Your web analytics solution is only as good as the data being passed to it, and sometimes the data being passed is just plain spooky.
Tags break, pages are coded incorrectly, tools conflict, implementations fail—you name it. Things go wrong and hinder your organization’s ability to make data-driven decisions, increase revenue, and improve user experiences.
But don’t be spooked. By implementing a handful of analytics best practices, the aforementioned trap doors don’t have to become major setbacks.
This Halloween, take your analytics data from bone-chilling to bona fide with the following seven steps.
1. Promote a Pumpkin King: Assign Ownership to Data Quality
A data quality owner is someone with administrative authority and responsibility over data related-practices in an organization. Having a data quality owner is critical, as they will oversee data QA processes, ensure compliance with data privacy laws, control access and responsibilities around data, and maintain buy-in from executives regarding the company’s data procedures.
Your data quality leader should have the following four qualities:
- A strong technical background. Your leader must be competent with company technology and be familiar with the platforms associated with relevant digital properties.
- A position of seniority. Your leader must have seniority in your organization, providing them with adequate analytics resources and clout to influence data quality processes.
- An awareness of the business value of data. Your leader must be able to understand and create a dialogue about the requirements, purposes, and methods of data collection.
- A data-oriented mindset. Your leader must be a data-oriented steward who understands the effects of breakage, knows where to look for problems, and will choose effective corrective actions.
2. Gather Your Goblins: Define and Deploy a Tag Governance Strategy
Tag governance is the discipline of overseeing and continually maintaining the integrity of a company’s tagging strategy, especially as it pertains to analytics tags. At its core, tag governance involves using automation software to test your implementation as you implement your analytics strategy.
In order to effectively deploy a tag governance strategy, you will want to consider the elements of the tag governance framework, which are as follows:
- Plan. Before you ever start deploying tags or configuring variables on your site, you need to create a plan—your tagging plan. This plan documents your measurement and marketing strategy—outlining what each tag is doing and why.
- Comply. Tag governance helps companies comply with internal and external regulations. As part of your strategy, you should know exactly what types of data you should and should not collect, and with a tag governance solution, you can be alerted when anything in your tagging implementation is not in compliance.
- Deploy. With a tag governance solution, a development team can deploy and scan their tag implementations in a pre-production environment to make sure tags and data layer variables are populating according to the requirements included in the tagging plan. This capability allows companies to fix tagging errors before they get expensive.
- QA. Tag governance solutions help QA engineers automate their iterative tests, so they can ensure tags are working under various conditions. This automated capability keeps website release cycles agile while also minimizing broken code.
- Validate. A tag governance solution will allow you to replace manual spot-checking of your implementation with automated tag auditing, monitoring and validation. This automation can help you test your tag implementation more accurately and at a much faster rate.
- Monitor. A tag governance solution will allow you to stop wasting human effort meticulously monitoring tags manually. Instead you will be able to periodically scan your live site with automation, and be alerted as errors crop up.
3. Zombies in Your Implementation? Quickly Identify and Fix Errors with Automation
Tagging implementations are complex, especially when your site or app is large and stacked with functionality. Furthermore, implementations go through rapid updates with your site or app, only further adding to the associated complexity.
This complexity makes attempting to manually validate your entire implementation cumbersome, inefficient, and prone to human error.
Manually validating your implementation hinders your ability to scale and become agile, as you often don’t have adequate time and resources to continually validate that your technologies are working properly on an ongoing basis. This failure to continually validate your entire implementation can lead to unresolved errors and remnants of past strategies remaining in zombie mode on your site, doing who knows what.
You know errors are going to crop up as time goes on. As a result you should free yourself from the chains of manual validation and move to an automated solution. Using automation to validate your implementation will help you quickly find and fix errors, all while saving you vast amounts of time and resources. Automation will also help you find and eliminate old, archaic tagging elements, which may improve data accuracy and your site’s load time.
4. Things That Go Bump in the Night: Close Communication Gaps Between Teams
Sometimes setbacks in your tagging implementation come from communication gaps between development and analytics teams. As your site goes through regular updates, things can get missed if the two teams aren’t on the same page about release dates, site goals, and customer knowledge.
As a result, you will need to consistently work to keep lines of communication open between analytics and development teams. You can improve communication through the following methods:
- Align team terminology. Aligning terminology will give both teams a solid baseline for communication and help eliminate potential misunderstandings.
- Align team goals. Aligning high-level team goals will position both teams as partners tackling a problem, even though each team will have differing low-level objectives. This partnership mentality will encourage open communication and collaboration between teams.
- Align customer knowledge. Aligning customer knowledge between teams is critical to delivering the best possible customer experience. Each team has valuable knowledge about customers and this knowledge should be openly shared to improve customer experiences.
Ensuring alignment between teams will require a leader in the organization to take responsibility for the tasks mentioned above. This leader should create systems and processes that encourage alignment and collaboration on an ongoing basis.
