It is not hard nowadays to find talks from conferences and blog posts on the web claiming that data analytics, or data science as it is now called, can do wonders for your company. Sure! However, identification of the relevant problems and their formalization into available data vs. the desired output remain the biggest obstacles to a realistic implementation of any data-driven project.
Indeed, the most important part of a data analytics project is always at the beginning, when the problem to be solved is selected and formally defined. Problems usually are many. How do we select the most remunerative and the least work intensive?
I encounter this blockage often when I’m out giving presentations and discussing data analytics strategies. So, I thought, it might be useful to describe the path followed in some past projects. Of course, the ones presented here are actually just a subset of the many solutions built and refined with KNIME Analytics Platform.
You can find them
- in the KNIME EXAMPLES server under folder 50_Applications*,
- in the use case web page on the KNIME web site,
- or in the web page dedicated to whitepapers still on the KNIME web site.