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Advantages & Disadvantages of Big Data

Big data is a rapidly generated collection of both structured and unstructured data that is extremely large in volume. Big data production increases exponentially over time, and it is anticipated that this product will double every two years.

Scala and Hadoop are two of the most well-liked open-source big data frameworks. The use of programming languages is crucial for big data analytics. For instance, MapReduce applications can be created in Python, C++, or R, whereas the Hadoop big data framework is implemented in Java.

Advantages & Disadvantages of Big Data

Search engines, social media platforms, mobile devices, service networks, public records, and connected devices like smart TVs are the primary sources of big data collection. Businesses can access additional information sources to obtain big data. Huge datasets can be stored in a structured, unstructured, or semi-structured database for later processing and analysis after they have been collected. Big data is frequently stored in NoSQL databases because they offer high performance when handling huge data volumes at scale.

Types Of Big Data

The following are the types of Big Data:

1. Structured

Any data that can be stored, accessed and processed in the form of fixed format is termed as a 'structured' data. Over the period, developed technology in computer science has achieved greater success in developing techniques for working with such kinds of data (where the format is well known in advance) and also deriving value from it. However, nowadays, we are foreseeing issues when the size of such data grows to a huge extent; typical sizes are in the range of multiple zettabytes.

2. Unstructured

Any data with an unknown form or structure is classified as unstructured data. In addition to the huge size, unstructured data poses multiple challenges regarding its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc. Nowadays, organisations have a wealth of available data. Still, unfortunately, they don't know how to derive value from it since this data is in its raw form or unstructured format.

3. Semi-structured

Semi-structured data can contain both the forms of data. We can see semi-structured data as structured in form, but it is not defined with e.g. a table definition in relational DBMS. Example of semi-structured data is a data represented in an XML file.

Big data has advantages and disadvantages of its own, just like any other technology. There are times when the disadvantages of big data outweigh some of its advantages when it comes to practical applications. Therefore, before utilising big data, businesses must consider both its advantages and disadvantages. Let's talk about Big data's benefits and drawbacks after defining it.

Advantages of Big Data

1. Making wiser decisions

Businesses use big data to enhance B2B operations, advertising, and communication. Big data is primarily being used by many industries, such as travel, real estate, finance, and insurance, to enhance decision-making. Businesses can use big data to accurately predict what customers want and don't want, as well as their behavioural tendencies because it reveals more information in a usable format.

Big data provides business intelligence and cutting-edge analytical insights that help with decision-making. A company can get a more in-depth picture of its target market by collecting more customer data.

Business trends and behaviours are revealed by data-driven insights, which also help businesses compete and grow by enhancing their decision-making. Additionally, these insights help companies develop more specialised goods and services, strategies, and intelligent marketing campaigns to compete in their sector.

2. Cut back on the expense of business operations

According to surveys done by New Vantage and Syncsort (now Precisely), big data analytics has helped businesses significantly cut their costs. Big data is being used to cut costs, according to 66.7% of survey participants from New Vantage. Moreover, 59.4% of Syncsort survey participants stated that using big data tools improved operational efficiency and reduced costs. Do you know that Hadoop and Cloud-Based Analytics, two popular big data analytics tools, can help lower the cost of storing big data

3. Detection of Fraud

Financial companies especially use big data to identify fraud. To find anomalies and transaction patterns, data analysts use artificial intelligence and machine learning algorithms. These irregularities in transaction patterns show that something is out of place or that there is a mismatch, providing us with hints about potential fraud.

For credit unions, banks, and credit card companies, fraud detection is crucial for identifying account information, materials, or product access. By spotting frauds before they cause problems, any industry, including finance, can provide better customer service.

For instance, using big data analytics, banks and credit card companies can identify fraudulent purchases or credit cards that have been stolen even before the cardholder becomes aware of the issue.

