Nowadays, the term Big Data is making an appearance in many areas of everyday life. The benefits of Big Data gather extensive appraisals among those who integrated it. Therefore, almost any company that collects data in its operations starts considering potential Big Data technologies to use. Sometimes the goal is to integrate analytics. Every company wants to make use of data to understand their customers or processes better. It is also done to modernize a company and cut costs.

In any case, Big Data is an upward trend that is highly beneficial for data-aware companies. Turning to the Big Data field is often celebrated as a breakthrough. But is it really?

This article will gradually explain why Big Data is such a popular tool and who may actually need it.

What is Big Data?

This is one of those cases where the definition given by Wikipedia is just spot on and precisely explains what Big data is:

“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.” – Big Data by Wikipedia.

This is precisely what Big Data resembles at its core. It is how to operate amounts of data that are too large or unconventional to be handled by traditional data-processing technologies. As Big Data comes into play for such large data quantities, it is unproductive to operate within the usual business data setup.

So Big Data is the other way to manage your data system. But where and when do we start seeing the need for it?

Multiple V’s: Setting the Data Rules

The classical way to define Big Data is described by “three V’s”: volume, velocity, and variety. These three measures usually set a divide. One side of it is the Big Data Field, and the other is still within the traditional bounds.

With the development of Big Data and its benefits, other V’s started to appear alongside these original three. Most popular additional measures include value and veracity. But for the general case, we will concentrate on these agreed-upon bases.

The First V: Volume

First of all, the volume. The exact volume of information needed for classification as Big Data-applicable was and still is an object of great debate.

However, there is one more or less generally agreed-upon threshold. If you have at least a terabyte of data to process, you surely need the Big Data infrastructure. Sounds big? For some companies, it is not. Just consider how much data is shared through Amazon purchases every single second. But not every company is as big as Amazon.

Below the terabyte threshold, things get more vague. Modern commercial processing capabilities are usually large enough to work through gigabytes and gigabytes of data safely. If you need to process them only once in a while, this is mainly for you. Even a single processing unit can handle a lot of data if speed is not the defining factor. Do you need to rebuild an operational line for this?

The critical point you will likely need to understand here is that there is only a very selected circle of companies that can generate so much data. Modern processing tools, and here we are considering the classic ones, are already powerful enough to withstand much load. Even many millions of records generally can be processed perfectly with them.

The Second V: Velocity

Many companies who use Big Data need to process incoming data quickly, sometimes in real-time. If you are Facebook, you want to offer users new advertising based on their visit. But it is again safe to say that such a concentration of events does not occur in every business.

In order for the turn to Big Data to be justifiable, the business needs to evaluate its effect on every task. If the stream of events is not closely straining the operational capabilities, or you can redistribute it, you will probably be fine without Big Data.

If the business is only in the initial stages, it is crucial to apply the same strategy as in other aspects. Can a traditional database work for the task just fine? The hyped Hadoop ecosystem will probably not offer a lot of value to the business only wasting the precious development efforts.

A lot of disappointment with Big Data comes from the fact that businesses often expect it to do wonders while it just… works. Implementation of Big Data is functional when it brings value.

The Third V: Variety

Sometimes, the very stream of events is not that gigantic. However, it can have many sources and types. Just imagine what kinds of data a simple email attachment can contain. Or an e-commerce platform, which will store a lot of images and text. Add a historical dimension to it, and you will receive the informational chaos.

When you have many types of data, a database table is usually not an option. Streams of incoming text, images, video feeds, and any other data are flowing into the company and need to be somehow stored together. And when the number of instances with various data types exceeds millions, managing and storing everything becomes a challenge. But it is still not a reason to turn to Big Data!

Often, problems with data storage arise simply from its organization. Companies are so used to doing it the traditional way that they don’t see the options for restructuring. But often, there are more fitting non-Big Data solutions on the market. Such a solution can be a distributed database family or storing data in other formats. Ask a data engineer to provide you with an elegant solution that suits you best. 

When data becomes too complex, it is a signal to make it simpler. Big Data should be accepted for the answer only after it is clear that there are other problems than storage structure. Use all three Vs to refer to the problem.

So, Where Does It Go Wrong for Many Companies?

The correct answer to this question lies in understanding the processes. Big Data technologies need to have at least a terabyte of data for the advantages to become apparent. This means hundreds of millions and sometimes billions of records. The costs of maintaining a Big Data infrastructure are often not compared with the price tag of implementation without it. 

Let’s not assume otherwise: it is a potent tool, and Big Data’s benefits are numerous. However, similar to Neural Networks for analytics, it is nuclear weaponry of data processing. Can you use it in most of your business tasks? Sure. Will it deliver progress to your business? Most likely. Will it be the most efficient tool for you? This one is doubtful. And here’s why.

