Data analysis can be crucial for making well-founded decisions. But when does the amount of data become a challenge? How do you use data analysis in your supply chain?
-Very positive, as a decision support tool
-Overwhelming, with too much data
-Neutral, depends on how it is used
-Hardly any influence, data is little used
Share your experiences, challenges and best practices.
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The challenge lies not only in the amount of data, but also in the ability to use it sensibly. With the right tools and technologies such as AI, we can effectively analyze large amounts of data and gain valuable insights from it. The technology helps us to filter out and interpret the relevant information.
Data analysis is supposed to help us make better decisions. But I wonder whether the flood of data often becomes a real challenge. How many companies are really able to draw useful insights from the amount of data?
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This sounds like an ideal approach, but not all companies have access to the latest technologies or the know-how to use them properly. How do you deal with the flood of data when resources are limited?
There are always ways to make progress, even with limited resources. The key is to prioritize and use targeted analytics tools that are specific to the needs of the business. It is also important that data analysis is integrated into the decision-making processes in order to achieve real benefits.
But even with the best tools, the interpretation of data can be subjective. How do you ensure that the decisions are really well-founded and not just influenced by the analysts' prejudices?
This is a legitimate question. Ideally, data analysis should be supported by objective algorithms that aim to minimize bias. There should also be a continuous exchange and feedback process to ensure that the analyses remain realistic and objective.