What Is Big Data Analytics and How Useful Is It to Your Business?

Do you know that with more than 2.5 billion users worldwide, Facebook, one of the top five bet social networking sites in 2020, generates over 4 petabytes every day? It’s an enormous amount of data that requires a unique way of processing. We define it as Big Data. It gradually becomes on-trend, and businesses now consider it a useful tool to make profits. In fact, many of them are currently making use of it in plenty of ways.

There has been “Data”, and now we have “Big Data”. As mentioned above, we need to process it to save it from going to waste. Theoretically, we define Big Data Analytics as the systematic computational analysis of Big Data. If you expect to find out more about Big Data Analytics, then you arrive at the right place. This article is truly a comprehensive guide on Big Data Analytics.

What is Big Data Analytics?

Prior to defining Big Data Analytics, let’s discover Big Data to gain some background knowledge first.

Big Data is a term that has been commonly and frequently mentioned these days. In fact, most people have come across this term at least once, but not everybody has a deep insight into Big Data. So, how does Big Data differ from the data you ever know?

There have been various definitions of Big Data so far. Still, in general, Big Data refers to the voluminous and complex sets of data that almost all conventional data processing software fails to control. As time goes by, Big Data keeps increasing in its size and is commonly characterized by the so-called Vs (usually 3Vs): Volume, Velocity, and Variety.

Volume:

The amount of Big Data has yet to be determined now, and people have kept discussing how voluminous it is to be called Big Data. The size of Big Data is ever-increasing and variable, ranging from terabytes (TB) to exabytes (EB), even up to zettabytes. In brief, its huge size comes as a surprise to us, to the extent that we may not imagine.

Variety:

Big Data contains data coming from various sources with inconsistencies in formats and categories. The kinds of data stored in Big Data grow more diverse over time, including structured (the traditional type), unstructured (texts, videos, audios, images, etc.), or even semi-structured like json or xml files.

Velocity:

Due to the giant size, variety, and complexity, Big Data needs to be processed within a short period, usually and expectedly homologous with real-time.

Besides the 3Vs mentioned above, Big Data is also defined by other Vs, such as Variability, Veracity or Value. Regardless of how we define it, its usefulness and profitability are the things that matter. Big Data, a potential mine of gold, is still useless we use it for a particular purpose.

Big Data poses several challenges for analyzing, accumulating, examining, and transmitting due to its giant amount. However, the benefits it brings to us are undeniably enormous as it has a substantial impact on modern life nowadays, especially the industrial revolution 4.0 — the opening of digital life.

Currently, in the digitalization era, every one of us cannot ignore the great potential of Big Data for making profits. To take advantage of Big Data is to fulfill the need to store, analyze, accumulate, and recall data. Big Data Analytics emerged as one of the essential things if we want to utilize Big Data. So, what is Big Data Analytics?

Big Data Analytics is the process of analyzing data to arrive at some conclusions that are useful for businesses or Big Data users. In fact, Big Data Analytics is the process of scrutinizing large and various data to disclose hidden patterns, correlations, and other insights from this data. Now companies and organizations commonly use Big Data Analytics to foster their development and reach well-informed decisions.

In particular, Big Data Analytics comprises 9 stages:

1. Business Case Evaluation:

This is the initial stage to determine the purpose or the goal of analyzing data. The data analyzing team will access available resources that need to be utilized and be aware of the challenges they may face. Henceforth, they can calculate the costs of carrying out the analysis to avoid wasting money, time and effort.

2. Identification:

Related data sets and their sources will be identified to increase the likelihood of uncovering hidden patterns and correlations amongst data.

3. Accumulation and Filtering:

After being identified, data is collected from the sources found in the previous stage. Then nonsense and useless data will be discarded.

4. Extraction:

The format of some collected data may be incompatible with the analyzing process, so various types of data will be extracted and converted into compatible versions.

5. Validation:

Data gathered in Big Data may be unstructured, complex, and redundant. That’s why it needs to be validated.

6. Aggregation:

Data comes from various data sets and joins together through common fields, regardless of different formats.

7. Analysis:

Analysis will be repeated until hidden patterns and correlations are revealed.

8. Visualization:

This stage helps business users interpret the analysis result and give feedback on it. There are various ways to present the results, which greatly influences the process of data interpretation.

9. Utilization of analysis results:

Businesses take advantage of the analysis results to make decisions or optimize their business activities.

As you can see, with 9 stages above, Big Data Analytics is not an easy-done task, so it requires highly-skilled and professional Data Analysts and Scientists — those whose job is to cope with Big Data Analytics.

