1) Webpage - Web pages are the most pivotal part of this hierarchy. These can be HTML, ASP, ASP.NET etc or any other page hosted on a Web Server.
3) Web Server – This is the datacenter/Web Server that is responsible for storing the parameters captured by the page tag. These Servers are usually very powerful and can store TBs of data and are also responsible for dropping cookies in the client’s machine. These cookies are the crux of Web Analytics and are crucial for calculating user behavior.
4) Log Files – The parameters captured by the Analytics Tag are stored in the Server Logs which are systemically designed to store data in the form of text files which can be in TXT or CSV format. Again these log files are huge and it is recommended to store them as compressed Archives.
5) Processing – The log files are then processed by the operations or database team via an ETL (Extract, Transform, Load) process. This is a very complex step and a team having strong technical expertise can do the job. This team is also responsible for filtering out the so called bot traffic.
6) DataWarehouse – A DataWarehouse stores the filtered data that will be displayed in the Web Analytics Tool. These databases are mostly used by Digital Analysts who want to create custom reports usually not possible with the help of Web Analytics tools. They can write their own custom queries and create a report not present in the analytics tool.
7) Web Analytics Tool – This is the final step of the Web Analytics Data Lifecycle and is the GUI form of data. Tools like Adobe Analytics, Google Analytics etc are the backbone for all the Analytics that take place nowadays. Anything like exporting data to creating graphs, charts are the basic features of these tools. They help organizations make the business decisions that generate revenue and make the appropriate changes to the Web Pages to retain users and also entice them to come back.
So you read how a simple web page forms the basis of such complex processes that are used to transform a simple http request into data that generates revenue. I hope you liked this article and would appreciate if you can critique it by commenting.