{"id":349986,"date":"2025-11-09T18:46:00","date_gmt":"2025-11-09T17:46:00","guid":{"rendered":"https:\/\/sauldie.org\/de\/datenintelligenz-big-data-smart-data-strategisch-nutzen\/"},"modified":"2026-03-14T12:43:52","modified_gmt":"2026-03-14T11:43:52","slug":"using-data-intelligence-big-data-and-smart-data-strategically","status":"publish","type":"post","link":"https:\/\/risawave.org\/en\/datenintelligenz-big-data-smart-data-strategisch-nutzen\/","title":{"rendered":"Data Intelligence: Strategically utilising Big Data & Smart Data"},"content":{"rendered":"

Data Intelligence: Strategic Utilisation of Big Data and Smart Data<\/h2>\n

In the digital age, the sheer volume of available information presents businesses with significant challenges. This is precisely where data intelligence comes in: it connects the unmanageable amounts of data, known as Big Data, with intelligently processed and relevant information, so-called Smart Data. This creates strategic value, enabling informed decisions and generating competitive advantages.<\/p>\n

Datenintelligenz im Kontext von Big Data und Smart Data bezeichnet die F\u00e4higkeit, aus grossen und komplexen Datens\u00e4tzen, die oft unstrukturiert und in Echtzeit anfallen (Big Data), wertvolle und umsetzbare Erkenntnisse zu gewinnen. Diese Erkenntnisse erm\u00f6glichen es Unternehmen, bessere Entscheidungen zu treffen, Prozesse zu optimieren und neue Gesch\u00e4ftsm\u00f6glichkeiten zu erschliessen.\n\nIm Kern geht es bei Datenintelligenz darum:\n\n* **Daten zu verstehen:** Nicht nur die Daten selbst, sondern auch deren Ursprung, Kontext und m\u00f6gliche Beziehungen zueinander.\n* **Daten zu analysieren:** Mittels fortschrittlicher Analysetechniken, maschinellem Lernen und k\u00fcnstlicher Intelligenz Muster, Trends und Korrelationen aufzudecken.\n* **Daten in Wissen umzuwandeln:** Die gewonnenen analytischen Ergebnisse in verst\u00e4ndliche und handlungsrelevante Informationen zu \u00fcberf\u00fchren.\n* **Massnahmen zu ergreifen:** Dieses Wissen zu nutzen, um konkrete gesch\u00e4ftliche Entscheidungen zu treffen und operative Massnahmen abzuleiten.\n\n**Der Unterschied zu Big Data und Smart Data:**\n\n* **Big Data:** Bezieht sich auf die schiere Menge, Vielfalt und Geschwindigkeit von Daten, die traditionelle Datenverarbeitungssysteme \u00fcberfordern k\u00f6nnen. Es geht um die Verf\u00fcgbarkeit riesiger Datenmengen.\n* **Smart Data:** Ist eine Weiterentwicklung von Big Data, bei der der Fokus darauf liegt, die relevanten, n\u00fctzlichen und brauchbaren Datens\u00e4tze aus der riesigen Menge von Big Data zu extrahieren und aufzubereiten. Es geht darum, die *richtigen* Daten f\u00fcr eine bestimmte Aufgabe zu identifizieren.\n* **Datenintelligenz:** Ist der Prozess und die F\u00e4higkeit, aus sowohl Big Data als auch Smart Data aussagekr\u00e4ftige Erkenntnisse zu gewinnen und diese in Gesch\u00e4ftswert umzuwandeln. Sie ist das \"Gehirn\", das die Daten interpretiert und Aktionen antreibt.\n\nZusammenfassend l\u00e4sst sich sagen, dass Datenintelligenz der entscheidende Schritt ist, um den Wert von Big Data und Smart Data voll auszusch\u00f6pfen. Ohne Datenintelligenz bleiben Big Data oft nur eine unstrukturierte Sammlung von Informationen, und Smart Data sind nur aufbereitete, aber noch nicht interpretierte Daten.<\/h2>\n

Big Data refers to a collection of large, diverse, and often unstructured datasets that arise in various industries \u2013 for example, transaction data in the financial sector, sensor data in industry, or customer data in retail. The sheer volume of this information is almost impossible to evaluate manually.<\/p>\n

Data intelligence involves the ability to precisely filter these vast data sets and transform them into meaningful smart data. Smart data is characterised by high quality, relevance, and clear contextualisation. This means that not all data sets are valuable, but rather those that deliver real added value and insights can be sustainably integrated into business processes.<\/p>\n

Strategic application of data intelligence across different industries<\/h2>\n

The range of application areas for data intelligence is vast. Three examples from various fields illustrate its practical benefits:<\/p>\n

In retail, intelligent data analysis helps filter out patterns from millions of customer interactions that provide clues about the purchasing behaviour of specific target groups. This allows marketing campaigns to be more precisely tailored to needs, leading to increased customer loyalty and revenue growth.<\/p>\n

In the automotive industry, data-driven methods support predictive maintenance. Sensor values are continuously analysed to provide early warnings of potential faults. This allows for better planning of workshop appointments, avoidance of breakdowns, and increased customer satisfaction.<\/p>\n

Healthcare also benefits enormously when patient data, laboratory results, and other health information are intelligently linked and interpreted. On this basis, highly individualised therapeutic approaches can be developed, which can improve the course of treatment and at the same time reduce costs.<\/p>\n

Data intelligence in practice: process optimisation and efficiency improvement<\/h3>\n

Industrial manufacturing illustrates how data-driven action can significantly optimise processes. Real-time monitoring and algorithmic evaluation can reduce downtime and make maintenance work more targeted.<\/p>\n

BEST PRACTICE at the customer (name hidden due to NDA contract)<\/b> The implementation of data intelligence in the production process led to reduced response times for machine failures. By using intelligent algorithms, maintenance appointments were optimally scheduled. The result was a noticeable increase in manufacturing efficiency and improved product quality.<\/i><\/p>\n

A logistics company also used data intelligence to dynamically adjust routes. The analysis of traffic data and order requirements enabled flexible supply chain design, thereby reducing delivery times and deploying resources more efficiently.<\/p>\n

In retail, stock levels have been optimised through intelligent analysis of inventory and sales data. This precise control prevents overstocking, reduces storage costs, and improves product availability.<\/p>\n

Practical tips for introducing data intelligence in the company<\/h2>\n

To successfully utilise data intelligence, several essential steps should be considered:<\/p>\n