The mountain of data available on the Internet and in companies – this fact is described as big data – is getting bigger, more confusing and difficult to process. Increasingly technologically sophisticated tools and programs are supposed to tame the flood of data (source Big Data Insider).

The flood of data is growing and presents companies with the challenge of storing, preserving and, above all, evaluating them. The magazine Big-Data Insider goes on to say that, on the one hand, it describes the ever-increasing amount of data; on the other hand, it is also about new and explicitly powerful IT solutions and systems with which companies can advantageously process the flood of information.

But many readers still wonder: what is big data? Is this easy to have a big Excel or a large amount of paper documents? Is this big data or, if not, what is big data? I would like to get to the bottom of this question in the article.

What is Big Data

If you enter the search term on Google, you will find the following definition of ReserachEnterpriseSoftware: Big Data is a general term used to describe large amounts of unstructured and semi-structured data that companies produce on a daily basis. This data takes a lot of time and money to load into a relational database for analysis.

The Gabler Economic Dictionary provides a definition: “Big data” refers to large amounts of data that originate from areas such as the Internet and mobile communications, the financial industry, the energy industry, healthcare and transport, and sources such as intelligent agents, social media, credit and customer cards, smart metering systems, assistance devices, surveillance cameras, and aircraft and vehicles, and which are stored, processed and evaluated with special solutions.

A great explanation can also be found at Chip:

  • A large amount of data is called big data when the scope is too large or too
    complex
    to process by hand. This is especially true for data that is constantly changing.
  • Big data, these can be innocuous data from climate research. However, data about people are also collected: communication behaviour, consumer behaviour or surfing behaviour of Internet users. You can see the effects of big data analysis on
    the Internet
    every day. A typical example is personalized advertising.

In conclusion, it can be said that big data is first of all large amounts of data, which are unstructured and cannot be evaluated by hand.

Big Data – Pros and Cons

Large amounts of data offer our society a lot of new possibilities, so at least if we are able to evaluate them. So I found some great examples at dice, which I would like to show.

On the one hand, crime can also be better combated. As Dice says, it may not be quite like the film “Minority Report,” which shows a society in which police officers successfully arrest individuals for crimes they haven’t committed. But, in fact, data masses have already helped regional and local authorities to identify difficulties before they can become a major problem.

Furthermore, diseases can be predicted. As dice says, predicting events based on existing data could provide individual medical care for each patient. By analyzing digitally collected medical data and similar patient disease histories, personal disease risk profiles could be created. Doctors could then prescribe preventive treatments or check related symptoms.

Another great example is Netflix. This is the case with dice: For Netflix, the visualization of data is of the utmost importance in order to be able to continue its success story. Netflix can use data calculations to determine what viewers want to see and how they want to present the content.

So there are many great examples. For more you can simply read on dice. In the following, however, big data may not always be a success factor and I would like to present the downsides.

This is what the magazine CIO says, criticizing big data “is probably due to the widespread “algorithm weakness” among machines, i.e. the inability to draw the right conclusions from a lot of collected information. Apart from that, there are two main reasons why companies do not benefit from big data or not enough. The first is that they come up with results with the help of data analysis that they could have had with not so big data.”

The second is that big data produces results and ideas that, for whatever reason, cannot be implemented in practice. For example, a major U.S. retailer found in a model trial that sales would increase if you put a special offer product on the shelves for a while before it was priced and left there when the offer price is no longer valid. (CIO).

In this paragraph we have now seen some good examples of big data, but also some that may not be quite as meaningful. So, as always, it’s important that we use big data properly and not just do it because it’s just hype.

AI, Deep Learning and Machine Learning

One potential that involves mastering large amounts of data is the ability to give machines intelligence, i.e. artificial intelligence. So a machine could evaluate the Controlling Report directly and take action, right?

On the one hand, one finds the term artificial intelligence. Thus says t3n: All technologies used in connection with the provision of intelligence services, which were previously reserved for humans, can be found under the umbrella term of AI.

Furthermore, there is the possibility of machine learning: Machine learning describes mathematical techniques that enable a system, i.e. a machine, to independently generate knowledge from experience (t3n). However, deep learning goes a step further: “Deep learning” with artificial neural networks is a particularly efficient method of permanent machine learning based on statistical analysis of large amounts of data (big data) and the most important future technology within AI (t3n).

Conclusion: What is Big Data?

Big data is a lot of unstructured data that we cannot evaluate by hand. Controlling such data can lead to great potentials, but also “backfire”. The potentials of this data mastery are new methods such as artificial intelligence, which can offer an enormous market advantage. I hope to have given an insight into the subject with this article and I am pleased about my first article on this subject.

READ MORE: What is big data

If you want to learn more about the question: What is big data, you can participate in the roundtables and discuss relevant topics with me and other experts. Or write in the comments how to deal with this trend. Read also my article on Big Data.

Tip: Book suggestions for big data

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I blog about the impact of digitalization on our working environment. For this purpose, I present content from science in a practical way and show helpful tips from my everyday work. I am a manager in an SME myself and I wrote my doctoral thesis at the University of Erlangen-Nuremberg at the chair of IT Management.

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