Tag Archives: socio-technical

Leadership – No Pushing Required

Brene Brown on leadership

When I was younger, I felt like I was pretty smart. Then I turned 23, was thrown into the fast-faced world of helping CxOs try to straighten out their wayward enterprise software implementations, and realized just how little I knew. My turning point came around 6pm on a hot, sticky, smelly evening on Staten Island in a conference room where a director named Mike Davis was yelling at a bunch of us youngster consultants. I thought he was mad at us, but in retrospect, it’s pretty clear that he just wanted something simple, and no matter how clearly he explained it, no one could hear him. Not even me, not even when I was being smart.

The customer was asking for some kind of functionality that didn’t make sense to me. It seemed excessive and unwieldy. I knew a better way to do it. So when Mike asked us to tell him, step by step, what user scenario we would be implementing… I told him THE RIGHT WAY. After about five attempts, he blew up. He didn’t want “the right way” — he wanted “the way that would work.” The way that would draw the most potential out of those people working on those processes. The way that would make people feel the most engaged, the most in control of their own destiny, the way that they were used to doing (with maybe a couple of small tweaks to lead them in a direction of greater efficiency). He knew them, and he knew that. He was being a leader.

Now I’m in my 40s and I have a much better view of everything I don’t know. (A lot of that used to be invisible to me.) It makes me both happier (for the perspective it brings) and unhappier (because I can see so many of the intellectual greenfields and curiosities that I’ll never get to spend time in — and know that more will crop up every year). I’m limited by the expiration date on this body I’m in, something that never used to cross my mind.

One of the things I’ve learned is that the best things emerge when groups of people with diverse skills (and maybe complementary interests) get together, drive out fear, and drive out preconceived notions about what’s “right” or “best”. When something amazing sprouts up, it’s not because it was your idea (or because it turned out “right”). It’s because the ground was tilled in such a way that a group of people felt comfortable bringing their own ideas into the light, making them better together, and being open to their own emergent truths.

I used to think leadership was about coming up with the BEST, RIGHT IDEA — and then pushing for it. This week, I got to see someone else pushing really hard for her “best, most right, more right than anyone else’s” idea. But it’s only hers. She’s intent on steamrolling over everyone around her to get what she wants. She’s going to be really lonely when the time comes to implement it… because even if someone starts out with her, they’ll leave when they realize there’s no creative expression in it for them, no room for them to explore their own interests and boundaries.  I feel sorry for her, but I’m not in a position to point it out. Especially since she’s older than me. Hasn’t she seen this kind of thing fail before? Probably, but she’s about to try again. Maybe she thinks she didn’t push hard enough last time.

Leadership is about creating spaces where other people can find purpose and meaning.  No pushing required.

Thanks to @maryconger who posted the image on Twitter earlier today. Also thanks to Mike Davis, wherever you are. If you stumble across this on the web one day, thanks for waking me up in 2000. It’s made the 18 years thereafter much more productive.

Happy 10th Birthday!

10 years ago today, this blog published its first post: “How Do I Do a Lean Six Sigma (LSS) Project?” Looking back, it seems like a pretty simple place to have started. I didn’t know whether it would even be useful to anyone, but I was committed to making my personal PDSA cycles high-impact: I was going to export things I learned, or things I found valuable. (As it turns out, many people did appreciate the early posts even though it would take a few years for that to become evident!)

Since then, hundreds more have followed to help people understand more about quality and process improvement in theory and in practice. I started writing because I was in the middle of my PhD dissertation in the Quality Systems program at Indiana State, and I was discovering so many interesting nuggets of information that I wanted to share those with the world – particularly practitioners, who might not have lots of time (or even interest) in sifting through the research. In addition, I was using data science (and some machine learning, although at the time, it was much more difficult to implement) to explore quality-related problems, and could see the earliest signs that this new paradigm for problem solving might help fuel data-driven decision making in the workplace… if only we could make the advanced techniques easy for people in busy jobs to use and apply.

We’re not there yet, but as ASQ and other organizations recognize Quality 4.0 as a focus area, we’re much closer. As a result, I’ve made it my mission to help bring insights from research to practitioners, to make these new innovations real. If you are developing or demonstrating any new innovative techniques that relate to making people, processes, or products better, easier, faster, or less expensive — or reducing risks and building individual and organizational capabilities — let me know!

