The intersection of big data and privacy: Essential insights

Big data has been around for longer than most might realize. From the first large data sets being handled in the 60s and 70s, to an influx of data seen in 2005 from social media sites such as Facebook and YouTube, there is certainly no shortage of big data in our digitally advanced society. However, what impact does big data have on customer privacy?

For consumers, there is no telling just how much personal data and sensitive information is stored within these complex data sets. Every online action, from making a purchase through an online store to simply browsing the web, leaves behind personal information that contributes to big data, and can therefore be accessed and handled by large organizations.

For the receiving organizations, on the other hand, with big data comes opportunities to drive business efforts using data analysis techniques, but the implications from a privacy perspective cannot be ignored.

What impact does big data have on customer privacy?

For organizations to effectively use big data, they must first gather the necessary information to create a comprehensive data set. At any point during a consumer’s online journey, user data can be harvested in a variety of ways; whether by filling out a subscription form to a newsletter, or making an online purchase.

This data oftentimes contains personal information relating to online consumers, and can include sensitive data such as Personal Identifiable Information (PII), financial information, healthcare records, online browsing activity, and more. Hence, consumer privacy must be made a paramount concern for organizations wishing to harness big data.

Additionally, with more and more data privacy regulations in place to safeguard consumer privacy (such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA)) , companies who actively handle big data as part of their business strategy must ensure total compliance with these privacy laws to avoid legal pitfalls and hefty fines.

Big data and privacy: What is it and what are the risks?

Big data is traditionally characterized by three key elements: a wide variety of data types, the increasing volume of these data types, and the velocity at which they are generated. These characteristics are known as the three V’s of big data.

Once obtained, big data is stored within a database which is then managed and maintained by an organization. The data management strategy of an organization can differ depending on business function. Due to the large, complex nature of these data sets, traditional data processing software cannot solely maintain them, and so specialized big data technologies and tools are required to manage, process, and analyze the information effectively.

Big data analytics can serve numerous purposes for an organization. They can aid in better understanding consumer behaviors, product performance, and website usability. When it comes to the handling of personal information within big data, however, there are numerous risks and challenges involved. Predominantly, the exposure of personal data to breaches and ransomware remains of paramount concern to businesses. Not only this, but if organizations are found to be negligent in their compliance with data protection laws and regulations, substantial penalties can be imposed—such as fines and legal action.

To promote the safe and effective handling of big data, whilst safeguarding the personal data of consumers, organizations must look to implement a robust data governance framework. Designed to promote data security and drive data literacy, a data governance framework can provide organizations with a means of meeting compliance requirements. In doing so, data integrity can be upheld, allowing for effective processing and evaluation of big data to improve business efforts.

Types of big data

There are two main types of big data that are harvested and stored within large data sets. These include structured data, and unstructured data. The data quality of the two depends entirely on business needs and functions, and with this considered, it is possible for organizations to tailor their data collection to reflect these.

Structured data is made up of strictly textual or numerical values. It is the easiest big data type to be stored, as it takes up only a small amount of storage space and can be kept within files such as an Excel spreadsheet. The types of personal data that form structured data include customer names and addresses, dates of any kind (date of birth, date of death), location data, social security numbers, and financial information (such as credit card numbers). Data analysis using structured data is far easier than unstructured data, as specific filters and sorting techniques can be applied.

The second type of big data is Unstructured data. This type of data is made up of images, video, audio, or even social media data, and is typically far heftier in terms of storage size. This type of data is also much harder to comb through when it comes to data analysis.

When it comes to big data privacy, it is important to recognize the types of data and personal information that is collected. In doing so, organizations can be more informed about the data protection strategies that should be employed to ensure all data, regardless of its type or quality, is safeguarded to mitigate potential privacy risks.

What is big data used for?

There are several benefits to an organization in utilizing big data and big data analytics. Big data goes beyond simply collecting personal data from consumers, and instead offers organizations the chance to evaluate usage patterns and behaviors also. In conducting big data analysis, businesses can uncover greater insights into audience demographics, and understand how consumers are engaging with the business online.

However, big data analysis relies not only on base-level evaluations, but input from insightful business analysts and executives who can ask the right questions about customer data in order to follow up with informed, data-driven decisions.

Risks of big data

Whilst dealing with big data is an effective way of driving business efforts, there are several privacy risks involved that professionals must be aware of. Privacy and trust go hand-in-hand, and so to ensure trust between business and consumer, organizations must regularly conduct privacy risk assessments when it comes to their big data strategy.

Some key big data privacy risks to look out for include:

• Compliance violations: Data privacy regulations such as the GDPR and CCPA serve to protect consumers from unlawful data privacy practices. Organizations in possession of big data should ensure total compliance with such laws not only to avoid legal ramifications, but to guarantee a respectable level of data protection practice.
• Security concerns: Online crime is only increasing due to the sheer amount of high-value data stored on the web. Cybercriminals will therefore look to big data sets as a high-value target for information theft, identity theft, and fraudulent actions. Implementing a robust cybersecurity solution into an organization is therefore of paramount importance for upholding big data privacy and protection.
• Lack of data control: Whilst accessibility to big data is good for organizations, having control over it is far more imperative. Ensuring that the correct user access controls are in place can guarantee that data is protected from unwarranted users. Intrusion detection and prevention should therefore be considered within a big data privacy framework.

Developing big data privacy and protection strategies

With numerous privacy concerns in regards to big data, organizations must ensure that their efforts in safeguarding consumer information are prioritized, and that they are continually revised to ensure total compliance with regulation. Factors that should be considered within a big data privacy framework include:

• Prioritising consumer trust over transactions: Whilst big data can be a driving factor behind business development and innovation, it should not hinder consumer trust. Making clear your intentions with consumer data is highly important in fostering trust, and can be achieved in many ways. For example, a privacy policy is essential in communicating any data handling procedures and purposes to your consumers, and is also a legal requirement in many circumstances.
• Continuing commitments to data integrity: Once established, you should never assume that your big data privacy framework is as robust as it can be. Developments in technology, particularly with AI, mean that future legislation and requirements for data protection are likely to occur. Therefore, you should continuously seek to improve your data privacy efforts wherever you can.
• Acknowledgement of regulation: Guidelines such as the GDPR and CCPA should seldom be viewed as a hindrance to your big data analytics. Data privacy regulation serves not only to safeguard your business from legal action, but serves to inform your data privacy efforts in order to safeguard consumer information and personal data. Ensuring total compliance with relevant privacy regulation not only protects your organization, but your customers too.

How to make big data and privacy work for you

When it comes to the consents of consumers in the collection of their personal data, organizations may look to implement a Consent and Preference Management solution (CPM) to aid in collecting the appropriate consents from consumers before handling any kind of data.

Big data can be highly influential in developing your business’ strategies and innovations. However, it is critical to acknowledge the role of the consumer in your big data practices. Consumer data and personal information should be handled with care, and every effort should be made to ensure total protection.