{"id":110815,"date":"2025-09-24T14:33:19","date_gmt":"2025-09-24T12:33:19","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=110815"},"modified":"2025-09-29T15:08:44","modified_gmt":"2025-09-29T13:08:44","slug":"assistance-work-environment","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/assistance-work-environment\/","title":{"rendered":"Data-Driven Assistance Systems in the Working Environment"},"content":{"rendered":"\n

This article reports on initial findings from action research [1] on the implementation of digital assistance<\/a> systems in four pilot cases as part of the project \u201cPerspektiveArbeit Lausitz\u201d. It examines how systematic development can prevent bad investments or project cancellations.<\/p>\n\n\n\n

Initial situation: Lack of BI tools for data visualization<\/h2>\n\n\n\n

In the data-driven world of work, accurate and timely information is essential for sound decision-making and competitiveness. Business intelligence (BI) dashboards play a key role in this. BI tools, such as Tableau, consolidate data from various software programs in use within companies, for example for order management or production data acquisition, and enable analyses of patterns and trends, transforming complex data sets into dashboards.<\/p>\n\n\n\n

In this way, they make an important contribution to sound business decisions, either on the basis of real-time data evaluations or by integrating data from different sources for the optimization of processes and decision support [2-5]. Dashboards facilitate communication between departments by providing all participants with the same overview of relevant data. This promotes alignment on goals and improves coordination within the company. <\/p>\n\n\n\n

However, every company has specific requirements for data visualization and reporting. Dashboards must therefore be customizable to provide exactly the information that is most relevant to the company in question. Designing dashboards independently reduces reliance on external service providers or proprietary solutions, on the one hand. On the other hand, however, it constitutes a challenging process that can only be carried out by those with adequate skills and resources for implementation. As pilot cases show, many SMEs are currently overwhelmed by this prospect.<\/p>\n\n\n\n

The four pilot companies reported on here demonstrate a high degree of dependence on available IT capacities (for example Group IT) and on the willingness of external IT service providers to give access to their underlying data and metadata. While the EU Data Act [6] grants users of software formal rights with regard to transparency and data access, these rights are currently claimed only hesitantly. High-quality BI dashboard solutions incur considerable licensing and implementation costs. The risk of a bad investment therefore represents a further hurdle for smaller companies. Co-creation by companies and academia brings together different areas of expertise, helping to minimize risk.<\/p>\n\n\n\n

The pilot companies do not currently use any BI tools for data visualization. Instead, dedicated employees create their own diagrams using office software, most often Microsoft Excel. Obtaining and processing data from various source systems involves considerable manual effort. The limited functionality of Excel, the often-restricted data access, and the resulting manual replacement of missing data can lead to distorted visualizations.<\/p>\n\n\n\n

Data modeling is extremely challenging: employees often lack either the knowledge to model business processes and the associated data collection or access to the company data stocks, which would have to be provided by the suppliers. Inconsistent or incorrect data from different systems leads to reduced data quality and significantly impairs the accuracy of dashboards. These issues often occur due to gaps and deviations in the organizational processes for data collection.<\/p>\n\n\n\n

The scope and design of dashboards<\/a> is largely determined by their intended use [7]. Relevant use cases in the pilot companies are:<\/p>\n\n\n\n