Big Data: How to Avoid Privacy Risks

Data is the new gold. Information is being collected from various sources: IoT devices, M2M communications, facial recognition software, etc. These vast data sets allow companies to understand customer needs better, identify significant correlations between seemingly unrelated variables, support decision-making, predict customer behavior, weather patterns, market fluctuations, etc.

Data collection, structuring, and processing are not accessible, but it is achievable even for small organizations. 

Given that businesses can extract so much value for so little money, the gold rush of data acquisition should come as no surprise.

However, with great power comes great responsibility. Any piece of personal information that becomes available to a company is a profound opportunity to invade privacy and damage fundamental human rights.

 Unauthorized collection, lax protection, or irresponsible processing carries severe risks for both the individual and the company. Balancing the risks and opportunities of big data should be a top priority for organizations.

Big data consulting is more relevant today than ever before: failure to comply with security and privacy regulations can lead to huge fines, lawsuits, and a permanently tarnished public image. For example, penalties for violating the GDPR may be as high as 20 million euros, or 4% of a company's annual revenue. 

Moreover, the authorities can prohibit companies from processing data at all, which in many cases means termination of operations.

Let's look at the big data privacy issues companies may face and the steps they need to take to ensure that software product development stays within the law.

Despite the tremendous benefits of big data analytics, data should never come at the cost of privacy.




Big Data Privacy Issues

Businesses have been using data analytics to gain a competitive advantage and increase profits for decades. This raises the question: why has big data privacy only recently become relevant?

The problem lies in scale. The volume and variety of data have increased dramatically due to significant technological advances, making privacy concerns more apparent. In this regard, we can identify the following data privacy issues:

 

Lack of control

The wide variety of data sources in different systems makes control over personal information much more difficult. This is where many businesses become perplexed. Even if your service is compliant with the requirements, it must still be documented. This poses a major technological challenge for companies to inform their users accordingly. Surveillance camera data or Internet searches are great examples of often unmanaged data processing.

 

Data availability

Companies collect data for a reason. For example, medical organizations need your medical history for safe treatment. However, when this data gets into the hands of third parties without your consent, it can cause many ethical problems.

Over-automation and profiling

Companies that use big data, such as those in e-commerce, are looking for ways to profit from it. 

The importance of targeting cannot be overstated. 

The better you know your customer, the more effective your marketing and pricing strategies will be.

On the other hand, customer profiling can frequently result in prejudice and unequal distribution of advantages based on ethnicity, age, region, and so on. For example, modern online retail platforms often employ ML-based price differentiation, which effectively increases revenue. However, in the case of incomplete data, this practice can lead to errors that deprive fully qualified people of the prices or bonuses they are entitled to.

Tips for GDPR compliance

GDPR compliance with big data privacy becomes crucial since fines can reach up to 20 million euros. Let's look at what steps companies can take to avoid getting in trouble.

Encourage awareness

Ensure that everyone involved in developing your software is aware of GDPR by offering adequate training. 

The more your staff are aware of data privacy, the less time and resources will be required to police it. 

However, it's vital to keep the onus of duty on your engineers and management to a minimum. 

Any stress brought on by the need to meet regulatory criteria can impair performance. 

A Data Protection Officer (DPO) would most likely be required, whose only task will be to guarantee that the finished product meets regulatory criteria. The DPO will also be responsible for maintaining documentation and ensuring that the products or services released collect and process data legally. 

Make documentation a must

This is where many businesses become perplexed. Even if your service is compliant with the requirements, it must still be documented. This applies to software projects as well as business operations in general. 

Assess compliance upfront

 In addition to conventional ways of delivering products and services, a privacy strategy should also be among the early stages of software development. Privacy Impact Assessment (PIA) is an effective method of ensuring that your service can be delivered within legal boundaries. Not only does it help in the early identification of privacy risks, but it can also serve as reliable documentation in the event of privacy disputes. 

Conclusion

From now on, less data is correlated with less risk. Determine what data sets are needed, and anonymize or encrypt as much of it as possible.

Implementing a data privacy policy is more than just risk management. Privacy by design should be an integral part of any big data initiative. This means that security and privacy should not be an afterthought in software development but a deeply ingrained feature and a core requirement.



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