Unlike Business Intelligence, the buzzword Industry 4.0 and its predecessors can look back on a long history that began in the 18th and 19th century with Industry 1.0 and thus took its first steps far ahead of those in the field of data analysis. Let us take up this analogy and draw it into the picture of modern data processing. An exact differentiation according to the following categories is not always possible because the boundaries of the individual tools are often blurred. However, let’s give it a try.
Management Information System – Industry 1.0
In the early 1960s, BI encompassed a broad spectrum of applications and technologies for decision-oriented collection, processing and presentation of business-relevant information. Management Information Systems (MIS) are computer-supported systems that provide managers in various hierarchical positions with information from a given database. However, in the 1960s, this approach was the first attempt of IT-based generation of information for decision-making that was based on inefficient hardware and software.
The data provided here is still highly operational. The ever-increasing volume of data in this area became a problem because the data was not filtered, cleaned or compressed. Only an automation of the standard reporting was possible by batch generated reports. However, evaluations in the shape of data rows are far too confusing to reach a useful level of decision support. As a result, instead of senior management, only controllers and decision makers used the services. Thus, the MIS failed to meet one of the key requirements: the provision of aggregated, centralized information. The discrepancy between high expectations and technical feasibility could not be overcome. Nevertheless, similar systems are still responsible for automated standard reporting in many companies today.
Decision Support Systems – Industry 2.0
Decision support systems are interactive and adaptable systems that process the given data using models and methods. The focus here is not on the detailed supply of data, but rather on support in the planning and decision-making process. A pronounced model orientation and method orientation is therefore characteristic for decision support systems. However, this is also the biggest disadvantage as complex models have to be understood. In a man-machine dialogue, the user side must be given more consideration. A study from 1988 showed that only 29% of the managers used a computer at that time.
Decision support systems, like management information systems, were supposed to provide an enterprise-wide model for planning, but decision support systems could not fulfill this approach either. Nevertheless, DSS are still used for subproblems that require a high level of competence. In addition to problem structuring, the focus here resides in the evaluation and generation of alternatives. Integration in Enterprise Resource Planning (ERP) can also be observed. Within a metaphorical application pyramid, DSS are hierarchically placed above MIS.
Executive Information Systems – Industry 3.0
The approach of management information systems was taken up again by the increasing performance of personal computers and the progressive networking of data processing systems. The term Executive Information System was established for dialogue- and data-oriented information systems, which provide internal and external information through easy-to-use and individually adaptable interfaces. As a user is aimed at the top management level, but since this concept was very well received, the target group expanded considerably, so that EIS was called “Enterprise Information System” or “Everybody’s Information System” in the mid-1990s.
Executive Information Systems continue to remain popular due to new and increasingly powerful technologies, mature concepts, the ever-increasing knowledge of IT managers and the increased acceptance of new information technologies in and for companies. Historically, they are regarded as a further development of management information systems and usually represent the top of the company application pyramid.
However, one of the decisive disadvantages of management information systems was the relational database. Performance within large amounts of data across many tables suffered greatly from this type of data modeling. The use of a more suitable data structure for analysis in the context of a data warehouse ultimately led to systems that fall under the definition of Business Intelligence. The systems mentioned above are often combined. Such a combination is called an Executive Support System (ESS). Here the advantages in the field of visualization and presentation of executive information systems are rejuvenated with the models and methods for analysis, forecasting, simulation and optimization by decision support systems.
Thus, the performance of an ESS is significantly higher than the indivual performance of EIS and DSS. In order to serve the different problem-solving needs of a company a combination of the presented systems is necessary. The function-enhancing combination of different components in one ESS is hence a logical conclusion.
Business Intelligence – Industry 4.0?
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