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Start » Data Intelligence: Strategically utilising Big Data & Smart Data

9 November 2025

Data Intelligence: Strategically utilising Big Data & Smart Data

Automation Big data and smart data Digital transformation Artificial intelligence Digital Toolbox

Data Intelligence: Strategic Utilisation of Big Data and Smart Data

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.

Datenintelligenz im Kontext von Big Data und Smart Data bezeichnet die Fähigkeit, aus grossen und komplexen Datensätzen, die oft unstrukturiert und in Echtzeit anfallen (Big Data), wertvolle und umsetzbare Erkenntnisse zu gewinnen. Diese Erkenntnisse ermöglichen es Unternehmen, bessere Entscheidungen zu treffen, Prozesse zu optimieren und neue Geschäftsmöglichkeiten zu erschliessen. Im Kern geht es bei Datenintelligenz darum: * Daten zu verstehen: Nicht nur die Daten selbst, sondern auch deren Ursprung, Kontext und mögliche Beziehungen zueinander. * Daten zu analysieren: Mittels fortschrittlicher Analysetechniken, maschinellem Lernen und künstlicher Intelligenz Muster, Trends und Korrelationen aufzudecken. * Daten in Wissen umzuwandeln: Die gewonnenen analytischen Ergebnisse in verständliche und handlungsrelevante Informationen zu überführen. * Massnahmen zu ergreifen: Dieses Wissen zu nutzen, um konkrete geschäftliche Entscheidungen zu treffen und operative Massnahmen abzuleiten. Der Unterschied zu Big Data und Smart Data: * Big Data: Bezieht sich auf die schiere Menge, Vielfalt und Geschwindigkeit von Daten, die traditionelle Datenverarbeitungssysteme überfordern können. Es geht um die Verfügbarkeit riesiger Datenmengen. * Smart Data: Ist eine Weiterentwicklung von Big Data, bei der der Fokus darauf liegt, die relevanten, nützlichen und brauchbaren Datensätze aus der riesigen Menge von Big Data zu extrahieren und aufzubereiten. Es geht darum, die richtigen Daten für eine bestimmte Aufgabe zu identifizieren. * Datenintelligenz: Ist der Prozess und die Fähigkeit, aus sowohl Big Data als auch Smart Data aussagekräftige Erkenntnisse zu gewinnen und diese in Geschäftswert umzuwandeln. Sie ist das "Gehirn", das die Daten interpretiert und Aktionen antreibt. Zusammenfassend lässt sich sagen, dass Datenintelligenz der entscheidende Schritt ist, um den Wert von Big Data und Smart Data voll auszuschöpfen. Ohne Datenintelligenz bleiben Big Data oft nur eine unstrukturierte Sammlung von Informationen, und Smart Data sind nur aufbereitete, aber noch nicht interpretierte Daten.

Big Data refers to a collection of large, diverse, and often unstructured datasets that arise in various industries – 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.

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.

Strategic application of data intelligence across different industries

The range of application areas for data intelligence is vast. Three examples from various fields illustrate its practical benefits:

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.

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.

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.

Data intelligence in practice: process optimisation and efficiency improvement

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.

BEST PRACTICE at the customer (name hidden due to NDA contract) 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.

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.

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.

Practical tips for introducing data intelligence in the company

To successfully utilise data intelligence, several essential steps should be considered:

Integrate data from different sources, such as CRM systems, IoT devices, or external data providers.
Improving data quality by identifying and removing erroneous or duplicate datasets.
Using machine learning and statistical methods to identify patterns and make predictions.
Visually prepare results for quick and understandable decision-making.
Clearly define data protection and governance rules to ensure security and compliance.

This systematic approach not only creates a solid foundation for data-driven work, but also supports companies on their journey to more efficient processes and better decisions.

Technological prerequisites and challenges

An important prerequisite for successful data intelligence is technological systems that can ingest, process, and make large volumes of data usable for analysis purposes. These include data lakes, cloud infrastructures, and specialised analytical tools. Despite modern technology, the extraction of smart data often still requires human expertise, as it is important to assess the context and quality of the data.

Many companies report that the use of Artificial Intelligence can increasingly automate the processing of data. This leaves more time to incorporate the insights gained from it into strategic decisions.

My analysis

Data intelligence is a crucial success factor in today's information society. It enables companies to derive relevant, high-quality insights from the abundance of available data. This leads to informed decisions that optimise processes and open up new opportunities. The combination of Big Data and Smart Data is key to achieving the greatest possible benefit. Companies that support and strategically implement this approach create sustainable competitive advantages.