Author Archives: Nicole Radziwill

A Chat with Jaime Casap, Google’s Chief Education Evangelist

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“The classroom of the future does not exist!”

That’s the word from Jaime Casap (@jcasap), Google’s Chief Education Evangelist — and a highly anticipated new Business Innovation Factory (BIF) storyteller for 2015.  In advance of the summit which takes place on September 16 and 17, Morgan and I had the opportunity to chat with Jaime about a form of business model innovation that’s close to our hearts – improving education. He’s a native New Yorker, so he’s naturally outspoken and direct. But his caring and considerate tone makes it clear he’s got everyone’s best interests at heart.

At Google, he’s the connector and boundary spanner… the guy the organization trusts to “predict the future” where education is concerned. He makes sure that the channels of communication are open between everyone working on education-related projects. Outside of Google, he advocates smart and innovative applications of technology in education that will open up educational opportunities for everyone.  Most recently, he visited the White House on this mission.

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The current system educational system is not broken, he says. It’s doing exactly what it was designed to do: prepare workers for a hierarchical, industrialized production economy. The problem is that the system cannot be high-performing because it’s not doing what we need it to for the upcoming decades, which requires leveraging the skills and capabilities of everyone.

He points out that low-income minorities now have a 9% chance of graduating from college… whereas a couple decades ago, they had a 6% chance. This startling statistic reflects an underlying deficiency in how education is designed and delivered in this country today.

So how do we fix it?

“Technology gives us the ability to question everything,” he says.  As we shift to performance-based assessments, we can create educational experiences that are practical, iterative, and focused on continuous improvement — where we measure iteration, innovation, and sustained incremental progress.

Measuring these, he says, will be a lot more interesting than what we tend to measure now: whether a learner gets something right the first time — or how long it took for a competency to emerge. From this new perspective, we’ll finally be able to answer questions like: What is an excellent school? What does a high-performing educational system look (and feel) like?

Jaime’s opportunity-driven vision for inclusiveness  is an integral part of Google’s future. And you can hear more about his personal story and how it shaped this vision next month at BIF.

If you haven’t made plans already to hear Jaime and the other storytellers at BIF, there may be a few tickets left — but this event always sells out! Check the BIF registration page and share a memorable experience with the BIF community this year: http://www.businessinnovationfactory.com/summit/register

Data Quality is Key for Asset Management in Data Science

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:

dont-archive-it

 

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:

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

 

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

Quality and Diversity, Especially Women in Tech

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

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

The newly launched R Consortium has announced its inaugural Board members, and not one of them is a woman. (Even more unfortunately, I don’t think any of them are active R users; although I’m sure he’s used it, the new President’s bio establishes him as a SAS and S-PLUS user.)

Although I’m sure the lack of diversity is an oversight (as it so often is), I’ve gotten my knickers in a knot a lot more about this issue lately. It’s probably just because I’m getting older (I’ll be 40 next year), but it’s also due to the fact that I’ve been reflecting an awful lot more lately: about what I’ve done, and what I’ve chosen not to do. About how I’ve struggled, and the battles I’ve chosen (versus those I’ve chosen to ignore). About how the subtle and unspoken climate of women in technology is keeping them out, and chasing them away, even though the industry needs more.

I really love programming. I’ve been doing it since 1982, when I realized that I could make my Atari 800 beep on command.

But in the workplace, I never really felt comfortable as a programmer. Whether they intended to or not, male colleagues always gave off a vibe of mistrust when they integrated my code… they always had a better way to design a new module, or a better approach to resolve a troubleshooting issue. When I got an instrumentation job that required field work on the hardware, I’d hear comments like “maybe you can stay here… girls don’t like to get dirty.” I felt uncomfortable geeking out with other women because I even felt like I’d be judged by them… like if they were some technical rock star, they would find my skills an embarrassment to other women like themselves who were trying to become experts.

So I went into software development management, where my role was much more accepted. My job was to let the coders do their job, and just keep everyone else out of their hair. I remember hearing comments like “you know a lot more about code than I thought you would.” I wanted to get a lot deeper into the technical aspects of the work, but I never felt like one of the guys. So I stopped trying.

