2.5 trillion bytes of data are produced every day worldwide. If one gigabyte weighed one kilogram, the daily volume of data would be about 350 times the weight of the Eiffel Tower. Emails, documents, presentations and photos – whether at work or at home – contribute to the exponential growth in the amount of data. 90 percent of the 44 zettabytes of data existing today has been produced in the last two years. This gigantic amount of data is called big data. We will explain to you what this term means and how you can discover valuable insights within the large amounts of data.
Figure 1: Exponential data growth – the diverse data sources of big data
When can we describe data as “big”?
The term big data describes large amounts of data. Due to ongoing digitalization, this data is obtained from a wide variety of sources: Text data from social media articles, medical data from test series, sensor data from factories or image data from video recordings are just a few examples. The formats and structures of this data are varied. What exactly characterizes big data can be explained using the three Vs:
The amount of data produced on a daily basis is constantly rising, so the volume of data is also increasing. In addition to the data that an organization produces itself, more and more data is flowing in from outside. This data must be stored and made as easily usable as possible. The multitude of data sources leads to a variety of data structures. Around 20 percent of the data is available as what we call structured data, which is easy to process. The remaining 80 percent is unstructured data, which is difficult to process. With more and more people and devices generating data, the speed (velocity) at which data is produced also increases. For example, sensors can collect data more than 1,000 times per second, and log data and cookies are also collected for every single user interaction on the internet.
These characteristics all pose challenges to organizations when processing and analyzing big data. Not only do large amounts of data require a lot of storage space, but they also need a great deal of computing capacity. The variety of formats have to be brought together and prepared for the systems. These requirements exceed the capacities of conventional technologies and methods, such as classic databases or reporting solutions.
What are big data technologies?
Big data technologies and big data analytics methods are used to filter, analyze and query the data volumes. When combined with powerful IT solutions and systems, they unlock a wide range of innovative technologies which are applied depending on the amount and type of data. For example, data mining refers to statistical-mathematical methods that are used to recognize patterns. Based on existing data, various algorithms can be used to identify patterns and trends. By contrast, business intelligence solutions support the process of systematically collecting, evaluating and presenting data in order to optimize value creation.
The purpose of any big data technology is to transform data into useful information in order to derive new knowledge. The more data used in the methods, the more accurate the derived knowledge can be. The goal of developing big data technologies is therefore to develop cost-effective and fast forms of data processing. Big data analytics can therefore generate new insights and valuable knowledge in real time. This eases decision-making and facilitates the automation of processes.
Figure 2: From data to decisions – the big data processing stages
Big data in practice: Why is data so valuable?
The data world is changing and with it the situation for organizations. Data is becoming the differentiating factor in many industries. The relevance and the need for organizations to be able to process large amounts of data quickly and strategically is increasing rapidly. The intended use of big data offers the opportunity to improve existing products or processes on the one hand, and to open up new fields of business and support the development of new business models on the other.
In practice, this potential can be used across all sectors and departments in an organization. For example, in order to offer a unique and individual range of products and services, in marketing customer data such as interests, gender or age are analyzed. Big data technologies make it possible to identify patterns in purchasing behavior. With specific marketing and sales measures, cross-selling can be conducted, or customer churn can be counteracted with a discount campaign. Big data also offers advantages in production and manufacturing. The sensors in factory and industrial plants serve as an external interface and transmit data concerning the status of the plant and the production process every second. This means that malfunctions and failures can be predicted early on based on sensor data and long downtimes can be prevented. Complex structures such as supply chains in logistics can also benefit from using big data. By efficiently evaluating data on warehousing, demand and sales planning as well as transport routes, reliable forecasts can be made for distribution and logistics networks, for example to avoid delivery bottlenecks. Proactive planning can also reduce transport costs or use location data to adjust transport routes in real time. Due to the large number of available figures and data, the financial sector also makes use of many of the opportunities big data offers. Forecasts can be derived, and scenarios created based on existing data, especially in risk management. This makes it easy to react quickly to risk factors and market developments.
The potential of big data and the use of technologies can therefore be summarized in many ways:
Strengthening customer orientation and customer loyalty
Increasing profitability and optimizing processes
Calculating and minimizing risks
Increasing profits and reducing costs