Is Big Data Regulation A Thing Now?

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2018 has been the year of data. Companies who have moved from a static marketing approach to a more detailed, data-driven one, have experienced massive growth in the past 7 months. After what happened with the Cambridge Analytica scandal last year, greatly covered in the recent Netflix movie "The Great Hack", there have been some changes in regards to how big data is acquired in today's business world. Let's analyse the matter in more detail.

What's Big Data And How Is It Collected?

Big data is the term which is related to numerical values associated with a specific user. The simplest example of big data in today's industry would be cookies. Although the matter is in constant evolution, with far more tailored and precise algorithmic values which are generating far better big data, the collection of such data is still related to numPy and Pandas, two very popular Python (programming language) libraries used throughout the entire data science sector. The collection of such data happens with the combination of these libraries with (mainly) Java and Javascript-based architectures which are storing these values for their future usage in marketing/lead generation processes.

The GDPR Variable

As mentioned above, big data has been recently shaken by the Cambridge Analytica scandal: in 2018, the infamous British company bought a vast number of data points (very tailored big data based on users' keyword research) from Facebook in order to launch dozens (if not hundreds) of extremely personalized paid social ads for both the Trump campaign and the Brexit campaign. This illegal buying has cost Facebook a $2 billion fine and, most importantly, a specific GDPR section on big data regulation. As of today, in fact, it is mandatory for every business entity to state how data is acquired, stored and processed, in order to let the user decide whether if he/she wants his/her data to be collected.

The Future

The future of data regulation is definitely related to how the technologies used to either get and store pieces of data will evolve. Python is a programming language which is constant evolution and its power in data acquisition will most likely embrace the mobile sector, like a big company which focuses on app development, has recently stated. The UK still is the European powerhouse in terms of technology and its usage, so we can safely say that this insight is very much worth.

To Conclude

Data has definitely been recognized as a powerful yet dangerous tool in today's business world. Its usage, acquisition and everything in between are hot topics and it's good to see that their complete regulation is almost here.

rproffitt commented: Good post but them seems like spam when I read the London app dev reference. -3

Dani AI

Generated

Short answer: yes — regulation of large-scale personal-data collection is real and already being enforced. The EU General Data Protection Regulation (GDPR) remains the baseline for companies dealing with personal data, setting data‑subject rights, lawful bases for processing, DPIA requirements and significant penalties. (eur-lex.europa.eu) Regulators outside Europe have also shown they will act; for example, the U.S. FTC’s 2019 enforcement against Facebook is a clear demonstration that privacy enforcement can carry major business consequences. (ftc.gov)

Cross‑border transfers and automated profiling are two of the biggest compliance traps. The Court of Justice’s Schrems II decision removed the EU‑US Privacy Shield and forced organisations to reassess transfers to the U.S. on a case‑by‑case basis. (europeansources.info) The EU adopted a modern set of Standard Contractual Clauses in 2021; those SCCs are the common fallback for transfers that lack an adequacy decision, but they often require additional technical or contractual safeguards. (op.europa.eu)

U.S. federal privacy law is still fragmented, but states moved fast: California’s CCPA/CPRA now gives consumers expanded rights (know, delete, opt‑out, limit use of sensitive data), and other states have followed with their own rules. (oag.ca.gov) Practical, immediate priorities for marketing and data teams: map and classify every data flow; persist consent and opt‑out flags in event/user records; minimise collected identifiers used in models; add automated retention/deletion; run DPIAs for high‑risk profiling; and require Data Processing Agreements with all vendors.

For Python/data engineers: treat consent as a first‑class data field, pseudonymize identifiers before analytics, keep re‑identification maps under strict access control, version training sets and log lawful bases for each dataset. Involve legal or a DPO before political or sensitive targeting. As noted about the business upside from data, the upside is real — but sustainable growth now depends on solid governance and documented compliance.

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