Today’s data needs can often not be met by a classic Data Warehouse. Especially because today we have started using data for far more than just reports. This has resulted in a major development of new technologies and approaches to data. The biggest challenge with this development, however, is that the individual technologies often only cover very limited needs, and you therefore need multiple tools to solve all your data challenges. This combination of tools is what we today call a modern data stack. In this blog post, we will give you some insight into how to choose the right foundation for your data stack.
A data strategy must support your strategic goals, and ensure that data collection and utilization of this gives your company a competitive advantage. The data strategy must ensure that your entire data value chain is optimized so that it best supports your business and what you want to use data for. In this blog post I will give an insight into what a data strategy is and why it is so important.
If you collect data in your company, you have been faced with the choice of how data should be stored. When we aggregate data, we lose information and this can have consequences especially when we store the data. For example, if we need to use raw data later, it will never be possible for us to recreate it with aggregated data. So we have to start all over again with collecting data. In this blog post, we dive into the consequences of choosing different methods to aggregate data and whether raw data is a better alternative.
Digitization is a word that is mentioned in most meeting rooms. We have met many companies that have a 2025 goal of digitizing parts of their business. But what exactly is digitization? What are the benefits of becoming more digital? When is it satisfying and how can one kickstart the process? Is it important to your business at all?
In this blog post, we will discuss why you should stop focusing on Big Data and especially the big part, which is often interpreted as the number of observations we have in a dataset.
In a previous blog post we showed how Machine Learning can estimate the Remaining Useful Life of a turbofan engine.
In this blog post, we will put our internal library, Cohen to the test, by estimating the Remaining Useful Life on NASA’s Turbofan engine with six different conditions (0 to 20,000 feet), and one fault mode, HPC Degradation.
Afterwards, we will test it when having two fault modes: HPC Degradation and Fan Degradation.
Often when we talk about predictive maintenance, we refer to the method by which we predict the breakdown of an engine or a bearing, using data such as vibration and rotational speed. However, there is another method where we use cameras to be able to detect wear on, for example, frying belts, conveyor belts, rust on wind turbines or leakage in pipes.
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