Top 3 challenges of data governance and performance measurement
If there’s one thing the sudden acceleration of digital engagement in 2020 is indicative of, it’s that analytics for understanding consumer behavior online are more important than ever.
And while the opportunities that real-time data offers in terms of informing strategy and decision-making pertaining to customer experiences is massive, challenges exist, too.
In this article, we examine three sticking points, as well as how having a data governance and performance management plan in place can help you move past them.
Challenge 1: Navigating vast amounts of data
First the good news: All the work your organization likely put into analytics technology during the past few decades has paid off. Your business is now able to collect vast amounts of customer data about nearly every element of your website.
However, more data does not always mean better data. If anything, it means more time and resources required to sort, clean, and understand the data; the more you have, the harder it becomes to ensure its accuracy. Furthermore, not all data is created equal. Collecting and analyzing data outside of what’s most critical for your business can waste time and energy on work that only marginally impacts ROI.
Also time-consuming: setting up and maintaining front-end data collection processes. Indeed, analytics implementations for robust websites can be massively complex, containing thousands or even millions of analytics tags to help you understand and monitor customer behavior. The sheer volume of tags makes ongoing tag debugging, updating, and maintenance quite an endeavor. If you’re attempting to manage everything manually, know that doing so takes a ton of time, is prone to human error, and isn’t sustainable long-term as you grow your business both during and after this economic crisis.
Challenge 2: Team misalignment
A common story in the world of data governance is as follows: A team sets up a system and process that’s used by multiple departments to collect accurate data. Then at a later date, someone does something that breaks the system and process because the teams didn’t clearly communicate their goals with each other.
Different teams working on the same website and analytics implementation will always have different objectives. For example, a marketing team’s objectives around website analytics will likely focus on customer experience and ROI, while an IT team will be more focused on the site’s functionality and security. Due to these differing team goals, ongoing blunders (such as interrupted customer journeys, mistyped URLs, or double-tagging) are inevitable when teams aren’t aligned.
Challenge 3: Time and resource allocation
Growing your brand by acquiring and retaining customers is no easy feat, especially since there are seemingly endless ways business leaders can allocate time and resources to accomplish those goals.
A core component of this challenge resides in a company’s ability to obtain accurate campaign attribution. Without this, a company lacks the necessary insights to efficiently allocate budget.
Solution: Data governance and performance measurement
How can you overcome these challenges? The answer lies in QA testing and data governance.
Manual spot-checking and QA testing can help improve data accuracy, but at the same time it can also introduce other issues, such as draining time and resources, and creating more spreadsheets to manage. However, an effective data governance and performance measurement process and solution can help manage tagging and QA complexity by allowing you to automate ongoing audits that ensure tags are functioning properly in the correct location before, during, and after each release.
A recommendation for either manual or automated testing: While the inclination would be to run tests on your entire site, an all-inclusive testing strategy of your live production environment is not recommended. Websites are large, and running comprehensive tests on a regular basis, and doing so after a release, would take excessive time and resources to execute. Additionally, running all-inclusive tests in production would return vast amounts of data to sift through and often only after tagging errors have caused some damage to your data quality.
Instead, a more targeted approach done in your preproduction environments and on your most critical pages, before they go live, is a best practice to catch errors. Your website likely follows the 80/20 rule, in that roughly 80% of your website’s revenue comes from about 20% of your website’s functionality. This 20% of your website is where you will want to focus your automated or manual testing efforts, before the errors go live and impact your data quality.
Focusing on specific, scalable testing-especially before each release goes live will allow you to efficiently navigate the problems created by tagging errors on vast amounts of data.
Align teams on analytics goals
The first step here is to establish communication by aligning standards, goals, and knowledge among teams. This requires team leaders to meet specifically about standards and language. Then these leaders need to align their teams on terminology around KPIs, goals, and terms for how each team conceptualizes different work elements, such as what project completion looks like and which team owns specific tasks.
When IT, analytics, and marketing teams unite on common terminology around KPIs, goals, and workflow items, communication gaps close and collaboration improves.
Again, automating can help here by making sure that you can establish user permissions which will safeguard your data from unauthorized use and prevent cross-team data blunders. Also, set up notifications so you are alerted whenever something changes or goes sour in your tagging implementation.
Allocate budget with accurate attribution
In order to allocate time and resources effectively, you need accurate attribution. This is where tag governance and performance measurement come into play.
The key is in predefining data standards before you ever start collecting data, which ensures unification for all the data you collect, even offline customer touch points. This allows teams to obtain accurate data insights throughout all of your campaigns, so you know exactly how to allocate budget to maximize your ROI.
Some organizations still manage attribution using spreadsheets. However, manual campaign management via spreadsheet can be a complicated way to derive insights and can lead to human errors and lost time. By automating data governance and performance measurement, you will be able to move away from spreadsheets to manage attribution, and more effectively and accurately understand where to invest.
You have two options when it comes to tag governance and performance measurement automation. The first option is to build your own automation solution, which requires teams of developers with comprehensive expertise in data collection, processing, storing, and querying. They would also need to know to incorporate functional visualization, UX/UI, notifications, and reporting functionality.
Undoubtedly, you would need to dedicate extensive hours and resources to the creation, customization, and maintenance of such a solution.** **This option realistically only makes sense for large teams that have vast resources of time, money, and people power, and the ability to provide support and continued maintenance for the solution over time.
Many teams, however, opt to go the third-party route due to the labor-intensive nature of building and maintaining an automated testing solution that can be configured and customized to their specific needs.
And since the solution is already built and maintained externally, all you need to do is allocate the people to utilize it, to set up automated tests and monitor the results to ensure quality data insights.
Whichever way you go, implementing tag governance and performance measurement processes are key to achieving actionable and accurate insights that drives your organization forward. And with such an influx of digital activity caused by the events of 2020, automating these processes is one of the most efficient and effective ways to ensure data-driven success.
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