Types of Big Data Analytics

In general, Big Data Analytics falls into 4 types:

  • Processing, interpreting, and summarizing historical and raw data to draw useful and understandable comparisons and conclusions.
  • Examining historical data in data mining and data aggregation to answer the question: “What has happened?”
  • Being demonstrated through graphic patterns like bar charts, pie charts, line graphs, etc to lay a solid foundation for further analytics.
  • Drilling down and discovering historical data, finding out correlations amongst events from this data to help answer the question: “Why did something happen in the past?
  • Finding out the true meaning of data and the underlying cause of past success or failure to improve the future and better decision making.
  • Using algorithms to obtain the missing data with reasonable guesses based on the existing data as well as the correlation amongst various data sets. It’s used to calculate probabilities of potential risks, opportunities, or trends and answer the question: “What might happen in the future?”.
  • Forecasting the likelihood of future from structured data, which is easier than unstructured data.
  • Analyzing historical data to make predictions about future outcomes. This aids businesses and organizations in any decision or action they intend to take.
  • Using descriptive and predictive analytics to suggest options and help answer the question: “What should be done?”
  • Showing why something is likely to happen to recommend what decision should be made to take advantage of opportunities and avoid risks.

In a nutshell, 4 aforementioned types of Big Data Analytics predominantly concentrate on exploiting and analyzing historical data to anticipate the future and give businesses advice on what should be done to achieve optimization and efficiency.

Why Big Data Analytics?

As mentioned above, Big Data is only a large amount of useless data that needs to be analyzed to make sense. The demand for Big Data Analytics is increasing, so people raise many questions about how useful it is to businesses. In other words, Big Data Analytics is considered a way to utilize Big Data, preventing it from going to waste.

Big Data Analytics now becomes widely-used by businesses in order to reap benefits or make profits from it. I am going to tell you 3 reasons why it is necessary for businesses in particular as follows:

Businesses always bring customers’ satisfaction into their major focus. It is best to gain insights into what customers really want to meet their demands. In this way, Big Data Analytics offers a direct method of understanding customers without asking every single customer.

Through Big Data Analytics, businesses can investigate or accumulate customers’ feedback on a product or service they offer. As a result, they can minimize their weaknesses as well as improve the quality of their products or services.

Do you know “information power”? Indeed, those who possess information and data that others don’t will hold power. In the age of information and data nowadays, it is the speed of obtaining information that matters. In this case, we can’t help but mention Real-time Big Data Analytics. It means Big Data is processed simultaneously with its arrival so that businesses can obtain the most up-to-date information.

Take stock charts as a typical example: they need updating minute-by-minute or even second-by-second for the sake of the most real-time notifications. Traders or stockholders feel a need to gain the most up-to-date information on their stock to make any decisions or changes over their stock selling or buying. Financial businesses may lose their customers if there is any latency in offering data on stock charts. That’s why Real-time Big Data Analytics is on increasing demand.

By applying Big Data Analytics, businesses can minimize their expenses. They needn’t try to ask every single customer’s opinions in real life, but they can just research Big Data, then gain valuable insights in this way.

For example, hospitals and patients can reduce their costs via online treatment. Patients will provide doctors with their symptoms, and the doctors, using Big Data Analytics, can diagnose quite accurately and quickly. In this way, hospitals don’t need to spend money on facilities, and neither do patients — they don’t need to go to hospitals.

Big Data use cases

It’s not difficult to see cases of using Big Data nowadays. Let’s take Shopee and Google as examples.

Thanks to Big Data it possesses, Shopee easily grasps its customers’ behavior via search results and online shopping bills. Therefore, they know which products are more popular, affordable, and preferable than the others.

Also, they can put forward recommendations for customers during the process of selecting products. For instance, when a customer chooses a pencil, Shopee will recommend relevant things like erasers, rulers, or sharpeners. It demonstrates that using Big Data Analytics can bring customers the best buying experiences.

Another case is Google, whose predominance is undeniable. Of course, it owns Big Data. People perceive Google as an online encyclopedia, thanks to the tremendous amount of data it possesses. Based on the keywords that users input, Google will provide users with plenty of useful search results with various formats and categories like texts, images, videos, audios, or news.

Besides, Big Data is widely used in other fields such as education, health care, banking, etc.

Trends in using Big Data Analytics

Thanks to plenty of benefits it offers users, Big Data Analytics has grown in popularity. It becomes an integral part of businesses.

According to Accenture, 79% of business executives warn that companies that do not use Big Data Analytics are likely to end in failure.

Besides, thanks to the increasing demand for Big Data Analytics, it creates many job opportunities for job seekers. Data Analyst and Data Scientist are globally trending jobs with an average base salary of about $100.000 per year. The candidates must learn about things closely linked with Big Data Analytics like the Hadoop ecosystem or NoSQL databases.

Also, machine learning and AI keep continuing to evolve as a result of utilizing Big Data Analytics. They grow smarter and smarter over time, opening the door to the future advancement of automation in particular and technology in general.

All in all, Big Data Analytics touches every aspect of life and is considered a mine of gold for those who want to make huge profits. Notably, it’s utterly beneficial and essential for businesses. In modern and competitive life, companies should be concerned about using Big Data Analytics. What’s more, they need to equip themselves with information and knowledge related to Big Data Analytics to avoid being technologically backward.

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