If a company is looking for Big Data infrastructure, it is almost sure to value the data or, at the very least, to use it. It has most likely started as a data-driven company or is going through a Digital Transformation. Thus, the question that arises here should be familiar: does the company use its data and resources most efficiently?

Led by the Hype

Immediate cost-saving calculations attract many companies. And they make perfect sense, too. Even the sheer hardware costs without running them are enough to demonstrate it. For most regular hard drives, the cost per 1 GB starts at around $0.025.

And now, let’s multiply: storage for 1 TB will start from around $25. For a petabyte, it is already $25,000. It is easy to see how cutting just 10% of data storage costs may impact developing businesses. And for more prominent companies, the data volume rises accordingly.

The question is, how likely are you to encounter such V’s. The estimates for how much data the business may have vary from one company to another. But if you are not sure if one of the basic Vs applies to you, it is essential to review the entire decision and look for possible alternatives. You may need only several reinforced points with distributed operations. Also, the issue may be in other company processes. Departments may be separate data storages, the workload may be unevenly distributed over time, or even faster network connection may solve it. Correctly estimating how Big Data will perform is only possible when you remove the other digital bottlenecks.

In some cases, such miscalculations start from the wave of popularity that surrounds the field. Sometimes, the solution you are seeking can be found with traditional tools used in a sophisticated manner. Misuse of Big Data has already been a well-known issue for businesses for quite some time. In the 2016 survey by Blazent, 42% of polled executives indicated that wrongly used Big Data can impair revenues.

The usual bottleneck is that for some companies redesigning the very way they function proves to be more costly at the moment. Thus, the Big Data tools are put on hold or implemented only partly, not being used where they are needed.

Not Using All Data

Many companies do not fully use the data available to them. The phenomenon of “dark data” is sometimes estimated to cover 55% of all collected data. Other estimates of unused data go as high as 73%. The usual reasons for not using all of their data include the company’s lack of understanding of how to use it or possibly not even being aware of it. Sometimes, the data is just incomplete or unstructured.

Therefore, while working on improving the processing costs, it is also essential to justify them. While it is true that you may collect some of the data as an asset for potential future use, at the same time, there can be a lot of obviously useless information or duplicates.

Too simple to happen? Many reports indicate that there is a lot of data companies are not even aware they have. Once you hit a specific capacity, it becomes impossible to check everything that comes in on the fly. Consider conducting a thorough review of everything in your data as a potential solution. If you collect a lot of information, some of it may be extraneous.

Interpreting the Term

In the last 3-4 years, the term Big Data and its benefits became so popular it turned into a marketing point. As such, everything that is somehow related to enormous amounts of data is by default referred to as Big Data. Similarly, companies can attribute all the pros of Big Data technology to them.

Some businesses may indeed state that they cut costs or optimized operations by using it. In many cases, they mean that they used analytics on the impressive amount of data they gathered. Or they may have just started their data-driven Digital Transformation, and the results of the entire process have a remarkable influence. In any case, for many companies, it is more optimal to implement all related Big Data technologies at once. This way performance in total can be attributed to the changes in general. While in reality, it can be the Analytics part of the field that made the biggest improvement.

The difference in perception is evident. If you start using Big Data, you are innovative and are moving with the curve. If you start using analytics, the instant question arises why you did not use it before.

Always keep in mind this fact when checking the new Big Data implementation. Using demand prediction or price optimization is fantastic for business. The only issue here is if it comes in similar time frames with Hadoop or related instruments. This coincidence can lead to misjudging their effects. Sometimes this leads to crediting the KPI growth not to the tools which contributed to it the most. Therefore, the business needs to understand which issues the transformation has resolved.

Conclusions

The field of Big Data came into prominence because it addressed the defined business problems. Those problems were too pressing at the moment, so Big Data benefits received wide acceptance in the field. Despite the popularity, it is still primarily a tool designed to work with amounts of data deemed too inconvenient.

There are several agreed-upon ways to understand what data qualities are needed for the benefits of Big Data to show themselves. The most popular of them is to have enough data volume, enough processing velocity, and enough variety of data types. Often turning to Big Data is sensible only when these needs are impossible to fulfill otherwise.

However, many companies still get it wrong in the process. The biggest challenge is not to let the popularity drive decision making. Also, it is necessary to make use of the resources available and clearly understand what you are trying to do. Sometimes, more analytics or data processing can have a much more significant impact than the introduction of Big Data. And at one moment, you may understand that you will never need Big Data.