Question - Explain the Pros and Cons of Big Data?
Answer -
Pros of Big Data are:
- Increased productivity: Recently, it was found that 59.9% of businesses use big data tools like Hadoop and Spark to develop their sales. Current big data tools enable analysts to examine instantly, which enhances their productivity. Also, the insights inferred from the analysis of big data can be used by organizations to increase productivity in different forms throughout the company.
- Reduce costs: Big data analytics help businesses reduce their costs. In most companies, big data tools had served them to enhance operational performance and decrease costs, and in few other companies had started using big data to decrease expenses. Interestingly, very few companies selected cost reduction as their primary goal for big data analytics, suggesting that this is merely a very welcome side benefit for many.
- Improved customer service: Improving customer service has always been one of the primary goals for big data analytics projects, and it has been a success for many companies with the help of this. Various customer contact points like Social media, customer relationship management systems, etc., transfer a lot of information about their customers. And this analysis and data is used to improve the services for the customers
- Fraud detection: The primary purpose of using Big data analytics is in the financial services industry for detecting frauds. The advantage of big data analytics systems is that it depends on machine learning, because of which they are great at recognizing patterns and irregularities. As a result, these techniques can give banks and credit card companies the capacity to detect stolen credit cards or deceitful purchases, usually before the cardholder knows that something is wrong.
- More significant innovation: A few companies have started investing in analytics with the sole purpose to bring new things and disturb their markets. The reason behind this is if they can see the future of the market with the help of insights before their competitors, they can come out strong from that situation with a few new goods and services and capture the market quickly.
On the other hand, implementing big data analytics is not as easy as we think; there are a few difficulties too when it comes to implementing it.
Cons of Big Data are:
- Need for talent: The number one big data challenge that we have been facing for the past three years is the skill set required for it. A lot of companies also face difficulty when designing a data lake. Hiring or training staff will only increase the cost considerably, and also, imbibing big data skills takes a lot of time.
- Cybersecurity risks: Storing, especially sensitive big data will make those businesses a prime target for cyberattackers. Security is one of the top big data challenges, and cybersecurity breaches are the single greatest data threat that enterprises encounter.
- Hardware needs: Another critical concern for businesses is the IT base necessary to help big data analytics drives. Storage space for storing the data, networking bandwidth for transferring it to and from analytics systems, and calculating resources to achieve those analytics are costly to buy and keep.
- Data quality: The disadvantage in working with big data was the requirement to address data quality problems. Before companies can use big data for analytics purposes, data scientists and analysts need to ensure that the data they are working with is accurate, appropriate, and in the proper format for analysis. This slows the process, but if companies don't take care of data quality issues, they may find that the insights produced by their analytics are useless or even harmful if performed.