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Ultimately, data provides you and your teams with information and customer insights to improve customer experience. Gaining the ability to perceive your actual customers’ behaviours through data, and leveraging this knowledge to make well-informed business decisions that refine their customer journey, requires placing a 100% reliance on data Adopting a data-centric approach where everyone places their trust in customer data is essential. This article delves into several prevalent risks associated with upholding data quality, the necessary prerequisites for maintaining it, such as effectively managing data privacy and compliance, and the criteria for establishing a data-centric organization.

The most common data quality risks

  1. Accidentally including personally identifiable information (PII), such as email addresses of your paying customers in the URL parameters. Violating GDPR legislation with fines and reputational damage is a result. Not very common, but comes with an extremely high risk. We spotted this once last year.
  2. Important decisions cannot be fully supported with actionable information due to a loss of analytics measurements after a development update. A gap in the data (a week or even longer) is a common practice at multiple organizations. And as you can imagine, this makes it harder to rely on long term analyses, since you will not be able to draw any conclusions based on year-to-year analyses in the same time period. We see these challenges appear on a quarterly basis.
  3. Overload of Data and Tracking Methods: With the abundance of data available today, it’s easy to become overwhelmed by the sheer volume of information. It’s important to prioritize collecting data that aligns with your organisation’s goals and KPIs. Additionally, having a streamlined approach to tracking methods can help ensure consistency and accuracy.
  4. Attribution Models and Discrepancies: Different advertising and social media platforms often have varying attribution models, leading to discrepancies in reported numbers. Establishing a standardized attribution model or understanding the differences among platforms can help mitigate confusion. Regularly auditing and cross-referencing data from different sources can also help identify inconsistencies.
  5. Lack of Consistency in Reporting & Defining of KPIs: Consistency in reporting is crucial for building trust in data-driven decisions. Having standardised reporting templates, guidelines, and protocols can help ensure that data is interpreted consistently across departments. Ideally, key performance indicators (KPIs) and tracking methods should be defined consistently across the organisation. This promotes a unified understanding of goals and ensures that everyone is working toward the same objectives. However, it’s important to allow some flexibility to adapt to specific departmental needs while maintaining alignment with the overall organizational strategy.

Mastering data privacy and compliance

Proper configuration leads to privacy and compliance. Incorrect privacy settings can cause the unintended collection and storage of sensitive data. Mastering data privacy and compliance is ensuring that privacy settings are configured and maintained correctly to comply with applicable legislation, such as the GDPR. You of course want to avoid fines and reputational damage. Using a GDPR Monitor, including a dashboard featuring real-time alerts can assist with this by automatically overseeing the presence of personal identifiable information (PII) in the data.

Who has access to what personal identifiable information (PII)?

A crucial aspect of compliance involves using user permissions accurately and appropriately managing access to GA4 data. Mishandling user permissions and access controls may lead to unauthorized access to GA4 data. It’s imperative to confine access strictly to authorized users only to prevent sensitive data from falling into the wrong hands. This helps to prevent data breaches and avoid potential legal consequences.

The importance of continuously collecting, tracking and storing valuable data

A proper analytics implementation is of course required to collect and store data to become information and finally insights. On the one hand, we see that a lot of data is labelled ‘invaluable’ due to the incorrect way the data was gathered. Knowing you only have one chance of collecting data, the way you collect data should be correct. Maintaining is another necessity. For instance, we often see that historical information is no longer available due to an incorrect setting of the retention period. Now trends can no longer be spotted and valuable analysis is no longer possible.

A 6-stage data model to handle your data
You can utilise the 6-stage data model below to ensure proper data handling, thereby acquiring valuable insights that serve as the foundation for your actions. The initial phases of this model encompass establishing and maintaining data collection, as well as managing data storage. These initial steps, involving data gathering and storage, are of utmost importance. The subsequent stages—’Transformation,’ ‘Visualisation,’ ‘Analysis,’ and even ‘Activation’ of data—rely heavily on these foundational data aspects: the proper setup and maintenance of data collection, as well as accurate data storage with appropriate retention periods.

How to maintain data quality?

Since analytics platform vendors constantly change their existing features on their platform or launch new ones, organisations often struggle with an outdated analytics set-up. This renders valuable data useless, leading to a squandering of data investments. To counteract this issue, an automated data quality monitor diligently oversees your analytics configuration, providing real-time notifications to the team when adjustments are necessary. This mechanism guarantees the upholding of stringent data quality standards at minimal expenses.

How can you minimize data loss?

