The modern working world is changing rapidly. Companies are faced with the challenge of utilising large amounts of data in a meaningful way. Data analysis plays a central role in this. It helps to gain valuable insights from raw data and make well-founded decisions. Data analysis is becoming increasingly important, especially in the context of big data and smart data. Many clients come to me because they don't know how to analyse their data effectively. This is exactly where transruptions coaching comes in: as support for data analysis projects.
What does data analysis mean today?
Data analysis is more than just analysing figures. It is about recognising patterns, understanding correlations and deriving recommendations for action. Companies use data analysis to optimise processes, better understand customer needs and gain competitive advantages.
Example 1: An online shop analyses the purchasing behaviour of its customers. This enables it to make personalised recommendations and increase the conversion rate.
Example 2: An industrial company analyses sensor data from machines. This enables it to recognise maintenance requirements at an early stage and avoid breakdowns.
Example 3: A hospital uses data analysis to optimise treatment processes and improve patient care.
Data analysis and smart data
From big data to smart data
Big data alone does not create added value. Only by analysing data can huge volumes of data be turned into meaningful information. Smart data is created when relevant data is analysed and processed in a targeted manner. Companies use smart data to improve their business processes and make strategic decisions.
Example 1: A logistics company analyses traffic data in order to optimise delivery times and increase customer satisfaction.
Example 2: An energy supplier analyses consumption data in order to better predict energy requirements and use resources more efficiently.
Example 3: A financial services provider uses data analysis to detect fraud attempts at an early stage and minimise the risk.
Practical application of data analysis
Data analysis is not a one-off process, but a continuous cycle. Companies collect data, analyse it and derive measures from it. These measures are then implemented and evaluated. This creates a learning process that continuously improves data analysis.
Example 1: A manufacturer of consumer goods analyses customer data in order to develop new products that are better tailored to the needs of the target group.
Example 2: An insurance company analyses claims data in order to create risk profiles and make premiums fairer.
Example 3: A training provider uses data analysis to measure the learning success of participants and adapt the course content.
BEST PRACTICE with one customer (name hidden due to NDA contract) and then the example with at least 50 words.
A medium-sized company in the automotive industry was faced with the challenge of improving the quality of its products. By analysing production data, we were able to identify patterns in rejects. Together with the customer, we derived targeted measures. This enabled us to significantly reduce the reject rate and increase customer satisfaction. The data analysis was continuously developed and integrated into the daily work process.
My analysis
Data analysis is an indispensable tool for modern companies. It helps to create added value from data and make well-founded decisions. Data analysis is becoming increasingly important, especially in the context of big data and smart data. Many clients report that data analysis has given them new impetus for their projects. Data analysis is not an end in itself, but a process that needs to be continuously developed. With the right support and the right methods, data analysis can create sustainable added value.
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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