7 Advantages and Disadvantages of Big Data | Drawbacks & Benefits of Big Data

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7 Advantages and Disadvantages of Big Data | Drawbacks & Benefits of Big Data

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7 Advantages and Disadvantages of Big Data | Drawbacks & Benefits of Big Data

7 Advantages and Disadvantages of Big Data | Drawbacks & Benefits of Big Data


Even on their own, social networking sites and search engines produce vast amounts of data. A significant amount of data is produced every second, and this amount grows yearly. As the term suggests, big data refers to extremely large volumes of data. Both structured and unstructured data are included.

There are now two well-liked big data frameworks. That's Scala and Hadoop. Each one analyzes large amounts of data using a different computer language. Certain information enterprises have access to large data. Big data is used by these businesses to inform important business choices. 


Simple methods cannot be used to obtain the insights since the data in this case is too large. Beyond tetabyte, the data can also be stored in petabyte and yettabyte formats. The volume of the data makes typical data processing software inadequate for handling it all. The data is collected from a variety of sources.

Big data is primarily used for analysis, or the process of looking through and analyzing data. We call this big data analysis. Aside from that, big data benefits all businesses. Particularly for the big businesses. Big data is viewed as a useful asset by many of them. While there are many potential advantages to big data, there are also many formidable obstacles to overcome. A corporation must consider the benefits and drawbacks of any technology before implementing it.

I'll be outlining the 7 Advantages and Disadvantages of Big Data | Drawbacks & Benefits of Big Data in this post. You will learn about the pros and cons of using big data from this post.

Now let's get started,

Advantages of Big Data

1. Cost Effective

Big data allows a business to save a lot of money. Big data is cost-effective due to its increased productivity and efficiency as well as its streamlined operational strategy. 


The initial expenditure may appear high, but over time, it will save a significant amount of money.

In general, it lowers costs in a variety of ways. Big data, for instance, assists in anticipating a company's real needs so that production is constrained. 


In this manner, the businesses can reduce the expenses related to making large purchases of raw materials.

2. Research Time

Big data can analyze information more quickly. The decision-making process eventually speeds up. Sophisticated software is available for extensive data analysis. 


A corporation needs to spend a lot of time conducting research if it wants to compete. 


Big data is typically created automatically, allowing for real-time analysis. Only when the company is capable of making prompt decisions.

3. Fraud Identification

Industries such as banking are required to detect fraudulent activity. If not, a bank's reputation could be ruined. 


Artificial intelligence (AI) and machine language are used in large data analysis to make this possible. Big data typically excels in spotting trends and abnormalities.

These kinds of fraudulent actions are frequently found in bank accounts and credit cards. If there are any questionable activity, the system alerts the institution right away. 


The company's security system personnel is immediately alerted to any suspicious transactions. 


4. Decision Making

Improving decision-making abilities is a crucial component of running a business. Putting money into big data is a terrific method to get more adept at making business decisions. 


AI and machine language are used in big data analytics to process data quickly. 


Their tactics can reveal patterns, linkages, and insights that are typically unidentifiable. Businesses are able to successfully compete in their field as a result. 

5. Productivity

Big data offers businesses numerous methods to boost productivity. Big data analytics uses an insight-based approach that is very effective at analyzing complex and enormous amounts of data. 


Organizations are compelled by this to upgrade the areas where productivity could be increased. 


This is the rationale behind businesses' increased interest in big data analytics investments, which boost output.

6. Agility

Big data is essential for organizations to develop new business strategies. Adopting quick changes in the industry is vital. 


Big data analytics provide insights that are critical to new developments. 


There would be no appropriate marketing strategies without them. Eventually, this aids in the creation of new products.

7. Customer Service

Although there are other ways to collect client data, such as through email transactions and CRM (customer relationship management), social media is one of the most popular platforms. 


Big data makes it easier for firms to understand consumer behavior and what interests them. 


Targeting can be done according to the unique tastes of each customer. You can raise client satisfaction levels overall with the help of this data.

Disadvantages of Big Data

1. Security

Big data security is a worry. Big data analytics is susceptible to numerous security risks, even being utilized to detect fraudulent activity. 


Big companies have frequently complained about data breaches. 


Big data demands a significant quantity of storage due to its vast size. It requires a secure network for storage as a result. 


If not, there is a chance that it will be compromised. This attracts the interest of online predators.

2. Technical Expertise

Big data analytics is a difficult procedure that requires technical know-how to complete. Big data analytics experts are the ones who do this kind of research using large amounts of data. 


These individuals are extremely rare and have high compensation demands. 


Occasionally, the enterprise budget may not cover the cost of these personnel. Data analysis is one of the most difficult jobs in the world due to its intricacy.

3. Compliance

Data that a business uses in big data is extremely private and secret. They must therefore abide by the stringent data privacy laws set forth by the government. 


If not, there will be legal repercussions for them. 


Government regulations must be followed when managing and storing data. Furthermore, handling a larger amount of data becomes more challenging. 



4. Data Quality

Big data quality is still debatable. The majority of data are unstructured since they are gathered from many sources. 


It is challenging to evaluate and research this material without first cleaning and filtering it. 


Therefore, mistakes and inaccuracies are possible. Businesses who make judgments based on shoddy data may suffer unfavorable effects.

5. Data Integration

The majority of big businesses don't keep their data in one single place. The information is dispersed throughout the surroundings in a number of distinct places. 


Thus, gathering and combining those data is a difficult task. It takes more time and work. 



6. Hardware Requirement

Dealing with massive data typically results in infrastructure issues for enterprises. 


Big data operations cannot be carried out by businesses without an adequate IT infrastructure. Distributed storage and additional analytics devices are also required. 


The cost of investing can be further increased by the potential expense of purchasing all of this.

7. Rapid Evolvement

Rapid technical advancement should lead to a corresponding shift in culture. Big data is subject to the same rules. 


Organizations must adapt their cultures in order to embrace the big data notion. 


This could cause some issues for the organization. For instance, new company strategies need to be introduced and new staff members need to be hired.

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