4. A rise in productivity

A survey by Syncsort found that 59.9% of respondents said they were using big data analytics tools like Spark and Hadoop to boost productivity. They have been able to increase sales and improve customer retention as a result of this rise in productivity. Modern big data tools make it possible for data scientists and analysts to analyse a lot of data quickly and effectively, giving them an overview of more data.

They become more productive as a result of this. Additionally, big data analytics aids data scientists and analysts in learning more about themselves to figure out how to be more effective in their tasks and job responsibilities. As a result, investing in big data analytics gives businesses across all sectors a chance to stand out through improved productivity.

5. Enhanced customer support

As part of their marketing strategies, businesses must improve customer interactions. Since big data analytics give businesses access to more information, they can use that information to make more specialised, highly personalised offers to each individual customer as well as more targeted marketing campaigns.

Social media, email exchanges, customer CRM (customer relationship management) systems, and other major data sources are the main sources of big data. As a result, it provides businesses with access to a wealth of data about the needs, interests, and trends of their target market.

Big data also enables businesses better to comprehend the thoughts and feelings of their clients to provide them with more individualised goods and services. Providing a personalised experience can increase client satisfaction, strengthen bonds with clients, and, most importantly, foster loyalty.

6. Enhanced speed and agility

Increasing business agility is a big data benefit for competition. Big data analytics can assist businesses in becoming more innovative and adaptable in the marketplace. Large customer data sets can be analysed to help businesses gain insights ahead of the competition and more effectively address customer pain points.

Additionally, having a wealth of data at their disposal enables businesses to assess risks, enhance products and services, and improve communications. Additionally, big data assists businesses in strengthening their business tactics and strategies, which are crucial in coordinating their operations to support frequent and quick changes in the industry.

7. Greater innovation

Innovation is another common benefit of big data, and the NewVantage survey found that 11.6 per cent of executives are investing in analytics primarily as a means to innovate and disrupt their markets. They reason that if they can glean insights that their competitors don't have, they may be able to get out ahead of the rest of the market with new products and services.

Disadvantages of Big Data

1. A talent gap

A study by AtScale found that for the past three years, the biggest challenge in this industry has been a lack of big data specialists and data scientists. Given that it requires a different skill set, big data analytics is currently beyond the scope of many IT professionals. Finding data scientists who are also knowledgeable about big data can be difficult.

Data scientists and big data specialists are two well-paid professions in the data science industry. As a result, hiring big data analysts can be very costly for businesses, particularly for start-ups. Some businesses must wait a long time to hire the necessary personnel to carry out their big data analytics tasks.

2. Security hazard

For big data analytics, businesses frequently collect sensitive data. These data need to be protected, and security risks can be detrimental if they are not properly maintained.

Additionally, having access to enormous data sets can attract the unwanted attention of hackers, and your company could become the target of a potential cyber-attack. You are aware that for many businesses today, data breaches are the biggest threat. Unless you take all necessary precautions, important information could be leaked to rivals, which is another risk associated with big data.

3. Adherence

Another disadvantage of big data is the requirement for legal compliance with governmental regulations. To store, handle, maintain, and process big data that contains sensitive or private information, a company must make sure that they adhere to all applicable laws and industry standards. As a result, managing data governance tasks, transmission, and storage will become more challenging as big data volumes grow.

4. High Cost

Given that it is a science that is constantly evolving and has as its goal the processing of ever-increasing amounts of data, only large companies can sustain the investment in the development of their Big Data techniques.

5. Data quality

Dealing with data quality issues was the main drawback of working with big data. Data scientists and analysts must ensure the data they are using is accurate, pertinent, and in the right format for analysis before they can use big data for analytics efforts.

This significantly slows down the reporting process, but if businesses don't address data quality problems, they may discover that the insights their analytics produce are useless or even harmful if used.

6. Rapid Change

The fact that technology is evolving quickly is another potential disadvantage of big data analytics. Businesses must deal with the possibility of spending money on one technology only to see something better emerge a few months later. This big data drawback was ranked fourth among all the potential difficulties by Syncsort respondents.

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