Top 8 Big Data Security Best Practices for 2021

Big data security is defined as the protocols and tools utilized to protect data that organizations collect. The term comprises all measures taken to protect data from cyber attacks, data thefts, and any malicious online activity that could compromise an organization. This article delves into the key challenges organizations face in big data security and the best practices to follow in 2021. 

Table of Contents

• • What Is Big Data Security?
  • Key Challenges in Big Data Security
  • 8 Best Practices for Big Data Security in 2021

What Is Big Data Security?

Big data security is defined as the protocols and tools utilized to protect data that organizations collect. The term comprises all measures taken to protect data from cyber attacks, data thefts, and any malicious online activity that could compromise an organization. Like other online security protocols, big data security protects a business from online and offline attacks.

Today, companies leverage big data to make better decisions, identify potential markets and revenue streams, and enhance performance. However, with the massive use of data comes the concern of security. In 2019, Forbes reportedOpens a new window that the number of IT professionals using big data descriptive and predictive statistics grew from 40% to 60%. Additionally, research by the Federal Bureau of Investigation (FBI) Internet Crime Complaint Centre (IC3) showed that more than $3.5 billionOpens a new window was reported lost due to cybercrimes in 2019 alone. 

Several companies work on cloud today, and the big data security challenges they face are multi-faceted — data thefts, ransomware attacks, DDoS attacks — the list is long. Companies that store their clients’ personal information, particularly credit card and contact details, are especially vulnerable to such attacks. These attacks result in dire consequences like a debilitating financial loss, legal costs, a wide range of fines and sanctions, and high-value compensation to clients.

Big data security needs a multi-pronged approach. Before understanding it, let’s understand the multiple facets of big data security.

Also read: What Is Application Security? Definition, Types, Testing, and Best Practices 

Added vulnerability with regulated enterprises 

When a company is bound by regulations, it faces more challenges while storing, managing, transmitting, mining, and analyzing its data. Consider the HIPAA rules that govern the healthcare sector, for example. There are several privacy guidelines that healthcare professionals have to follow concerning patient information, its storage, and usage. 

In most cases, organizations use a third-party cloud storage provider. The challenge here is that healthcare providers do not retain complete control of the data but, the onus of its safety continues to be on them, according to stipulated regulations. Here are some other companies face with big data security.

1. Complications from BYOD

59% of organizations today follow a bring-your-own-device (BYOD) work model, while another 13% plan to allow its do so within a year, says research Opens a new window by Tech Republic. According to Global Market Insights, the BYOD trend is estimated to be valued at $366.95 Opens a new window billion by 2022. 

The increase in the adoption of BYOD has led to a rise in the utilization of third-party applications. This adds to the challenges of big data security for an enterprise. Since security is not inherently built into individual devices, enterprises will have to work on retrofitting measures onto each one which may be feasible for enterprises in the long run.

2. Security risks from third-party cloud applications 

Big data depends extensively on the cloud; however, security risks arise primarily from third-party applications used to access it. Applications not compliant to set standards pose a greater risk of a breach of enterprise networks. 

The BYOD trend adds to security concerns among remote workers. The number of users on the network can also add to security concerns. Businesses need to implement appropriate measures for endpoint security, privileged access to data, and employ well-defined security policies. However, organizations have to contend with the fact that these measures are not always fool-proof and will have to be evaluated constantly.

Also read: Top 8 Disaster Recovery Software Companies in 2021

Key Challenges in Big Data Security

1. Falling victim to fake data

Fake data is introduced into data streams by cyber-criminals who hack into enterprise systems. Let’s consider a fabrication unit that is usually set to churn out a specific number of products in a given time frame. With automated measures, floor managers ensure that employees meet their targets and things flow smoothly. However, cyber criminals can hack into systems and falsify data to trick machines into believing that the right numbers are being manufactured, whereas, in reality, there may be a slow-down. This can lead to a severe dip in the output. A fraud detection approach is necessary to deal with this kind of intrusion.

