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Data-Driven

Managing data governance across event lifecycles

Christy Hollingshead
April 8, 20233 min read
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Let’s face it: if you’re dedicated to building great digital experiences, you’ll know already that the value of your data is only as good as its quality. That’s why we need to master two elements of data governance: data collection and data management.

In the agile product delivery age, our digital experiences evolve all the time– and so does our dataset. That's why data governance is so critical. It instills trust in our data by providing an effective framework for collecting, managing, securing, and deriving value from it. In other words, data governance is the key to unlocking the true potential of our data. 

In this blog post, we’ll dive into what data governance really is, why it’s important, and the framework you need to use to ensure accurate and trusted data is accessed safely across your organization.

What is data governance?

Data governance is a set of processes, policies, and standards that keeps your data accurate, consistent, and secure. The more rigorous your data governance approach, the more you can trust your data to be up-to-date and available to the right people in your organization. Data governance can often be broken down into three goals:

  1. Accuracy: ensuring that the data foundation used for analytics is trustworthy and complete. 

  2. Organization: ensuring that the data is structured, labeled, and stored in a way that promotes consistency, useability, and discoverability. 

  3. Security: ensuring that the data is handled in a way that complies with relevant regulations, respects consumer privacy, and minimizes the risk of security issues.

Best Practice | Governance- Goals

The role of data governance in the lifecycle of a digital event 

Although data governance can be applied to lots of different data types, I want to focus on data governance when it comes to digital analytics plans and the events of digital products and experiences.

When you design a feature or a product you expect users to use it a certain way. But more often than not, users will find a way to surprise you. The reality is that with today's rich and complex digital experiences, there are countless potential user journeys and flows out there. And it's almost impossible to identify and define each one before shipping. This framework is designed to help product, data, and growth teams better understand how to maintain a complete, trusted dataset. It will help you stay up to date with user behavior insights no matter how your experience evolves, or how users decide to interact with it.

The building blocks of this framework are your individual digital user interactions, also known as digital events. A digital event is when you capture granular detail about a user’s interaction with a digital property–things like navigating to a page, clicking a button, or filling out a form. These events are crucial because they offer insights into the quality of your digital experience.

There are three main ways to capture digial events:

  • Manual tracking: where engineers manually insert tracking code for each event and property before shipping a digital experience.

  • Autocapture: which automatically captures the majority of user interactions with a single Javascript snippet and unlocks retroactivity.

  • Hybrid capture - mixes both of the above in different levels for maximum operational efficiency and speed of insight (e.g. automatically capture 90% of user actions, and manually track 10%).  

When comparing different methods of data collection and management, it’s important to consider the time and resources required to turn the data from these events into useful insights.

We can break down this process into four steps:

  1. Mapping customer journey events. This refers to you identifying the events that will provide you with actionable insights.

  2. Validating events. This is the process of verifying that data is being captured reliably, in a way that’s consistent with the intended definition of the event. 

  3. Monitoring events. This is the process of ensuring that data is flowing consistently, without interruption. 

  4. Evolving events. This is the process of ensuring that events can be updated and redefined. 

Lifecycle of a Digital Event

What do these approaches look like in practice?

So far we’ve covered what data governance is, the different ways to capture the data you need, and the steps to take to transform your captured data into useful insights.

As a final step, let’s go through each step we covered in the context of a digital event to see what this looks like in practice. 

Data Gov- Applying Steps

—

And that’s it! By understanding the lifecycle of a digital event and following the four steps we’ve laid out in this blog post, you and your team can start designing the best data stack and management practices to meet your current and future analysis needs. Soon your data stack will be able to support all current and

As a next step, you are invited to learn more about different data governance approaches and best practices for implementing change management processes successfully.

Christy Hollingshead, VP, Customer Education & Engagement

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