customers, or making forecasts about future demand, big data can contribute to your decision-making process.

We are all familiar with how ecommerce companies track your browsing and shopping history to anticipate your needs and recommend deals you can’t refuse. But it can go a step further. Amazon has patented a practice called “anticipatory shipping”. It uses big data to anticipate what its customers need and ship products out to distribution centers before receiving a single order. This reduces delivery time and costs, and it increases the customers’ wow factor. It is up to visionaries like yourself to come up with the next prime example of how big data can transform our business models.

● Academic Research and Public Policy
It should come as no surprise that advanced computing plays a big part in modern scientific research. If superb processing power is the engine that drives human knowledge forward, big data is the fuel. Whether you are analyzing human behavior in social sciences or meteorological data in climate studies, big data can give you the boost to help you arrive at more accurate and actionable conclusions.

Useful academic research can also help shape public policy. Take Spain’s Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) as an example. In 2021, it participated in the “DISTANCIA-COVID” research project, which analyzes big data—specifically, mobile phone data—to understand how changes in user mobility affects the spread of COVID-19. The benefits of social distancing are also studied. The IFISC purchased GIGABYTE’s R282-Z91 and R272-Z32 R-Series Rack Servers to support this project, because the servers offered stable storage and low-latency data transfer, which are attributes that are very useful for dealing with big data.

Learn More:
《More information about GIGABYTE’s Rack Server》
《Success Case: IFISC Tackles COVID-19, Climate Change with GIGABYTE Servers》
《Success Case: Japan Decodes the Storm with GIGABYTE’s Computing Cluster》

● Bespoke Entertainment and Beyond
The use of big data to track, record, and anticipate our needs is prevalent on company websites, ecommerce portals, and even streaming services. The fact that a platform knows what you like and can curate its displays accordingly goes a long way toward improving user experience and guaranteeing return visits. Every time you see something like “You May Also Like…” on an ecommerce website, chances are, you are witnessing big data in action.

Popular streaming platforms like Netflix are another excellent example. You might notice the list of programs self-adjust every time you binge (or turn off) a show. The platform is using big data to reshape your streaming experience in real time. And it is building on your next course of action (as well as the response from many other viewers like yourself) to make its big data application smarter and more personalized.

At the conclusion of our Tech Guide, we hope you have gained a clearer understanding of what big data is, and how you can benefit from it. If you are looking for server solutions to help you on your big data journey, look no further than GIGABYTE. We have a comprehensive line of products built to serve different functions, such as computing and storing big data.

● Compute
- H-Series High Density Servers
GIGABYTE’s High Density Servers are optimized for HPC and hyper-converged infrastructure (HCI). It houses a highly dense configuration of nodes in slim chassis, and supports Intel® Xeon® Scalable, AMD EPYC™, or Ampere® Altra® processors based on the “cloud-native” ARM architecture. The H-Series is your go-to choice if you want to squeeze a lot of computing power into limited space.

Learn More:
《More information about GIGABYTE’s High Density Server》
《Glossary: What is Hyper-Converged Infrastructure (HCI)?》
《Glossary: What is Node?》
《GIGABYTE Tech Guide: The Advantages of ARM, From Smartphones to Supercomputers》
《Success Case: GIGABYTE’s ARM Server Boosts Development of Smart Traffic Solution by 200%》

- G-Series GPU Servers
G-Series products are suited for parallel computing and heterogeneous computing because they are designed to support GPGPUs—general-purpose graphics processing units. This means they are faster at processing data that can be converted to graphical form. Choose GIGABYTE’s GPU Servers if you expect to encounter a lot of graphical data in your big data journey.

Learn More:
《More information about GIGABYTE’s GPU Server》
《Glossary: What is Heterogeneous Computing?》
《Glossary: What is GPGPU?》
《GIGABYTE Tech Guide: Let’s Set the Record Straight—What is HPC?》

- R-Series Rack Servers
Whereas the H-Series and G-Series were envisioned to be more specialized, GIGABYTE’s Rack Servers were designed with versatility in mind. They provide an optimal balance between efficiency and reliability that makes them ideal for business-critical workloads. They can also be used in conjunction with H-Series or G-Series products to help you reach performance and budget goals.

● Storage
- S-Series Storage Servers
The role of storage servers is self-explanatory; they are where you will store your big data, especially during the early “organization” phase. GIGABYTE’s ultra-dense, highly scalable Storage Servers were built for this purpose. In addition to features such as SDS and RAID that offer better protection and performance, GIGABYTE servers also offer proprietary features such as Smart Crises Management and Protection (SCMP) and Smart Ride Through (SmaRT) to better guard your valuable big data.

Learn More:
《More information about GIGABYTE’s Storage Server》
《Glossary: What is Scalability?》
《Glossary: What is Software-Defined Storage (SDS)?》
《Glossary: What is RAID?》

Thank you for reading GIGABYTE’s Tech Guide on “Big Data”. We hope this article has been helpful and informative. For further consultation on how you can benefit from big data in your business strategy, academic research, or public policy, we welcome you to reach out to our sales representatives at marketing@gigacomputing.com.