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If Japan Can, Why Can’t We? A Retrospective

Posted on December 5, 2015 by Nicole Radziwill 5 comments

June 24, 1980 is kind of like July 4, 1776 for quality management… that’s the pivotal day that NBC News aired its one hour and 16 minute documentary called “If Japan Can, Why Can’t We?” introducing W. Edwards Deming and his methods to the American public.

The video has been unavailable for years, but as of 2018, it’s posted on YouTube. So my sophomore undergrads in Production & Operations Management took a step back in time to get a taste of the environment in the manufacturing industry in the late 1970’s, and watched it during class.

The last time I watched it was in 1997, in a graduate industrial engineering class. It didn’t feel quite as dated as it does now, nor did I have the extensive experience in industry as a lens to view the interviews through.

What did surprise me is the challenges they were facing then aren’t that much different than the ones we face today — and the groundbreaking good advice from Deming is still good advice today.

Before 1980, it was common practice to produce a whole bunch of stuff and then check and see which ones were bad, and throw them out. The video provides a clear and consistent story around the need to design quality in to products and processes, which then reduces (or eliminates) the need to inspect bad quality out.
It was also common to tamper with a process that was just exhibiting random variation. As one of the line workers in the documentary said, “We didn’t know. If we felt like there might be a problem with the process, we would just go fix it.” Deming’s applications of Shewhart’s methods made it clear that there is no need to tamper with a process that’s exhibiting only random variation.
Both workers and managers seemed frustrated with the sheer volume of regulations they had to address, and noted that it served to increase costs, decrease the rate of innovation, and disproportionately hurt small businesses. They noted that there was a great need for government and industry to partner to resolve these issues, and that Japan was a model for making these interactions successful.
Narrator Lloyd Dobyns remarked that “the Japanese operate by consensus… we, by competition.” He made the point that one reason industrial reforms were so powerful and positive was that Japanese culture naturally supported working together towards shared goals. He cautioned managers that they couldn’t just drop in statistical quality control and expect a rosy outcome: improving quality is a cultural commitment, and the methods are not as useful in the absence of buy-in and engagement.

The video also sheds light on ASQ’s November question to the Influential Voices, which is: “What’s the key to talking quality with the C-Suite?” Typical responses include: think at the strategic level; create compelling arguments using the language of money; learn the art of storytelling and connect your case with what it important to the executives.

But I think the answer is much more subtle. In the 1980 video, workers comment on how amazed their managers were when Deming proclaimed that management was responsible for improving productivity. How could that be??!? Many managers at that time were convinced that if a productivity problem existed, it was because the workers didn’t work fast enough, or with enough skill — or maybe they had attitude problems! Certainly not because the managers were not managing well.

Implementing simple techniques like improving training programs and establishing quality circles (which demonstrated values like increased transparency, considering all ideas, putting executives on the factory floor so they could learn and appreciate the work being done, increasing worker participation and engagement, encouraging work/life balance, and treating workers with respect and integrity) were already demonstrating benefits in some U.S. companies. But surprisingly, these simple techniques were not widespread, and not common sense.

Just like Deming advocated, quality belongs to everyone. You can’t go to a CEO and suggest that there are quality issues that he or she does not care about. More likely, the CEO believes that he or she is paying a lot of attention to quality. They won’t like it if you accuse them of not caring, or not having the technical background to improve quality. The C-Suite is in a powerful position where they can, through policies and governance, influence not only the actions and operating procedures of the system, but also its values and core competencies — through business model selection and implementation.

What you can do, as a quality professional, is acknowledge and affirm their commitment to quality. Communicate quickly, clearly, and concisely when you do. Executives have to find the quickest ways to decompose and understand complex problems in rapidly changing external environments, and then make decisions that affect thousands (and sometimes, millions!) of people. Find examples and stories from other organizations who have created huge ripples of impact using quality tools and technologies, and relate them concretely to your company.

Let the C-Suite know that you can help them leverage their organization’s talent to achieve their goals, then continually build their trust.

The key to talking quality with the C-suite is empathy.

You may also be interested in “Are Deming’s 14 Points Still Valid?” from Nov 19, 2012.

