The ability to gain precise, relevant insights from large amounts of data is known as Data intelligence. Especially in today's world, where companies are surrounded by ever more extensive data streams, this expertise is becoming increasingly important. In contrast to the sheer volume of so-called big data, data intelligence is about processing data in a targeted manner and converting it into smart, targeted information.
Data intelligence: From the flood of data to targeted information
Every day, many organisations are faced with an unmanageable mountain of structured and unstructured data, known as big data. These huge amounts of data range from customer data and sensor information to feedback from social media. The challenge lies in the fact that big data is often heterogeneous, unstructured and difficult to utilise. This often results in data silos and decision-makers being overwhelmed and losing track of the sea of data. This is where data intelligence comes in: it filters and processes the raw data, sorts out the essentials and forms smart data, i.e. intelligent data that provides targeted and contextualised insights.
This makes it easier for companies to optimise operational processes, minimise risks and develop sustainable strategies. One practical example is the retail sector, where smart data provides targeted recommendations for product range management and marketing based on the analysis of purchasing behaviour and stock levels. In finance, too, smart data helps to precisely anticipate market trends and assess default risks in a structured manner. In the manufacturing industry, the data-intelligent evaluation of machine sensors enables predictable maintenance cycles and minimises unplanned downtime.
Fields of application and advantages of data intelligence at a glance
In the field of logistics, for example, the combination of real-time data and historical information provides decision-making aids to make supply chains more efficient and flexible. Precise control prevents bottlenecks and optimises stock levels. By simulating different scenarios, potential risks can be recognised at an early stage and strategic measures based on smart data can be planned.
Marketing also benefits enormously from data intelligence. Instead of simply collecting large amounts of data, smart data focuses on the targeted segmentation of customers. This allows campaigns to be better tailored to individual needs, which promotes customer satisfaction and loyalty. Another example can be seen in the healthcare sector: Here, intelligent data supports personalised therapy planning based on patient data and clinical studies. This improves the quality of treatment and the efficiency of resource utilisation.
BEST PRACTICE at the customer (name hidden due to NDA contract) An international manufacturer of industrial plants was able to reduce machine downtimes by 15 % by using data intelligence. Using intelligent analysis of sensor data, the team recognised potential faults at an early stage and reacted proactively, resulting in considerable savings and higher production quality.
Important features of data intelligence
Key characteristics set data intelligence apart from the mere amount of data:
- Data quality: Smart data is cleansed, accurate and structured.
- Targeted: Information is specifically adapted to the context of a company.
- Real-time capability: Insights can be utilised immediately and enable agile decisions.
- Increased efficiency: data preparation reduces noise and irrelevant information.
- Support for AI & machine learning: Intelligent data increases the training quality of algorithms.
Smart data not only helps with strategic decisions, but also with the automation of intelligent processes, which is becoming increasingly important in sectors such as telecommunications, production and financial services.
Data intelligence as support for decision-makers
Decision-makers need reliable and well-prepared information. The high availability of big data alone is usually not enough. Data intelligence coaching and support helps managers to successfully manage data-driven projects. Clients often report that the integration of smart data gives them far-reaching impetus for their innovation processes. Transparent and comprehensible presentation facilitates acceptance within the company and helps to communicate the added value of data-driven measures.
BEST PRACTICE at the customer (name hidden due to NDA contract) A large logistics company was able to significantly improve its transport planning through targeted coaching on data intelligence. The data-based insights enabled more flexible scheduling decisions and strengthened competitiveness through faster response times to market changes.
Service providers who use data-intelligent systems in marketing or customer management also report optimised customer approaches and measurably better results in campaign management. The key to success here is the close integration of technical expertise, process understanding and clear objectives.
My analysis
The implementation of data intelligence is a key lever for deriving tangible and reliable decisions from the unmanageable flood of data. Companies that utilise smart data often achieve more sustainable success as they can target the right areas more effectively. Practical experience shows that data intelligence is about far more than just data volume - it is about clarity, context and accessibility. With the right support during the course of the project, data projects can be implemented more effectively and achieve better results in the long term.
Further links from the text above:
Difference Between Big Data and Smart Data - Esa Automation
Big data vs. smart data: is more always better? - Netconomy
Smart data definition, challenges and difference to big data - Appvizer
From big data to smart data: AI in data automation - iPaaS Blog
Big data vs. smart data - DATAVERSITY
From big data to smart data: data intelligence as the key - Xpert Digital
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