Today, data analysis is a key building block for companies that want to grow sustainably and hold their own against the competition. With the right approach, valuable insights can be gained from huge amounts of data. The step from big data to smart data is particularly important. Because only those who analyse the right data in a targeted manner can create real added value. In this article, you will learn how to master data analysis and advance your projects with targeted methods.
What does data analysis mean in practice?
Data analysis helps companies to derive specific recommendations for action from raw data. It enables patterns to be recognised, processes to be optimised and decisions to be supported. Many organisations today collect large amounts of data, but only a few make targeted use of it. They often lack the right methodology or the necessary expertise.
Practical examples show this: In production, companies analyse sensor data to predict machine downtimes. In healthcare, data analyses help to optimise treatment strategies. In e-commerce, customer behaviour and purchasing patterns are evaluated in order to create personalised offers.
Data analysis is therefore not just an IT topic. It affects all areas of a company and requires a holistic approach.
Data analysis and smart data: the path to added value
Data analysis as the key to smart data
Smart data is created when relevant information is specifically extracted from big data. This requires a clear question and a structured approach. Data analysis is the decisive step here in order to turn raw data into a smart basis for decision-making.
An example from industry: a company analyses sensor data from production in order to optimise energy consumption. The data analysis shows that certain machines consume a particularly large amount of electricity at certain times. From this, the company derives measures to reduce consumption.
Another example from the healthcare sector: A clinic analyses patient data in order to improve treatment processes. The data analysis helps to recognise high-risk patients at an early stage and provide them with targeted care.
A third example from e-commerce: an online shop analyses the purchasing behaviour of its customers. The data analysis shows which products are frequently bought together. From this, the company derives recommendations for cross-selling.
Practical methods for data analysis
Data analysis with explorative methods
Exploratory data analysis helps to recognise patterns and correlations in large amounts of data. It is particularly useful when the research question is not yet clear. Valuable insights can be gained using methods such as data mining, machine learning and statistical analyses.
An example from logistics: A company analyses delivery data in order to optimise the flow of goods. The data analysis shows that certain routes often lead to delays. From this, the company derives measures to shorten delivery times.
Another example from the energy industry: an energy supplier analyses consumption data in order to better predict energy demand. The data analysis helps to optimise the grid load and avoid outages.
A third example from medicine: a research institute analyses patient data in order to develop new treatment approaches. Data analysis helps to recognise correlations between different factors.
Data analysis in SMEs: opportunities and challenges
Small and medium-sized companies also benefit from data analysis. They can use it to optimise their processes, reduce costs and develop new business models. However, many companies are reluctant to get started because they believe that data analysis is only suitable for large corporations.
An example from production: A medium-sized company analyses production data in order to improve the quality of its products. Data analysis helps to identify sources of error and optimise processes.
Another example from the retail sector: a retailer analyses sales data in order to optimise its product range. The data analysis helps to identify products that are doing particularly well and those that are less in demand.
A third example from the service sector: a consulting company analyses customer data in order to improve its services. Analysing data helps to better understand customers' needs and respond to them in a targeted manner.
BEST PRACTICE with one customer (name hidden due to NDA contract) A medium-sized manufacturing company was struggling to improve the quality of its products. With the help of data analysis, we were able to recognise patterns in the production data that pointed to specific sources of error. From this, we derived specific measures that led to a significant improvement in product quality. The data analysis helped the company to optimise its processes and reduce costs.
My analysis
Data analysis is a powerful tool for creating added value from data. It helps companies to optimise processes, support decisions and develop new business models. The step from big data to smart data is particularly important. Only those who analyse the right data in a targeted manner can create real added value. With the right methods and the necessary expertise, even small and medium-sized companies can enter the world of data analysis.
Further links from the text above:
Smart + Big Data | Artificial Intelligence
Intelligent Data Analysis Methods for Engineers (Master)
Big and smart data - from statistics to data analysis
Big data explained simply: definition and significance for the ...
Smart Data: Definition, application and difference to Big ...
Making decisions with smart data
Data analytics: Data and methods - Fraunhofer SCS
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