Even while working as a manager, the organizations I was a part of were always male-dominated, in both the hierarchy and the style and tone of the work environment. (It was much like the masculine, emotionally void environment of so many of the classrooms I’d spent time in during my youth.) I felt lots of pressure to be firm and decisive, to never show emotions, and to work a 60 hour week even when I had a newborn at home. When I was firm and unyielding, I was called “difficult” and “strident.” I changed my approach and became “not assertive enough.” The women who I saw as being successful were all decidedly masculine, and I couldn’t transform my personality to become an ultra-productive, emotion-suppressing machine. (I’ve got the personality of an artist, and I’ve got to flow with my ideas and inspiration.)

Eventually I lost my mojo, switched careers entirely and went into higher education. (What do I teach? Mostly R… so I’m having fun, and I get to code pretty much every day.) But I still fantasize about getting back into the technical workforce and being one of those rare women leaders in technology (which I try to rationalize is not that rare at all, because I know plenty of women scientists, engineers, and technicians). But yeah, comparatively, we are a minority.

My situation is not unique. So why does this tend to happen? Gordon Hunt of Silicon Republic reports that gender stereotypes, a small talent pool, and in-group favoritism are to blame. I’ll agree with the gender stereotyping – even women do it to each other. My college roommate called me “Nerdcole” and it was sort of endearing, and sort of not. As a hiring manager, I remember being surprised every time a resume from a woman crossed my email box, and giving it a second look no matter what. I remember feeling guilty every time I thought “oh, well, she can’t be as serious about doing this as the guys are.” As for in-group favoritism, I think it’s hard not to favor naturally masculine people for jobs in a naturally masculine environment. 

The role of diversity in achieving quality and stimulating innovation has not been deeply explored in the research. Doing a quick literature search, I could only find a few examples. Liang et al. (2013) found that diversity does influence innovation, but due to inconsistent outcomes they couldn’t recommend a management intervention. Feldman & Audretch (1999) found that more innovation occurs in cities because of greater diversity. Ostergaard et al. (2011) explored the breadth of a firm’s knowledge base and its influence on innovation. And in one of my favorite papers ever, Bassett-Jones (2005) explains that diversity creates a “combustible cocktail of creative tension” that, although difficult to manage, ultimately enhancesa firm’s innovation performance.

I found no papers that looked at a link between diversity and quality performance.

But I would love to have a combustible cocktail of creative tension right now.

A 15-Week Course to Introduce Machine Learning and Intelligent Systems in R

lantz-ml-in-rEvery fall, I teach a survey course for advanced undergraduates that covers one of the most critical themes in data science: intelligent systems. According to the IEEE, these are “systems that perceive, reason, learn, and act intelligently.” While data science is focused on analyzing data (often quite a lot of it) to make effective data-driven decisions, intelligent systems use those decisions to accomplish goals. As more and more devices join the Internet of Things (IoT), collecting data and sharing it with other “things” to make even more complex decisions, the role of intelligent systems will become even more pronounced.

So by the end of my course, I want students to have some practical skills that will be useful in analyzing, specifying, building, testing, and using intelligent systems:

  • Know whether a system they’re building (or interacting with) is intelligent… and how it could be made more intelligent
  • Be sensitized to ethical, social, political, and legal aspects of building and using intelligent systems 
  • Use regression techniques to uncover relationships in data using R (including linear, nonlinear, and neural network approaches)
  • Use classification and clustering methods to categorize observations (neural networks, k-means/KNN, Naive Bayes, support vector machines)
  • Be able to handle structured and unstructured data, using both supervised and unsupervised approaches
  • Understand what “big data” is, know when (and when not) to use it, and be familiar with some tools that help them deal with it

My course uses Brett Lantz’s VERY excellent book, Machine Learning with R (which is now also available in Kindle format), which I provide effusive praise for at https://qualityandinnovation.com/2014/04/14/the-best-book-ever-on-machine-learning-and-intelligent-systems-in-r/

One of the things I like the MOST about my class is that we actually cover the link between how your brain works and how neural networks are set up. (Other classes and textbooks typically just show you a picture of a neuron superimposed with inputs, a summation, an activation, and outputs, implying that “See? They’re pretty much the same!”) But it goes much deeper than this… we actually model error-correction learning and observational learning through the different algorithms we employ. To make this point real, we have an amazing guest lecture every year by Dr. Anne Henriksen, who is also a faculty member in the Department of Integrated Science and Technology at JMU. She also does research in neuroscience at the University of Virginia. After we do an exercise where we use a spreadsheet to iteratively determine the equation for a single layer perceptron’s decision boundary, we watch a video by Dr. Mark Gluck that shows how what we’re doing is essentially error-correction learning… and then he explains the chemistry that supports the process. We’re going to videotape Anne’s lecture this fall so you can see it!

