How data privacy has shifted the paradigm for building data pipelines

The emergence of data as a product has brought data privacy to the forefront of data engineering

May 17, 2023

The emergence of data as a product

In the past, data was often viewed as a byproduct of business operations, something that was collected incidentally and then analysed for insights. However, with the rise of big data and the increasing importance of data in business decision-making, many companies now view data as a product in itself.

This means that data is treated as a valuable asset that can be leveraged to drive business growth and innovation. Data has become a commodity that can be packaged, marketed, and sold to other businesses or consumers. Companies collect and process data with the explicit goal of creating monetary value, much like they would with a physical product or service.

This shift in thinking has increased the need for effective data processing pipelines that can handle large volumes of data quickly and accurately. Another key implication is the importance of data privacy. When data is viewed as a valuable asset, the risks associated with data breaches and privacy violations become more significant.

Issues revolving around data ownership, data sharing, data breaches, data profiling, and lack of transparency have taken centre stage. These issues have also affected the building of data pipelines. Data privacy is now a priority from the outset, and not an afterthought. Strong security measures and data governance policies are enforced to ensure that sensitive information is protected.

With the mindset of treating data as a valuable asset, companies can unlock new insights and create new opportunities for growth and innovation. However, this approach also requires careful attention to data quality, data pipelines, and privacy, in order to maximise the value that data can provide.

Shift in the way engineers think about data pipelines

Traditionally, data pipelines were built to serve internal purposes, with the primary focus being on data quality, processing speed, and accuracy. Data was collected from different sources, transformed, and loaded into a central repository where it was used by analysts and data scientists to generate insights.

Now, the primary concern is no longer just the quality and accuracy of the data, but also the privacy and security of the individuals who generate it. In the past, data was collected anonymously, and individual privacy was not a primary concern. But now, with regulations such as GDPR and CCPA, companies are required to ensure that personal data is collected and processed in a secure and transparent manner.

In this new paradigm, engineers prioritise data privacy and security in their data processing pipelines. It is essential to protect sensitive data from unauthorised access or disclosure. Engineers use different techniques and design data pipelines to ensure data privacy. They build systems in which data is collected with user consent, and users can control how their data is used. Data privacy is no longer an afterthought in data pipelines, but a necessary prerequisite.

data as a product
data privacy
data security
data pipelines
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