When web analytics data becomes more integrated to company's enterprise data, it becomes even more critical to manage the data integration. In order to reflect consistent integration and expectation of reporting outcome, web analytics and other data need to be properly managed through different iteration of version changes within the Business Intelligence (BI) system.
For example, your web analytics might be integrated to your Microsoft SQL server, Oracle, SalesForce, or other enterprise solution; you could be feeding site visitors from your web analytics tool and run it against sales or market data. (CRM and supply chain data integration with web analytics are pretty common in ecommerce.)
When your BI solution goes through different changes or version releases you got to make sure the joins and data integrations aren't affected by it. In my experience, common challenges are filter requirements or scalability of data across multiple sources due to difference in definition of data and its application within the original source.
Good example of this is reporting different sales regions. You can apply different data attributes to identify regions. Here are some examples:
Page tagging values like Country code, Cities, Continent, Regions (AMR, EMEA, APAC), web analytics visitors data based on IP address, language ID, etc.
So when you mesh two data from different sources, using CRM and web analytics data as an example; CRM data may have user profile for where they reside (e.g. California), and web analytics data may feed traffic data based on country/language value tagged on the page (e.g. us-en). What that means from BI integration perspective, you need to have mapping table that maps California with us-en, so BI reporting will properly scale to recognize two data sources when users select "USA" from the BI reporting.
This example was a simple one, but it gets more tricky when different sources are involved (imagine when you have different subdomains or data managed by 3rd party, that you want to integrate...).
My point is, depending what the business goals and reporting needs are, you need to be careful, well planned, document the requirements, caveat the data, and do what ever it takes to not waste resources so at the end of the day you're not spending more resource than the value that will be expected out of the BI solution. Why I am saying this when it should be a project manager's work and not analytics analyst? At the end of the day you're likely to work with project manager to feed web analytics data (possibly other sources of data, too), and web analyst will be doing the analysis out of that BI solution and be accountable for that reporting/analysis.
All of that careful planning and execution makes web analyst valuable when involved in such BI projects, and got to be accountable for the reporting end when you use that data as an analyst. You definitely don't want ad hoc changes and poorly managed change management process to affect your analysis work. Good data coming into Web Analytics tool and feeding good data to BI solution can still yield bad data going out.
"Great data in Garbage out" is possible when you don't have the right change management or poorly planned BI solution.
When I talk to various experts in the industry, it is common to find few companies that do all of these integration with ease. If there is a company that did that well, a lot of props go out to them, and I bet they went through many challenges with many sweat and tears.
As web analytics get more sophisticated and integrated with many business data, web analysts will need to be more wise and educated in this field of data management.
Definition of change management (from Wikipedia):
Change Management is an IT Service Management discipline. The objective of Change Management in this context is to ensure that standardized methods and procedures are used for efficient and prompt handling of all changes to controlled IT infrastructure, in order to minimize the number and impact of any related incidents upon service. Changes in the IT infrastructure may arise reactively in response to problems or externally imposed requirements, e.g. legislative changes, or proactively from seeking improved efficiency and effectiveness or to enable or reflect business initiatives, or from programs, projects or service improvement initiatives. Change Management can ensure standardized methods, processes and procedures are used for all changes, facilitate efficient and prompt handling of all changes, and maintain the proper balance between the need for change and the potential detrimental impact of changes.
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