Here is the syllabus I am using for Fall 2015. Please feel free to use it (in full or in part) if you are planning a similar class… but do let me know!

Newsletter of the American Society for Quality (ASQ) Innovation Division – Issue #1, July 2015

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A bamboo forest in Kyoto, Japan. (Image Credit: Nicole Radziwill)

MESSAGE FROM THE CHAIR

Our first newsletter is a milestone for the Innovation Division of ASQ. A special thanks goes to Nicole Radziwill, our newsletter editor, for bringing this together.

Another milestone for the Division was achieved at this year’s World Conference. We were recognized as the Division with the highest percentage of certified members! That speaks to the very high knowledge and competence level we have in the division.

Please note that our Innovation Division Conference in Charlottesville, Virginia will soon be upon us. Jane Keathley has been working hard with Kevin Groth and Sarah Rosebruck in the Blue Ridge Section (1108) finalizing the program.

The Conference starts on September 19th with a reception on the Friday evening, with concurrent sessions on Saturday, and a half day workshop on Sunday. You can register online. With the Early Bird Special of only $199 this is truly an outstanding value. 

As we hit the 600 mark for membership (and move towards our first thousand) we are gradually building the infrastructure we need to operate as an effective division, and to give you as a member what you need in terms of knowledge about the exciting and growing field of Innovation. We rely so much on the officers in our core innovation team, and with 2016 approaching fast please let me know if you are interested in being a core team member. Ian Meggarrey is our Chair Elect, and as he takes on the role of chair in 2016 we will need to fill his role of managing the division website. There are many other opportunities if you are interested in joining the core innovation team.

Wishing you success in our World of Innovation.

Peter Merrill
Chair, ASQ Innovation Division
pm@petermerrill.com

ANNOUNCEMENTS AND CALLS FOR PARTICIPATION

+ Attend the 2015 ASQ Innovation Division Conference in Charlottesville, VA (September 18-20, 2015)
+ What is the PAR Innovation Award? Find out how you can get involved
+ Become a Guest Blogger for Quality & Innovation
+ Contribute an Article or Announcement for the Next Newsletter
+ Join the Division!

ARTICLES AND BOOK REVIEWS

+ Dr. Deming on Joy of Work, Innovation, and Leadership (by Tanmay Vora, @tnvora on Twitter)
+ The Innovation Think Tank Executive Summary
+ Why is Innovation “Quality for Tomorrow”?
+ Book Review: The Innovation Book by Max McKeown

CONTACT US

+ On the Webhttp://asq.org/innovation-group/
+ On Twitterhttps://twitter.com/asqinnovation
+ On YouTubehttps://www.youtube.com/channel/UCPebRUdPsYPszn0yOZmNiag/


Attend the 2015 ASQ Innovation Division Conference in Charlottesville, VA (September 18-20, 2015)

Want to get together with other people passionate about quality and innovation? In 2015 the ASQ Innovation Division hosts its third annual conference, following an inaugural meeting in Sacramento in 2013, and a very successful 2nd meeting in Toronto in September 2014 that brought approximately 80 attendees together for talks and networking. We look forward to an even more dynamic and inspiring time together at the end of this summer in Virginia! Topics will include innovation culture, managing innovation, innovation in processes, statistics and innovation, how to innovate in established organizations, and the pathway from quality to innovation. See the complete flyer here: 2015 3rd ASQ IC Flyer

innov-conf-announce

Although the call for presentations is now closed, the registration form for the conference is available at http://asq.org/innovation-group/about/2015-innovation-conference-registration-form.html?shl=116820


What is the PAR Innovation Award? Find Out!

