Big Data: Essential Steps To Improve Data Security

big data security

In the era of big data, data security is essential. In fact, companies generate 2,000,000,000,000,000,000 bytes of data in total every day, and the value of this data is set to be worth around $77 billion by 2023. Nevertheless, data breaches are a huge problem for businesses, now costing an average of $8.19 million each — up from $3.45 million in 2006. By taking steps to protect big data security, businesses can stay compliant, prevent data breaches, and protect their bottom line. 

Don’t just focus on perimeter protection

Nearly 90% of security budgets go towards firewall technology. However, if hackers are able to penetrate your network perimeter, you still need to make sure your big data itself is protected. So, ensure controls are placed either within or as close as possible to the data and data stores. In fact, embedding security in the data center cluster itself is one of the most effective ways to give your big data an extra layer of protection. For example, implementing fine-grained access control like role-based access control (which limits who’s able to access certain data) will ensure your sensitive data remains protected even if there’s a perimeter breach.

Adopt privacy-enhancing technology

Industries that deal with a huge amount of sensitive data on a regular basis (like healthcare and financial services, for example) are prevented from sharing data by privacy regulations (namely, the U.S. Health Insurance Portability and Accountability Act (HIPAA)). Consequently, as much as 73% of enterprise data ends up going unanalyzed. Fortunately, by adopting technology designed to enhance computer and communications security, businesses can share sensitive data in an encrypted space without breaching privacy. With access to privacy-enforced algorithms, your business can access new data otherwise unavailable due to privacy concerns and glean sharper insights.

Delete redundant data

When it comes to managing big data sets, redundant data — defined as having the same data set stored in two or more places — can often be inevitable. And, as your volume of redundant data grows, the more storage space it takes up on your servers. Not only does this slow down operational efficiency, but it also increases the risk of corrupt reports or analytics as users may not realize which data they need to access or update. Implementing a routine data checking schedule is essential for preventing data redundancy. Regular data checks will reveal duplicate entries with transactional information running the biggest risk of redundancy (although you should still run regular checks across the whole database to identify identical data). It’s also important to delete databases you no longer need as you go to further prevent data redundancy. For example, if you transfer customer data to a new database, don’t forget to immediately delete the duplicate data from the old database. 

Big data security is key for business success. By embedding security in data clusters, adopting privacy-enhancing technology, and deleting redundant data, you can make sure your data stays safe and protect your business’s bottom line.

Cover Photo by Nao Triponez from Pexels

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