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Context of data should be clear before data analysis

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Before data analysis is carried out, it is important to understand the context of the available data. For example, if we analyze hotel reviews data and draw conclusions without understanding the context of the hotel reviews, then we could be drawing totally wrong conclusions.  Let's assume that we have hotel reviews data. Firstly, we should ask several questions about the hotel reviews data before we try to find answers about business from hotel reviews data. Questions such as: Who is the provider of the reviews?  How are the reviews provided? When are the reviews provided?  What are the limitations involved in the review collection process? In how many languages can guests provide reviews in the reviews system? What % of the hotel guests provide reviews? What % of the hotel guests were able to read at least one of the languages in which reviews collection system works? Which channels are there for guests to provide reviews? When did review collection start? What is the sequence an
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ETL developer vs Data engineer

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Rephrased question : ETL developer vs Data engineer Answer : Unfortunately there are no strict industry standards on these job titles. That is just one part of it. Before ETL tools such as DataStage, Informatica, Ab Initio, etc., became popular, developers were hand coding every ETL flow. These ETL tools shortened the ETL flow development time to a great extent and allowed ETL developers to focus on business rule/logic/requirement (what to implement) than how to code it or optimize the code. There are many other benefits of using a tool but I won’t go into that in this answer. So an ETL developer with experience in these tools without any programming (coding) experience was/is able to design and develop end to end data flows. Whenever new types of source/target data format comes up, these tools catch up but it takes time, i.e., the ETL tool provider (e.g. Microsoft, IBM, etc.,) adds new components/connectors within the ETL tool to be able to work with new data format. For example, let’
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3 years of IBI

This week (on 19th Feb 2020) I completed 3 years of IBI. To celebrate it I have created a Dashboard in Google Data Studio. So from now on instead of using the charts in the Google Sheets I will use the Dashboard. Google Data Studio is more user friendly and mobile friendly too, also it comes with easy to use filters and drill down/up capabilities. PDF version of the first 2 pages of the shareable dashboard is attached below. I am happy to share the first 2 pages of the IBI Dashboard, reach out to me if you would like to get the Google Data Studio IBI dashboard. Once you get the dashboard (without my data of course) you can make a copy of it and point it to your IBI data (assuming you have started), add more charts, pages, etc., and use it for your benefit. Like with every year, this IBI year too I have learnt a lot about myself, made new mistakes, changed several things about me. The benefits of IBI is much more than we can think of. Use it to know it. And what are some of the
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