Data intelligence is becoming increasingly important in the digital age. Decision-makers are faced with the challenge of extracting real added value from huge amounts of data. Data intelligence describes the ability to generate precise and contextualised smart data from the flood of big data. It is not just about the sheer quantity, but also about the quality and meaningfulness of the information. This intelligently processed data helps companies to make well-founded decisions and develop innovative solutions.
Big data and smart data: the crucial difference
Big data refers to large, heterogeneous and often unstructured volumes of data. This raw information comes from numerous sources such as IoT sensors, transactions or user interactions. Without analysis, however, it offers little direct benefit. Smart data, on the other hand, is high-quality, filtered and contextualised information that is extracted from big data. It is precise, relevant and enables fast and reliable decisions.
An example from production: A manufacturer collects data from machines and sensors. This raw data alone does not help. Smart data that predicts maintenance requirements and prevents breakdowns can only be created through intelligent analysis.
Another example from the retail sector: a retailer analyses customer data to create individual offers. The raw data is confusing, but targeted filtering and AI-supported processes create smart data that enables personalised marketing measures.
A third example from the healthcare sector: A hospital collects data from patients and devices. Intelligent analysis creates smart data that supports diagnoses and optimises treatment plans.
Data intelligence: From a mountain of data to valuable knowledge
Data intelligence is the key to creating real added value from big data. It supports decision-makers in acting more efficiently, securely and with foresight. In numerous industries, the targeted use of smart data facilitates strategic decisions and operational processes.
An example from logistics: a haulage company uses data from vehicles and routes. Intelligent analyses create smart data that shortens delivery times and saves costs.
Another example from the financial sector: a bank analyses transaction data to identify cases of fraud. The raw data is confusing, but targeted filtering and AI-supported processes create smart data that minimises risks.
A third example from the energy sector: an energy supplier collects data from consumers and grids. Intelligent analysis produces smart data that optimises energy consumption and prevents outages.
Data intelligence in practice: best practices
Data intelligence is not just a theoretical concept, but is being successfully implemented in practice. Companies report that big data alone only brings the hoped-for benefits to a limited extent, as the data is often unstructured and incorrect. Smart data is carefully cleansed, filtered and processed with the help of artificial intelligence or machine learning into meaningful information that enables decisions to be made in real time.
An example from the automotive industry: a manufacturer collects data from vehicles and customers. Intelligent analyses create smart data that supports product development and increases customer satisfaction.
Another example from the telecommunications sector: a provider analyses usage data in order to optimise networks. The raw data is confusing, but targeted filtering and AI-supported processes create smart data that increases performance.
A third example from the food industry: a manufacturer collects data from production processes and customers. Smart data is created through intelligent analysis, which improves quality and increases efficiency.
My analysis
Today, data intelligence is an indispensable resource for companies that want to hold their own in dynamic markets. While big data provides the raw mass of information, it is the smart, contextualised data that generates real insights. For decision-makers, this means using less data but more targeted information to optimise strategic decisions and operational processes. The implementation of data-intelligent solutions opens up numerous opportunities, from increasing efficiency and minimising risk to better customer targeting.
Further links from the text above:
Big data vs. smart data: is more always better?
Smart data: definition, application and difference to big data
Big data: the utilisation of large amounts of data
How to secure your lead with Big & Smart Data
Unleash data intelligence: Mastering Big Data & Smart Data
Data intelligence - big data and smart data for decision-makers
Big Data & Smart Data specifically for decision-makers
Smart + Big Data | Artificial Intelligence
Data Intelligence Guide: For more transparency and trust
How to turn big data into smart data
Smart data - definition of terms in the AI glossary from clickworker
Big and smart data - from statistics to data analysis
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