Web Analytics and Data Warehouse
By Michael Beiene
What is Web Analytics?
The Digital
Analytics Association defines web analytics as the measure, collection,
analysis and reporting of internet data for purposes of understanding and
optimizing web usage. There are two ways of collecting digital analytics data.
The older method
of collecting digital analytics data has been from web servers’ log file. Web
servers record file requests from browsers. By opening the web servers log
file, it used to be easy to count how many times the site has been accessed
identifying unique users based on their IP addresses. But, this method started
to die out with the start of search engine and dynamically assigned IP
addresses.
·
Incorrect hit count because of search engine
- With the start of search engines, like Alta Vista, Yahoo and Google, result
of a web search by a user will be logged on the web servers even if the user
has not actually opened the site. This gives a site a hit count because the
name of the site has appeared as a search result even if the user has not
opened the web site.
·
Incorrect his count because of dynamically
assigned IP addresses – Web servers log file uses IP address to identify
unique users. Back in the days, IP addresses used to be static. This is to say,
a user will have the same IP address all the time which makes it easy to count
how many unique users have accessed a site from the web servers’ log file. But,
mid-way to 1990’s, dynamically assigned IP addresses were introduced. Instead
of using the same IP address all the time, users’ IP address change after a
certain period of time. This made it harder to count how many times users have
accessed a certain web site based on their IP addresses.
The newer method
of collecting digital analytics data is called page tagging. Instead of
analyzing log files, this method uses JavaScript embedded within the web site
page code which send page rendering requests to a third-party
analytics-dedicated server. Adobe’s SiteCatalyst and Google’s Google Analytics are the widely used
third-party analytics tools.
What is Data Warehouse?
Data Warehouse
or sometimes referred as Enterprise Data Warehouse is a central repository of
data that is created by integrating data from different sources. Unlike
database, which stores only current data, Data Warehouse stores current as well
as historical data for report, data analysis and to make projections based on
trends. In order to create a Data Warehouse, ETL process needs to be performed.
ETL stands for Extract, Transform and Load. First, the data is Extract from the
different sources (marketing database, Sale database….). Then, the data is transformed
to an appropriate format on a staging database. Finally, the data will be
loaded to the data warehouse. While database focuses on transaction, Data
Warehouse focuses on data analysis. The below diagram illustrates the process
of ETL.
Web Analytics and Data Warehouse
In today’s competitive market,
there are terabytes of data from web analytics tools. Moving or importing these
data to a data warehouse not only will make analysis of the data easier, but
also will have the following advantages.
·
It will be easier to extract web analytics data
from different sources or different sites and combine them together for better
analysis. For example, General Motor Corporation may want to see and analyze
web analytics data from different authorized car dealers’ web sites. By using
data warehouse, they can combine the web analytics data for better analysis of
the data.
·
When the behavior of a visitor changes, that
signals for opportunities. It is easier to notice these changes when web
analytics data is in data warehouse, since data warehouse is built for better
analysis of data.
·
Data warehouse provides more data visualization
capabilities compared with web analytics tools.
·
Data warehouse provides advanced segmentation of
data capability compared with web analytics tools.
·
Data warehouse provides complex, automated
distribution of customized reports compared with web analytics tools.
These
are only some of the advantages of moving or importing web analytics data to a
data warehouse instead of trying to analyze it within web analytics tools.
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