{"id":349488,"date":"2025-05-08T09:07:37","date_gmt":"2025-05-08T07:07:37","guid":{"rendered":"https:\/\/sauldie.org\/de\/datenanalyse-unternehmen-big-data-smart-data-entscheidungen\/"},"modified":"2025-05-08T09:07:37","modified_gmt":"2025-05-08T07:07:37","slug":"data-analysis-company-big-data-smart-data-decisions","status":"publish","type":"post","link":"https:\/\/sauldie.org\/en\/data-analysis-company-big-data-smart-data-decisions\/","title":{"rendered":"Mastering data analysis: KIROI step 3 to Big & Smart Data"},"content":{"rendered":"

Successfully mastering data analysis is a key challenge for many companies. It is not just about collecting large amounts of data, but above all about the targeted processing of this data in order to gain smart, useful insights. Modern data analysis helps companies to generate valuable smart data from big data and thus make better decisions. The following section presents important approaches and practical examples from various industries that show how professional data analysis can succeed in day-to-day business.<\/p>\n

The importance of data analysis for companies<\/h2>\n

Data analysis means more than just collecting data. It is about analysing the data in a meaningful way and deriving specific impulses for business processes from it. In industry, for example, well-founded data analysis helps to optimise production processes, predict machine downtimes and thus reduce costs. In the healthcare sector, data analysis is used to recognise patterns that enable better patient care. Similarly, retailers can create personalised offers and improve their marketing strategies by analysing customer behaviour.<\/p>\n

Big data - the sheer mass of data - is being transformed into smart data through specialised analysis methods. This smart data is characterised by relevance and quality and provides users with valuable insights for optimisation and innovation. For the individual, this means using modern technologies and intelligent processes to obtain data-driven decision-making aids that support strategic decisions.<\/p>\n

How to master data analysis in the company<\/h2>\n

Firstly, companies should define clear objectives: Which issues or business processes should be improved by analysing data? For example, a manufacturing company can focus on optimising the supply chain in order to shorten delivery times and manage stock levels more efficiently. A service provider, on the other hand, could improve service quality by analysing customer feedback.<\/p>\n

In the second step, data preparation becomes important. Data from different sources must be standardised, quality-assured and processed. An automated process facilitates the preparation of large volumes of data and ensures that only relevant information is included in the analysis. In the energy sector, for example, consumption data from different systems can be combined and analysed in order to identify potential savings.<\/p>\n

Suitable analysis methods are then used. These range from traditional statistical methods and data mining to machine learning and artificial intelligence. One logistics company, for example, uses predictive analyses to forecast vehicle requirements depending on seasonal fluctuations. In mechanical engineering, anomaly detection helps to identify faults at an early stage and prepare for maintenance.<\/p>\n

Examples from the practice of data analysis<\/h3>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> A medium-sized industrial company was able to increase production capacity utilisation by 15 % through data-based process optimisation. The data analysis revealed bottlenecks in the material supply, which were eliminated through targeted adjustments.<\/i><\/p>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> A logistics service provider used data analysis to make route planning smarter. By using traffic data and historical delivery times, it was possible to increase efficiency and significantly improve delivery reliability for customers.<\/i><\/p>\n

BEST PRACTICE with one customer (name hidden due to NDA contract)<\/b> A healthcare company relied on predictive models that recognised risks at an early stage based on patient and treatment data. This enabled better resource planning and improved individualised patient care.<\/i><\/p>\n

Recommendations for the successful use of data analysis<\/h2>\n

Those who use the possibilities of data analysis correctly should consider the following:<\/p>\n