In the age of digitalisation Data intelligence is becoming increasingly important. Today, companies are faced with the challenge of not only storing information from huge amounts of data, but also transforming it into valuable knowledge. The terms „big data“ and „smart data“ play a central role here. While the former describes large, often unstructured volumes of data, the latter focuses Data intelligence on intelligent utilisation and analysis in order to create real added value.
Data intelligence as a bridge between big data and smart data
The broad mass of data referred to as big data comes from a wide variety of sources. For example, retail companies collect millions of customer interactions or product scans every day. In industry, sensor data is generated by machines and systems, and numerous transaction data records are created in the financial sector. Without targeted processing of this data, this raw data is often of little use for decision-making.
This is where the Data intelligence It uses intelligent technologies to transform big data into smart data - i.e. qualitative, relevant data sets that provide directly usable insights. The aim is more efficient decision-making based on sound data.
BEST PRACTICE with one customer (name hidden due to NDA contract) In the area of logistics, the project led to a significant reduction in delivery delays. Thanks to the intelligent analysis of transport, warehouse and traffic data, the customer was able to react in good time and optimise its processes.
Big data, smart data and their significance for data-intelligent companies
Big data is primarily described by the three Vs: Volume, Velocity and Variety. These data volumes are so large and complex that traditional methods of data processing often reach their limits. For example, a networked car generates millions of sensor data within a very short space of time, which must be processed in order to provide relevant warnings and maintenance information.
Smart data, on the other hand, does not focus on quantity, but on quality and targeting. In the healthcare sector, for example, only the information that is relevant for personalised treatment is filtered out of extensive patient data. This makes therapeutic approaches much more precise and treatment more effective.
Supported by the use of algorithms and artificial intelligence Data intelligence The company can extract suitable data from huge sets and use it in a targeted manner.
BEST PRACTICE with one customer (name hidden due to NDA contract) In the retail sector, the smart data class enables an effective customer approach. By analysing purchase history and time spent on websites, customers can develop personalised offers that lead to a measurable increase in sales.
How data intelligence simplifies everyday work
The intelligent processing of data helps employees to make decisions more quickly and organise processes more flexibly. In mechanical engineering, sources of error can be recognised at an early stage thanks to intelligent data analysis. This minimises downtime and saves costs.
Smart data also helps in marketing to better target campaigns. For example, a telecommunications provider can identify trends in the use of mobile services and use them to develop customised tariffs.
BEST PRACTICE with one customer (name hidden due to NDA contract) In the insurance sector, data-intelligent risk assessment was used to make claims settlement more efficient. Relevant data was analysed automatically, resulting in faster and more precise decisions.
Practical tips for utilising data intelligence
To realise the potential of Data intelligence companies should consider the following steps:
- Identify and systematically collate the relevant data sources.
- Establish quality standards for data collection and maintenance to ensure valid results.
- Use intelligent analysis tools and AI technologies to filter and analyse data automatically.
- Involve interdisciplinary teams from specialist departments and data scientists to take the needs of all departments into account.
- Regularly review results and derive potential for optimisation.
These impulses support the targeted implementation of data-intelligent projects and the creation of sustainable added value.
My analysis
The importance of Data intelligence is growing steadily because companies are increasingly faced with the challenge of making sense of large and complex amounts of data. The combination of big data and smart data is essential here: while big data forms the basis, smart data creates the actual added value. Successful data-intelligent applications provide practical insights for targeted decisions, support better processes and enable a personalised customer approach. The benefits are already being utilised in numerous sectors - from industry and retail to healthcare - and are leading to measurable improvements.
So that Data intelligence becomes a sustainable pillar for your company, you should systematically promote data quality, use modern technologies and open up your organisation to data-driven approaches.
Further links from the text above:
[1] Data intelligence: big data and smart data for decision-makers
[2] Big data vs. smart data: is more always better?
[3] Smart data: definition, application and difference to big data
[4] Big data: the utilisation of large amounts of data
[6] What is smart data?
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