Did you know that your Section, Division, or LCM can be recognized for its innovative initiatives? The ASQ Performance Award and Recognition (PAR) Program includes an Innovation Award for member units who use new information and knowledge in ways that benefit their members. Introduced in 2014, the award is intended to encourage increased member value through innovations, expand innovation process expertise, and share innovation efforts with other member units.

Applications are developed by the member unit and describe the unit’s problem or opportunity and how it was identified, how new information was used to come up potential solutions, how the solutions were narrowed down to a final selection, how that solution was developed and then how it was deployed. The applications are submitted to a judges’ panel for review and scoring. Each submitting unit receives a feedback report from the judges, and awards are based on the judges’ assessments. The applications are then shared with other members by posting on the ASQ website.
Seven member units were recognized in the 2014 cycle of the award:
 
Silver Innovation Award – Portland Section 0607
Bronze Innovation Award – Greater Fort Worth Section 1416
Honorable Mentions:
  • Reliability Division
  • Merrimack Valley Section 0102
  • Orange Empire Section 0701
  • Akron-Canton Section 0810
  • Greater Atlanta Section 1502
Applying for an Innovation Award is a great way to stimulate innovative thinking in your member unit, learn more about the innovation process, and share your ‘lessons learned’ with others in ASQ. What is your member unit doing that is outside-the-box, unusual, or revolutionary??? Consider applying for the PAR Innovation Award!
 
Contributed by Jane Keathley


Contribute an Article or Announcement for the Next Newsletter

This is only the first Newsletter for the ASQ Innovation Division! We plan to provide our members with regular quarterly updates about Division news, business, and opportunities, and we’ll need your help to make this a useful and dynamic resource. Please send anything you’d like to share with the membership by email to Nicole Radziwill (nicole.radziwill@gmail.com). Thank you for your interest and involvement!


Become a Guest Blogger for Quality & Innovation

Do you occasionally write about topics and issues that span the domains of quality improvement and innovation? If so, consider submitting a short article to Nicole Radziwill (nicole.radziwill@gmail.com) who contributes to ASQ’s Influential Voices program at http://qualityandinnovation.com.


Dr. Deming on Joy of Work, Innovation and Leadership

Having worked in Quality management role for a long time, I could not have afforded to miss insights from Dr. W. Edwards Deming whose thinking was way ahead of time. Dr. Deming is remembered for transforming Japan into a formidable business competitor through his management and leadership practices, especially Deming’s 14 principles.

In 1994, at the age of 92, Dr. Deming gave his last interview to IndustryWeek magazine which I read with great interest.

In part 1 of his interview, Deming says,

The source of innovation is freedom. All we have—new knowledge, invention—comes from freedom. Somebody responsible only to himself has the heaviest responsibility.

3M is a 100 years old company that thrives on innovation. 3M’s William McKnight first instituted a policy known as 15% rule – that engineers can use 15% of their time on whatever projects or initiatives they like. Later, Google also had a similar policy. McKnight used to tell his managers,

“If you put fences around people, you get sheep. Give people the room they need.”

This is even more crucial when an organization grows and if you want good people, you cannot manage them traditionally. They would want to do things in their own way. Providing a conducive space for performance is one of the primary responsibilities of a leader.

In the same interview, Dr. Deming also touched upon a topic businesses are still struggling with – how can leaders enable joy at work? He suggested,

The alternative is joy on the job. To have it, people must understand what their jobs are, how their work fits in, how they could contribute. Why am I doing this? Whom do I depend on? Who depends on me? Very few people have the privilege to understand those things. Management does not tell them. The boss does not tell them. He does not know what his job is. How could he know? When people understand what their jobs are, then they may take joy in their work. Otherwise, I think they cannot.

If we keep all the glorification of leaders aside, the two fundamental tasks of a leader are to get great talent (good people who care) and then help them succeed by providing clarity, reiterating the vision, mentoring and serving to their needs with a focus on achieving business outcomes.

After reading insights by Deming in this interview, I was only wondering about the depth of Dr. Deming’s passion about better business and better leadership that kept him engaged even at 93!