I’ve also learned a lot in the past decade, most of which I’ve spent helping undergraduate students develop and refine their data-driven decision making skills, and more recently at Intelex (provider of integrated environment, health & safety, and quality management EHSQ software to enterprises and smaller organizations). Here are some of the big lessons:

  1. People are complex. They have multidimensional lives, and work should support and enrich those lives. Any organization that cares about performance — internally and in the market — should examine how it can create complete and meaningful experiences. This applies not only to customers, but to employees and partners and suppliers. It also applies to anyone an organization has the power and potential to impact, no matter how small.
  2. Everybody wants to do a good job (and be recognized for it). How can we create environments where each person is empowered to contribute in all the areas where they have talent and interest? How can these same environments be designed with empathy as a core capability?
  3. Your data are your most valuable assets. It sounds trite, but data is becoming as valuable as warehouses, inventory, and equipment. I was involved in a project a few years ago where we digitized data that had been collected for three years — and by analyzing it, we uncovered improvement opportunities that when implemented, saved thousands of dollars a week. We would not have been able to do that if the data had remained scratched in pencil on thousands of sheets of well-worn legal paper.
  4. Nothing beats domain expertise (especially where data science is concerned). I’ve analyzed terabytes of data over the past decade, and in many cases, the secrets are subtle. Any time you’re using data to make decisions, be sure to engage the people with practical, on-the-ground experience in the area you’re studying.
  5. Self-awareness must be cultivated. The older you get, and the more experience you gain, the more you know what you don’t know. Many of my junior colleagues (and yours) haven’t reached this point yet, and will need some help from senior colleagues to gain this awareness. At the same time, those of you who are senior have valuable lessons to learn from your junior colleagues, too! Quality improvement is grounded in personal and organizational learning, and processes should help people help each other uncover blind spots and work through them — without fear.

 

Most of all, I discovered that what really matters is learning. We can spend time supporting human and organizational performance, developing and refining processes that have quality baked in, and making sure that products meet all their specifications. But what’s going on under the surface is more profound: people are learning about themselves, they are learning about how to transform inputs into outputs in a way that adds value, and they are learning about each other and their environment. Our processes just encapsulate that organizational knowledge that we develop as we learn.

How to Assess the Quality of a Chatbot

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

Quality is the “totality of characteristics of an entity that bear upon its ability to meet stated and implied needs.” (ISO 9001:2015, p.3.1.5) Quality assurance is the practice of assessing whether a particular product or service has the characteristics to meet needs, and through continuous improvement efforts, we use data to tell us whether or not we are adjusting those characteristics to more effectively meet the needs of our stakeholders.

But what if the entity is a chatbot?

In June 2017, we published a paper that explored that question. We mined the academic and industry literature to determine 1) what quality attributes have been used by others to determine chatbot quality, we 2) organized them according to the efficiency, effectiveness, and satisfaction (using guidance from the ISO 9241 definition of usability), and 3) we explored the utility of Saaty’s Analytic Hierarchy Process (AHP) to help organizations select between one or more versions of chatbots based on quality considerations. (It’s sort of like A/B testing for chatbots.)

“There are many ways for practitioners to apply the material in this article:

  • The quality attributes in Table 1 can be used as a checklist for a chatbot implementation team to make sure they have addressed key issues.
  • Two or more conversational systems can be compared by selecting the most significant quality attributes.
  • Systems can be compared at two points in time to see if quality has improved, which may be particularly useful for adaptive systems that learn as they as exposed to additional participants and topics.”

What Protests and Revolutions Reveal About Innovation

The following book review will appear in an issue of the Quality Management Journal later this year:

The End of Protest: A New Playbook for Revolution.   2016.  Micah White.  Toronto, Ontario, Canada. Alfred A. Knopf Publishing.  317 pages.

You may wonder why I’m reviewing a book written by the creator of the Occupy movement for an audience of academics and practitioners who care about quality and continuous improvement in organizations, many of which are trying to not only sustain themselves but also (in many cases) to make a profit. The answer is simple: by understanding how modern social movements are catalyzed by decentralized (and often autonomous) interactive media, we will be better able to achieve some goals we are very familiar with. These include 1) capturing the rapidly changing “Voice of the Customer” and, in particular, gaining access to its silent or hidden aspects, 2) promoting deep engagement, not just in work but in the human spirit, and 3) gaining insights into how innovation can be catalyzed and sustained in a truly democratic organization.

This book is packed with meticulously researched cases, and deeply reflective analysis. As a result, is not an easy read, but experiencing its modern insights in terms of the historical context it presents is highly rewarding. Organized into three sections, it starts by describing the events leading up to the Occupy movement, the experience of being a part of it, and why the author feels Occupy fell short of its objectives. The second section covers several examples of protests, from ancient history to modern times, and extracts the most important strategic insight from each event. Next, a unified theory of revolution is presented that reconciles the unexpected, the emotional, and the systematic aspects of large-scale change.

