Data management requires efficiency, particularly in large organizations with a lot of throughput. As more data flows through a network, various protocols need to be in place to ensure each bit and byte get where they need to be, but bottlenecks can bring things to a screeching halt.

If you’re a data management professional is who concerned about bottlenecks in your network, below are three common causes of data management bottlenecks and some solutions:

Failure to Measure Data Quality

Poor-quality data has the potential to cause bottlenecks since having the wrong data leads to lost productivity. When poor-quality data takes up valuable space on a server, data professionals and processing resources have to sift through unnecessary clutter instead of being able to quickly find and focus on data that matters. This wastes time and causes other data tasks to get backlogged.

To overcome this challenge, you may consider active metadata management. Since metadata is the data that describes data, it can make sorting much easier. Active metadata management can reduce the potential for bottlenecks since it allows high-quality data to be surfaced faster.

Data Synchronization

Data that isn’t synced often enough can also lead to bottlenecks. In addition, data synchronization issues can cause duplicate data, further clogging networks and leading to lost productivity.

To remedy this, you can create tighter synchronization schedules, but you can also introduce redundancy tools that automatically sync data upon entry into a network. Even with automatic backup systems in place, it’s a good idea to run audits from time to time to make sure all data is properly synced. Real-time data streaming can be another solution to this challenge.

Evaluate Your Training

Although system performance is often the reason for data management bottlenecks, performance-based bottlenecks can also occur as a result of improper training. If all team members are not trained in how to manage different types of data, bottlenecks tend to happen more often.

To correct this, you may need to evaluate your training materials. Asking workers for feedback can also help to identify training issues that could lead to bottlenecks in data management.

Author Resource:-

Emily Clarke writes about the best data catalog tools and data analysis softwares. You can find her thoughts at data management blog.