Typical Problems and Challenges in Data Science
What are the common Issues and how can they be solved?
Especially when companies start to deal with the topic of Data Science and begin to implement first PoC and projects, typical problems can arise. Here, I would like to present these well-known problems, but also those that I have observed myself, and show possible solutions.
Challenge 1: No Data
The first problem is the most obvious one, missing data. Whether it is due to a lack of IT and systems in the company or the lack of access. Without data or better said a lot of data, Machine Learning & Co. do not work. Of course, even with little data, small projects in the area of business intelligence or statistics are possible, but the big Data Science use cases will probably not be there. However, it is important to make the best out of the data — even small insights from e.g. reports or empowering employees in the form of self-service BI is worth a lot.
Challenge 2: No Infrastructure and Platform
The second problem is more technical in nature. In order to enable evaluations and Machine Learning or Deep Learning, sufficient data storage is required, which should be as inexpensive as possible, but also scalable systems that provide more…
