When it comes to the manufacturing world, we have had process control systems and data historians for decades, but we have mostly lagged behind the “simpler” industries, like retail, when it comes to converting data into actionable information. If you look at the ways in which online advertising and big chain stores use data, you will perhaps feel a bit exposed to how much they can glean from our various buying and browsing habits. But aside from the problems associated with the inescapable monitoring of everyone’s behavior in our society, we have to admit that it is a well-oiled machine that delivers huge value to online and brick-and-mortar retailers. Even beyond the marketing and advertising, those same retailers have developed sophisticated, data-based algorithms to understand their supply chain and logistics, predicting problem areas and taking proactive actions to avoid those problems before they arise. So, how do industries like ours have had data for many more years but struggle to convert that data into something meaningful and valuable?
There are several reasons that have led to our current situation, but diving deep into that history isn’t particularly useful unless it helps us to make progress in our own endeavors to convert data into information. So, instead of focusing on what has happened, let’s focus on what we can do to change the trajectory and deliver value from our data.
The most critical and primary action is to organize and contextualize our data. On its face, this shouldn’t be too hard because we have engineers and process technicians who understand the data and have a general idea of which input variables will impact which output variables. However, mimicking human thought and pattern detection with data models and predictive algorithms is not an easy task. There are a few high-level steps that must be taken with our data to develop this kind of automated pattern detection and subsequent action:
1. Gather: Build a place where all data streams can reside. Automate the ingestion of data from various sources into that single location. Label parameters well and then organize them into simple, easy-to-navigate data tables. Map your process in total and see if there are variables that you know or suspect are important but which are not currently being recorded automatically.
2. Integrate: Most of our data streams are not immediately compatible. Consider an injection molding process where resin and additives are mixed before going to the injection molding machine.