Data processing capacity can have a major impact on the performance of any data visualization tool. Finding the right data visualization tool is about balancing your business’s on-premises computing resources and your data processing needs. What determines data processing capacity and how can businesses balance computing power and data visualization tools?
Choosing The Right Data Visualization Tool
Data processing capacity can put a ceiling on the performance of any data visualization tool you decide to use. If you have a weak PC or laptop, any software you try to run on it will be working around a bottleneck. This is especially true for tasks like data visualization, which can be very processor-intensive. The integrated GPUs and low-power CPUs found on many budget laptops simply cannot handle intensive data processing.
As a general rule, the larger the data set you need to process, the more processing power you will need to run it through a data visualization tool. Your business’s unique data processing demands play a big role in the importance of data processing capacity in your data visualization projects. If you need to display large, complex data sets or multiple data sets together, you will need a higher processing capacity device.
Choosing the right data visualization tool for your business is all about finding a tool that complements the computing infrastructure you have. If your business has powerful computers with advanced CPUs and high-power GPUs, you can be more selective about finding the most feature-packed tool. Even if your business has lower-powered computers, you still have options.
The rise of cloud computing over recent years opens up new opportunities for businesses that lack conventional on-premises computing capabilities. If your physical computer hardware is not high-performance, then cloud-based data visualization tools will be ideal since they move processing demands to the cloud. Your computer essentially acts as a command center rather than the main processing device.
Popular cloud-based data visualization tools include Plotly, Power BI, and even Google Charts, which is good for basic visualizations. With cloud-based visualization tools, on-premises data processing capacity is much less influential on overall performance.
There are several key features to look for in data visualization tools for any business. Some are great to have regardless of processing capacity. For example, in-app learning tools and tutorials are fantastic features for businesses in particular. If an application is easier to use, employees will be more likely to take advantage of it and more willing to give it a try. In-app tutorials and guidance can improve employee engagement and simplify onboarding for new employees.
Additionally, look for a data visualization with many different visualization options. This is a basic feature that all good data visualization tools should include. A high-value data visualization tool will include many different chart and graph types to choose from. A good bonus feature to have is the ability to include multiple data sets in the same visualization, although this may require more processing power.
Documentation and tech support are also crucial features for a good user experience. These are especially important to consider if your business is new to using data visualization apps. Once you compile a list of a few data viz programs that look like they could be a good fit, check user reviews for insights into tech support experiences and research the documentation the company has available online.
What Determines Data Processing Capacity?
Data processing capacity refers to the transfer of raw data through a computer’s CPU and RAM. Every computer has a maximum processing capacity, although this depends heavily on the specific components of the computer. The GPU or iGPU can also impact data processing performance.
An efficient, powerful combination of CPU, GPU, and RAM will be able to process larger amounts of data and do so faster than a weaker set of parts. The CPU usually plays the biggest role in determining the data processing capacity of a computer. There are many factors that influence a CPU’s potential max performance. The clock speed and the number of cores are usually the most important factors to make note of.
When it comes to data visualization in particular, the GPU is also important. Your GPU not only determines the quality of graphics your computer can display, but it also contributes to the overall data processing capacity of your computer. A more powerful GPU will improve processing capabilities.
However, it is important to remember that the CPU creates a performance ceiling for the GPU. If you have a weak CPU, it could potentially bottleneck your GPU, leading to poor performance and slow data processing, regardless of the data visualization tool your business is using.
Determining and Improving Data Processing Capacity
So, before investing in a data visualization tool, you need to analyze the computer infrastructure they currently have or the budget they have to invest in new tech. Check the specifications for the computer or computers that may be used for data visualization. There are free PC performance benchmark tests available online that you can use to compare and measure your performance capabilities.
If you discover that your business’s current data visualization PCs are getting low-performance scores on benchmark tests, you may want to consider investing in more powerful hardware. Pay close attention to the CPU and RAM in new data analytics computers. A few good CPUs for this purpose include Intel’s Core i7 and Core i9 processors as well as Apple’s M1 series of chips. In terms of RAM, aim for a laptop or PC with 16GB or more.
Additionally, before buying completely new hardware, make sure to check in with the manufacturer of your current computers. You may be able to upgrade your RAM rather than buying a completely new device.
Balancing Processing Power and Data Visualization
Data visualization tools can be limited by the data processing capacity of your business’s computers. The more data you want to process, the more processing power you will need. Cloud-based data visualization tools offer a solution for businesses with limited on-premises computing resources.