Everything has two sides, and big data analysis has advantages and disadvantages. Data analysis refers to using appropriate statistical analysis methods to analyze a large amount of collected data, summarizing, understanding and digesting them, in order to maximize the development of data functions and play the role of data. So, what are the advantages and disadvantages of big data analysis?
1.Advantages
Identify errors quickly – Let’s assume an error occurs and needs to be resolved as quickly as possible. With real-time big data analytics, this error can be immediately identified and quickly fixed. This can help prevent additional and/or more severe failures. This also helps the business's reputation in the long run, and quick error correction can help gain more customers.
Savings – Although the implementation of real-time big data analytics can be expensive, the high value of immediate data analytics can more than make up for this expense.
Step-by-step services – Monitoring products and services through big data analytics may lead to higher conversion rates for customers, which may lead to higher profits. Upcoming errors and issues can be easily predicted through analytics, which also helps focus more on customer needs.
Real-time fraud detection – Teams managing system and server security can quickly and easily be notified of fraud and take action in real-time once fraud is detected.
Strategies against competitors – Competitors are suffering many setbacks in the market today and big data analytics can help you provide a detailed picture of your competitors such as launching new products, lowering/increasing prices at a specific time or focusing on users in a specific location.
Insights – Sales insights are critical to understanding the position of sales. These insights can lead to additional revenue, such as not losing customers in the long term, checking bounce rates, and finding the best ways to grow sales by analyzing real-time big data analytics.
Trends – Decision-making by analyzing customer trends can be done through real-time big data analytics. This may include products, advertising, customer needs, offers available during specific seasons, etc. Therefore, it can also improve long-term decisions.
2. Disadvantages
Hadoop incompatibility – As mentioned earlier, Hadoop is the most widely used big data analytics tool and currently cannot handle real-time data. Therefore, some additional tools are required and it is expected that in the future Hadoop will add functionality for real-time methods.
New approaches are needed – Some organizations are used to receiving insights on a weekly basis. However, as real-time big data continues to flow in, a completely different approach is required. This may be a challenge for some organizations and may lead to the reshaping of some decisions and plans.
Possible Failures – Some organizations may view real-time big data analytics as a shiny new toy and want to implement it immediately. However, if not implemented correctly, it can cause many problems. If a business isn't processing data at such a fast pace, that could lead to incorrect analysis, which could cause bigger problems for the organization.
Big data technology can improve productivity, improve marketing decisions for the industry, and bring better development prospects to enterprises. Although the development of big data technology is currently in its infancy, it has a strong momentum of development and more industries will be involved in big data in the future. The application of data technology and the future development of big data technology are great!