Weighing the Pros and Cons of Real-Time Big Data Analytics

In this age of data explosion, organizations are collecting and storing data at ever-increasing rates. However, simply collecting that data for your organization doesn't have any business value. Real-time analysis and visualization of this big data turn this mass of data into valuable statistics. While this real-time insight can be of great value to your organization, it does have both pros and cons.

What Is Big Data, and How Is It Different From Real-Time Big Data Analytics?

Before moving further, let's discuss big data – what exactly is it? Traditionally, data was stored much more easily since there was so much less of it. Big data came into existence when there became a need to store data sets in much larger quantities. It is not only data or a data set, but a combination of tools, techniques, methods and frameworks.

Big data can come from nearly anything that generates data, including search engines and social media, as well as some less obvious sources, like power grids and transportation infrastructure. This data can be categorized into three types: structured, semi-structured and unstructured.

Big data is usually collected and analyzed at predefined intervals. However, with real-time big data analytics, the collection and analysis is continuous, giving a business up-to-the-minute insight. (For more on big data analytics, see How Big Data Analytics Can Optimize IT Performance.)

Hadoop is the most well-known tool for analyzing big data, but it isn't well suited for handling real-time big data analytics. Some real-time big data tools include:

• Storm – This is a real-time distributed computation system which works with any programming language and is scalable. It is currently owned by Twitter.
• GridGain – This is an enterprise open-source grid computing tool. It is compatible with Hadoop DFS which offers a substitute to Hadoop’s MapReduce.

Pros

Now let's discuss some of the advantages of real-time big data analytics.

• Quickly recognize errors – Let's assume an error has occurred, and needs to be resolved ASAP. With real-time big data analytics, this error can be recognized immediately and quickly remedied. This can help prevent more numerous and/or more severe failures. In the long term, this also helps a business' reputation – rapid error corrections could help in gaining more customers.
• Savings – Even though implementation of real-time big data analytics can be expensive, the high value of immediate data analysis can make up for this expenditure.
• Progressive services – Monitoring products and services through big data analytics could lead to higher conversion rates for customers, which in turn could lead to higher profits. Imminent errors and issues can be easily predicted with analytics, which could also help in focusing more on customer needs.
• Real-time fraud detection – The team managing the security of the systems and servers can be quickly and easily notified of fraud, allowing them to take measures in real time, as soon as the fraud is detected. (To learn more about fraud detection, see Machine Learning & Hadoop in Next-Generation Fraud Detection.)
• Strategies toward competitors – Competition scares many people in the market today, and big data analytics assists in providing a detailed picture of competitors, such as launching a new product, lowering/increasing prices for a particular duration or focusing on users from a specific location.
• Insight – Sales insights are vital for knowing where sales stand. These insights could lead to additional revenue, such as not losing a customer in the long term, checking the bounce rate and finding optimal ways of increasing sales through analyzing real-time big data analytics.
• Trends – Decisions by analyzing customer trends can be done with real-time big data analytics. This could include offerings, advertisements, customer needs, offers available for a particular season and others. Therefore, it can also improve long-term decisions.

Cons

Now let's have a look at the cons.

• Hadoop not compatible – As mentioned earlier, Hadoop, the most widely used tool for big data analytics, is not currently able to handle real-time data. Therefore, some other tools are required, with an expectation that in the future Hadoop will add functionality for a real-time approach.
• New approach required – Some organizations are used to receiving insights once a week. However, with the constant inflow of real-time big data, a completely different approach is required. This could be a challenge for some organizations and could lead to remodeling of some decisions and plans.
• Possible failure – Some organizations may see real-time big data analytics as a shiny new toy, and want to implement it immediately. However, if not implemented properly, this could cause a multitude of problems. If a business isn't used to handling data at such a rapid rate, it could lead to incorrect analysis, which could cause larger problems for the organization.

Conclusion

Real-time big data analytics can be of immense importance to a business, but a business must first determine if the pros outweigh the cons in their particular situation, and if so, how those cons will be overcome. This is still a relatively new technology, so it is expected to evolve in the future and hopefully resolve some of its current challenges.