7 Big Data Risks, Threats, and Their Solutions

big data risks and threats

We live in an Era where Data is everything. The Big Data Analytics market is about to reach up to $655.53 billion by 2029, according to fortunebusinessinsights. Businesses are working on big data technology to enhance business productivity, make profitable decisions, and understand market needs. However, working with Big data comes with major risks for organizations that need to be prepared.

This guide has covered Big Data, its importance, and some major risks of using big data in organizations.

📌 Table of Contents

What is Big Data?

Big Data is used to represent a large amount of data, including structured, semi-structured, and unstructured data. Big Data is high-volume, high-velocity, and contains various data from different sources.

Nowadays, almost every business, from small to enterprise-level, is leveraging the power of big data analysis to find meaningful insights that help their business make profitable business decisions and understand customers’ needs and new innovative ideas.

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Major Risks & Threats come with Big Data

Analyzing such a large amount of data can come with various risks and threats that can affect the business heavily. So organizations need to understand these risks and threats and identify the best possible ways to minimize the risks. Let’s find out!

1. Privacy and Data Protection

When companies are collecting big data, then the first risk that comes with big data is data privacy. This sensitive data is the backbone of many big companies, and if it leaks to any wrong hand, like cybercrime or hackers, it can badly affect the business and its reputation. In 2019, 4.1 billion records were exposed through data breaches, according to the Risk-Based Security Mid-Year Data Breach Report.

So businesses should mainly focus on protecting their data’s privacy and security from malicious attacks.

Big data is not easy to store in pockets; companies need to manage big servers to hold this crucial information and protect it from the outside world. It’s a very challenging and risky process, but it’s a need for businesses to keep their big data protected. 

Various companies are adapting new privacy regulations to protect their database. Recently, many hackers have been attacking giant companies to steal their data for monetary benefits.

So it clearly shows that the bigger the data company has, the more the chances of getting malicious attacks. So companies must ensure the security of the data with high-level encryption. 

2. Cost Management

Big data requires big costs for its maintenance, and companies should do the calculation of collecting, storing, analyzing, and reporting the big data costs. So all companies need to budget and plan well for maintaining big data.

If companies don’t plan for the management, they may face unpredictable costs, which can affect the finances. The best way to manage big data costs is by eliminating irrelevant data and analyzing the big data to find meaningful insights and solutions to achieve their goals.

3. Unorganized Data

As we’ve discussed, Big Data is a combination of structured, semi-structured, and unstructured data that is the major problem companies face while managing big data, i.e., Unorganized Data. It’s a complex process to categorize the data and make it well-structured.

From small business to enterprise-level, handling unorganized data becomes hectic. It requires a well-planned strategy to collect, store, diversify, eliminate and optimize data to find meaningful insights that help businesses make profitable decisions. 

4. Data Storage and Retention

Big Data is not just information that can be stored in a computer; it’s a collection of structured, semi-structured, and unstructured data from different sources that can be the size of zettabytes. To store the big data, companies need to take a big server area where all the big data is stored, processed, and analyzed.

This way companies should be concerned about the storage space of big data. Otherwise, it can be a complex issue. Nowadays, companies leverage the power of cloud-based services to store data and make accessibility easy and secure.

5. Incompetent Big Data Analysis

It’s estimated that the amount of data generated by users each day will reach 463 exabytes worldwide, according to weforum. The main aim of big data is to analyze and find meaningful information that helps businesses to make the right business decisions and innovations. If any organization doesn’t have a proper analyzing process, big data is just trash that seems unnecessary.

The analysis makes big data Important, and companies should hire the best data analyst and software that helps to analyze the big data and find meaningful insights with the help of professional analysts and technology.

Thus, before planning to work on big data, each business, from small to enterprise-level, should hire professional analysts and use powerful technologies to analyze big data.

6. Poor Data Quality

One key risk of getting big data is that organizations may reach poor quality, irrelevant or out-of-date databases that will not help their business to find something meaningful.

In Big Data, when everything is stored, whether structured, semi-structured, or unstructured data then it’s a risk for organizations to collect and analyze the data because it may be useful or not for their business based on the data relevancy.

Many challenges come across while analyzing big data, and organizations must be prepared for these outputs, try to eliminate irrelevant data, and focus on analyzing relevant data to get meaningful insights. 

7. Deployment Process

Deployment is a core process of an organization to collect and analyze big data and deploy meaningful insights in a time period. In this situation, companies have two options for data deployment, i.e. first is to use an in-house deployment process where the big data is collected and analyzed to find meaningful insights, but this process takes a good amount of time.

Instead of setting up their server infrastructure, a cloud-based solution is more convenient, easy, and beneficial because there’s no internal infrastructure to store big data. The amount of time to deploy meaningful insights from big data is important.

Big Data Security Issues and their solutions

Big Data comes with several security issues that can heavily impact organizations. So it’s important for businesses to understand and resolve these security issues. Here are some common big data security issues with the solution. Let’s begin!

1. Data Storage

Problem: When businesses plan to store big data, the first problem they face is storage space. Many companies are leveraging the power of cloud space to store data, but due to online access to data, there’s a chance of security issues. So some companies prefer to own their physical server storage to store the database.

One of the major data storage issues faced by Amazon in 2017, where AWS cloud storage is full & doesn’t have space to run even basic operations and later Amazon resolved the issue and maintained the storage to prevent this problem in future.

Solution: To resolve the problem, companies should store their sensitive data in an on-premises database, and less sensitive data can be stored in cloud storage. But still, there are security issues that can be resolved by hiring cybersecurity experts.

It may increase the cost of organizations, but database value is more worth it. 

2. Fake Data

Problem: Another Big Data issue many organizations may face, i.e., fake databases. When collecting data, companies require a relevant database that can be analyzed and used to generate meaningful insights. However, having irrelevant or fake data can waste any organization’s efforts and costs in analyzing the data.

In 2016, Facebook faced the issue of fake databases because the algorithm’s didn’t recognise real or fake news differences and ended up with nonsense political issues, according to Vox.

Solution: To validate the data source, organizations should do periodic assessments and evaluations of databases to find irrelevant data and eliminate it. So that only relevant data is left to analyze and generate results. 

3. Data Access Control

Problem: When users get access to control the data like view, edit or remove, it may affect the business operations and privacy.

Here’s an example:

Netflix which reported the loss of 200,000 subscribers in Q1 because users are sharing their login details with friends/family to log in with the same account. Later Netflix takes charge and controls data accessibility to limited users on a single account.

Solution: Its solution is used to work with Identity Access Management (IAM) to simplify the process of controlling the data via identification, authentication, and authorization. By following the ISO standards, organizations can protect their access to IAM. 

4. Data Poisoning

Problem: Nowadays, almost every website has Chatbots on their website, and it’s a target of hackers to attack these Machine Learning models that lead to Data Poisoning, where organizations’ databases can be manipulated and injected. 

Solution: The best way to resolve this issue is through outlier detection. It helps to separate injected elements from the existing data distribution.

Is Big Data Dangerous?

Big Data comes with various risks and challenges that make Big Data Dangerous for organizations, but the best thing is that these risks and security issues can be resolved.

Organizations can minimize the risks by focusing on the solutions and preparing for them.

By enhancing security and data protection, organizations can resolve the issues and use big data to analyze and find meaningful insights that help grow their businesses and make the right decisions.

📌 Supplementary resources