Open Access

In zunehmend disruptiven Märkten wird die Fähigkeit, Latenzen in Bezug auf die organisatorische Adaptions- und Entscheidungsfähigkeit zur Reaktion auf neue Marktbedingungen zu minimieren, für das langfristige Bestehen von Unternehmen immer bedeutsamer. In Reaktion auf diese Entwicklungen realisieren immer mehr Unternehmen Strategien, um durch die informationstechnologiebasierte Generierung, Speicherung und Verarbeitung von Daten die datengetriebene Entscheidungsfindung voranzutreiben und datenbasierte Entscheidungen zu treffen. In der Konsequenz generieren und speichern die Unternehmen zunehmend große Mengen an Daten. Um diese in umsetzbare Erkenntnisse zu überführen und eine datenbasierte Entscheidungsfindung zu realisieren, sind fortgeschrittene statistische Verfahren und Funktionen (Analytics) notwendig, die unter dem Begriff Business-Analytics zusammengefasst werden. Trotz der hohen zu erwartenden Potentiale stehen viele Unternehmen großen Herausforderungen gegenüber, welche durch den Einsatz der hochkomplexen Systeme und Technologien induziert werden und große Auswirkungen auf die Organisation und ihre Mitarbeiter haben. In Abwesenheit geeigneter Hilfestellungen und Instrumente zur Berücksichtigung und Gestaltung der relevanten technischen und sozialen Faktoren, welche den erfolgreichen Einsatz von Business-Analytics beeinflussen, bleiben die Unternehmen auf sich allein gestellt. Die Arbeit hat zum Ziel, den erfolgreichen Einsatz verschiedener Typen von Business-Analytics durch soziotechnische Gestaltungsinstrumente sicherzustellen. Das Ergebnis besteht somit in der systematischen Entwicklung konkreter Gestaltungsinstrumente, um unter Berücksichtigung des wechselseitigen Einflusses zwischen Technologieeinsatz sowie der Organisation und deren Mitarbeiter den erfolgreichen Einsatz verschiedener Typen von Business-Analytics zu gewährleisten.

Digitalization offers companies strategic advantages through the simplification and optimization of business processes and is an important lever for long-term growth. Numerous industries already benefit from optimized coordination of capacities and resources by integrating digital technologies into business processes. Digital platforms enable, e. g., the demand-based adjustment of asset capacities at peak loads on the basis of data-based forecasts. In particular, small and medium-sized enterprises in the German quarrying industry have so far been denied access to such technologies due to high investment and operating costs, high implementation efforts and heterogeneous machine fleets. This paper presents the functions of the data-centric platform demonstrator “PROmining” developed in an AiF research project and validates them using case studies with companies in the industry. The platform demonstrator offers marginally digitalised companies a tool with functions ranging from simple operational data collection, the evaluation of capacity utilization, to scenario development for regional demand, which can be used as a blueprint within their own company. Thus, companies in the quarrying sector are provided with a low-effort entry into the digital transformation and a contribution to long-term competitiveness.

In diesem Artikel wird das Potenzial einer datenzentrierten digitalen Plattformlösung für die S&E-Branche mithilfe der Ergebnisse einer Nutzen- und Potentialanalyse erläutert.

The use of Business Analytics (BA) helps to improve the quality of decisions and reduces reaction latencies, especially in uncertain and volatile market situations. This expectation leads a continuously rising number of companies to make large investments in BA. The successful use of Business Analytics is increasingly becoming a differentiator. At the same time, the use of BA is not trivial, rather, it is subject to high socio-technical requirements. If these are not addressed, high risks arise that stand in the way of successful use. In particular, it is important to consider the risks in relation to the different types of BA in a differentiated way. So far, there is a lack of suitable approaches in the literature to consider these type-specific risks with regard to the socio-technical dimensions: people, technology, and organization. This paper addresses this gap by initially identifying risks in the use of Business Analytics. For this purpose, possible risks are identified using a systematic literature review and verified with a Delphi survey with various partners experienced in dealing with BA. Subsequently, the identified and validated risks are assigned to three different types of Business Analytics (Descriptive, Predictive and Prescriptive Analytics) and assessed in order to systematically address and reduce the risks. The result of this paper is an overview of the interactions between the socio-technically assigned risks, summarized in a risk catalog, and the different types of Business Analytics.

The successful use of Business Analytics is increasingly becoming a differentiating competitive factor. The ability to extract data-driven insights and integrate them into decision-making is becoming growingly important. The underlying technologies are evolving exponentially, the value proposition differs from simple descriptive applications to automated decision-making. Existing approaches found in literature and practice to classify those levels only insufficiently mark down the boundaries between the different technology levels. As a consequence, it is often unclear which characteristics of the technology interact with the working environment, which can be described as a socio-technical system. Using a systematic literature review, this paper identifies the characteristics of Business Analytics and delineates three types of Business Analytics based on case studies. Thus, a starting point for the socio-technical system design and optimization for the use of Business Analytics is created.