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

 

Search engines and social media platforms alone generate enormous
amount of data. In every second large amount of data is produced and the amount is increasing every year. Big data as the name mentions is the high voluminous amount of data. It consists of both structured and unstructured data. 

Currently there are two popular frameworks for big data. That is Hadoop and Scala. Each uses different programming languages for the big data analysis. The access to big data is provided to some information businesses. These companies rely on big data for making vital business decisions. Since the data here is too big, the insights cannot be performed through simple methods. Further from tetabyte, the data can also go in the form of petabyte and yettabyte. The data is so large such that a traditional data processing software alone will not be able to manage. The information is gathered from multiple sources. 

The primary purpose of using big data is for the analysis part where the data is examined and analyzed. This is known as big data analysis. Apart from that, big data is helpful for every enterprise. Especially, for the large scale companies. Many of them consider big data as a valuable asset. Big data offers various range of potential benefits but we cannot also deny that it faces significant challenges. It is essential for a company to give attention to both advantages and disadvantages before using it. 

In this article, I will be listing out 7 Advantages and Disadvantages of Big Data | Drawbacks & Benefits of Big Data. Though this post, you will know the pros and cons using big data.

Let's get started,

Advantages of Big Data

1. Cost Effective

Big data offers significant cost savings for a company. Improved productivity and efficiency, streamlined operation approach is all which make big data cost effective. Even though the initial investment seems expensive, it will same save a lot of money in the long run. 

It overall reduces expenses in many ways. For an example, Big data helps to predict the actual demands of a business so that the production is limited. This way the companies can cut the cost incurred from purchasing excessive raw materials.

2. Research Time

Big data is able to process data in increased speeds. Eventually the decision making is faster. Advanced software are available to analyze large data sets. Research time is very essential for a company for staying with the competition. Usually big data is generated automatically which makes it possible to analyze in real time. Only though the business are able to make timely decisions. 

3. Fraud Identification

It is compulsory for industries like banking to identify fraudulent activities. If not, it can destroy a bank reputation. This is made possible in big data analysis though the use of machine language and Artificial Intelligence (AI). Normally big data are good at identifying patterns and anomalies. 

Often, these kind of fraudulent activities are prevalent in credit cards as well as bank accounts. The system immediately notifies the institution if there is any suspicious activities. Once there is an unusual transaction, the security system team of the company is notified instantly. 

4. Productivity

Big data allows business to increase productivity levels in many ways. The method of insight used in big data analytics is highly efficient to analyze large and complex data. This makes organizations to improve the area where it could be more productive. This is the reason why companies are more interested in investing in big data analytics which in return increases productivity.

5. Agility

Organizations depend on big data to implement new business strategies. It is must for adopting frequent changes in the market. Insights calculated from big data are essential for innovations. Without them, there is no proper marketing techniques. This eventually helps in product development. 

6. Decision Making

Optimizing decision making skills is an important aspect of business to business operations. Investing in big data is a great way to improve the business decision making skills. Big data analytics makes use of AI and machine language to process data quickly. The pattern, relationships and insights which is not normally identifiable can be known based in their strategies. This allows business to effectively compete across their industry.

7. Customer Service

Social media is one of the biggest platform used to gather customer information, al though there are other forms like CRM (Customer Relationship Management) and Email transactions. Big data helps to identify customer behavior, businesses will be able to know what their customers are interested. Each customer can be targeted based on their individual preferences. Through this data, you can overall improve the customer satisfaction.

Disadvantages of Big Data

1. Data Quality

Quality is still questionable in big data. Since data is collected from different sources, most of them are unstructured. Without cleaning and filtering it is difficult to analyze and study this data. So there is a chance for inaccuracies and errors. Business which use poor quality data for making decisions can cost them with negative consequences.

2. Security

Security is a concern for big data. Even though big data analytics is used to identify fraudulent activities, it is itself vulnerable to many security threats. Often, large business enterprises have complained of data breaches. Since big data is large in size it requires massive amount of storage. Therefore, it needs secured network for storage. Otherwise, it has the tendency to get breached. This draws the attention of cyber attackers.

3. Technical Expertise

Big data analytics is a complex process which cannot be performed without technical expertise. The professionals who carry out this big data research are known as big data analytics. They people are very less in number and demands high salary. Sometimes the enterprise budget might be not sufficient enough to pay for this people. The complexity of analyzing the data makes it one of the demanding professions in the world.

4. Compliance

Informations used in big data for a company is highly confidential and personal. Hence, they must adhere to the strict data privacy rules of a government. Otherwise, they are going to face some legal consequences. Storing and handling the data must be according to the government regulations. Moreover, the task of managing becomes more difficult as the size of data increases.

5. Hardware Requirement

Most often organizations undergo infrastructure problems when dealing with big data. Business cannot carry out their big data operations without proper IT infrastructure. In addition to that, it needs other analytics hardware and distributed storage. All this can be expensive for buying which further increases the cost of investment.

6. Data Integration

Most of the large organizations do not confine their data into one single location. The data is distributed across variety of different locations inside the environment. So collecting and integrating those data is not an easy process. It requires extra time and effort.

7. Rapid Evolvement

If there is any rapid technological evolvement, there should be a change in culture. The same applies to big data as well. For adopting to the concept of big data, the organizations must change its culture accordingly. This might lead to some complications within the company.  For an example, new staffs must be hired and new business strategies needs to be implemented.