Last night I had the honor to speak at the ASQ Boston Section monthly meeting on some of the exciting work Thermo Fisher Scientific is doing in mixed reality and how it fits into the industrial transformation that we are all taking stabs at, as well as the broader concept of Quality 4.0.
A small group, but it was really fun to discuss some of the stuff I’ve gotten involved with in the 5 months I’ve been here, and where we see it going.
Slides are available here.
The US Food and Drug Administration’s proposed regulatory framework for artificial intelligence- (AI) and machine learning- (ML) based software as a medical device (SaMD) is fascinating in what it exposes about the uncertainty around the near-term future of a lot of industry 4.0 initiatives in pharmaceuticals and medical devices.
While focused on medical devices, this proposal is interesting read for folks interested in applying machine learning and artificial intelligence to other regulated areas, such as manufacturing.
We are seeing is the early stages of consensus building around the concept of Good Machine Learning Practices (GMLP), the idea of applying quality system practices to the unique challenges of machine learning.
My day 2 at WCQI is Day 1 of the conference proper. I’m going to try to live blog.
Today’s morning keynote is the same futurist as at the LSS in Phoenix last month, Patrick Schwerdtfeger, and not only was I dismayed that it was they exact same I was reminded yet again how much I dislike futurists. I’m all about thinking of the future, but futurists seem to be particularly bad at it. It is all woowoo and bro-slapping and never ever a serious consideration of the impact of technology. Futurists are grifters.
These grifters profit by obscuring facts for personal gain. They are working an angle, all of them: the health gurus and the life hackers peddling easy solutions to difficult problems, the futurists who basically state current trends as revelations. They are all trying to pull off the ultimate con – persuading people they really matter.
They are selling themselves: their books, their podcasts, their websites, their supplements, their claims to some secret knowledge about how the world works. But I fundamentally doubt that anyone who gave 40 talks in the last year has the bandwidth to do anything that really matters. It is all snake-oil. And as quality professionals, individuals who are dedicated to process and transparency and continuous improvement, we deserve better.
I’m not sure how these keynotes are selected but I think we need to holistically view just what we want to be as a society and the pillars we want for our conferences.
Anyone know a good article that evaluates futurists and life hackers with the prosperity gospel? Seem like they are coming from a similar place in the American psyche.
Ooh, artificial intelligence. Don’t get me wrong there is real potential (maybe not the potential people feel like there is) but most discussions on artificial intelligence is hype and bluster, and this presentation is no different. Autonomous vehicles block chains. Hype and bluster.
There is definitely people thinking this seriously, offering real insights and tools. We’re just not there. The speaker admits he gets 90% of his income from speaking. Pretty sure he isn’t actually doing that much. He might be an aggregator (as most of his slides with real content were attributed) but I keep struggling to see value here.
Gratuitous Steve Jobs picture.
After this there is some white space for vendor stuff.
At least I could multi-task and did some work.
I didn’t attend many of these last year because they were all standing and had a thrown together feeling. This year the ASQ seems to have upped the game. Shorter sessions can be good if the presentation is tight. At 15 minutes that is a hard bar to set.
First up we have Nicole Radziwill on “Mapping Quality Problems to Machine Learning Solutions” Nicole’s very active in the software section, which under the new membership model I’ll start paying a lot more attention to.
In this short presentation Nicole focuses on hitting the points of her 2018 Quality Progress article. Talking about quality 4.0’s path from Taylorism as “discovery & learning.”
She references Jim Duarte’s article Data Disruption.
Hit on big data hubris and the importance of statistics and analysis. The importance of defining models before we use them.
Covers machine learning problem types at a high level.
High level recommendations were domain expertise, statistical expertise, data quality and human bias.
Next up is Beverly Daniels on “Risk and Industry 4.0”
It is telling that so many of the talkers make Millennial jokes. As a Gen-Xer I am both annoyed because no one ever made Gen-Xer jokes (the boomers never even noticed we were at the conference) AND frustrated because this is something telling about the graying of the ASQ.
Quick review of risk as more than probability. Hit on human beings as eternally optimistic and thus horrible at determining probability. As this was a quick talk it left more questions than answers. Getting rid of probability from risk is something I need to think about more, but her points are aligned to my thoughts on uncertainty.
Focusing on impact and mitigation is interesting. I liked the line “All it does is make your management feel good about not making a decision.”
A critical skill of a quality professional (of any professional), and a fundamental part of Quality 4.0, is managing data — knowing how to acquire good data, analyze it properly, follow the clues those analyses offer, explore the implications, and present results in a fair, compelling way.
As we build systems, validate computer systems, create processes we need to ensure the quality of data. Think about the data you generate, and continually work to make it better.
I am a big fan of tools like the Friday Afternoon Measurement to determine where data has problems.
Have the tools to decide what data stands out, use control charts and regression analysis. These tools will help you understand the data. “Looks Good To Me: Visualizations As Sanity Checks” by Michael Correll is a great overview of how data visualization can help us decide if the data we are gathering makes sense.
Then root cause analysis (another core capability) allows us to determine what is truly going wrong with our data.
Throughout all your engagements with data understand statistical significance, how to quantify whether a result is likely due to chance or from the factors you were measuring.
In the past it was enough to understand a pareto chart, and histogram, and maybe a basic control chart. Those days are long gone. What quality professionals need to bring to the table today is a deeper understanding of data and how to gather, analyze and determine relevance. Data integrity is a key concept, and to have integrity, you need to understand data.
As we all try to figure out just exactly what Industry 4.0 and Quality 4.0 mean it is not an exaggeration to say “Data is your most valuable asset. Yet we all struggle to actually get a benefit from this data and data integrity is an area of intense regulatory concern.
To truly have value our data needs to be properly defined, relevant to the tasks at hand, structured such that it is easy to find and understand, and of high-enough quality that it can be trusted. Without that we just have noise.
Apply principles of good master data management and data integrity. Ensure systems are appropriately built and maintained.
Understand why data matters, how to pick the right metrics, and how to ask the right questions from data. Understand correlation vs. causation to be able to make decisions about when to act on analysis and when not to is critical.
In the 2013 article Keep Up with Your Quants, Thomas Davenport lists six questions that should be asked to evaluate conclusions obtained from data:
1. What was the source of your data?
2. How well do the sample data represent the population?
3. Does your data distribution include outliers? How did they affect the results?
4. What assumptions are behind your analysis? Might certain conditions render your assumptions and your model invalid?
5. Why did you decide on that particular analytical approach? What alternatives did you consider?
6. How likely is it that the independent variables are actually causing the changes in the dependent variable? Might other analyses establish causality more clearly?
Framing data, being able to ask the right questions, is critical to being able to use that data and make decisions. In the past it was adequate enough for a quality professional to have a familiarity with a few basic tools. Today it is critical to understand basic statistics. As Nate Silver advises in an interview with HBR. “The best training is almost always going to be hands on training,” he says. “Getting your hands dirty with the data set is, I think, far and away better than spending too much time doing reading and so forth.”
Understanding data is a key ability and is necessary to thrive. It is time to truly contemplate the data ecosystem as a system and stop treating it as a specialized area of the organization.
