We’re back!
In this summer sequel to June’s premiere of (R)Evolutionizing Manufacturing, Jeff Winter and Smart Industry’s Scott Achelpohl take on data, “The Good, the Bad, and the Ugly” of a basic and perhaps most important issue in any digital transformation: the information your company has and how it’s used.
At the end of the day, it’s all about data.
“All the cool new technologies and transformational initiatives you hear about revolve around properly capturing and extracting value from data,” as Jeff points out in this new episode.
And if a company doesn’t do this, it can cost millions a year; a lot of money is wasted on bad data. So much of it is being collected, so it’s critical to distinguish the good data from the bad.
As we’ll do in every episode, Jeff also answers questions that our listeners left on our social channels here, here, and here or send to the program via email. So please reach out; we want to hear from you! And enjoy Episode 2 of (R)Evolutionizing Manufacturing. We had fun with it.
Below is a partial transcript of this episode:
Scott Achelpohl: In the spirit of summer blockbusters and it being July, this episode is “the great sequel” to our June premiere … like “The Empire Strikes Back” or “The Godfather: Part II” … but hopefully not “Jaws 2.”
After last month’s premiere episode, me and Jeff got to thinking about the foundations of a company’s digital transformation, and we concluded a lot of it has to start with data, whether a manufacturer has a handle on all the data that feeds every single one of its processes—hour in, hour out, day in, day out, shift after shift of production run time.
See also: Episode 1 of (R)Evolutionizing Manufacturing: Where to begin a digital transformation
Jeff Winter: That's right. We started talking about quite a few different topics for future episodes, and we concluded that at the end of the day—it’s all about data.
It's the secret sauce that helps manufacturers predict problems before they happen, streamline processes, and make smarter decisions faster. It's not just about keeping up with the competition; it's about staying ahead and setting new standards.
Without data, you're missing out on all the efficiencies and innovations that make modern manufacturing so exciting.
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Scott: I got to thinking about the movie “Apollo 13”—yes, the Tom Hanks blockbuster, he of “Houston, we have a problem.” Great movie, right!?
If you recall from that film (and the real mission in 1970), the Apollo astronauts are stranded, headed on a perilous slingshot course around the moon, and the team on the ground at NASA has to figure out how the three crew can conserve power and generate more for the crippled Odyssey command module to keep the heroes alive for several long days in space before splashdown. There’s a line from that hit movie that stands out: “Power is everything.”
Well, in our case, think about it, we can draw the analogy with data. Data is everything.
Lots of manufacturing companies have told us they couldn’t build a solid foundation for ANY project related to digital transformation without getting a handle on their unbounded and oftentimes disorganized and disjointed data.
So, Jeff, I want to draw on your expertise with this, what are some other examples in digital transformation where intact data is absolutely critical for success?
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Jeff: I laugh because the answer is simple. The answer is … all of them. There is no shortage of digital transformation use cases out there, but all revolve around one key element: being digital—and that means data.
If you look at IoT Analytics in their 2022 Industry 4.0 Adoption report, they tracked the top 15 smart manufacturing use cases and 16 smart product use cases. All 31 revolve around collecting, transferring, or using data.
So, what is most important? The answer is obviously good data. It’s important because it helps us make informed decisions, identify trends, and solve problems effectively. Without good data, you're not just guessing; you’re making informed decisions, not bad ones.
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Most data companies out there will tell you there are seven main characteristics of data quality:
- Completeness: Measures how much of the data is present and usable.
- Uniqueness: Ensures no duplicate data exists.
- Validity: Checks if data matches the required format and business rules.
- Timeliness: Ensures data is available within the expected timeframe.
- Accuracy: Confirms data correctness based on a designated "source of truth."
- Consistency: Compares data across different datasets to ensure similar trends and reliable relationships.
- Auditability: Data is accessible, and changes are traceable. Bad data costs us dearly. According to Gartner research, “the average financial impact of poor data quality on organizations is $12.9 million per year.” And that was back in 2021. IBM also discovered that, in the U.S. alone, businesses lose $3.1 trillion annually due to poor data quality.
So yeah, it matters.
