Benefits and challenges of real-time big data analytics

With a new methodology of big data analytics, called real-time big data analytics, organizations can analyze their ever-changing operational environment in real-time. This has significant benefits and challenges that must be considered when developing an implementation plan. This post covers real-time data analytics, its benefits, and its challenges.

What is Real-Time Big Data Analytics?

Real-time big data analytics is a new way to interpret, analyze, and report on fast-changing data streams. It is also a new approach to business intelligence. A key benefit of this methodology is that it can be used to interpret and make decisions in near real-time. It enables businesses to be aware of their changing operations and improve performance by analyzing these events in real-time.

Real-time big data analytics extracts information from a continuously updated stream of data and immediately shows them to the decision-makers who can act on them. Real-time data processing also provides the capability to detect potential issues and respond quickly. Analytics is a software capability that gathers data from multiple sources. It helps in searching and interpreting patterns into human-readable form.

Benefits of Real-Time Big Data Analytics

1. Collect and Manage Performance Data

Real-time big data analytics enables enterprises to collect, analyze, and decide on their performance data. It provides business intelligence across disparate sites and for different teams that can immediately report on performance. It produces a root cause analysis of performance issues and provides immediate recommendations for improvement.

2. Real-Time Decisions

This methodology brings forth real-time decisions through the analysis of big data. These decisions can enable a business to catch up with its competitors or even outdo them. If a company detects an unfavorable situation and takes measures to resolve it, it can save itself from financial loss.

3. It Improves IT Security

Businesses need to make sure their IT systems are secure. Real-time big data analytics can identify potential security risks, such as cybersecurity threats. This helps prevent a security breach, and a company can avoid any potential financial loss or damage to its reputation.

4. Increased User Efficiency

Real-time big data analytics can help in improving workplace efficiency and can make employees more productive by enabling them to act on decisions instantly. It can provide the company with accurate information and act as a guide for better decision-making. This is because of the speed at which it processes data, unlike traditional methods that take longer due to human intervention.

5. Rapid Monitoring and Troubleshooting for IT Operations

After a system is built, it must be monitored on an ongoing basis. Real-time big data analytics can be used for this purpose because it can provide detailed and instantaneous information about a system’s performance. It can also detect potential issues before they occur.

In addition, this methodology helps make the best use of limited resources by providing real-time analysis of operating systems and applications that are not functioning properly. This helps save money spent on extra person-hours to troubleshoot these issues.

6. Improve Customer Service with Up-To-Date Information

In many industries, timely and accurate information is key to a company’s success. Customers expect to receive information on their orders when they place them. Therefore, businesses must provide customers with the current status of their orders. Providing this information enables businesses to answer questions immediately and make more informed decisions on the use of resources.

7. Increase Business Agility and Optimization

Real-time big data analysis is a significant aspect of business agility and optimization. It enables businesses to make short-term decisions that help optimize their operations and provide them with better insights into the efficiency of their assets.

Drawbacks of Real-Time Big Data Analytics

1. Data Integration Challenges

Data integration is not a simple process. You will have to integrate data from different sources, use different analytical tools, and involve decision-makers in the outcome. Data integration usually consumes considerable time and effort, and the process may not be entirely automatic.

2. Insufficient Expertise

Finding people with the necessary skills and expertise to develop an effective real-time big data analytics implementation can be challenging. This is because the most suitable people for this work are professionals with experience in statistical analysis, computer programming, and business intelligence.

3. Lack of Tools

Businesses need to find the best tools for performing real-time big data analytics. These tools should have a scalable architecture deployed on multiple systems. They also need to have a low cost of ownership and should require minimal changes in organizational processes to use them effectively.

4. It Requires a Change in Processes

This methodology may require a change in organizational processes to use it effectively. For instance, it may involve new operational procedures and roles. An organization will have to modify existing business information management systems that produce the data being analyzed.

5. Expensive to Maintain

It may be difficult to find skilled analysts and programmers as these specialized skills require to be paid well and cannot be easily acquired or trained. Maintaining the tools and systems required for real-time big data analytics is also expensive.

Conclusion

Real-time big data analytics is a very effective method of business intelligence and should be highly considered by companies depending on the benefits it can provide. This is because real-time big data analytics can help enterprises with rapid decision-making, improved customer service, maintenance of data security, and numerous other benefits.