Tanmay Vora (@tnvora on Twitter) – Reprinted with permission from author


Innovation Think Tank Executive Summary

Two years ago, as the seeds for the ASQ Innovation Division were being planted, the ASQ Board of Directors commissioned a panel to establish the foundations for exploring innovation from the perspective of quality and improvement. If you’re a member of the Innovation Division and haven’t yet explored this resource, you should! It provides an excellent basis for framing your understanding of innovation, both from the philosophical and practical perspectives. Some of the key points are:

  • Whereas the discipline of quality emphasizes articulating and meeting customer requirements, innovation is captured in the white space of sensing and responding to unspoken or anticipated unmet needs in effective ways. (This can also involve creating needs where none previously existed.)
  • “Willingness to fail” (which can also be considered a willingness to engage in a process of continuous learning) — and the willingness to be less than comfortable throughout the process — must be part of the culture.
  • Innovation can be considered at the system, product, or process levels. At the system level, organizational structure or business models may be the subject of the revitalization efforts.
  • Innovation (and the knowledge exchange that supports it) must be measured. Risk management (including considering the opportunity costs of not taking risks) is also essential. 
  • The innovation process, by its nature, is natively ambidextrous — that is, both the creation and execution phases must be attended to equally.

ASQ Members can access the 2013 White Paper entitled “Innovation is Quality for Tomorrow” at http://asq.org/innovation-group/2013/11/asq-innovation-think-tank-executive-summary.html?shl=113585.

Contributed by Nicole Radziwill


Why is Innovation “Quality for Tomorrow”?

In December 2014, I went to an event hosted by Joyce Krech of the Shenandoah Valley Business Development Center (SBDC), a fantastic organization that helps to connect people in western Virginia to build new ventures, create new value, and promote local and regional economic development. I shared a little bit of the story of the ASQ Innovation Division with everyone, and learned how each person is addressing innovation in their own domain of expertise.

When Meghan Williamson presented her story about how innovation is happening right now in our local area, she reminded everyone of the result from the ASQ Innovation Think Tank a couple years ago, which has become a tagline for our innovation group at ASQ:

Innovation is Quality for Tomorrow.

When it was my time to speak, I clarified what this means to me in terms of my favorite definition of quality – the one that comes from ISO 9000 (para 3.1.5). Keep in mind that “entity” can be a project, a product, a process, or even a person:

Quality is the totality of characteristics of an entity that bear upon its ability to satisfy stated and implied needs.

How is innovation related to quality? I used this definition to provide a more specific description of our position that quality is innovation for tomorrow:

Innovation is the totality of characteristics of an entity that bear upon its ability to satisfy unmet, emerging, anticipated, and/or unanticipated needs.

Innovation is not so much our ability to create something new. More realistically, it is our ability to create new value by meeting needs. Which also implies that WE ARE ALL INNOVATORS. We all have the capacity and capability to create value, to meet needs, and to anticipate and tap into emerging needs. Why? Because each of us has a unique perspective, shaped by unique experiences and dispositions, with the unique talent for understanding some very important slice of humanity.

We are all innovators. Let’s collectively imagine the needs of tomorrow, and figure out how to satisfy them!

Nicole Radziwill (@nicoleradziwill on Twitter) – Reprinted with permission from author


Book Review: The Innovation Book by Max McKeown

The Innovation Book. 2014. Max McKeown. Harlow, UK: Pearson International. 258 pages.

According to Peter Merrill, founding leader of ASQ’s newly developing Innovation Division, innovation can be considered “quality for tomorrow”. We can maintain high quality in our products, processes, and organizations, and embark on continuous improvement efforts to maintain our competitive edge. But if we fail to acknowledge or embrace those forces that will keep us relevant in the future, we will not maintain our success. As a result, learning how to become conscious innovators (both as individuals and organizations) is a priority for many quality managers.

