What Is Big Data and Why Is It a Big Deal?

Big Data is the buzzword around the tech scene these days. Like the cloud, AI and machine learning, the concept is quite tricky to explain.

Little wonder so many conspiracy theorists are having a field day. They now teach their disturbing versions to the curious public. First off, there is no link between this concept and world domination. You can rest easy now.

So what does big data mean?

It means a massive volume of data. But it doesn’t stop there. It also encompasses studying this enormous amount of data with the goal of discovering a pattern in it. It is a cost-effective and complicated way of processing information to find useful insights.

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Today the estimated volume of data online is about 2.7 zettabytes. To put things in perspective, one Zettabyte is equal to one billion terabytes!

The trend is not slowing down. Studies show that Facebook servers receive 500 terabytes daily. Also, we send about 290 billion emails every day. We expect that by 2020 we will produce 44 times more data than we did in 2009!

The above stats are intriguing. The amount of data we produce in two days is equal to the amount we generated from the dawn of time until 2003.

The volume of data we have today is a direct result of the invention of the computer and Internet. The information uploaded to social media platforms, forums, businesses, etc. are all part of this concept.

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Big data has five characteristics that describe its use and philosophy:

  1. Volume – Of course, unless the size of the data is a significant amount, you cannot refer to it as big data. Volume is the primary characteristic of the concept.
  2. Variety – This attribute addresses the nature and type of data computers will analyze.
  3. Velocity – Big data is always available in real time, implying that even while analyzing substantial data sets, you can still access the data.
  4. Variability – The consistency of the datasets determines the extent to which the data conforms to this concept.
  5. Veracity – Veracity is the quality of the data used for analysis. Only quality data can produce quality inferences and patterns. Otherwise, it’s a waste of time.

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Analysing such large volume of data is very complicated. Every day, programmers write newer algorithms to process massive data sets. This level of complexity also means that a lot of complicated hardware has to take part in the process.

But for simplicity sake, here’s a high-level rundown of the processes involved.

1. Capturing the Data

The first step is to capture the data. You can only grow your data library if you have a means to obtain data. Use a sophisticated algorithm to find the data needed to populate your data library.

2. Curation

The system curates the captured data and sorts them into smaller units. An algorithm is also responsible for this process. The reason for this sorting is to allow for simplification in the later stage of the process.

3. Indexing the Data – Making the Data Searchable

Due to the velocity of data flow, data scientists organize data sets into a searchable library. The system organizes and indexes everything. That way anyone can look through it and pull up information – in real time.

4. Storage

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While all the above processes are going on, the system is simultaneously storing data. But because it is still raw and untouched, data is only temporarily stored. Indexing and storage happen concurrently. So at any moment, the algorithm in control knows where to find a data set.

5. Analysis of the Data

In this stage a lot of things are going on under the hood of the infrastructure. Plenty of algorithms are running, and computer processors are heating up. The system examines the stored data sets and analyzes the patterns.

6. Sharing and Transfer

Here, the system makes the analyzed dataset shareable and transferable. This new data generated is also still prepared to go through the entire process again.

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7. Visualization

The patterns discovered in the analysis of the data create visual descriptions using an algorithm. These illustrations show the relationships between various data sets and data types. It also provides patterns and inferences.

8. Information Privacy

All the processes above are expensive. They are also confidential and should not leak out of the concerned company. Information privacy is the final process in this concept.

Realize that while the system serializes the entire process, it all happens concurrently in real life. A lot of processors may be handling one set of operations while others cater to other sets.

big-data-explained-benefits

A lot of corporations are investing big in this technology. For a good reason, too. The benefits of implementing this concept in business strategy justify the investment.

  1. Saves money: Implementing the concept helps companies study the most cost-effective ways to do business.
  2. Saves time: Developing more straightforward methods by analyzing vast volumes of data about a process saves you time.
  3. Understand your competition: Implementing the big data concept helps businesses stay ahead of their competition as well as grow their profits.
  4. Develop new and better products: Due to the large volume of data being examined, your chances of a new product idea are high.
  5. Understand the consumer or market: It is not unlikely that the system studies consumer behavior and develops a pattern.

big-data-explained-pros-and-cons

Yes, Big Data can help in making your work a breeze, more enjoyable, and profitable. But it’s not all roses without thorns. Users have encountered some of the pitfalls listed below:

  • This concept doesn’t lend itself to bespoke query solutions.
  • Turning your collected data into useful insights can be onerous and complex.
  • Data analysis can mislead you.
  • Big data demands speed of data delivery to keep up with accurate updates. If your rate of real-time data delivery isn’t fast enough, your analysis will be false or inferior in quality. And sometimes, data isn’t available at all.
  • High overhead expenses.

Big Data is a complex subject and will need intensive research and maybe some real-life practice to fully understand it. But with this article, you’re on the right path. The benefits are far-reaching, and the advancement is not slowing down soon. If you’re a business seeking innovative solutions, you’ll want to hop on this bandwagon NOW!

3 comments

  1. “there is no link between this concept and world domination”
    Depends how you define “world domination”. If you mean one country invading another, then NO. But, since Information is power, whoever controls most of it can be said to dominate.

    BTW – riddle me this – IF there is no connection between the concept of Big Data and world domination, WHY ARE any and all entities so hell bent on gathering as much data as they can?!

    “You can rest easy now.”
    Whenever someone says that, I really start to worry. That sounds so much like the Great Oz trying to convince Dorothy and friends “not pay attention to the man behind the curtain”.

    “Only quality data can produce quality inferences and patterns.”
    That may be true when you collect only quality data. However, when everybody hoovers up any and all data they can get their hands on just so they can have zettabytes of it, any talk of ‘quality data’ is ludicrous.

    “They are also confidential and should not leak out of the concerned company.”
    Unfortunately, we all know how big a joke that is. Many entities are so hellbent on collecting zettabytes of data that they neglect to properly secure it.

    “Data analysis can mislead you.”
    Lies, damned lies and statistics

    You did not mention another CON of Big Data – the bigger the database, the bigger and more inviting target it is.

    All in all, this article has been an interesting discourse on the theory of big data. Unfortunately, in real life Big Data is much different.

  2. Your article’s focus seems entirely on the capitalist use of Big Data, to the neglect of more leftist use, to which I am more inclined. I don’t see how to use Big Data to solve problems such as the growth of the Underclass, or the First Nations peoples, or the fact that Latinos in America outnumber those of Black-American heritage. Judging by your summary, these people do not count. I obect.

    A.

    • Propaganda, leftist or rightist, capitalist or communist, is still propaganda. Data is data. It is how you spin it that makes it leftist or capitalist. You have not been reading the spin you agree with. I suggest you change the source of your reading material.

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