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KIROI - Artificial Intelligence Return on Invest: The AI strategy for decision-makers and managers

Start » Data Intelligence: With Big & Smart Data for Better Decision-Making

4 November 2025

Data Intelligence: With Big & Smart Data for Better Decision-Making

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

In today's business world, the ability to meaningfully interpret large volumes of data is becoming increasingly important. Data intelligence forms the basis for generating relevant and high-quality information from the flood of Big Data. This intelligent data supports companies in making informed decisions, unlocking efficiency potential, and remaining competitive.

Datenintelligenz im Kontext von Big und Smart Data bezieht sich auf die Fähigkeit, aus grossen und komplexen Datensätzen, die oft als Big Data bezeichnet werden, Erkenntnisse und Wissen zu gewinnen. Sie umfasst die Prozesse, Technologien und Methoden, die notwendig sind, um Rohdaten zu sammeln, zu speichern, zu verarbeiten, zu analysieren, zu interpretieren und daraus handlungsrelevante Informationen zu extrahieren. Im Wesentlichen geht es bei Datenintelligenz darum, Daten in intelligente Daten umzuwandeln. Hier sind die Schlüsselkomponenten und Aspekte von Datenintelligenz im Kontext von Big und Smart Data: * Datenintegration und -bereinigung: Big Data stammt oft aus verschiedenen Quellen und in unterschiedlichen Formaten. Datenintelligenz beinhaltet Mechanismen, um diese Daten zusammenzuführen und zu bereinigen, um Konsistenz und Qualität sicherzustellen. * Datenverarbeitung und -analyse: Dies ist das Herzstück. Es geht um den Einsatz von Werkzeugen und Techniken (wie Machine Learning, KI, statistische Analysen, Data Mining), um Muster, Trends, Korrelationen und Anomalien in den Daten zu erkennen. * Echtzeit-Analyse: Bei Smart Data, das oft auf Echtzeitdaten abzielt, ist die Fähigkeit zur sofortigen Verarbeitung und Analyse entscheidend, um zeitnahe Entscheidungen zu ermöglichen. * Datenvisualisierung und -kommunikation: Die komplexen Erkenntnisse aus Big Data müssen für Entscheidungsträger verständlich aufbereitet werden. Datenintelligenz schliesst die Erstellung von Dashboards, Berichten und Infografiken ein. * Kontextualisierung: Datenintelligenz fügt den Daten Bedeutung hinzu, indem sie sie in den richtigen Kontext setzt – sei es geschäftlicher, operativer oder wissenschaftlicher Natur. * Automatisierung und Entscheidungsfindung: Ein fortgeschrittener Aspekt ist die Automatisierung von Prozessen auf Basis der gewonnenen Erkenntnisse und die Unterstützung oder gar Übernahme von Entscheidungen durch intelligente Systeme. * Datenqualitätsmanagement: Sicherstellen, dass die Daten korrekt, vollständig und aktuell sind, ist eine ständige Aufgabe. * Sicherheit und Datenschutz: Bei der Verarbeitung grosser Datenmengen sind strenge Massnahmen für Sicherheit und Datenschutz unerlässlich. Unterschiede und Verknüpfungen mit Big Data und Smart Data: * Big Data: Bezieht sich auf die grossen Mengen, die Geschwindigkeit und die Vielfalt der Daten. Datenintelligenz ist die Fähigkeit, mit diesen Big Data umzugehen und daraus Wert zu schöpfen. * Smart Data: Bezieht sich auf Daten, die bereits eine gewisse "Intelligenz" oder einen Kontext aufweisen, sodass sie leichter analysierbar und nutzbar sind. Es kann auch bedeuten, dass die Daten so aufbereitet sind, dass sie für spezifische Zwecke wertvoller sind. Datenintelligenz hilft dabei, Big Data in Smart Data zu verwandeln oder Smart Data weiter zu verfeinern. Zusammenfassend lässt sich sagen, dass Datenintelligenz der Prozess und die Kapazität ist, aus der Fülle und Komplexität von Big und Smart Data meaningfulle, handlungsrelevante und wertvolle Erkenntnisse zu gewinnen.