Management consultant Max McKeown, who practices in the UK, has produced an excellent guidebook for stimulating innovation both personally and in organizations. His academic background is evident in his well-structured arguments, yet he maintains an informal tone throughout that is reminiscent of the style of Tom Peters (though not nearly as irreverent). His book has six sections: Your Creative Self, Leading Innovators, Creating Innovation, Winning With Innovation, Innovator’s Turning Points, and The Innovator’s Toolkit. The first section focuses on tools for developing and enhancing creativity, while the second specifically addresses leadership challenges that are encountered while actively managing for innovation. The third and fourth sections frame these creative processes in terms of context of use, that is, that great ideas only become innovations when they are made useful. The fifth section is short, but reassuring: McKeown points out several examples of how very imperfect ideas still launched waves of innovation that were both notable and profound.

The clear strength of this book, however, is the sixth and final section – which provides an overview of 24 research-supported models for generating ideas, developing sound strategies, and engaging the social and organizational networks that support innovation. He covers older and more well known approaches like Altshuller’s TRIZ, which incorporating newer approaches like Alex Osterwalder’s Business Model Canvas. It is an effective blend of the established and the novel, subtly and effectively demonstrating how even our perspectives on innovation can be innovated! In short, this is a useful guidebook that is certain to catalyze ideas on how to improve both your personal innovative capacity and your ability to lead an innovative organization.

Review by Dr. Nicole Radziwill  (originally appeared in Quality Management Journal, July 2015)

Simulation for Data Science With R

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

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

Hey everyone! I just wanted to give you the heads up on a book project that I’ve been working on (which should be available by Spring 2016). It’s all about using the R programming language to do simulation — which I think is one of the most powerful (and overlooked) tools in data science. Please feel free to email or write comments below if you have any suggestions for material you’d like to have included in it!

Originally, this project was supposed to be a secret… I’ve been working on it for about two years now, along with two other writing projects, and was approached in June by a traditional publishing company (who I won’t mention by name) who wanted to brainstorm with me about possibly publishing and distributing my next book. After we discussed the intersection of their wants and my needs, I prepared a full outline for them, and they came up with a work schedule and sent me a contract. While I was reading the contract, I got cold feet. It was the part about giving up “all moral rights” to my work, which sounds really frightening (and is not something I have to do under creative commons licensing, which I prefer). I shared the contract with a few colleagues and a lawyer, hoping that they’d say don’t worry… it sounds a lot worse than it really is. But the response I got was it sounds pretty much like it is.

While deliberating the past two weeks, I’ve been moving around a lot and haven’t been in touch with the publisher. I got an email this morning asking for my immediate decision on the matter (paraphrased, because there’s a legal disclaimer at the bottom of their emails that says “this information may be privileged” and I don’t want to violate any laws):

If we don’t hear from you, unfortunately we’ll be moving forward with this project. Do you still want to be on board?

The answer is YEAH – of COURSE I’m “on board” with my own project. But this really made me question the value of a traditional publisher over an indie publisher, or even self-publishing. And if they’re moving forward anyway, does that mean they take my outline (and supporting information about what I’m planning for each chapter) and just have someone else write to it? That doesn’t sound very nice. Since all the content on my blog is copyrighted by ME, I’m sharing the entire contents of what I sent to them on July 6th to establish the copyright on my outline in a public forum.

So if you see this chapter structure in someone ELSE’S book… you know what happened. The publisher came up with the idea for the main title (“Simulation for Data Science With R”) so I might publish under a different title that still has the words Simulation and R in them.

I may still publish with them, but I’ll make that decision after I have the full manuscript in place in a couple months. And after I have the chance to reflect more on what’s best for everyone. What do you think is the best route forward?


 

Simulation for Data Science With R

Effective Data-Driven Decision Making for Business Analysis by Nicole M. Radziwill

Audience

Simulation is an essential (yet often overlooked) tool in data science – an interdisciplinary approach to problem-solving that leverages computer science, statistics, and domain expertise. This easy-to-understand introductory text for new and intermediate-level programmers, data scientists, and business analysts surveys five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling). The book focuses on practical and illustrative examples using the R Statistical Software, presented within the context of structured methodologies for problem solving (such as DMAIC and DMADV) that will enable you to more easily use simulation to make effective data-driven decisions. Readers should have exposure to basic concepts in programming but can be new to the R Statistical Software.

Mission

This book helps its readers 1) formulate research questions that simulation can help solve, 2) choose an appropriate problem-solving methodology, 3) choose one or more simulation techniques to help solve that problem,  4) perform basic simulations using the R Statistical Software, and 5) present results and conclusions clearly and effectively.

