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These 4 Issues May Cause Trouble for Your Big Data Efforts Featured

These 4 Issues May Cause Trouble for Your Big Data Efforts Yogendra Singh

Big data analytics has become a leading area of investment for many organizations that seek to take advantage of technology to improve their services. While this technology holds great promise in this age, it has some obstacles that one must overcome to realize full potential. However, before investing in big data analytics, you must investigate various data analytics issues, explore the potential causes, and find possible solutions to these problems. Here are four problems that may cause trouble for your big data efforts.

  • Business analytics may fail to provide new and timely business insights

Big data analytics solutions are needed to tap insights that would help your organizations make the right decisions. However, the new big data systems can sometimes provide insights that you already have regarding their level and quality. This can be due to many reasons. The two most common reasons for this are lack of data and systems taking a long time to respond. On the other hand, inadequate data in analytics means enough insight will not be generated. This can result from a lack of integrations or poor organization of data. The long data response occurs when insights are needed in real-time, but the system does batch processing. This means data will not be available as needed as it takes time to be collected or pre-processed. These two issues can hinder decision-making for your business.

  • Inaccuracy of analytics

There’s nothing as bad to any organization as making decisions based on the wrong information. This can be a disaster of monumental proportions that may impact the delivery of services substantially. Some of the reasons for inaccurate analytics are poor quality of data source or defects in the system that affects data flow. If your system relies on deficient data that is incomplete and full of errors, you will get poor results. You will need a rectification plan as soon as possible to solve this issue. To avoid this challenge, enhance your data quality management at all stages of your organization.

On the other hand, if the system is deficient due to requirements not being met or due to human error or failure of verification processes, you may also encounter problems. Your big data initiative may run into a significant hitch in such a scenario, and your decision-making may also be adversely affected. These problems can be reduced by thoroughly testing and verifying the development lifecycles of your big data systems.

  • Long system response time

Often, insights are needed in real-time to make decisions that are critical for daily operations. Sadly, a system may take too much time analyzing data even if input data has already been availed. This means the reports cannot be generated on time for real-time systems. This failure can cost thousands of dollars to any serious organization. This challenge can result from the inefficient organization of data and big data analytics infrastructure and resource utilization issues. The organization of data must be in a way that makes it easy for a system to operate. Check if your data warehouse is designed based on the needed case scenarios and ensure you have sufficient hardware.

  • High maintenance cost

Like any other system, big data systems require investment in the management and maintenance of the infrastructure. This is sometimes difficult because most businesses want to minimize their expenditure. The high cost of maintenance can result from outdated technologies, non-optimal infrastructure, or over-engineered systems. Old systems process little data volumes compared to modern ones hence the need to upgrade them. However, the high cost of replacing or maintaining the infrastructure may serve as a threat to your business and your big data efforts at large.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

scottkoegler.me/

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