Data isn’t everything. But it’s perhaps the main thing standing between you and a successful project. Continuous improvement takes effort, but more than anything, it takes solid information and analysis. In other words, wouldn’t it be more helpful to use statistical process control to find out where your process is going wrong, rather than just a hunch?

Decisions based on data are more likely to succeed than gut feelings. Box and whisker plots, control charts, histograms, and design of experiments (DOE) all can help improve data analysis.

According to “The Quality Toolbox, Second Edition,” ASQ Quality Press, referenced on the ASQ site, data collection and analysis tools vary and can be used in many applications and many industries. It goes on to say: “Data collection and analysis tools are defined as a series of charts, maps, and diagrams designed to collect, interpret, and present data for a wide range of applications and industries. Various programs and methodologies have been developed for use in nearly any industry, ranging from manufacturing and quality assurance to research groups and data collection companies.”

Today, with the ever-changing technological improvements, data is a must. And the sheer quantity of it is growing all the time. The ballooning amounts of data available today are a tremendous resource for companies. Or, rather, they have the potential to be a resource. If they data is too overwhelming and just ends up being ignored, that doesn’t help anyone.

Say that the organization does understand the importance of data analysis however. In this case, it can help in a myriad of ways, from tracking defects to improving workflow.            

Determining the cost of quality, for example, is one area that necessitates data analysis. The cost of recalls is always unwanted and unnecessary, but less obvious costs related to additional inspection should also be monitored. This is critical when an organization is struggling, but it’s important even when things are going well.

According to a McKinsey article, “Even within manufacturing operations that are considered best in class, the use of advanced analytics may reveal further opportunities to increase yield.”

The article cited a European manufacturer that did just that, despite skepticism from staff. The article explains: “It boasted a strong history of process improvements since the 1960s, and its average yield was consistently higher than industry benchmarks. In fact, staffers were skeptical that there was much room for improvement. “This is the plant that everybody uses as a reference,” one engineer pointed out.”

But after using a form of advanced analytics, even this star plant was able to improve. The company was able to track some variability in the process and eventually reduce energy consumption and improve yields.

So even if you are working on at impressive plant that is held up as a beacon to others in the industry, there may be room for data to help you along the way. If you happen to be at a location that needs some work, data can obviously help in that case as well. While there are many ways to find solutions to vexing problems, asking the right questions can help.

As Tanvir Haque writes in an article for Entrepreneur, “It’s not enough to know what data you want to gather, you also need to know what you’re going to do with the data and insights once you have them. What problem is your company looking to solve? How is this data going to solve it?”

This sounds straightforward, but obviously implementing these ideas can be anything but simple. If you are confused about what to do with data gathered, you’re in good company. According to a paper from Applied Sciences, today there is a tendency “for manufacturers to possess only limited knowledge of the relative value of smart manufacturing data collected.” The paper goes on to discuss how determining the value of data leads to better results. The authors say, “A generalized approach to provide clarity as to what input data is valuable and what input data is not valuable, perhaps with both a quantitative and qualitative dimension, can shape analysis decisions in the big-data environment.”

Make the decision to make data work for you. Gather the right information and then chart a course from there. With the best information at your fingertips, you’ll put yourself in a position to succeed. Let your competitors be scared of this trend; you will be looking ahead and adopting new methods to improve.

The ever-increasing amounts of data are not going away. Big data is not a idea that should be dismissed. Ignoring the information surrounding your business may seem easier, but maintaining the status quo won’t help you stand out. Successful organizations gather the right data and then employ it to improve the business.