{"id":346860,"date":"2025-10-22T12:11:34","date_gmt":"2025-10-22T10:11:34","guid":{"rendered":"https:\/\/sauldie.org\/de\/smart-data-nutzen-unternehmen-erfolg\/"},"modified":"2026-03-14T13:21:36","modified_gmt":"2026-03-14T12:21:36","slug":"companies-use-smart-data-for-success","status":"publish","type":"post","link":"https:\/\/risawave.org\/en\/smart-data-nutzen-unternehmen-erfolg\/","title":{"rendered":"Smart Data: How decision-makers increase their success with Big Data"},"content":{"rendered":"

In the digital age, data is a crucial success factor for businesses. However, not all data is equally valuable. In particular, the use of Smart Data<\/strong> supports decision-makers in transforming complex big data sets into targeted, profitable insights. This intelligently prepared data provides well-founded impulses for strategic decisions and helps to effectively shape business processes.<\/p>\n

Smart Data as the key to better decision-making<\/h2>\n

Many companies have a multitude of data, but it is often unstructured and lacks significance. Smart Data<\/strong> denotes datasets that exhibit high data quality after careful review and filtering, making them immediately usable. Such data enables executives to act more quickly and precisely because relevant information is readily available.<\/p>\n

A manufacturing industry example shows how production planning is optimised using Smart Data: sensors capture real-time machine data, which is then filtered and analysed. This allows for predictive maintenance scheduling, thereby reducing unplanned downtime and increasing productivity. Similar approaches are found in logistics, where real-time data is used to manage supply chains, and in retail, where customer analysis enables more targeted marketing campaigns.<\/p>\n

BEST PRACTICE at the customer (name hidden due to NDA contract)<\/b> By using Smart Data, customer behaviour data in a consumer goods company was analysed to create personalised offers automatically. This led to a significant increase in conversion rates and higher customer satisfaction.<\/i><\/p>\n

From Big Data to Smart Data: Quality over Quantity<\/h2>\n

The term Big Data refers to huge volumes of data that arise within companies. These contain valuable information but are often unstructured and difficult to manage. Smart Data<\/strong> is created when this raw data is filtered, cleaned, and contextualised. This results in high-quality datasets that are specifically tailored to the respective industry and company goals.<\/p>\n

For example, in healthcare, patient data from various sources is consolidated, enabling doctors and nursing staff to make well-founded diagnoses more quickly. In the energy sector, smart data analyses help to accurately predict energy consumption, thus using resources more sustainably. In the banking sector too, credit decisions benefit from cleanly processed data that allows for better risk assessment.<\/p>\n

Typical challenges such as data overload or quality defects are reduced in this way, so that employees can work with clear, usable information. This is important because clients often report that mere access to large amounts of data without an intelligent filter leads to overwhelm.<\/p>\n

Practical tips for the successful use of Smart Data<\/h3>\n

So that companies Smart Data<\/strong> To use it effectively, it is recommended to observe some basic principles:<\/p>\n