2. Loss of speed with encryption

Encryption is a well-established means of ensuring the security of sensitive data. However, regular encryptions and decryptions of large amounts of data can slow the entire processing system. Therefore, companies tend to ignore its need and store data in the cloud without encryption. Achieving a balance between speed and security is a challenge.

3. Vulnerable mappers

When big data is collected, it is split, and a mapper is used to process and send it to multiple storage silos. This distribution is based on a mapper code. When cybercriminals gain access to mapper codes, it becomes easy for them to alter parameters, making the data useless as it does not get stored in the right silos. They can also add alien codes to destroy data processing setups, thereby rendering any results based on this data faulty. This problem is especially difficult to deal with because big data technologies largely depend only on perimeter security systems and don’t have a multi-layered approach, making them vulnerable.

4. Vulnerability to information mining

Perimeter security ensures that the entry and exit points for your data are secure. However, the data inside the system can still be open to corruption by unscrupulous personnel who have access to it. It can be stolen, destroyed, or be utilized in corporate fraud to help rivals – which is often the case with businesses trying to get an edge over the competition.

Big data needs to be protected with multiple layers of additional perimeters. Anonymization can be useful here. This means that the accessed data will not display details like names, addresses, or telephone numbers, rendering the information useless to hackers.

Also Read: What Is Data Security? Definition, Planning, Policy, and Best Practices

5. Problems of granular access control

A challenge with big data is granting access to specific groups of people. With medical big data, for example, medical professionals will need to access personal information from treatment charts, but for researchers, it may be restricted to details like age, gender, and ailment. Granting such granular access in big data is not simple because the technology is not created to work in such a manner. Considering the complexity of granular access, big data can slow down system performance and maintenance as the data keeps growing each day exponentially.

6. Data provenance difficulties

Most enterprises are unaware of the fact that in big data storage, each bit of information has its provenance. All the information related to the data’s origin, the manipulations done with it, and the many ways it may have been influenced — it all gets stored. This results in a massive cache of metadata, which also needs to be secured. Data provenance poses a massive data security concern because its misuse can lead to several problems.

• • Unauthorized changes in the metadata can lead employees to wrong data sets, making it challenging to find the right information when needed.
  • The inability to trace data sources can lead to major obstacles while investigating security breaches and the generation of fake data cases when data provenance has been compromised.

7. Evolution of NoSQL databases

According to Allied Market Research, the NoSQL market size is projected to reachOpens a new window $22,087 million by 2026. NoSQL databases are increasingly being used to store big data, and they are constantly evolving to newer demands. On one hand, this is a good thing for the future of big data, but on the other, there is a gaping issue when it comes to security. Often in the race to evolve and introduce newer features, security is placed on a back-burner, making the system vulnerable.

8. Lack of security audits

For any organization that uses big data, regular audits will help find and plug security loopholes. Companies are advised to do this regularly, but it is rarely followed. When working with big data, there are several challenges — lack of time to keep up with the ever-evolving nature of the data, resources to maintain systems, qualified personnel to use the system well, and a lack of clarity about the kind of security measures needed. In such scenarios, audits only add to the chaos. 

Also read: What Is Biometric Authentication? Definition, Benefits, and Tools

8 Best Practices for Big Data Security in 2021 

Now that we have examined the challenges involved in big data security let’s look at the best practices that can help businesses gain maximum benefits from their data. 

1. Set business and technology goals for big data

Big data has the potential to open up several opportunities for businesses. Naturally, enterprises aim to employ every latest technology. While doing so, they often lose sight of what the primary business goal is. A good practice is to first understand what the end goal of the business is. With this firmly in place, big data can then be leveraged with analytics. Companies should clearly outline the outcomes and results they are hoping to achieve.

The management and IT departments should work in tandem to ensure that the collected data is streamlined to meet business goals.