Taking the appropriate measures to align your teams and create open lines of communication will allow your business to deliver amazing customer experiences, increase your bottom line, and experience fewer errors.
5. Got Ghosts? Prevent Unauthorized Data Collection
You may not see flickering lights or hear a poltergeist plodding down the hallway in your analytics architecture, but that doesn’t mean unauthorized data collection isn’t occurring on your site.
In the white paper Data Quality and the Digital World, Eric Peterson warns that companies have started to leak data through the multitude of tag-based data collectors deployed across their digital properties:
“Given the relative ease with which they can be added to a website, combined with the fragmented approach companies take to digital measurement, analysis, and optimization, it should be no surprise companies have started to leak data.”
Deploying data collection systems without a clear plan for maintaining accuracy, validity, and security of data collected is undoubtedly a poor practice. Additionally, new data privacy regulations (like GDPR and CCPA) are cropping up left and right, and these regulations often carry heavy fines for companies that fall out of compliance.
For example, according to BBC, in August 2018, British Airways lost a significant amount of customer data to hackers. This stolen data included email addresses, names, and credit card information, and the breach put hundreds of thousands of British Airways customers at risk of experiencing credit card fraud.
The exact cause of the breach hasn’t been disclosed, but data privacy professionals suspect that hackers planted a rogue script on the British Airways site via a third-party tag. After the breach, the Information Commissioner's Office fined British Airways £183 million for failing to comply with GDPR regulations.
While high-level criminal acts that successfully steal data may be a relatively uncommon occurrence, there are several other more common data privacy issues to be aware of.
Consider these five scenarios:
- An employee leaves the company to work for a direct competitor but maintains access to traffic and revenue data through unknown deployment of analytical tools.
- An agency deploys tools with questionable Personally Identifiable Information (PII) collection and handling practices.
- A tag management solution (TMS) is deployed in an effort to consolidate the chain of authority for controlling site tags, but that doesn’t prevent other groups from circumventing this process and deploying tags outside of the TMS to meet their own needs.
- A third-party vendor deploys tracking that sells data to other third-parties, which potentially exposes your data to direct competitors.
- A policy bans particular technologies on all sites, yet those tags continue to appear.
To navigate all the potential ways you can get into trouble with data privacy, we recommend using an automated tag governance solution, as an automated solution can alert you when unexpected scripts appear on your site. Using an automated tag governance solution will help you avoid both large and small-scale data privacy incidents.
6. Count Your Candy: Validate Your Data Collection Continuously Over Time
Your development team constantly adds, changes, and retires website content. As a result, you must manage the corresponding changes to your analytics implementation on an ongoing basis.
A fourfold validation strategy is prescribed for managing your analytics implementation through ongoing website changes:
- Test frequently during the development process. Validate your implementation multiple times during any large-scale deployment effort. Share the results with developers and project managers when appropriate.
- Test often during quality assurance phases. Closely monitor the data being passed to your analytical systems after your development project has passed into your QA environment.
- Test immediately following deployment to confirm data is being collected as planned. Confirm data collection has successfully migrated from the development and staging environments to the production environment. When problems are identified in this phase, act quickly to correct them.
- Monitor data collection over time. Analytics implementations are not static objects. They shift, change, and move as your website goes through updates. As a result, you can’t simply set and forget your analytics implementation and hope for accurate data. In order to continuously maintain accurate data, you must monitor and validate your implementation on a regular basis.
Commitment to analytics data accuracy during initial deployment and throughout ongoing site updates means you will make better decisions, customers will get better experiences, and your bottom line will increase.
7. Beware! Keep Out! Danger! Caution! Define Rules to Test Against Your Digital Data Collection.
No two businesses or analytics implementations are exactly alike, and as a result, your data validation strategy should be catered to the specific needs of your business.
When using an automated tag governance solution, like ObservePoint, you can create custom rules that are specific to the objectives and goals of your analytics implementation. Customizing rules to your specific needs will allow you to create pass-and-fail standards that will enable you to be alerted whenever values in your implementation aren’t functioning as you would like.
Additionally, these custom rules are automatically saved into a rule library, where you can edit and access those rules for easy application to future tests.
Utilizing custom rules will allow you to improve how efficiently you can validate your entire implementation and will help you ensure your implementation is serving the specific needs of your business on an ongoing basis.
You Know You’ve Got Ghosts and Goblins in your Analytics Implementation. We’ll Help You Get Rid of Them.
All of the above steps—crucial as they may be to fixing your scary data—are labor intensive, expensive, and prone to human error when your digital analytics team tries to manage your analytics implementation manually. Using automation will help you save time and resources, improve your data quality, ensure privacy regulations are met, and increase the overall ROI of your marketing technology.
Furthermore, any tagging implementation is only as good as the data it processes, and using an automated testing platform like ObserverPoint confirms that the source of your data—the tags on your website—are deployed correctly and completely, all while helping you rid your implementation of any errors.
To see ObservePoint in action, schedule your demo now.