Big Data and Privacy: What Companies Need to Know

In recent years, cautionary tales of privacy issues and data breaches have made headlines across social media. Companies that handle large volumes of sensitive information are falling prey to data leaks, privacy issues, and consumer privacy laws (such as GDPR) are falling short. Your consumers have valid privacy concerns about their personal data in this digital age.

With big data comes an inevitable threat to data security — however, the data itself is not the problem. Weak data management is.

No privacy law can compensate for data that is poorly managed. Proper data management is essential for all organizations that handle sensitive information and large volumes of data. Those who prioritize data protection not only win customer loyalty by respecting individual privacy, but protect themselves in the long run with brand reputation and a team culture built around prioritizing informed decision-making and individual privacy.

Big data and privacy in your company

Big data is an asset in this digital age - and a privacy risk when managed poorly. Prioritizing cybersecurity in your company makes big data a big help, rather than a big roadblock.

Properly utilizing big data helps organizations like yours to better understand customers, build retention and marketing strategies, and promote intelligent decision-making at every level of the business.

Top 4 big data privacy risks

1. Data breaches: Data breaches occur when sensitive information is accessed without authorization. In most cases, data breaches are the result of out-of-date software, weak passwords, and targeted malware attacks. Unfortunately, they can cost an organization a damaged reputation and a great deal of money. Keeping software up-to-date, changing passwords often, and educating employees on best security practices can all help prevent data breaches.
2. Data brokerage: The sale of unprotected and incorrect data is considered data brokerage. Some companies gather and sell customer profiles, which contain false information that leads to flawed algorithms. Before buying data, organizations should do their research and make sure they are receiving data collection from a reputable provider that offers accurate data sets.
3. Data discrimination: Since data can consist of customer demographic information, organizations may develop algorithms that are profiling and can penalize individuals based on age, gender, or ethnicity. Organizations should always have a thorough and accurate representation of customers, account for biases, and put fairness above analytics.
4. Data collection and storage: Data storage of this sensitive information is often hosted on the cloud, rather than on a physical computer or network - to get that data on the cloud with minimal risk requires a carefully planned data management strategy and an in-depth understanding of the privacy risks.

How to make big data and privacy work for you

Employ real-time monitoring

Privacy issues can happen with no room for error — finding a solution that monitors big data in real-time helps to keep you on top of potential data breaches, and able to deploy data protection strategies faster and more efficiently.

Implement homomorphic encryption

Homomorphic encryption is a form of encryption that allows users to compute big data without decrypting it first. This form of encryption should be implemented to store and process information in the cloud to prevent organizations from revealing sensitive information to outside vendors.

Avoid collecting too much data

A bigger amount of data isn't always better. Companies handling large amounts of data big data should only collect the data that is absolutely necessary for big data analytics. An organization may not need the Social Security numbers of their customers; customer logins. Organizations should consider deleting any personal information that is not needed to best protect the individual privacy of their customers.

Prevent internal threats

Organizations are often exposed to internal privacy risks from employees. Human error is an inevitable factor. Educate your employees across all levels on best practices to avoid internal threats (even the basics, like changing passwords frequently and logging off unused computers.)

Big data privacy tools: What to look for

• Cloud-compatible: It’s essential for a big data privacy tool to be compatible with the cloud. If it only works on a physical server or computer, it’s likely an out-of-date solution that cannot keep up with today’s big data privacy challenges and privacy regulations.
• User-friendly design: Adoption is key, and data privacy is a team effort. The right tool should be easy to use on all levels of your organization. Finding an intuitive, user-friendly tool encourages confidence and adoption across your team.
• Automation: Manual protection is an admirable, but impossible feat. Opt for a tool that utilizes machine learning, and allows you to automate and optimize your data quality and privacy protection — letting you focus on making decisions confidently with trustworthy data.

Getting started with big data privacy

Data breaches, data brokerage, and data discrimination can occur if big data privacy isn’t taken seriously. For this reason, data governance and integration are vital for proper compliance and privacy management. If your organization is searching for a big data privacy solution, Talend Data Fabric collects, governs, transforms, and shares data with internal stakeholders while ensuring data privacy. Try Talend Data Fabric today to reduce the risk of privacy issues often associated with big data and ensure your company has data it can trust.