In 2012, Gartner predicted that by 2017 a company’s Chief Marketing Officer would be spending more on technology than its Chief Information Officer.
This prediction is on track to prove true as technology-based solutions are becoming integral in the marketing industry. There is even a rapidly developing subsection of the industry—MarTech (Marketing Technology).
Marketing has become a tech-powered discipline. The stunning rise in marketing technologies over the last three years continues to reveal the incredibly diverse set of needs and desires marketers have, and how each advancement of technology makes it easier to see additional areas for increasing marketing effectiveness.
As more and more of the marketing budget is being allocated to technological solutions, companies are investing in additional technologies and practices to ensure their MarTech tools are performing correctly.
More than 50% of enterprise organizations are now using 21 or more digital marketing solutions, and many of those are web analytics and tag management applications.
But this abundance of silver has a cloudy lining—with so many technologies making it possible to do just about anything, you can quickly end up with a morass of tools and technologies that don’t interconnect, report conflicting data or even interfere with one another’s functionality.
Here’s what the data-driven process should look like (A)—but all too often it ends up looking like image (B).
In an effort to leave no tech-solution stone unturned, many marketers actually find their marketing data in complete chaos. The scenario above is precisely why web analysts across enterprises from every vertical are becoming increasingly concerned with the ability to audit their marketing data.
Auditing Your Web Analytics Data
The word “audit” typically has a negative connotation surrounding it, but in the realm of Big Data, regular audits of your data collection processes can save your career.
In relation to digital marketing data, audits are a method of going through all your tracking and collection processes (tags and analytics solutions) and making sure
they’re working properly so that you can trust your data-based decisions. No matter your company’s size, industry, or format, you should be running regular audits of your analytics data to ensure that you are making accurately informed strategic business decisions.
Performing a comprehensive audit of your analytics data is an understandably daunting task, but it is necessary to trust your data and your resulting business decisions.
However, for most organizations, it would be nearly impossible to manually comb through the thousands of tags and millions of lines of code to complete an efficient audit. It would take countless hours and remarkable resilience to complete, and would still be susceptible to human error.
Thankfully, there is an easier way.
Automation—The Promised Land
Because of the sheer magnitude of the task, many enterprises have begun investing in automated auditing technologies to scan their web analytics processes to verify the accuracy of the data being collected and reported.
Putting this into context: automation is having technology do things for you so that you don’t have to.
What once took teams of analysts great amounts of time to do, can now be done efficiently, consistently and without human error.
Web tag validation or data quality assurance platforms automatically crawl specified websites and pages, performing comprehensive audits and report on potential issues such as:
- Missing tags
- Broken links
- Duplicate tags
- Slow loading pages
- Data leakage
- Third-party code
These solutions can be structured to regularly scan your web properties regularly and alert you to any issues that would prevent you from trusting your data-driven strategies.
And trust in your data is one of the big reasons automated audits empower your organization.
ObservePoint is the premier Data Quality Assurance platform which automates the process of checking and verifying your web analytics implementations and data. Start your free website audit now to see how automating the QA process improves the quality of your digital data.