Dangers of Big Data – Why Big Data is dangerous?

Big Data Dangers

Businesspeople are accustomed to trying new things; evaluating those risks and protecting against them comes easily; otherwise, we wouldn’t be in business forever! So there’s no reason to be scared of Big Data. But, of course, we must always be cognizant about hazards that may arise if we do not cover all of the bases. What else would concern you as an entrepreneur if that didn’t? Fighting the big data flood is no laughing matter because it comes with some significant threats to overcome.

What is Big Data?

Big Data is a massive data collection that is going to grow significantly over time. It is a data set that is so large and complex that traditional data management tools cannot store or process it efficiently. Big data is an extremely large type of data.

Benefits of Big Data

  • Big data analysis yields novel solutions.
  • Big data analysis aids in consumer perceptions and targeting.
  • It aids in the optimization of business operations.
  • It aids in the progress of science and studies.
  • It enhances healthcare and public health by making patient records accessible.
  • It is useful in stock markets, sports, polling, law prosecution, and so on.
  • Anybody can obtain vast amounts of information through survey data and provide answers to any question.
  • One framework can handle an infinite amount of data.
What is big data

Why is big data dangerous?

Because everything about us can be monitored, it can also be used for evil. The law governing privacy has not remained consistent with advances in technology and the kinds of data collected. Furthermore, the more data we collect, the simpler it is to segment and use it to market to specific segments of the population, resulting in a new type of racism. There have already been reports of data-driven discrimination; car insurance companies, for example, penalize people that drive late at night, but this can impact otherwise safe drivers who happen to work a swing shift and seem to be low-income.

It is already taking place. We know that organizations such as the NSA use data to spy on people. However, it has the potential to go much further. China has established a “social credit score” that is influenced not only by what you speak and do individually but also by what you post on social media.

Possessing all of our data in the cloud (or on the oceans) exposes it to threats and misapplication. Remember when bad guys had to biologically steal a laptop or hard drive to gain access to sensitive files? Not any longer. For each new security measure, there is a scammer or criminal working on a way to circumvent it. Furthermore, businesses rarely take security as seriously as they ought to.

In a nutshell, big data is dangerous. To make our data safer, we need a new legislative framework, more transparency, and possibly more influence over how it is used. But it will never be a passive force. Big data, in the incorrect hands, can have devastating results.

Examples of dangerous big data

Before delving into how we might address some of the issues raised by big data, consider some actual cases of how it has been abused.

The 2016 US Presidential Election and the 2016 Brexit referendum in the United Kingdom are two of the most visible examples of big data misuse. Following shocking results in both polls, Vote Leave in the UK and the Trump Campaign in the US was linked to Cambridge Analytica, a shady data analytics firm. The now-defunct firm used Facebook data obtained illegally to inform the communication skills for both polls. Since then, their influence has shaped the world’s political landscape. Again, intentional misuse is not the only risk posed by big data. Amazon’s Rekognition biometric authentication is a perfect example. In 2018, the software misidentified 28 states and Union Territories States Congress as convicted felons. While this revealed an issue with the software as a whole, a huge percentage of those misidentified were people of color. This is not an isolated incident; a growing body of research shows substantial racial bias in these types of technologies.

Why Big Data is Risky?

Data privacy is the most serious risk associated with big data. Sensitive data, personal client data, and strategic documents are used by businesses all over the world. When there is so much sensitive data floating around, the last thing you want is a data breach at your company. A security breach can not only compromise important data and harm your notoriety; it can also result in legal action and harsh penalties. Taking measures for data privacy is not just a good initiative anymore, it’s a compliance necessity.

Here are the reasons that shows why big data is risky?. Lets know more in details here

Data is in fragmented format

Big data is extremely adaptable. It exists in a range of forms and from a variety of sources. Data is being collected from both online websites. And the data piles up every day, every second. It is difficult for businesses to properly deal with such disorganized and siloed data sets. A well-planned governance strategy can help you emerge from the shadows of your facts and provide a sense of it.

Storage and data and archiving

When data accumulates at such a fast rate and in such massive quantities, the first major worry is its storage. Conventional data storage methodologies are simply insufficient for storing and retaining large amounts of data. To effectively store, archive and access big data, businesses today must migrate to cloud-based data storage solutions.

Managing Costs

Costs are incurred during the storage, cataloging, analysis, reporting, and management of large amounts of data. Many small and medium- businesses believe that big data is only for large corporations and how they can afford it. Big data costs, on the other hand, can be effectively mitigated with careful budgeting and resource planning. Once the expenses of initial setup, migration, and overhauling are covered, big data can be a fantastic revenue source for digital enterprises.

Ineffectual Data analysis 

Big data is nothing more than a pile of trash in your organization if it is not properly analyzed. Analytics is what makes data meaningful, providing useful insights to management to make business decisions and plan strategies. With data rising at such an alarming rate, there is a scarcity of skilled professionals and technology to efficiently analyze big data. It exposes businesses to the risk of data misinterpretation and poor decision-making. Hiring the right people and using the right tools are critical to making informed decisions from a big data project.

How to Reduce the Risks of Big Data

While there are obvious risks associated with big data, we should not throw the baby out with the bathwater. Big data has enormous potential for positive change. Fortunately for us, it is not a binary choice.

We can reap the benefits of big data while mitigating its risks by incorporating security measures and ethical rules. Here are a few ways data analysts and data scientists can advocate for safer big data use.

Maintain vigilance regarding security protocols

It is critical for any big data curator to have effective security measures in place and to keep these up with the latest. Although well front ends are common, recovery data is frequently stored in the backup and restore systems or test cases that are not always as well-protected.

Remove any unnecessary information

One of the most effective ways to avoid a data breach is to avoid having delicate data in the first place. Many businesses keep data they don’t use because they believe it will be useful in the future. Organizations, on the other hand, can keep the data required for their business operations while ridding what remains by carrying out regular audits.

Check for privacy and data protection legislation

Organizations need to invest appropriately in data safety and support, as well as follow other guidelines, to protect data. As a data analyst, you must endorse your company’s adherence to data security mechanisms.