The Data Cape: Architecture for rock-solid data
Collect data once, centralize compliance, distribute anywhere
Summary
In this article, we explain the Data Cape, a measurement and data architecture developed primarily for enterprise environments, based on over a decade of experience.
The Data Cape helps you to go from Data Erosion to Data Solidity; privacy law restrictions eating away at data completeness, or figuratively speaking, treacherous waters causing land erosion.
You will learn what makes data rock-solid and how only the combination of compliance and completeness, which you can measure using the Solidity Quadrant, lets you put individual data initiatives and your company as a whole on the road to lasting data success.
We live and breathe data, so if you ever need help with this approach, check out our rock-solid data offering, consisting of our compliance platform and our implementation services.
Table of contents
Our mission: User privacy AND business success
Please note that this document is still a work in progress.
1. Introduction
For more than a decade, we at Cape.ly have been helping companies gather user-centric data that is both legal and high-quality. During that time we have been confronted with the same set of beliefs over and over again:
- Maximizing business success requires maximizing user privacy violations.
- Maximizing user privacy leads to subpar data which negatively affects the business.
However, the implied assumption that user privacy and business success can’t be combined is absolutely false.
It is actually possible, it’s just not very easy to do. And because we care deeply about user privacy and want to help businesses, we have made it our mission to share and promote our approach as a framework to achieve both at the same time. It is closely connected to our concept of Data Erosion and Data Solidity, see here for the dedicated article.
2. Most use cases work well without invasive data
The most important thing to understand is that most use cases actually don’t require personally identifiable information (PII). It is just much easier to gather and work with user-related data. Additionally, basically the entire ecosystem runs on user-related data for historical reasons.
The very few use cases that actually require PII, and even that’s debatable, are ad networks with retargeting, and anything else that involves addressing specific users or devices individually, for example for, again, advertising purposes or security reasons.
All other use cases work very well with data that is not tied to an individual user or device. Some common examples include:
- Website and mobile app usage analysis
- Marketing campaign ROI analysis
- Funnel / navigation / user behavior analysis
These and almost all other use cases work with cohort-based data, consented PII and representative synthetic PII data.
3. Users deserve privacy, regardless of the law
Over the past decade, users have increasingly voiced concerns about privacy violations and demanded change. As a result, politicians around the world have created more and more privacy laws, with the European Union being the strictest at the moment.
However, even without any of these laws in place, businesses should have to realize one thing: Respecting their privacy is an essential part of the overall respectful behavior that companies should demonstrate towards their customers.
It is important to understand that respect for the law and customer privacy is a huge competitive advantage, especially when there is little to no affect on the usability of the data.
4. Violations are risky and ultimately expensive
Most violations of privacy laws can result in fines and/or lawsuits. And while there is certainly a lack of enforcement compared to the amount of violations, user-facing violations are relatively easy to detect and can therefore trigger a cascade of events at any moment.
The risk of fines and potentially losing all or a substantial portion of a business’ data foundation, is not a winning long-term strategy.
Once non-compliant data has been identified, businesses are often required to shut down and/or redo the way they gather data. In the end, they are spending more than if they did it correctly from the start. Shortcuts are risky and ultimately expensive.
6. Conclusion
We know how complicated user-related data, so we developed a set of tools and methods that protect both users and businesses:
- Users from privacy violations
- Businesses from legal risks due to non-compliant data
- Use cases and tools from issues due to incomplete data
We call our approach the Data Cape and hope this framework can help businesses to fortify and future-proof their data capabilities.
The Data Cape: Compliant AND complete data
Please note that this document is still a work in progress.
1. Introduction
One of the main advantages of the Data Cape is that data is only optimized for completeness once, and compliance is only done once, not multiple times or even once per tool / data consumer.
For businesses, it’s increasingly important to execute and adapt quickly to ever-changing environments and use cases. Especially automation, machine learning and artificial intelligence require very quick, high-quality data.
2. Tools are only as good as the data fed into them
There are more software tools than ever and most of them depend on data just as much as businesses on the tools themselves. Problems arise when the data that these tools work with is incomplete.
That can easily lead to situations where, figuratively speaking, the tools are blind on one eye. For most tools, this doesn’t mean that they stop working. However, it can quickly results in partially or completely incorrect results, further emphasizing how much more companies should focus on the input data.
3. Self-hosted data pipelines
Please note that this document is still a work in progress. This paragraph will later explain how to send data to self-hosted data pipelines, like for example:
4. Cloud platforms data pipelines
Please note that this document is still a work in progress. This paragraph will later explain how to send data to cloud platform data pipelines, like for example:
5. Customer data platforms (CDP)
Please note that this document is still a work in progress. This paragraph will later explain how to send data to customer data platforms, like for example:
6. Tag managers
Please note that this document is still a work in progress. This paragraph will later explain how to send data to server-side tag managers, like for example:
7. Analytics tools
Please note that this document is still a work in progress. This paragraph will later explain how to send data to analytics tools, like for example:
8. Marketing technologies (MarTech)
Please note that this document is still a work in progress. This paragraph will later explain how to send data to the thousands of MarTech tools, like for example:
9. Conclusion
Our long-term vision is that all tools can be powered from a single, central data source so that all resources can be spend to maximize legal compliance, and data completeness instead of being spread out. We at Cape.ly want everyone be able to focus on their job without worrying about any data at all.