The large amount of data that is generated every day in companies is a real challenge for managers, who constantly seek to extract the highest possible value from the data generated. In addition, as more data is available in companies, there are also more threats to information and more likely to be lost. In today’s article, we’ll cover the key security challenges that Big Data brings to companies.
Analysis of data may be compromised
Quantity doesn’t mean quality and sometimes we fall into the mistake of thinking that all the data we have at our disposal are useful for our business. One of the biggest challenges that Big Data brings to companies is the ability to analyze information correctly. Companies need to have the right tools to extract the insights they need to make the right decisions. Nowadays, you have to analyze the data in real time and one of the tools that helps companies deal with this great wave of information is Multipeers, a BAM system that allows you to analyze the business every second.
Loss of data and privacy
All artificial intelligence tools like chatbots, which help generate data for businesses, need to be very well configured, otherwise they can be a real problem for companies that need to keep their data private. You can not neglect good standards in these technologies (such as creating complex passwords, for example), as these can be incoming ports for improper access to the internal network. This is one of Big Data’s biggest challenges because the more information sources there are, the more entry-level doors to business exist.
Ensure mobility security
Mobile solutions are being adopted with great speed. Nowadays, it is no longer necessary for employees to go to the office to work because, because of the technology, they can work from any location and through any device. Undoubtedly this is a very important breakthrough and boosts the results. At the same time, it also requires a higher level of technical analysis because data security is more easily compromised, forcing the IT manager to take extra care.
Adopt data masking
Masking data has the primary purpose of protecting sensitive data from unauthorized access and is a key practice at a time when so much private data exists in companies. In practice, data masking tools create a version similar to the original data in terms of structure but without revealing its true information. In fact, its original format remains unchanged but the data presented is fictitious. Masked data can be used in test and auditing environments without compromising the result of the analysis, but always ensuring the confidentiality of sensitive information. A manual process to protect data consumes a lot of time and human resources so the best option is to resort to tools that do the process automatically, such as Datapeers.