{"id":349598,"date":"2025-11-07T04:03:53","date_gmt":"2025-11-07T03:03:53","guid":{"rendered":"https:\/\/sauldie.org\/de\/datenintelligenz-big-data-smart-data-unternehmen-9\/"},"modified":"2026-03-14T13:19:31","modified_gmt":"2026-03-14T12:19:31","slug":"data-intelligence-big-data-smart-data-companies-9","status":"publish","type":"post","link":"https:\/\/risawave.org\/en\/datenintelligenz-big-data-smart-data-unternehmen-9\/","title":{"rendered":"Unlocking Data Intelligence: Making the Most of Big Data and Smart Data"},"content":{"rendered":"

Data intelligence plays a crucial role today in extracting real added value from the vast amount of available information. Companies often face the challenge of gaining not just mass, but relevant insights from their large and unwieldy data inventories. Data intelligence offers the opportunity here to efficiently use big data and transform it into high-quality, targeted smart data. This supports business processes, promotes innovation, and creates competitive advantages.<\/p>\n

The art of data intelligence: from raw data to valuable information<\/h2>\n

Large amounts of data, known as Big Data, are generated in a wide variety of areas \u2013 from industrial manufacturing and retail to the healthcare sector. These massive data streams are often unstructured and complex. For example, companies in mechanical engineering collect sensor data from equipment, while the retail sector creates extensive purchasing profiles through customer interactions. However, the sheer volume of data is of little use if the right methods for analysis and preparation are not employed.<\/p>\n

This is where data intelligence comes into play. It enables the filtering of relevant data treasures from the deluge by algorithms recognising patterns and uncovering complex correlations. Smart data is created through the selective filtering, cleansing and contextualisation of data. For example, a logistics company can dynamically optimise route planning using data intelligence methods, thereby shortening delivery times and reducing fuel costs.<\/p>\n

In the financial sector, for example, data intelligence significantly improves fraud detection. Intelligent analysis methods make it possible to react in real-time and detect.<\/p>\n

Data intelligence in action: practical examples from various industries<\/h2>\n

In the automotive sector, manufacturers use data intelligence to analyse vehicle data in real time. This allows maintenance appointments to be predicted, defects to be detected early and service processes to be designed efficiently. In the pharmaceutical industry, data-intelligent systems support research through the evaluation of large study and patient data, which accelerates the development of new therapies.<\/p>\n

In the energy sector, smart data helps to precisely control energy consumption and better integrate renewable energies. Utilities can predict peak loads, thereby keeping the grid stable. In the media industry, intelligent analysis of user data provides better insights into content preferences, enabling the creation of personalised offers and increasing customer loyalty.<\/p>\n

BEST PRACTICE at the customer (name hidden due to NDA contract)<\/b> In a manufacturing company, the introduction of data-intelligent monitoring systems led to a significant reduction in unplanned downtime. The intelligent analysis of sensor data enabled predictive maintenance, thereby ensuring optimised plant utilisation. This not only resulted in higher efficiency but also in improved product quality.<\/i><\/p>\n

How companies become smarter with data intelligence<\/h3>\n

The key is to use the right tools and techniques to transform Big Data into Smart Data. These include:<\/p>\n