One of the greatest achievements of the ongoing digitization and networking of our world is certainly that companies today are collecting, evaluating and reusing more data than ever before. This creates a huge mountain of data: Big Data. In the following guide, we reveal which technologies are used to properly manage Big Data and why this area will become increasingly important in the future.
In the IT industry, the term Big Data now actually refers to two different things: First, the ever-increasing volume of data that companies collect every day. Secondly, it also refers to new technologies and models that allow this increasing volume to be used sensibly and to the benefit of the company.
Because even if data is described as the gold of the 21st century: Just collecting a lot of data is useless if you can't exploit this data and use it profitably. However, with the huge amounts of data that are now generated in modern companies, it is not that easy to implement. Big Data technology is therefore one of the most important technologies and fields of the digital age.
The phenomenon of too much data has been around since the invention of the Internet. But it wasn't until industry analyst Doug Laney precisely defined the term Big Data in the early 2000s that the term began to be used across the industry. Laney defined Big Data using the Three Vs model, which is still used today.
The three Vs:
Volume is used to describe that companies now collect an incredible amount of data from a wide variety of sources. This includes, for example, business or financial transactions and the countless sensors of the IoT. But also the collection of data from videos, messages, mails and especially social media activities is becoming increasingly important.
Velocity refers to the speed at which data is processed and collected in Big Data. Especially since the IoT (Internet of Things) and social media allow data sets to be collected in the shortest possible time, the data streams must be forwarded and processed at an ever-increasing speed. The goal is always real-time analysis of the data.
Variety is one of the biggest challenges with Big Data: the huge volumes of data come from a wide variety of sources and are therefore collected in a wide variety of forms: Text documents, videos, transaction files as well as numerical and structured data from databases. This makes it extremely difficult to find a solution that can read and utilize data from all sources.
Meanwhile, the definition of Big Data has expanded to include two more Vs: Variability and Veracity. Variability indicates that not only the sources of data, but also the actual amount of data collected can vary widely. Especially in areas that are highly user-dependent, such as social media, high variances occur.
Veracity adds another factor to this problem: the quality, or usability, of the data is also highly variable. As mentioned earlier, it is of little use to collect as much data as possible if it cannot then be utilized. Data from different sources must therefore not only be aligned with each other, but also checked for quality.
Since Big Data is primarily about collecting, storing and analyzing data, it is little wonder that the available storage hardware is crucial to the progress of Big Data. One of the most important advances is the ability to now store data using in-memory computing, rather than having to transfer it to external storage.
But of course, the hardware needed for Big Data also includes the devices that collect the actual data. For actual applications, this would primarily be sensors from the Internet of Things as well as mobile end-user devices such as smartphones and tablets. Again, in both areas, the trend is toward more and more comprehensive data collection.
The available cloud computing or software services for Big Data have evolved greatly in recent years, enabling increasingly effective use of Big Data. This includes, first and foremost, for Big Data analysis. After all, with today's data volumes, it would be absolutely ineffective and simply impossible to have all the data analyzed by a real person.
Big Data analytics is therefore used to eizuordnen the quality of the data and then provide the Data Scientists with only those data sets that are truly relevant. Furthermore, Big Data analysis is also used to predict load peaks and idle times in order to be able to achieve increased scalability. In addition to this, software solutions are used to actually collect the data, making it much easier to standardize, compress and forward the data streams.
The application areas of Big Data are as diverse as the application areas of digitization itself. No matter whether in finance, retail, healthcare, the insurance industry or governments: Big Data applications are already being used everywhere.