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Making decisions is not always easy – one possible quick-win for companies can be derived from corporate data: forward-looking data quality management, a process that unfortunately often receives far too little attention.
For long-lived software systems, maintenance costs far exceed initial development costs. Escape the cost trap through timely proactive modernization measures. An evolutionary approach guarantees predictable costs, continuous releases and immediate customer benefits with manageable risk.
Data engineering integrates data from a wide variety of sources and makes them effectively usable. This makes it a prerequisite for effective data science, machine learning and artificial intelligence, especially in the big data area.
How it is possible to minimize costs, respond flexibly to fluctuations in demand, and avoid production downtime due to disruption.
Since the trend of new CPUs is clearly towards ever higher numbers of processor cores, efficient parallelization is particularly important. With Fortran, the development of high-performance code is possible.
This article highlights a type of effort estimation that is particularly useful in agile projects: Story Points.
