When web analytics data becomes more integrated to company’s enterprise data infrastructure, it becomes even more critical to managing the data integration well.
Marketing analytics role and their accountability to manage critical data within an organization is something that shouldn’t be dismissed.
Many tech companies’ valuations are judged by their data that makes up ‘Active Users’, traffic to the site, page views, and many other key data points vital to online business’s success.
For example, your web analytics might be integrated to your SQL server, Oracle, SalesForce, Ad Tech, BI systems, and many other enterprise platforms.
With these integrations, most enterprises run their analysis against sales, customer data, financial data, third party data, or market data.
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 data management of multiple data sources due to the difference in definition of data and its application to the original source.
A good example of this is reporting different sales regions. You can apply different data attributes to identify regions, but the miss match in the convention naming will affect how analysts run the reports down the line.
Here is an example:
Page tagging values like Country code, Cities, Continent, Regions, web analytics visitors data based on IP address, language ID, etc. These tagged values from the website may differ from what your downstream systems may be classifying the region/country data.
So when you join two data from different sources, using CRM and web analytics data as an example; CRM data may have the 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 the mapping table that maps California with us-en, so BI reporting will properly 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 a 3rd party, that you want to integrate. That’ll be a lot more complex than my simple example.
My point is, depending what the business goals and reporting needs are, you need to be careful, plan well, document the requirements, caveat the data, and do whatever it takes to not waste resources so at the end of the day you’re not spending more resource than the value expected out of the BI solution.
Why am I saying this? At some point, someone needs to draw a line between what is a project manager’s work Vs. analytics manager’s work.
At the end of the day you’re likely to work with project manager to run projects to integrate marketing analytics data, and web analyst will be doing the analysis out of that BI solution and be accountable for that analysis and reporting. The accountability of the data management is surely something that needs to be clear.
If the reports from the analysts aren’t trusted, then fingers could be either pointed to the analysts or the project owner or the manager.
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 have to make sure you understand the data and it’s origins.
You definitely don’t want ad hoc changes and poorly managed change management process to affect your analysis work.
Good data coming into Marketing Analytics tools and feeding good data to BI solution can still yield bad data going out if not managed correctly.
“Great data in Garbage out” is possible when you don’t have the right change management or poorly planned BI solution. Note that, it is not always “Garbage In Garbage Out”.
When I talk to various experts in the industry, it is common to find few companies that do all of this integration with ease.
If there is a company that did that well, a lot of respects go out to them, and I bet they went through many challenges with many sweat and tears.
Although in recent days, there are emerging dashboarding tools like Datorama, Beckon, OrigamiLogic, and Sweetspot Intelligence. These tools will allow you to scale integrating marketing tech stack.
As web analytics get more sophisticated and integrated with many business data, web analysts will need to be wiser and educated in this field of data management.
What are your challenges and how are you overcoming them?
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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