3 Steps to Creating an Innovative Performance Culture

Posted on August 25, 2015 by Nicole Radziwill 4 comments

Image Credit: Doug Buckley of http://hyperactive.to

Want to leapfrog over your competitors by designing an extremely high-performance culture for your organization? If so, I have the secret formula.

It starts here: in his August post to ASQ’s View From the Q blog, guest blogger James Lawther asks:

What are your DOs and DON’Ts of creating a performance culture?

Citing Deming and Drucker, and noting how so many organizations rely on a “carrots and sticks” approach to performance management, he converges on the following recommendation: “The way to create a high performance culture is to seek out poor performance, embrace it and fix it, not punish it.” I think, though, that this is not a new approach… rather than improving upon poor performance, why don’t we seek out truly amazing performance and then just make more of it? These three steps will help you do it:

Eliminate power relationships. Power is poison! It creates and cultivates fear (which, according to Deming, we need to drive out). Unfortunately, our educational system and our economy are firmly steeped in power relationships… so we’re not accustomed to truly cooperative relationships. (In fact, being reliant on the income from our jobs shoehorns us into power relationships before we even begin working.) Holacracy is one approach that some organizations are trying out, but there are many possibilities for shifting from organizational structures that are designed around power and control, versus those that are designed to stimulate interest, creativity, and true collaboration.

Create systems to help everyone find (and share) their unique skills, talents, and gifts. This is the key to both engagement and high performance — and this isn’t a one-shot deal. These skills, talents, and gifts are extremely dependent on the organizational context, the external environment, and a person’s current interests… and all of these change over time!

Create systems to help people become stewards of their own performance. Accenture and Google have both recently given up performance reviews… and Deming has always warned about them! Unless we’re managing our own performance, and the process and outcomes are meaningful to us individually, we’ll just be dragged down by another power relationship.

Quality professionals are great at designing and setting up systems to achieve performance goals! Now, we have an innovation challenge: adopt the new philosophy, design quality systems that substitute community in place of power and control, and use our sophisticated and capable information systems to give people agency over their own performance.

“Creative teamwork utterly depends on true communication and is thus very seriously hindered by the presence of power relationships. The open-source community, effectively free of such power relationships, is teaching us by contrast how dreadfully much they cost in bugs, in lowered productivity, and in lost opportunities.” — E. S. Raymond in The Cathedral and the Bazaar

Data Quality is Key for Asset Management in Data Science

Posted on August 18, 2015 by Nicole Radziwill Leave a comment

This post was motivated by two recent tweets by Dr. Diego Kuonen, Principal of Statoo Consulting in Switzerland (who you should definitely follow if you don’t already – he’s one of the only other people in the world who thinks about data science and quality). First, he shared a slide show from CIO Insight with this clickbaity title, bound to capture the attention of any manager who cares about their bottom line (yeah, they’re unicorns):

“The Best Way to Use Data to Cut Costs? Delete It.”

I’m so happy this message is starting to enter corporate consciousness, because I lived it throughout the decade of the 2000’s — working on data management for the National Radio Astronomy Observatory (NRAO). I published several papers during that time that present the following position on this theme (links to the full text articles are at the bottom of this post):

First, storing data means you’ve saved it to physical media; archiving data implies that you are storing data over a longer (and possibly very long) time horizon.
Even though storage is cheap, don’t store (or archive) everything. Inventories have holding costs, and data warehouses are no different (even though those electrons are so, so tiny).
Archiving data that is of dubious quality is never advised. (It’s like piling your garage full of all those early drafts of every paper you’ve ever written… and having done this, I strongly recommend against it.)
Sometimes it can be hard to tell whether the raw data we’re collecting is fundamentally good or bad — but we have to try.
Data science provides fantastic techniques for learning what is meant by data quality, and then automating the classification process.
The intent of whoever collects the data is bound to be different than whoever uses the data in the future.
If we do not capture intent, we are significantly suppressing the potential that the data asset will have in the future.

Although I hadn’t seen this when I was deeply enmeshed in the problem long ago, it totally warmed my heart when Diego followed up with this quote from Deming in 1942:

In my opinion, the need for a dedicated focus on understanding what we mean by data quality (for our particular contexts) and then working to make sure we don’t load up our Big Data opportunities with Bad Data liabilities will be the difference between competitive and combustible in the future. Mind your data quality before your data science. It will also positively impact the sustainability of your data archive.

