Can you ever have too much data? Surprisingly, the answer is yes.

Collecting data is important, that much is true, but if too much data is collected, processes can become complicated and convoluted very quickly. SensrTrx Manufacturing Analytics software provides manufacturers visibility on the floor. It’s crucial to determine ahead of time the essential data needed to provide that visibility.  Think of it as a road map guiding manufacturers on the path to increased capacity, productivity, and efficiency.

However, the point of this blog isn’t to point out the road map you need to develop for your journey to visibility because there are other resources for that purpose, but instead, to explain why it is important to select a few key data collection points, and not gather all of the data possible.

SensrTrx software provides manufacturing analytics in a simple, straightforward way. But, what happens if you become inundated with too much data?

Too Much Data Can Turn into a Beast

Cue YAGNI. No, it’s not a yeti monster, but it does provide a good visualization, doesn’t it? YAGNI stands for  “You Ain’t Gonna Need It” or if we want to be grammatically correct, “You Aren’t Going to Need It”. When applied to manufacturing data, YAGNI explains the importance of collecting data that can provide valuable insights and actionable decisions. Many companies collect anything and everything because “you never know what you might find”, which is the wrong way to look at data collection.

But, don’t be fooled – the cost of doing this is high. When you just collect all of the data, without purpose or intent, it means you don’t know what problems you might want to solve, and you are collecting data “just in case”, which doesn’t help anyone.

You can see why this would be a problem. You’re wasting valuable resources on data that might not help you in the long run, and instead of focusing on problematic areas, you’re looking at too broad of a picture.

The idea behind manufacturing analytics is to start small, think big, move fast. Essentially, that means, start out by collecting the data you need to begin improving a few machines, but don’t forget YAGNI. Then, when you feel like you’ve conquered your first problem and have a laid out plan, think big and move on to other issues. Move fast along your journey and you will reach peak visibility and efficiency in no time.

 

What’s the Right Way to Gather Data?

That begs the question, “How do I figure out the right way to gather data?”. Look no further than the “What is Manufacturing Analytics?” guide we developed to answer that question specifically.

So after reading the guide, you’re now in the know – too much data can be overwhelming, and at times, detrimental to your business, but how do you determine how much is too much and where to draw the line? The answer is that it is different for each manufacturer.

In order to determine what data should be collected and contextualized, it’s important to first look at your factory floor as a whole. Where are your pain points? Where could you improve?

With that knowledge, you can begin collecting data, but wait, there’s one thing we should point out here. Don’t just collect data from the machines you regard as your “problem machines”. Collect data from those machines that are performing well, too. There’s a reason for this, and it is for comparison purposes. How will you know if the data collected from the underperforming machines is valid without some sort of comparison? That’s right – the well-performing machines will provide valuable insights, too.

You know you need to collect data from two types of machines, but don’t let the YAGNI monster scare you into thinking you’ll need to gather even more data. It simply means you should collect the same type of data, in a planned and pre-determined fashion. For example, if performance is low on one machine, track performance on another for, you guessed it, comparison purposes.

Remember, YAGNI and Only Collect the Data You Need

We’ve reached the end of the blog, and this is the point in time we recap what we’ve learned. So, to sum it up, data is important. When applied to everyday problems, it can help you create a more efficient shop floor. However, too much data can create even more problems. Establish a well thought out plan prior to collecting an endless amount of data that inevitably will leave you in a position of utter confusion on how to contextualize the data.

When in doubt, remember, YAGNI – You Aren’t Going To Need It.