The rapid pace of digitalisation means that companies today have to deal with enormous amounts of information. The ability not only to collect data but also to analyse it intelligently is becoming increasingly important. This is precisely where data intelligence comes in. It is the key to transforming raw data into valuable insights and thus securing a competitive advantage in the long term.
Data intelligence: increasing efficiency through smart 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.
In industry, for example, manufacturers use sensor data from production facilities to optimise maintenance cycles with the help of data-intelligent applications. This reduces unplanned downtimes and improves product quality. Retailers benefit in a similar way by using smart data to precisely analyse customer behaviour. Targeted marketing campaigns often lead to a noticeable increase in customer loyalty.
Data intelligence also comes into play in the financial sector. Sophisticated algorithms can be used to recognise and prevent attempted fraud at an early stage. Data intelligence therefore offers effective protection for companies and customers alike.
How valuable information is created from big data
The path from unstructured data to data-intelligent utilisation comprises several stages:
- Data integration of various sources such as IoT devices, customer management systems and external databases
- Data cleansing to eliminate errors, duplicates and inconsistencies
- Intelligent data analysis, for example with machine learning, to recognise patterns and create forecasts
- Visualisation of the results to enable well-founded decisions to be made more quickly
- Clear governance and data protection guidelines
For example, a logistics company uses smart data to dynamically adapt routes to traffic and weather conditions, thereby shortening delivery times. In the healthcare sector, intelligent analysis of large patient data samples can be used to develop more individualised therapy concepts.
BEST PRACTICE at the customer (name hidden due to NDA contract) The use of data intelligence in production led to a significant reduction in unproductive downtimes. The real-time monitoring of machines and algorithm-supported optimisation enabled maintenance to be planned in a targeted manner. This not only achieved better efficiency, but also a significant increase in product quality and savings in operating costs.
Data intelligence in everyday practice: three examples from different industries
1 In retail, smart data helps to recognise customer preferences in real time. This allows a fashion store to better predict seasonal trends and adapt its product range more specifically to demand. The result is higher sales and lower stock levels.
2 In the energy sector, consumption patterns are analysed with the help of data-intelligent systems. This enables an energy supplier to better forecast demand and optimise the integration of renewable energy sources. This not only supports sustainability, but also cost efficiency.
3 The automotive industry benefits from predictive maintenance. Here, vehicle conditions are continuously monitored using sensor data. Workshops are actively informed before faults occur. This reduces downtimes and repair costs.
Impulses for the successful use of data intelligence
In order to establish data intelligence in the company, those responsible should first thoroughly check the database. The quality and structure of the recorded data are crucial. Modern analysis tools and AI-supported algorithms then help to quickly recognise relevant patterns. It is equally important to train employees so that they can use data-intelligent tools in a targeted manner.
It is also advisable to implement clear data protection and security measures to ensure trust among customers and partners. This is the only way to create sustainable added value from the data.
Clients often report that external support provides valuable impetus, especially in complex projects. Transruption coaching supports the implementation of data-intelligent strategies and accompanies teams on the path from big data to smart data.
My analysis
Today, data intelligence is an indispensable skill for companies that want to strengthen their decision-making expertise and secure their competitive edge. The transition from unstructured big data to high-quality smart data requires technical expertise, strategic planning and consistent implementation. Numerous industries show how processes can be optimised, costs reduced and customer relationships improved through the intelligent use of data. The combination of modern technology, qualified employees and proven support is the key to success.
Further links from the text above:
[1] What is smart data?
[2] Data intelligence: With Big & Smart Data for better ...
[3] Data intelligence: Big data and smart data for decision-makers ...
[4] Smart Data: Definition, application and difference to Big ...
[5] Smart data - definition of terms in the AI glossary from clickworker
[6] Data intelligence: clever use of big data and smart data
[7] What is smart data? Definition and explanation of the term
[8] Big Data / Smart Data
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