Data intelligence refers to the ability to extract high-quality, reliable, and context-specific smart data from raw, mostly unstructured big data. Big data encompasses vast volumes of data generated at high speed from diverse sources – from sensor data in industry to customer interactions in retail. However, it is only through careful processing and analysis that this data flood becomes a valuable source of knowledge. For example, through data-intelligent analysis, companies can identify patterns and proactively derive measures from them.

Manufacturers, for example, use sensor data from production plants to optimise maintenance cycles and reduce unplanned downtime through data intelligence. In the financial sector, intelligent data analytics help to detect fraud attempts at an early stage. Smart data is also used in marketing to precisely target audience groups and increase customer loyalty.

How is data intelligence implemented in practice?

Data intelligence is created through several sequential work steps, which ensure that only relevant and quality-checked information is used for decision-making. The most important process steps include:

Data Integration: Linking diverse data sources such as CRM systems, IoT devices, or external services.
Data cleansing: Sorting out erroneous, duplicate, or irrelevant data.
Data Analysis: Use of algorithms, machine learning and statistical models for pattern recognition and forecasting.
Visualisation: Displaying key insights on dashboards for quick, clear decisions.
Data Protection and Governance: Ensuring the responsible handling of sensitive information.

For example, an energy provider uses smart meter data to predict consumption peaks and prevent grid bottlenecks. This data-intelligent control has improved the integration of renewable energies. The combination of Big Data and Smart Data acts as a driver for flexible, sustainable solutions.

In another case, data intelligence supports a medical technology company. There, automated analysis of large image datasets is supplemented by AI, which improves diagnostics and provides healthcare professionals with contextual guidance. This reduces susceptibility to errors and makes treatment more precise.

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

The introduction of data intelligence in manufacturing led to a significant reduction in unproductive downtime. Through real-time data monitoring and continuous optimisation of machine parameters, maintenance intervals could be precisely adjusted, and costly unplanned failures avoided. Additionally, product quality improved through targeted control of critical production steps.

Data intelligence as a competitive advantage: industry examples

In the field of logistics, data-driven applications are used to dynamically optimise routes. Intelligent analyses can shorten delivery times and reduce costs. Data intelligence ensures that the most up-to-date traffic and weather data are always incorporated.

In retail, smart data enables personalised customer engagement. Analysing purchasing behaviour and online interactions allows offers to be placed with precision, thereby increasing conversion rates. At the same time, data intelligence makes it possible to identify demand trends early and manage inventory levels efficiently.

In industry, networked sensors continuously monitor the condition of machinery. Data intelligence is used to analyse this information to proactively plan maintenance. This reduces downtime and extends the lifespan of equipment, a key efficiency factor in manufacturing.

Tips for the Successful Implementation of Data Intelligence

For businesses to effectively leverage data intelligence, a strategic approach is recommended. The following tips can be supportive:

Clear definition of goals: Data intelligence aims to answer specific questions such as: * What data do we have? * Where is it located? * What is its quality? * Who owns it and who is responsible for it? * How is it used and by whom? * What is its regulatory status (e.g., PII, GDPR)? * How can we gain insights from this data? * What are the business implications of our data assets? * How can we improve our data governance and management?.
Ensuring data quality Ensure regular data review and cleansing.
Use the right tools: Choose software solutions that efficiently process Big Data and provide Smart Data.
Engage experts: Involve data scientists and business departments early in the process.
Please observe data protection. Always consider legal requirements and ethical standards.

My analysis

The significance of data intelligence is continuously growing. Companies that not only collect Big Data but also transform it into Smart Data gain a crucial advantage. The ability to use relevant data quickly and meaningfully supports better decision-making and innovation. Data intelligence therefore effectively accompanies projects aimed at increasing efficiency, reducing costs, or improving customer experience.

The interplay of Big Data, Smart Data, and intelligent analysis forms the basis of modern decision-making processes. It opens up diverse opportunities, which are evidenced by practical examples from production, logistics, trade, and healthcare. With thoughtful implementation, data intelligence can sustainably support companies in achieving their strategic goals.