Today, data analysis is a central component of successful corporate strategies. Many organisations collect huge amounts of information, but it is only through targeted data analysis that real added value is created. The step from big data to smart data is particularly important. Because only those who identify, process and interpret the right data can gain valuable insights from it. In this article, you will learn how to master data analysis and generate targeted smart data from the flood of data.
The path from big data to smart data
Big data refers to large, often unstructured volumes of data. These are generated, for example, by sensors in production, transactions in retail or interactions in customer service. However, collecting data alone is of no use. Only by analysing data does this raw data become smart data - i.e. information that can be used in a targeted manner.
An example from industry: A manufacturer continuously collects sensor data from machines. By analysing the data, it recognises patterns that indicate upcoming maintenance requirements. This enables them to avoid breakdowns and increase efficiency. In the financial sector, too, intelligent data analyses help to detect attempted fraud at an early stage. In marketing, companies use smart data to precisely address target groups and strengthen customer loyalty.
Data analysis in practice: three concrete examples
1 A logistics company uses data analysis to optimise routes dynamically. This shortens delivery times and reduces fuel costs. Analysing traffic data, weather information and vehicle conditions leads to smart data that flows directly into planning.
2 In the healthcare sector, large image data sets are analysed automatically. By analysing data, diagnoses can be made more quickly and accurately. AI-supported systems support doctors in the interpretation of data and help to optimise treatment plans.
3 A retailer analyses customer data such as transaction histories, reviews and social media activities. This creates smart data that enables personalised offers and targeted advertising campaigns. Data analysis helps to better understand customer behaviour and adapt the marketing strategy.
Mastering data analysis: the most important steps
Several steps are necessary to turn big data into smart data. Firstly, data from various sources is integrated, such as CRM systems, IoT devices or external databases. This is followed by cleansing: Incorrect or duplicate data is removed. In the next step, patterns are recognised and forecasts are created, often with the help of machine learning and statistical models. The results are visualised so that decisions can be made quickly. Finally, governance and data protection ensure that sensitive information is handled responsibly.
Another example: a bank uses data analysis to better assess credit risks. The integration of credit history, income data and behavioural patterns creates smart data that supports decision-making. The analysis helps to identify risks at an early stage and optimise lending.
Data analysis and smart data: success factors
Successful data analysis requires not only technical expertise, but also a clear objective. Companies should ask themselves what questions they want to answer with the data. Only then can they select the right data and analyse it in a targeted manner. The quality of the data is also crucial. Incorrect or incomplete data leads to incorrect results.
An example from telecommunications: a provider analyses usage data in order to improve network quality. By analysing data, it recognises bottlenecks and can take targeted measures. Smart data helps to increase customer satisfaction and reduce operating costs.
Another example: an energy supplier uses data analysis to optimise electricity consumption. By analysing consumption data, weather information and market trends, smart data is created that makes the energy supply more efficient.
A third example: an education provider analyses learning behaviour in order to improve courses. The data analysis helps to recognise individual learning needs and adapt the course content.
My analysis
Data analysis is the key to turning big data into smart data. Only those who identify, process and interpret the right data can create real added value. The examples from various sectors show how diverse the potential applications are. Whether in industry, the financial sector, healthcare or marketing - data analysis helps companies to optimise processes, minimise risks and increase customer satisfaction. With the right methods and tools, it is possible to generate targeted smart data from the flood of data and thus secure a competitive advantage.
Further links from the text above:
Data intelligence - big data and smart data
Smart data: definition, application and benefits
From big data to smart data: the smart data principle
Smart data: the intelligent use of data
Data intelligence: clever use of big data and smart data
Smart data: definition, application and difference to big data
Smart data instead of big data
What is smart data? Definition and explanation of the term
From industrial big data to smart data
What is big data? Simply explained
From big data to smart data - the raw material of the 21st century
Big data, smart data: the most important keywords explained
Data analysis: from big data to smart data
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