Data Privacy Laws: Implications for Data Scientists

Some of the biggest businesses in the world today rely on data gathered from our own devices, our electronic transaction history, and data from other sources. Millions of small businesses and several startups rely on personal data to generate tailored digital offerings, market forecasts, and customer insights. The commercial usage of personal data has expanded in a wild-west manner over the last 20 years. But those days are rapidly passing due to a lack of consumer trust, government action, and fierce competition for customers.

The data economy was built around a “digital curtain” that was intended to hide the industry’s methods from the public and legislators throughout the majority of its life. Information was regarded as confidential information and firm property, even when it came from the private actions of the customers.

Since then, the curtain has come down, allowing users greater control over the data privacy laws they produce. All governments in the world have started to treat personal data as an asset owned by individuals and held in trust by businesses, rather than as a resource that may be freely mined. The tech companies have already been adapting and reforming the way in which they deal with their data to take account of the new realities around the fundamental data privacy laws of consent, insight, and flow.

As  data scientists, we should be well aware of all the Privacy Laws of Data and their implications for data scientists and should abide by the ethical norms of data ethics in data science.

One of the key principles is that personal data is an asset that belongs to the individuals who generate it, as evidenced by the new, straightforward legislation governing the data economy. However, breaking every rule completely is the same as breaking ingrained networks, patterns, and habits.

Consent takes a prominent place in this first rule. So far, firms have been massing as much information as they can, transaction by transaction, identities, interests, and habits of individuals. However, as customer control grows, the most valuable data will soon be that which is obtained with meaningful consent, as that will be the only data that businesses are allowed to act upon.

Companies must constantly build trust with clients by outlining in straight forward language how and why their data is being utilized, as well as the benefits to them. Businesses can take a sign from the recently established data cooperatives, which give consumers a variety of options for sharing data and the user finds it comfortable.

Businesses must reconsider how they gather information from one another as well as from their clients. Nowadays, businesses frequently sharing lots of personally identifiable information (PII) across intricate web agreements and risking the security and data privacy laws.

However, the technology of today, especially federated learning and trust networks, allows one to gain insight from data without actually obtaining or moving the data itself. Co-designing algorithms and data can streamline the insight extraction process by organizing each to better fit the requirements of the other. Consequently, the algorithms swap non-identifying statistics instead of transferring data.

An analogous method is employed by another company, Dspark, to get insights from extremely confidential yet highly valued personal mobility data. Every day, DSpark processes over a billion mobility data points, cleaning, aggregating, and anonymizing them. It never sells or transfers the data itself; instead, it uses it to create insights on anything from purchasing to demography that it then distributes to other businesses.

This final guideline serves as a new organizing principle for internal data teams and follows from the previous two. CIOs and CDOs no longer need to operate in silos, with one trying to keep data locked up and the other seeking to break it out.

Since all of your customer data has meaningful consent and gaining insight without moving data. Rather, with the same goal of getting the most insight possible from consented data for the benefit of the customer, CIOs and CDOs may collaborate to help insights flow.

In conclusion, the data economy is undergoing a significant change. Data has shifted from being a corporate asset to being personally owned, and this is the new reality that requires completely changing how businesses are to approach data operations.

Consent, Identity over Insight, and Flow, are the three the most critical data privacy laws. Organizations need to get meaningful consent from people before data collection; they must learn to best acquire insight without harming people’s privacy and learn how to share that insight amongst the data teams without risking privacy breaches. Building on these laws will bring confidence to customers’ and businesses, making them survive in the economy of data.