Papers where I talked about why NOT to archive all your data are here:

Radziwill, N. M., 2006: Foundations for Quality Management of Scientific Data Products. Quality Management Journal, v13 Issue 2 (April), p. 7-21.
Radziwill, N. M., 2006: Valuation, Policy and Software Strategy. SPIE, Orlando FL, May 25-31.
Radziwill, N.M. and R. DuPlain, 2005: A Framework for Telescope Data Quality Management. Proc. SPIE, Madrid, Spain, October 2-5, 2005.
DuPlain, R. F. and N.M. Radziwill, 2006: Autonomous Quality Assurance and Troubleshooting. SPIE, Orlando FL, May 25-31.
DuPlain, R., Radziwill, N.M., & Shelton, A., 2007: A Rule-Based Data Quality Startup Using PyCLIPS. ADASS XVII, London UK, September 2007.

What (Really) is a Data Scientist?

Posted on July 13, 2015 by Nicole Radziwill 6 comments

Drew Conway’s very popular Data Science Venn Diagram. From http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

What is a data scientist? What makes for a good (or great!) data scientist? It’s been challenging enough to determine what a data scientist really is (several people have proposed ways to look at this). The Guardian (a UK publication) said, however, that a true data scientist is as “rare as a unicorn”.

I believe that the data scientist “unicorn” is hidden right in front of our faces; the purpose of this post is to help you find it. First, we’ll take a look at some models, and then I’ll present my version of what a data scientist is (and how this person can become “great”).

#1 Drew Conway’s popular “Data Science Venn Diagram” — created in 2010 — characterizes the data scientist as a person with some combination of skills and expertise in three categories (and preferably, depth in all of them): 1) Hacking, 2) Math and Statistics, and 3) Substantive Expertise (also called “domain knowledge”).

Later, he added that there was a critical missing element in the diagram: that effective storytelling with data is fundamental. The real value-add, he says, is being able to construct actionable knowledge that facilitates effective decision making. How to get the “actionable” part? Be able to communicate well with the people who have the responsibility and authority to act.

“To me, data plus math and statistics only gets you machine learning, which is great if that is what you are interested in, but not if you are doing data science. Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods. On the flip-side, substantive expertise plus math and statistics knowledge is where most traditional researcher falls. Doctoral level researchers spend most of their time acquiring expertise in these areas, but very little time learning about technology. Part of this is the culture of academia, which does not reward researchers for understanding technology. That said, I have met many young academics and graduate students that are eager to bucking that tradition.” — Drew Conway, March 26, 2013

#2 In 2013, Harlan Harris (along with his two colleagues, Sean Patrick Murphy and Marck Vaisman) published a fantastic study where they surveyed approximately 250 professionals who self-identified with the “data science” label. Each person was asked to rank their proficiency in each of 22 skills (for example, Back-End Programming, Machine Learning, and Unstructured Data). Using clustering, they identified four distinct “personality types” among data scientists:

Data Businesspeople who are most focused on the information itself and how it is applied to business decisions. (These people were least likely to identify with the “data scientist” label.)
Data Creatives, the hackers with the broadest experience “from extracting data, to integrating and layering it, to performing statistical or other advanced analyses, to creating compelling visualizations and interpretations, to building tools to make the analysis scalable and broadly applicable.” They are chameleons who can shapeshift from one role into another as necessary (although they may not be extremely deep in any of the topic areas).
Data Developers, the wizards of the technical aspects of data management (accessing it, moving it around, archiving it, curating it), and
Data Researchers, those deeply familiar with the mathematical and statistical underpinnings of the work, who can develop new techniques as necessary (in addition to correctly selecting from available techniques).

As a manager, you might try to cut corners by hiring all Data Creatives(*). But then, you won’t benefit from the ultra-awareness that theorists provide. They can help you avoid choosing techniques that are inappropriate, if (say) your data violates the assumptions of the methods. This is a big deal! You can generate completely bogus conclusions by using the wrong tool for the job. You would not benefit from the stress relief that the Data Developers will provide to the rest of the data science team. You would not benefit from the deep domain knowledge that the Data Businessperson can provide… that critical tacit and explicit knowledge that can save you from making a potentially disastrous decision.