The third section speaks directly to innovation. Some of the book’s most powerful messages, the principles of revolution, are presented in Chapter 14. “Understanding the principles behind revolution,” this chapter begins, “allows for unending tactical innovation that shifts the paradigms of activism, creates new forms of protest, and gives the people a sudden power over their rulers.” If we consider that we are often “ruled” by the status quo, then these principles provide insight into how we can break free: short sprints, breaking patterns, emphasizing spirit, presenting constraints, breaking scripts, transposing known tactics to new environmental contexts, and proposing ideas from the edge. The end result is a masterful work that describes how to hear, and mobilize, the collective will.

 

Reviewed by

Dr. Nicole M. Radziwill

 

Free Speech in the Internet of Things (IoT)

Image Credit: from "Reclaim Democracy" at http://reclaimdemocracy.org/who-are-citizens-united/

IF YOUR TOASTER COULD TALK, IT WOULD HAVE THE RIGHT TO FREE SPEECH. Image Credit: from “Reclaim Democracy” at http://reclaimdemocracy.org/who-are-citizens-united/

By the end of 2016, Gartner estimates that over 6.4 BILLION “things” will be connected to one another in the nascent Internet of Things (IoT). As innovation yields new products, services, and capabilities that leverage this ecosystem, we will need new conceptual models to ensure quality and support continuous improvement in this environment.

I wasn’t thinking about quality or IoT this morning… but instead, was trying to understand why so many people on Twitter and Facebook are linking Justice Scalia’s recent death to Citizens United. (I’d heard of Citizens United, but quite frankly, thought it was a soccer team. Embarrassing, I know.) I was surprised to find out that instead, Citizens United is a conservative U.S. political organization best known for its role in the 2010 Supreme Court Case Citizens United v. FEC.

That case removed many restrictions on political spending. With the “super-rich donating more than ever before to individual campaigns plus the ‘enormous’ chasm in wealth has given the super-rich the power to steer the economic and political direction of the United States and undermine its democracy.” Interesting, sure… but what’s more interesting to me is that the Citizens United case, according to this source

  • Strengthened First Amendment protection for corporations, 
  • Affirmed that Money = Speech, and
  • Affirmed that Non-Persons have the right to free speech.

The article goes on to state that “if your underpants could talk, they would be protected by free speech.”

Not too long ago, a statement like this would just be silly. But today, with immersive IoT looming, this isn’t too far-fetched. 

  • What will the world look (and feel) like when everything you interact with has a “voice”?
  • How will the “Voice of the Customer” be heard when all of that customer’s stuff ALSO has a voice?
  • What IS the “Voice of the Customer” in a world like this?

A Robust Approach to Determining Voice of the Customer (VOC)

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

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

I got really excited when I discovered Morris Holbrook’s 1996 piece on customer value, and wanted to share it with all of you. From the perspective of philosophy, he puts together a vision of what we should mean by customer value… and a framework for specifying it. The general approach is straightforward:

“Customer Value provides the foundation for all marketing activity…
One can understand a given type of value only by considering its relationship to other types of value.
Thus, we can understand Quality only by comparison with Beauty, Convenience, and Reputation; we can understand Beauty only by comparison with Quality, Fun, and Ecstasy.”

There are MANY dimensions that should be addressed when attempting to characterize the Voice of the Customer (VOC). When interacting with your customers or potential customers, be sure to use surveys or interview techniques that aim to acquire information in all of these areas for a complete assessment of VOC.

The author defines customer value as an “interactive relativistic preference experience”:

  • Interactive – you construct your notion of value through interaction with the object
  • Relativistic – you instinctively do pairwise comparisons (e.g. “I like Company A’s customer service better than Company B’s”)
  • Preference – you make judgments about the value of an object
  • Experience – value is realized at the consumption stage, rather than the purchase stage

Hist typology of customer value is particularly interesting to me:

typology-customer-value

Most of the time, we do a good job at coming up with quality attributes that reflect efficiency and excellence. Some of the time, we consider aesthetics and play. But how often – while designing a product, process, or service – have you really thought about status, esteem, ethics, and spirituality as dimensions of quality?

This requires taking an “other-oriented” approach, as recommended by Holbrook. We’re not used to doing that – but as organizations transform to adjust the age of empathy, it will be necessary.

Holbrook, M. B. (1996) . “Special Session Summary Customer Value C a Framework For Analysis and Research”, in NA – Advances in Consumer Research Volume 23, eds. Kim P. Corfman and John G. Lynch Jr., Provo, UT : Association for Consumer Research, Pages: 138-142. Retrieved from http://www.acrwebsite.org/search/view-conference-proceedings.aspx?Id=7929

What (Really) is a Data Scientist?

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

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 popularData 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:

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?

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