Data analysis is an indispensable tool in the age of digitalisation. Companies are faced with the challenge of extracting valuable information from large amounts of data in order to make well-founded decisions. The correct application of data analysis can support efficient business processes and increase competitiveness. In this article, we will discuss the importance of data analysis and show how companies can gain valuable insights through the targeted use of big and smart data.
Data analysis in practice
Data analysis is essential for companies to optimise their business strategies. The combination of big data and smart data in particular offers enormous opportunities to increase efficiency and reduce costs. In the logistics sector, for example, the analysis of sensor data from vehicle fleets can help to optimise routes and reduce fuel costs[1].
In the healthcare sector, smart data supports the personalisation of treatment processes by only taking relevant patient data into account. This leads to individualised therapies and cost reductions[1].
Areas of application for smart data
Smart data is used in various industries to improve business processes. For example, the analysis of customer data in marketing can help to develop personalised advertising campaigns[2]. Smart data can also be used in supply chain design and inventory management to optimise delivery times and increase customer satisfaction[2].
BEST PRACTICE with one customer (name hidden due to NDA contract) and the following example: an industrial company used smart data analysis to optimise its production process. The combined evaluation of machine, process and environmental data significantly increased capacity utilisation. The targeted filtering and integration of the data led to a noticeable increase in efficiency and a reduction in downtime[1].
Data analysis to increase efficiency
Data analysis is crucial for increasing the efficiency of business processes. By focussing on relevant data, companies can make optimal use of their resources and effectively reduce costs. For example, the analysis of big data in e-commerce can help to understand purchasing behaviour and create targeted product recommendations[3].
Data analysis can also contribute to the optimisation of process stability and product quality in the area of production. By applying statistical methods and machine learning algorithms, companies can organise their production processes more efficiently and make forecasts for the future[4].
Important aspects of data quality
The quality of the data is crucial for effective data analysis. Companies must ensure that their data is up-to-date, complete and error-free. This can be achieved through automated checking processes to avoid duplicates and eliminate inaccuracies[1].
Transparency regarding the origin of the data and the establishment of review mechanisms are also important. These measures ensure that decision-makers can trust the analyses and use them as a reliable basis[1].
Data analysis and trusted data
Trusted data is the foundation of any effective data analysis. It must be trustworthy and reliable in order to enable well-founded decisions. Companies often report challenges with data quality, which can have a negative impact on the results of analyses[1].
For example, an insurer can use trustworthy customer data to create precise risk analyses and calculate tariffs better. Retailers also benefit from clean inventory data that prevents stock shortages and controls the flow of goods[1].
Data analysis in everyday life
Data analysis is not only relevant in the business world, but also in everyday life. Streaming services such as Netflix and Spotify use big data to offer personalised recommendations[3].
In medicine, big data can help to recognise illnesses at an early stage. In transport, analysing movement data enables more efficient navigation and supports smart city concepts[3].
My analysis
To summarise, data analysis is a crucial component in the digital age. By combining big and smart data, companies can optimise their processes and make informed decisions. The correct application of data analysis can not only increase efficiency, but also significantly improve the competitiveness of companies.
Further links from the text above:
Data analysis with Big, Smart & Trusted Data
Smart + Big Data | Artificial Intelligence
For more information and if you have any questions, please contact Contact us or read more blog posts on the topic Artificial intelligence here.
Deutsch 
English (UK)