Although most analysts and researchers who do screw up very innocently screw up their analyses by stumbling into misuses of statistical techniques, some unscrupulous folks might mislead other on purpose; although an extreme case, see I Fooled Millions Into Thinking Chocolate Helps Weight Loss.

Their complete results are available as a 30-page report (available in print or on Kindle).

#3 The Guardian is, in my opinion, a little more rooted in realistic expectations:

“The data scientist’s skills – advanced analytics, data integration, software development, creativity, good communications skills and business acumen – often already exist in an organisation. Just not in a single person… likely to be spread over different roles, such as statisticians, bio-chemists, programmers, computer scientists and business analysts. And they’re easier to find and hire than data scientists.”

They cite British Airways as an exemplar:

“[British Airways] believes that data scientists are more effective and bring more value to the business when they work within teams. Innovation has usually been found to occur within team environments where there are multiple skills, rather than because someone working in isolation has a brilliant idea, as often portrayed in TV dramas.”

Their position is you can’t get all those skills in one person, so don’t look for it. Just yesterday I realized that if I learn one new amazing thing in R every single day of my life, by the time I die, I will probably be an expert in about 2% of the package (assuming it’s still around).

#4 Others have chimed in on this question and provided outlines of skill sets, such as:

“Six Qualities of a Great Data Scientist“: statistical thinking, technical acumen, multi-modal communication skills, curiosity, creativity, grit
The Udacity blog: basic tools (R, Python), software engineering, statistics, machine learning, multivariate calculus, linear algebra, data munging, data visualization and communication, and the ultimately nebulous “thinking like a data scientist”
IBM: “part analyst, part artist” skilled in “computer science and applications, modeling, statistics, analytics and math… [and] strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.”
SAS: “a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved. They’re part mathematician, part computer scientist and part trend-spotter.” (Doesn’t that sound exciting?)
DataJobs.Com: well, these guys just took Drew Conway’s Venn diagram and relabeled it.

#5 My Answer to “What is a Data Scientist?”: A data scientist is a sociotechnical boundary spanner who helps convert data and information into actionable knowledge.

Based on all of the perspectives above, I’d like to add that the data scientist must have an awareness of the context of the problems being solved: social, cultural, economic, political, and technological. Who are the stakeholders? What’s important to them? How are they likely to respond to the actions we take in response to the new knowledge data science brings our way? What’s best for everyone involved so that we can achieve sustainability and the effective use of our resources? And what’s with the word “helps” in the definition above? This is intended to reflect that in my opinion, a single person can’t address the needs of a complex data science challenge. We need each other to be “great” at it.

A data scientist is someone who can effectively span the boundaries between

1) understanding social+ context,

2) correctly selecting and applying techniques from math and statistics,

3) leveraging hacking skills wherever necessary,

4) applying domain knowledge, and

5) creating compelling and actionable stories and connections that help decision-makers achieve their goals. This person has a depth of knowledge and technical expertise in at least one of these five areas, and a high level of familiarity with each of the other areas (commensurate with Harris’ T-model). They are able to work productively within a small team whose deep skills span all five areas.

It’s data-driven decision making embedded in a rich social, cultural, economic, political, and technological context… where the challenges may be complex, and the stakes (and ultimately, the benefits) may be high.

(*) Disclosure: I am a Data Creative!

(**)Quality professionals (like Six Sigma Black Belts) have been doing this for decades. How can we enhance, expand, and leverage our skills to address the growing need for data scientists?

Quality Has Always Been Global

Posted on March 1, 2015 by Nicole Radziwill One comment

Image Credit: Shutterstock, from http://asq.org/blog/2015/02/why-should-quality-go-global/

In his February post, ASQ CEO Bill Troy asks “Why Should Quality ‘Go Global’?” ASQ has, over the past several years, expanded its reach as a member organization… “going global” to expand awareness of quality tools and techniques. This is being done to more deeply realize ASQ’s mission to “increase the use and impact of quality in response to the diverse needs of the world”.

