The volume of data in companies has grown continuously over the past few years. Data is therefore considered the gold of the 21st century. The processing of this data is indispensable for the successful organization of day-to-day business but not as trivial as it may seem. New tools and programs should solve the problem.
In addition to the growing amounts of data, there are also unique and powerful IT solutions intended to help analyze this data. This includes new technologies such as machine learning or grid computing.
Definition & Explanation: What Does Big Data Mean?
The term big data comes from English and describes vast amounts of data. The data are primarily characterized by their size, complexity, fast pace and fundamentally weak structure. In German, experts usually also speak of mass data, although the definition is identical. In everyday parlance, the term big data is also used as a collective term for many digital technologies.
From a social point of view, these cause social upheaval and create an era of new and digital communication and processing options from a technical point of view. The term big data is not static but is subject to continuous change. As a rule, this term is used to describe the technology that enables collecting and analyzing this data.
The definition of the term also enables a subdivision into three different dimensions:
- Variety: Describes the range of data sources and types.
- Volume: Covers the book and the actual data volume.
- Velocity: Describes the speed at which the data volumes are generated and transferred.
Recently, the definition has been supplemented by the two terms value and validity. These stand for the added value that the company generates with this data and the data quality that must be ensured in the company.
What Is Big Data Analytics?
In principle, Big Data is of particular importance for Business Intelligence (BI). BI deals with the analysis of collected data. BI always uses systematic approaches and new software solutions for evaluating large amounts of data. Significant data software is characterized by particular functionalities that enable the parallel processing of large amounts of data.
The essential functions of such software are the processing of large data sets, the quick import of new data, the quick query and search of data, parallel processing of questions and the analysis of different types of information. Extensive data analysis therefore also represents one of the most important trends within the BI software industry.
Where Does The Data For Big Data Come From?
The data required for Big Data Analytics can come from various sources. Smart homes, connected vehicles, wearables, smartwatches, smartphones, customer cards and many other networked devices and platforms act as data sources. The technology also takes into account the user’s areas. For this reason, there are always conflicts between the wishes of industry and authorities and the personal rights of the individual.
In particular, public market participants want to evaluate the collected data to obtain additional and in-depth knowledge. The anonymization of the data offers a way out of this conflict of interest. Search engine operators and providers of social networks, in particular, are considered model users for data analysis.
The use of data analysis enables business processes to be improved in almost all functional areas of the company. The optimizations in marketing, information technology and technology development are particularly noticeable. With the help of data collection, users force the achievement of previously defined goals.
The various technologies are used in numerous industries, such as market research, medicine, intelligence services and administration. The focus is always on the efficient and beneficial use of the collected data. Typically, the data can make forecasts in individual business areas and thus anticipate future trends.
What Are The Advantages Of Using Big Data?
Classic relational database systems, in particular, have problems processing large amounts of data that are above average. Accordingly, the evaluation of such data falls within the scope of significant data software. Here the providers rely on new types of data storage and analysis systems.
As a rule, these access numerous processors and thus improve the speed of data processing. This has blatant advantages when processing many records and processing multiple columns within a single document. The import and export of large amounts of data can also be carried out faster and more efficiently. After importing, all data can be called up in real-time.
In addition, corresponding software solutions are characterized by low latency and processing times – even for complex inquiries. Several queries can be executed in parallel without any significant loss of performance. Ultimately, different types of information such as numbers, images or even texts can be examined for relevant information. Nevertheless, the development of the corresponding software is still in its infancy.
Examples Of Application Areas For Big Data In Companies
In fast-moving markets like today, competitive advantages are essential to building a good business position. This is where the data analysis comes into play. By analyzing large amounts of data, trends and patterns in the market can be identified and thus, competitive advantages generated. However, the realization of potential savings and the creation of new business areas are sometimes based on these data evaluations.
Example 1: Credit Scoring In Banks
In particular, the granting of loans can be improved with the help of big data. In this way, the creditworthiness of a large number of customers can be evaluated within a short time with the help of corresponding data analysis. The results of such big data scoring far exceed the classic creditworthiness decisions in terms of their objectivity and efficiency.
Example 2: Influencing Purchasing Behavior In Direct Marketing
Marketing is a classic area of application for extensive data analysis. However, in marketing, it is less about the data itself than about the knowledge that can be drawn from big data. The right decisions can be made on this basis, and the most profitable measures can be implemented. The data evaluation provides fundamental knowledge about the customers, who they are, what they want, where they shop and get information and how they want to be contacted.
With the extensive data analysis results, marketing experts learn how customer loyalty can be influenced and how lost customers can be won back. And this knowledge, in turn, enables a targeted, effective use of the marketing budget.
Example 3: Risk prophylaxis
Our last application example for big data in companies deals with fraud detection – known in German as risk prophylaxis. Again and again, customers use fraudulent scams to steal a product or service. With the help of extensive data analysis, possible irregularities can be identified at an early stage, which can then be checked more closely afterwards. Unwanted or incorrect transactions can also be placed in this way with minimal effort.
What Are The Challenges When Choosing A Powerful Data Software?
In the past few years, the amount of data available has increased continuously, so this problem is not entirely new for companies. Instead, the challenge for companies is to implement the often increasing, self-imposed requirements for data processing and evaluation.
Over the past few years, BI software has gained increasing strategic importance for companies. This, in turn, led to an increase in the number of users and their expectations regarding the topicality and short-term availability of the data. In addition, the relevance of the query performance of the respective systems also increased.
Overall, these high requirements only illustrate the challenges of the business world. Companies that react quickly, in particular, can use Big Data Analytics to gain competitive advantages in a highly competitive business environment.
Criticism Of Big Data Technologies
In addition to the undisputed advantages, however, there is also some criticism relating to big data. Numerous representatives see surveillance capitalism in the analysis of multiple data. But data protection is also the focus of numerous discussions, as companies have the option of always being able to assign the data to a customer.
A de-solidarization of insurance tariffs could also progress in this way so that high-risk users receive a corresponding tax. In addition, some experts criticize the inadequate basis of the data evaluations. Accordingly, the simple technical route is often chosen without taking a representative sample into account.
Big data is one of the relevant future technologies and helps companies generate high-quality, competitive advantages. At this point, the technology is still in its infancy. In the long run, however, there will be a breakthrough. Because of the rapidly growing flood of data, coping with it is one of the most critical aspects when designing digital solutions for corporate success.
Only an excellent big data approach with data from a wide variety of sources provides companies with the much-needed holistic and, at the same time, detailed overview. Therefore, big data will have a decisive influence on how companies, organizations, and their IT experts solve tasks in the future.