The EU’s GDPR renewed the data industry’s focus on user privacy, but new regulations are, in fact, a staple of the ebbing and flowing digital landscape. Although businesses are seeking new processes in response to current privacy laws, it pays more to stay ahead of the curve.
Changing regulations are always around the corner, so to maximize time and resources, organizations can adopt data privacy by design (DPBD). The DPBD framework, published in 2009, proactively ensures business operations are built with privacy in mind.
Today, companies can build minimal, transparent, and consistent systems that enable them to leverage data while safeguarding user privacy. Here’s how DPBD streamlines the way to future-proofing compliance, generating stronger business and product outcomes, and establishing trust from partners and customers.
The building blocks of privacy
Online users give off data with each action, interaction, and even inaction. This is valuable to marketers, media owners on the open web, and – when this data is used with transparency and intended use – consumers. All parties stand to gain from a stronger advertising experience, so how does DPBD allow businesses to safely make the most of data?
DPBD supports a holistic approach to data that can be applied to industry players across the ecosystem. Its principles include:
Collection limitation – By minimizing the amount of data they gather, organizations can reduce the risk of breaching privacy laws. Additionally, users must have a clear choice to opt out of data collection.Data quality – Focusing on data quality enables businesses to efficiently determine what information they truly need to drive results.Purpose specification – Identifying from the outset how data is being utilized to guarantee fair usage and productive outcomes. Use limitation – Businesses need a clear understanding of how data can be used throughout their operations, and specifying limitations shapes data practices. Security safeguards – One example of this is checking how data has been anonymized, pseudonymized, and encrypted and evaluating operations and enterprise measures to minimize breach, leakage, or other unintended intrusions. Openness – Transparency is no longer a nice to have; it’s a necessity. Knowing a data segment inside and out supports businesses in leveraging it safely. Individual participation – What inquiries can be made about the data? Just as consumers need control over their information, companies also need a view of what data they use. Accountability – When asked, can a company identify what the data is, where it is, and ensure it can be amended or deleted?
Altogether, these principles define how a business ingests, processes, and outputs data. Most importantly, DPBD constructs a malleable architecture that can be easily adapted as needs change over time.
What are the considerations of the DPBD system architecture?
To put DPBD into practice and build sustainable data flows, organizations must monitor how data enters their company. For instance, businesses should ask – what commercial product requires this data? Are there any characteristics that make this data sensitive or unique? When handling sensitive data, it’s vital to implement security parameters that guarantee it is retained securely.
Furthermore, businesses should be aware of what data is truly necessary for their products and what data can or cannot be combined to reduce any unintentional usage. By answering these questions, companies not only evaluate a product’s commercial viability but also how end-users receive it.
The importance of future-proofing
The best approach to take when future-proofing compliance can be summarized with Wayne Gretzky’s famous quote, “skate to where the puck is going, not where it has been.” If a company’s systems are built for data privacy as it stands today, they will quickly become redundant. Succinctly: the data industry continues to transform. Even independent of the ongoing regulatory changes, DPBD offers a way to stay ahead of these changes and improve systems when change does occur.
To illustrate, the principle of data minimization future proofs value. A big data footprint comes with the risk of big problems, but taking in as little information as possible produces a smaller footprint with built-in compliance. Delivering the most effective product with the least amount of data mitigates the impact of new regulations, whatever they may be. With a robust and consistent architecture, businesses can proactively sustain the value of their offerings and ensure agile, compliant data practices.
Building trust for partners and customers
Trust is founded on shared business values and, of course, results. Identifying use cases early on clarifies the core purpose of a company’s product, alongside how data should be used to achieve this. Organizations can then open discussions around data flows across departments, which facilitates more productive decision-making and stronger outcomes.
We can examine device graphs as an example, which is a method used by companies looking to connect individual behaviors to all their preferred devices. To increase a graph’s accuracy, it can be tempting to incorporate a broad range of data points, such as behavioral information from multiple sources, and tie these actions to real people, rather than just looking at the traffic being generated. Doing so, however, creates a high-risk proposition. As an alternative, businesses can look at key factors that, at a minimal level, facilitate data matching without ingesting or ascribing additional information beyond the initial purpose of connecting behaviors. Through keeping a clear focus on the graph’s purpose – in this case, to match behaviors across devices – companies can support effective ad targeting in a way that is privacy safe.
DPBD principles are more than an ideology – they guide tangible actions that all businesses can take to protect data privacy. Building them into operational workflows creates a consistent, long-term approach to establishing end-to-end compliance. Businesses must put in the hard work upfront to achieve data minimization, accountability, and ongoing trust. Only then can they walk the walk and preserve data privacy in the years ahead.
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