Big Data Analysis – Are Privacy Issues Real?

Today, data plays a significant role in various organizations and businesses. Business owners make critical decisions after analyzing data. That’s where big data analytics comes in. This method essentially improves analytics and brings innovative improvements for organizations. For instance, the retail industry can predict top items each season and boost their sales.

Furthermore, they can predict geographic areas with high demand. These are just a couple of big data analysis applications. Since big data analytics is a powerful tool, it has numerous privacy concerns along with positive business possibilities.

Big Data and Privacy Concerns

When big data is poorly managed, it brings many privacy risks. But, if an organization stops using this technology due to security concerns, they are making a huge mistake. Big data is highly important to understand customers and make smart decisions. By introducing big data analytics in your business, you can have a competitive edge over others.

Big data often require a cloud computing system for data storage. Physical computers and networks aren’t efficient to manage huge data. Analytics carefully plan data management strategies to ensure data transfer through the cloud. This results in minimal risk.

Major Big Data Analysis Privacy Issues

Big data offers a wide range of benefits to different organizations. Also, it comes with many privacy risks that you should consider; these risks include:

1. Data Breaches

Data breaches occur when someone accesses the information without authorization. The common reason for a data breach is outdated software, malware attack, and weak passwords. Data breaches damage business reputation in the market. Furthermore, cyber-criminal ask for a great deal of money after a data breach. You can prevent data breaches by:

• Educating employees
• Choosing strong passwords
• Repetitively changing passwords
• Timely updating the software

2. Data Brokerage

Selling incorrect or unprotected data comes under data brokerage. Some companies collect and sell data with false information. When companies use this data in machine learning algorithms, they won’t receive accurate data. Before purchasing data, businesses should conduct research and ensure that they receive accurate data. The best approach to prevent data brokerage is consulting a reputable provider to purchase accurate data.

3. Data Discrimination

Data contain customer demographic information. Therefore, various organizations can penalize individuals according to their gender, age, and ethnicity. They create an algorithm to extract data from the customers. Thus, businesses should put fairness above analytics. They should collect accurate customer data.

Methods to Ensure Privacy in Big Data Analysis

There are few traditional methods for data privacy in big data. Many big data analysis companies follow these methods to solve privacy issues. These techniques are:

• De-identification
• Privacy-preserving aggregation
• Operations over encrypted data

Conclusion

When businesses ignore data privacy, they can suffer from data brokerage, data breaches, and data discrimination. Therefore, privacy management and proper compliance are important for data integration and governance.

If big data analysis sounds difficult, you can outsource the task to collect, manage, and share the data without privacy issues. These businesses ensure data privacy through careful risk analysis.