Data Overload Is a Real Problem After Digitization
Humans are great at processing a few core variables, but beyond a handful of items, our processing systems tend to break down. It’s why we invented computers, which today largely handle the broad, complex, numerous variables we can’t wrap our heads around.
Ideally, a computer’s job is to process all those variables into easily digestible conclusions for our brains to consider. But in the information age, computers have become incredibly efficient at processing copious data and delivering innumerable insights, leaving us — oddly enough — back at square one, where we’re unable to process the information presented to us. It’s a problem aptly named “data overload.”
The factory ecosystem is rich with data
Today’s factories are packed with technology, and we have the capability to digitize almost anything. A factory ecosystem can include thousands of sensors generating terabytes of data day after day. But what do you do with all this data? Aggregate it, organize it, or do something else entirely?
Understanding what to do with data starts by addressing your need for the data. This is where many people get lost. You can have incredible value stream insights telling you what happens at every stage of the production process, but what does that matter if you’re not sure how to apply the data?
A data-rich factory ecosystem is only as useful as you imagine it to be. Are you trying to correct a problem? Create new efficiencies? Explore a new opportunity? Isolate a specific variable? Until you know the “what” or “why” of your need for insight, data is just data — and it can feel overwhelming.
More data isn’t always a good thing
Even if you have a plan to act on the data, the sheer volume of it can create problems for producers. Data coming from many directions — from thousands of sensors and machines, many of which may not even speak the same “language” — is a challenge for manufacturers to conceptualize. To add to this, factory data is constant, making it difficult to segment or sort in a static way.
The overwhelming amount of data and insight can lead to data overload, a problem related to “analysis paralysis.” Overwhelming amounts of information become noise: static that can block your ability to understand the important messages and themes buried in all the data. The answers might be right in front of you — but you can’t see them because they’re drowned out by less important data streams.
Factories need an enterprise data ecosystem
Data overload causes more than just static and noise. It can cause exclusionary insights, insight bias, and other conditions leading to false analysis. How do you avoid it?
Today’s manufacturers must invest in enterprise data ecosystems capable of collecting, sorting, analyzing, and transforming data into useful insights that optimize processes and efficiencies. And these processes would occur within dashboards designed to focus on the data that matters.
Investing in personnel is equally important. The modern factory needs data scientists and analysts who can simplify data collection and uncover the roots of problems. Well-trained people and digitized processes cut through the noise accompanying data overload, helping you understand every important nuance of your data.
