What is Data Governance?
The term “data governance” means different things to different people. However, when used in the context of user behavior data, most will agree that the end goal of data governance is that the data collected as the result of user actions in an application or website is reliable and usable.
Reliable means that you can trust the accuracy and completeness of your data in order to make key product or business decisions. If your user event data is missing events, missing properties, has duplicate events or contains other types of erroneous information, decisions based on the data can hurt your organization. On the other hand, high-quality and trustworthy data can be a goldmine in your efforts to optimize user flows and business KPIs.
Usable means that your event data meets and serves your business needs. If your dataset doesn’t contain the events you should be analyzing, or if it contains unknown/mystery events, or if it contains duplicate events with different names, it will be difficult, if not impossible, to leverage the data to meet your goals.
Effective Data Governance is Challenging
Unfortunately, there are many pitfalls on the road to capturing reliable and usable behavioral data. The fact is that many organizations suffer from user event data that is not very trustworthy, not very usable, or both. Some of the most common reasons for this situation include:
- The organization does not have, or does not consistently apply, a standardized naming convention/taxonomy.
- Different people in the organization may be involved with deciding on and/or implementing event and property names at different times, resulting in redundancies and/or inconsistencies.
- Differences in event and property names creep in at different stages between when they are first defined (e.g., in a tracking plan) and how they end up being implemented in the application or website (e.g., how they are coded or tagged).
- Developers may err, resulting in the partial or incorrect implementation of tracking code or tagged event triggers (causing missing events, missing event properties, or incorrect event/property names).
- It is difficult for business users to identify which actual user actions generated the events shown in the data.
- Applications and websites change frequently, which causes tracking to break and the data to be incomplete.
How to Achieve Effective Data Governance
There are a number of “best practices” that organizations can follow in order to achieve successful event data governance when manually instrumenting event capture. Unfortunately – due the very nature of manual event instrumentation – these practices contain their own pitfalls and limitations, often making them impractical to implement. These best practices include:
|Create a comprehensive event tracking plan||Improves coordination among stakeholders, serves as an organization-wide “dictionary” for user events||Time-consuming, slows down responsiveness to business requirements, difficult to maintain over time|
|Ensure adherence to a consistent naming taxonomy across events and properties||Improves data clarity and usability||Difficult to maintain over time, especially when multiple people are involved|
|Empower a single individual in the organization to be responsible for all event and property planning, naming, implementation and validation||Having a single central authority is the best way to ensure data consistency, completeness and accuracy||There often needs to be a number of people sharing this responsibility, due to different business needs, the involvement of different departments, or the sheer size of an organization|
|Manually test and validate every event||Helps ensure high data quality||Difficult to do continuously as applications and websites change and evolve|
AI-Driven Event Capture Delivers Intrinsic Data Governance
Manual event-tagging solutions – whether implemented using code, a tag manager or a UI-based event tagging tool – all suffer from the data governance challenges described above. On the other hand, AI-driven event capture solutions circumvent most of these issues, essentially providing a high degree of data governance intrinsically, i.e., without requiring any additional effort. Here’s why:
- No internal miscommunications – Because developers are not required to code, tag, name, customize or block/unblock events, there is no chance of an event being captured differently than defined by business users.
- No possibility of duplicate events – Because AI-driven event capture solutions group together on-page elements based on functionality and context (rather than on CSS selectors), it is impossible for duplicate events to appear.
- Consistency in event naming – Even if multiple people in the organization are defining event and property names, it is not possible for one event or property to have two different names, because there is only one place where these names are defined. Of course, it is still important for everyone to follow the organization’s standard naming conventions.
- Consistency in event properties – Because there are no duplicate events, there is no chance for the same event to contain different properties, or the same properties with different names or data types.
- No lost event data when applications/websites change – Because AI-driven event capture solutions capture on-page actions based on element functionality and page context (as opposed to inline code or CSS selectors), they provide “automatic tracking continuity.” This means that all user events and properties continue to be available, no matter how often an application or website changes.
- Simple event data validation – Long and manual event data QA processes are no longer required, and there is no more confusion or doubt in the organization regarding the source of each event; it is easy to go back from a generated event to the application or website UI, in order to understand exactly where it came from. Even the data’s end users can do this; they don’t need to be analysts, developers or administrators.
Data governance around user event data is a topic that gets a lot of attention, and rightly so. If an organization’s user event data is not both reliable and usable, it is mostly worthless (or worse, it can be damaging). Unfortunately, the conventional manual approaches to capturing user event data – whether via code, tag managers or UI-based event tagging tools – provide extensive data governance points of failure. The result is often duplicated, inconsistent, mysterious or missing user events.
On the other hand, AI-driven user event capture solutions exhibit “intrinsic data governance,” by completely eliminating most of the problems and challenges in this area. Not only do these modern event-capture solutions save a huge amount of time and effort for both tracking implementation and ongoing tracking maintenance, they provide higher-quality, more robust, more trustworthy and more reliable event data over time. Contact us to learn how AI-driven user event capture can boost the power of your analytics.