data scientists use critical thinking to generate valuable processes
data scientists use critical thinking to generate valuable processes
- Understand the current process
- Determine the data to collect based on the largest contributors to process performance
- Collect and analyze the data
- Develop the future process based on findings
What’s the current process?
it’s important to understand that data scientists use critical thinking to arrive at tenable conclusions. Good data scientists will ask a lot of questions, which may seem time-consuming, challenging, and even combative for outsiders to question the production experts. Critical questions are a vital part of the process, and they must be addressed for the sake of the goal and for the team’s well-being.
What data should we collect?
Once the current process is mapped out, it’s time to determine the metrics that contribute to good performance; your production experts will have good ideas about what’s important: inventory, cycle time, quality, and so on. Those familiar with lean manufacturing will be looking for areas of waste such as overproduction and transportation, so you might hear some of these metrics discussed.
Your data scientists might be thinking in another direction, though. If the goal is to find the biggest contributors to performance in each process step, then how do you know if you’re right? There may be a metric that has a huge impact on performance that nobody knows about. It’s important to encourage this critical line of questioning.
What does it all mean?
Once we know what data to collect, it’s time to start collecting and analyzing it. You expect data scientists to have copious ideas about collecting and analyzing operational data, but keep in mind that production experts also know a lot about collecting and analyzing the data that matters to them, and these experts know a great deal about the equipment that’s generating all this data—something the data scientists know nothing about. So, this is not the time for data scientists to be didactic about data collection or even analysis. What’s important to discover are the essential questions the production experts can’t answer or explain and that will eventually lead to a better tomorrow.
What process should be used in the future?
To understand how the process should perform, you must determine its theoretical best performance targets. Data scientists will use operational intelligence to see what’s truly possible instead of production experts making educated guesses. Then, data scientists can build sophisticated simulation models to see how the process will perform under different configurations. This would be impossible for production experts to do, even with a mildly complex process. So, it seems we can teach a few old dogs some new tricks—with a little help from big data analytics.
Good data science makes good sense
What’s different is how data science thinking, practices, and techniques add so much more value to the outcome. It’s more than just throwing in fancy algorithms and tools—it’s a data science philosophy.
At the heart of all good data science is curiosity. I find that most experts have more answers than questions, but data scientists are different—they have a lot of questions that generate more questions. It’s all part of critical thinking and the scientific process. This is a culture shift in most cases that requires proper leadership if you’re going to include data scientists in your efforts with experts from other disciplines. But it’s well worth it.
