Interested in the pros and cons of big data, a growing popular concept? You don’t need to wonder for much longer since we’ll go over its positive and negative influence on today’s world. If you’re unaware, big data refers to a collection of data so vast, intricate, and growing exponentially that traditional means cannot process it. It’s known for its three Vs: arriving with more velocity, in growing volumes, and consisting of an increasing variety. We will study this combination of unstructured, structured, and semistructured data and how it can be analyzed. Let’s get into the top 10 big data advantages and disadvantages.
Advantages of big data
Big datasets have a big impact on today’s business world. Here are several upsides of big data:
1. Anyone can access and query the big data database
Everyone is allowed to send queries to a big data database and study the results. While the efficiency and usefulness of big data require expertise and time, nothing is stopping people from branching into data analyst jobs. That leaves things fair and doesn’t give anyone or any entity a particular advantage. Additionally, it doesn’t prefer a particular programming language, since big data frameworks can be implemented in R, C++, Java, and Python, and use NoSQL for the DMBS (database management system).
2. Big data permits better and faster decision-making
Perhaps the biggest merit of using big data is the timely decisions it lets people and businesses make. With access to huge open-source databases such as Scala and Hadoop, businesses can hire data analysis and gain analytical insights and business intelligence well in advance. They can also learn a lot about their current and untapped customers and how trends change. Additionally, businesses may reevaluate risks at regular intervals and adjust strategies a lot faster than before. With a wealth of information at hand and constantly growing and improving, big data lets businesses make prompt and more accurate judgments based on customer data.
3. It’s a growing market with large predicted improvements
According to Statista, the big data analytics market size was $245.5 billion in 2021, with a predicted growth to $271.8 billion by the end of 2022. If forecasts are correct, it will cross the $500 billion mark in 2027 and go over $600 billion the next year. Of course, those numbers are based on a 2021 survey, and technology grows rapidly. With the increase in the number of artificial intelligence (AI) and machine learning (ML) projects and the speed, efficiency, and accuracy of data analysis, big data can expand even faster in the coming years. It’s merely a matter of how much and how fast.
4. Big data promotes productivity
Big data helps optimize processes, helping organizations deliver superb products and services in as little time as possible without a drop in quality. A 2021 Industry Report from Syncsort showed that 59.9% of the survey participants stated they increased their productivity by using tools for data analytics like Hadoop or Spark. The boost in productivity from big data analysis not only helps them gain more sales, balloon profits, and boost retention rate but also applies to the job. After all, the information also holds clues on how to improve the data analysis procedure, multiplying the benefits.
5. It aids marketing, research, and customer service
Big datasets help businesses, organizations, and people reach a bigger audience and deliver better products and services. That is because big data is the result of data collection from search engines, email conversations, mobile and smart devices, CRM (customer relationship management) systems, network and official information, public records, and more. Having access to that information lets businesses improve their marketing, advertising, and communication after understanding their customer. In turn, customer satisfaction grows through more personalized products, boosting their relationships and instilling loyalty.
Disadvantages of big data
Since there are two sides to a coin, let’s explore the downsides of big data:
1. Lack of skilled data scientists
The absence of knowledgeable talent is one of the most significant drawbacks of big data. The data mining field has pros and cons, too, and is still relatively new. Analyzing data requires a different skill set than programming or regular database management. Creating queries that provide succinct, desired, and accurate results is not something many IT professionals are adept in.
2. Big data is still largely unstructured and needs a long analysis
Another con of big data is that not all of it is useful or even worth analyzing. A lot of it is still unstructured and may never be organized because of newer data. Thus, it ends up at the bottom of the junk pile and needs to be purged. As mentioned, it takes an expert to know where to look, how deep, and how to structure the existing data and convert it into meaningful information. Even worse, even if the data analysis finishes, an influx of new data may noticeably skew the results or conclusions. That renders the analysis and application less efficient or even obsolete.
3. It invades privacy and poses a security risk
Although it collects data on such an enormous level that removes individuality, someone with the right information can cross-check information and create a digital footprint about users. That applies to financial, health, and all kinds of data users wouldn’t like others to see. That leaves companies and their users open to cyberattacks, social engineering, fraud, and data leaks.
4. Big data requires bureaucracy and legislation
Because personal information from several modern life aspects worldwide is part of big data, it has to be lawful. Therefore, data collection and publishing practices must adhere to local, country, and global laws. In contrast, companies using the data must ensure it was obtained legally, delete it if ordered, and use it for legal, beneficial means. After all, data-related accusations and malpractice are topics of many high-profile court cases in recent years. One example is the Facebook suit in the United Kingdom’s high court system.
5. It is expensive and not always cost-efficient
As we pointed out, due to the lack of trained personnel, data analysis is an in-demand, high-paying job in the field of data science. Therefore, the experts work for the most successful companies. Other businesses either cannot afford or find a proficient data analysis expert. Additionally, data storage and processing are constantly evolving, drastically increasing the cost of implementation, maintenance, and improvement of big data systems.