But this approach forgets that quality can’t go global… it already is global! The notion (and pursuit) of quality is evident in the history of water quality and sanitation dating back to Ancient Greece and Rome, the creation (over centuries) of the measuring instruments and standards that have made way for modern methods of industrial production, cave paintings discovered in Egypt that show quality assurance inspectors presiding over work, and other stories. Deming’s groundbreaking work took place in Japan, in the midst of a vastly different culture than Deming’s own. In 1990, Quality Progress ran a series of articles called “China’s Ancient History of Managing for Quality” that provides a very rich examination of quality practices in that region.

Sure, Frederick Winslow Taylor was an American manufacturer, meaning that the principles of scientific management were first tested and implemented here… but the chemist, Le Chatelier, very quickly translated Taylor’s work to French and introduced the principles to manufacturing plans during World War I. At the same time, Henri Fayol was conceptualizing similar techniques for assessing and managing quality in that country. The journal Quality Engineering ran a piece in 1999 that described the history of quality management in France in the 20th century, along with practices and trends from several other European countries.

Quality systems provide mechanisms for us to achieve and accomplish whatever it is that we value. Every culture has a long and vibrant history of using tools, techniques, and standards to make these things happen. Perhaps instead of aiming to simply push the message of quality beyond the United States, ASQ could also seek the message of quality that artisans, engineers, and citizens in vastly different environments and cultures have developed over the past several centuries to offer quality professionals everywhere.

Who Has Inspired You About Quality?

Posted on February 4, 2015 by Nicole Radziwill 5 comments

In his January post, ASQ CEO Bill Troy asks, “Have you met someone whose teachings on quality influenced you or inspired you? What were these lessons?” Although he acknowledges the “quality gurus” he encouraged us to think about people from beyond the domain of the quality profession. When I think about quality, I always start with my favorite definition to provide an anchor. According to this definition, quality is:

“The totality of characteristics of an entity that bear upon its ability to satisfy stated and implied needs.” — ISO 8402 (deprecated)

Even though they do not specifically teach about quality, I’d like to share two of my sources of inspiration: philosopher and activist Charles Eisenstein, and psychologist Barbara Fredrickson.

In Sacred Economics and The More Beautiful World Our Hearts Know is Possible, Charles Eisenstein encourages us to look beyond the subtle assumptions and limitations imposed upon us by being embedded in a market economy. What is quality in the absence of a commercial environment to exchange products and services?? How can we more effectively relate to ourselves and to one another, so that we can better satisfy our stated and implied needs? Eisenstein’s work inspires me because it encourages me to reflect on the unspoken assumptions of the quality profession, and how those assumptions might be holding us back from evolving our skill sets to meet the changing needs of society. (Sacred Economics is also available in print from Amazon.)

In Positivity, Barbara Fredrickson provides a simple, data-driven path (the “positivity ratio”) for improving our psychological health; in Love 2.0, she helps uncover ways for us to create substantive, authentic connections with one another. Her work can help us cultivate greater quality consciousness – because we are best able to satisfy others’ stated and implied needs when 1) we understand them, and 2) we are mentally and emotionally equipped to help deliver them! Although aspects of the positivity ratio have been criticized by researchers studying dynamical systems, I still find the concept (and measurement tool) very useful for raising the awareness of individuals and teams.

Postscript: Bill’s post made me think about another related question: “Who ARE the quality gurus?” I mean, everyone in the quality profession can call on Deming, Juran, or Crosby, but I’d toss luminaries like Csikszentmihalyi and Prahalad (plus others) in the mix as well. I searched online and found a nice “List of Gurus” that someone put together that includes my extra picks!

But!! There’s a problem with it.

WHERE ARE THE WOMEN? The one woman in this list is someone I’ve never heard of, which is odd, since I’ve read papers by (or about!) all of the other people referenced in the list. Which brings me back to my original point: WHERE ARE THE WOMEN QUALITY GURUS? It’s time to start celebrating their emerging legacy. If you are a woman who has made significant contributions to our understanding and/or practice of quality and improvement, PLEASE CONTACT ME. I’d like to write an article soon.

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