2 min readFeb 14, 2021


I wanted to be critical about the content though the formulations used make the reading more difficult than it should be.

Try to simplify and avoid confusing sentences/expressions! From the first sentences the reader can question what you wrote. For example, in an ideal world, there would be no gap between theory and practice. How can data storage become messy? Thumb rules aren’t strictly accurate and cvan be hardly considered as theoretical in the context of the theory. How can people be creative with the errors they commit? Are the errors introduced willingly?

An ETL pipeline is a series of data transformations. You refer eventually to the data warehousing processes and there are methodologies for building data models and data warehouses. It helps sometimes reading a book before writing or at least checking your information against other sources.

Data analysis means something else. Probably you refer to problem analysis or at most at data discovery/exploration to understand tables’ structure, dependencies and data’s meaning.

In Software Engineering one talks about requirements though probably requisites can be used as well. A satisfied requirement must exist.

What you mean by data quality is actually the data profiling. Data quality is the fit for use and the end-users typically decide whether an attribute is fit for use, respectively on whether the attribute is removed or on whether need to be better maintained.

Even if the attribute is not correctly maintained, it can still be meaningful for analysis. There are rules for dealing with missing values.

How do you want data sources talk to each other? Data integration is another topic, quite complex.

The same data (here attributes with the same meaning and role) can be found in different sources with different degrees of quality (timeliness, accuracy). One can still load all the data into the data warehouse (especially for checking the differences) though in the end must be declared which attributes will be further used. It can be a mix of attributes from the various sources.

Probably there are a few meaningful ideas for a professional though they get lost in scaffoldings with questionable value. Don’t build scaffoldings if they aren’t necessary for text’s understanding or for conveying relevant information!

Focus on narrower topics as content. It can be a challenge also for an experienced writer to summarize in a few hundred words topics that take usually books to write.

Keep writing and get some feedback before publishing, if you want to improve your content!




IT professional/blogger with more than 24 years experience in IT - Software Engineering, BI & Analytics, Data, Project, Quality, Database & Knowledge Management