Imagine that you own a tool and die shop. In a back corner is a room you use for storing a growing collection of tools you have acquired from other shops, auctions, and trade shows. Unfortunately, neither you nor any member of your staff knows how to use most of the tools you have collected. So what good are they? About as good as all that data you have sitting idly on company computers.
This fictional scenario accurately reflects what many businesses across the country do with the data they collect. Data collection is part and parcel of modern business, thanks to the ‘big data’ concept, but all of it is useless if an organization does not know how to analyze and use what they have collected. One possible solution is to introduce signal processing to that data.
At Rock West Solutions in Southern California, signal processing is something they specialize in. They recommend that companies not getting the most out of their data seriously consider looking at signal processing, detection and analysis. Whether they do so with applied sensor technology or through some other means, signal processing separates valuable data from useless noise.
A Simple Explanation of Signal Processing
Signal processing is the science of analyzing, synthesizing, and modifying signals in order to use those signals for specific purposes. Signal processing can improve transmission, create greater efficiency, measure quality, and so forth. Most importantly, it can help companies actually use the data they collect rather than just storing it.
Companies like Rock West Solutions can apply signal processing to both analog and digital signals. For example, analog audio and visual signals can be processed to enhance valuable information by separating it from noise. A good example would be enhancing a video image in order to facilitate a criminal investigation.
In terms of digital signals, engineers can utilize applied sensor technology to improve the effectiveness of diagnostic medical devices. This makes for clearer images and better diagnosis.
It should be noted that signal processing is not limited to conventional signals. In other words, engineers can take the same techniques used to analyze analog or digital signals and apply them to analyzing financial data. In such applications, new signals are not being generated by analog or digital systems. Yet the financial data is still being analyzed and focused so as to eliminate data that is not useful to the application at hand.
The best way to understand signal processing is to compare it to finding one of the tools in the fictional storage room described at the start of this post. If you were provided with a detailed description of the tool along with a photograph and the general location of that tool, you would be able to dig around and eventually find it.
Cut Costs and Improve Productivity
Applying signal processing to your data has a number of practical benefits. For starters, it can help your company or organization cut costs by identifying waste and inefficiency. A proper analysis of data can uncover things not previously seen, things that are unnecessarily adding to the cost of doing business.
Signal processing can improve productivity as well. Armed with a better understanding of productivity and its embedded inefficiencies, management can implement changes with real data behind them. Those changes can then be measured with new data points and subsequently improved.
It’s great that your company collects and stores a ton of data. But if you are not using it to your advantage, what good is it doing? Perhaps it’s time to combine that data with advanced signal processing.