2. Make big data a collective effort

A big data project should never have to be executed in isolation. Apart from the IT department, the management teams and service providers should also be involved in planning and implementation. If all three parties work together, they will get a clearer idea of the business goal, the possible challenges, and the means to address these issues without compromising the end output. 

Once big data is implemented, there should be a regular process of checks and balances to ensure that only relevant and accurate data is collected. Only then will the insights based on the data be of value to the business user. 

3. Test the quality of data

Even though an enterprise may have all the systems to collect big data, it doesn’t always ensure that the data is accurate or useful. The data may simply prove to be irrelevant to the organization. If parameters are not in place for collecting data, a business may end up with a large amount of disorganized data, which will not give the desired results. 

Therefore, it is very important to thoroughly examine the collected data and find what may be missing. These parameters can then be added to the system to collect the right data before implementing it in a project. It is impossible to know right from the get-go what data fields may be needed, but flexibility in the system must be maintained. 

No matter which way a business looks at it, collected data must be regularly tested to understand its efficacy. 

Also Read: What Is a Data Catalog? Definition, Examples, and Best Practices

4. Keep communication lines open

As with any other collaboration, big data is only effective when all stakeholders and the IT department collaborate regularly. Projects evolve at a fast pace, so the required data may move from one particular type of field to another — and this pace has to be met. It is crucial to put together a workflow chart that clearly outlines all the expected outcomes at different stages. If each stakeholder schedules regular check-ins, any desired change can be implemented at the right time. 

5. Start small and work your way up

Big data can seem overwhelming to a first-time user, so it’s important to start small. Begin with just one aspect of a business that can benefit from big data. Keep in mind that there are often aspects that can be addressed without big data, and a problem shouldn’t be forced. A small project can be something that doesn’t significantly impact business flow, should things not go according to plan. The best way to work with big data solutions for such initiatives is by working with agile techniques. The iterative approach to implementation makes it simpler to understand and easier to evolve. 

6. Be smart about big data technology requirements

Almost 90%Opens a new window of the data that is collected is unstructured. Irrespective of this, it has to be stored correctly — either using SQL or NoSQL (or the several variations in both kinds of storage). Business leaders need to ask the right questions to determine which storage works best for them – whether insights sought need to be in real-time or if fact evaluations will be done later. 

The kind of processing software used will be determined based on this. If data is stored across multiple locations, execs also need to decide what kind of data goes where and how. The kind of analytics required for each kind of data also needs to be ascertained. All these answers will help determine the best kind of software and hardware required for a big data project. It goes a long way in ensuring the safety of data. 

7. Align big data with the cloud

Using the cloud for big data has several benefits, but a business has to be prudent since its use is metered. Ideally, a public cloud can be provisioned and rapidly scaled up when needed. Many such cloud services also offer quick prototyping. With the ability to rapidly prototype the environment, a business can use a data subset and the several tools provided by the cloud to create a development and test environment in a short period; and utilize it as a testing platform. An operating model can only be moved back on-premise when it is well-tested.

Additionally, cloud also allows users to collect all the data and business needs and simply set it down there — it doesn’t need to be moved on-premise. Several big data applications and databases support multiple data sources that are both cloud and on-premises. 

8. Hire the right people

Data privacy is a massive issue, and several regulations are being put in place to make things harder. Restricting free access to big data is essential. Determining what kind of data stays on-premises and what can go into the cloud is also a critical governance step. Business managers need to realize that big data is constantly evolving. Because of its complexity, it does not rely on experts being self-taught, as is the case with Python or Java programmers. It is crucial that access to the data is only shared with individuals who need it. 

Recruiters should remember that there are no standard courses of study offered for big data. Thus, hiring solely on the basis of a data science degree may not be the optimal way forward. It is crucial to ascertain skill sets and experience in the field of big data.

Also read: What Is Network Security? Definition, Types, and Best Practices

Takeaway

The bottom line remains that the best practices for big data are continually evolving, as is the field of data analytics. Businesses will have to be on top of their toes to compete with the latest strategies being implemented.

Did you find this article helpful? Let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!