With the help of a data quality monitor, you’ll be able to automatically compare trends in today’s data with those from the previous day. Comparing your day-to-day data gives you critical alerts, enabling you to identify instances where a conversion (former goal completion) has been altered due to changes on your website. Comparing day-to-day traffic data to, for example, flag tagging issues, can then be fixed directly. It also ensures the seamless flow of qualitative data into your data storage location. Subsequently, the process of transforming, visualizing and analyzing data can begin.

How to do a reliable data analysis: the famous ‘360 customer view’

First of all, in order to do a reliable data analysis, you first need to make sure events are set correctly and filters are configured accurately to ensure reliable reports. Misconfiguration can result in inaccurate data and analysis when certain events or traffic for example is excluded. This can lead to wrong conclusions, poor decision-making and missed opportunities for improvement.

Moving forward, additional focal areas of significance encompass understanding the distinctions between universal analytics and GA4, navigating the intricacies of varying reported conversion figures, and constructing attribution models.

The difference between Universal Analytics and GA4 output

We all have seen the differences between the output in Universal versus GA4 analytics. These differences result in a decrease in the data trust among our colleagues; the people we want to convince of our analytics insights. There also is a difference between the data shown in the GA4 interface and the raw data. Although Google might say that they’re showing you all the data, GA4 is not showing you 100%. The reason behind this is the focus on speed. Google wants to compete with other analytics platforms based on loading time in the interface. One technique they employed for achieving this, involves session estimation. This is based on a smaller subset of the data. This also explains the differences between UA and GA4 output.

Why do reported conversion digits on social platforms differ from those in your analytics platform?

You’ve might have noticed the differences in how conversions are attributed to paid advertising or social channels. For instance, why does TikTok report a higher count of conversions than your analytics platform? META is also an often heard name within our agency when differences in conversion reports are discussed. These discrepancies stem from the underlying business model of the advertising and social platforms. They profit from a higher number of conversions. How the conversions are assigned to the platform and why the number of conversions differ is because of attribution. Different methods are used that assign credit to various marketing channels or touch points along the conversion path. GA4 now uses three different attribution models:

  1. Standard channel group for new users: First Click
  2. Standard channel group for sessions = Last Click
  3. Standard channel group for conversions = Data Driven

Build your own attribution model

You can put your organization in control over the challenges posed by differences between attribution models by creating and managing your own attribution model. If you want to make full use of all available GA4 data, making use of BigQuery can be a viable option. Trough the integration of the BigQuery plugin, the raw data can be used. With the help of SQL, your team can reproduce the reporting options available in GA4 and even customize it. This makes it possible to define and use rule-based marketing attribution models, using logic that you own and can change. This puts you in the driver seat of attribution!

What are the conditions required to transform to a data centric organization?

While data-driven digital marketing focuses on using data as a tool, data-centric digital marketing goes a step further by viewing data itself as a valuable asset. It means seeing data as an essential business asset that is central to making decisions and developing marketing strategies. Collecting, storing and managing data is key to gaining valuable insights into customer behavior and trends. A data-centric approach is essential for organisations that want to grow and compete in a digital environment. By seeing data as a valuable asset, companies can differentiate themselves from their competitors and gain valuable insights that lead to effective marketing strategies and a better customer experience.

The four fundamental aspects for a data-centric organization

  1. Maintenance: Data quality is set-up properly and maintained constantly;
  2. Knowledge: Uniform understanding among all company stakeholders regarding tracked elements and the significance of various metrics;
  3. Application: Every employee knows how to use data whenever relevant and possible;
  4. Trust: Fostering a sense of confidence and reliance on data throughout the organization.

An instance illustrating how our client assists all stakeholders across the organisation in comprehending both the tracked elements and the various metrics is through the utilisation of a ‘KPI catalog.’ This catalog encompasses all triggers and definitions of measures presented in a comprehensible language for all stakeholders within their organisation.

Conclusion

Research underscores the paramount importance of data quality in a data-centric approach to business. Understanding customer preferences, driving decisions, and enhancing customer experiences depend on accurate and reliable data. Aspects such as data privacy, consistency, and proper setup play vital roles in maintaining data quality. Organisations must establish strong data fundamentals, automated quality monitoring, and reliable analytics implementation to navigate the challenges and unlock the benefits of a data-centric approach. Trust, knowledge, application, and maintenance are the cornerstones of such a transformation, enabling effective decision-making and superior customer engagement. Reliable data and employee data trust are fundamental for building thriving data-centric organisations in the future.

Jesse Terstappen

Data Analyst | OrangeValley

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