Objectives and achievements

The reader will:

  • Learn about essential and foundational concepts in modeling and simulation
  • Determine whether a simulation project is also a data science project
  • Choose an appropriate problem-solving methodology for effective data-driven decision making
  • Select suitable simulation techniques to provide insights about a given problem
  • Build and interpret the results from basic simulations using the R Statistical Software

SECTION I: BASIC CONCEPTS

  1. Introduction to Simulation for Data Science
  2. Foundations for Decision-Making
  3. SECRET NEW CHAPTER THAT YOU WILL BE REALLY EXCITED ABOUT

SECTION II: STOCHASTIC PROCESSES

  1. Variation and Random Variable Generation
  2. Distribution Fitting
  3. Data Generating Processes

SECTION III: SIMULATION TECHNIQUES

  1. Monte Carlo Simulation
  2. Discrete Event Simulation
  3. System Dynamics
  4. Agent-Based Modeling
  5. Resampling Methods
  6. SECRET NEW CHAPTER THAT YOU WILL BE REALLY EXCITED ABOUT

SECTION IV: CASE STUDIES

  1. Case Study 1: Possibly modeling opinion dynamics… specific example still TBD
  2. Case Study 2: A Really Practical Application of Simulation (especially for women)

Chapter 1: Introduction to Simulation for Data Science – 35 pages

Description

This chapter explains the role of simulation in data science, and provides the context for understanding the differences between simulation techniques and their philosophical underpinnings.

Level

BASIC

Topics covered

Variation and Data-Driven Decision Making

What are Complex Systems?

What are Complex Dynamical Systems? What is systems thinking? Why is a systems perspective critical for data-driven decision making? Where do we encounter complex  systems in business or day-to-day life?

What is Data Science?

A Taxonomy of Data Science. The Data Science Venn Diagram. What are the roles of modeling and simulation in data science projects? “Is it a Data Science Project?” — a Litmus Test. How modeling and simulation align with data science.

What is a Model?

Conceptual Models. Equations. Deterministic Models, Stochastic Models. Endogeneous and Exogenous Variables.

What is Simulation?

Types of Simulation: Static vs. Dynamic, Stochastic vs. Deterministic, Discrete vs. Continuous, Terminating and Non-Terminating (Steady State). Philosophical Principles: Holistic vs. Reductionist, Kadanoff’s Universality, Parsimony, Sensitivity to Initial Conditions

Why Use Simulation?

Simulation and Big Data

Choosing the Right Simulation Technique

Skills learned

The reader will be able to:

  • Distinguish a model from a simulation
  • Explain how simulation can provide a valuable perspective in data-driven decision making
  • Understand how simulation fits into the taxonomy of data science
  • Determine whether a simulation project is also a data science project
  • Determine which simulation technique to apply to various kinds of real-world problems

Chapter 2: Foundations for Decision Making – 25 pages

Description

In this chapter, the reader will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. The social context of data science will be explained, emphasizing the growing importance of collaborative data and information sharing.

Level

BASIC

Topics covered

The Social Context of Data Science

Ethics and Provenance. Data Curation. Replicability, Reproducibility, and Open Science. Open, interoperable frameworks for collaborative data and information sharing. Problem-Centric Habits of Mind.

Selecting Key Performance Indicators (KPIs)

Determining the Number of Replications

Methodologies for Simulation Projects

A General Problem-Solving Approach

DMAIC

DMADV

Root Cause Analysis (RCA)

PDSA

Verification and Validation Techniques

Output Analysis

Skills learned

The reader will be able to:

  • Plan a simulation study that is supported by effective and meaningful metadata
  • Select an appropriate methodology to guide the simulation project
  • Choose activities to ensure that verification and validation requirements are met
  • Construct confidence intervals for reporting simulation output

Chapter 3: Variability and Random Variate Generation – 25 pages

Description

Simulation is powerful because it provides a way to closely examine the random behavior in systems that arises due to interdependencies and variability. This requires being able to generate random numbers and random variates that come from populations with known statistical characteristics. This chapter describes how random numbers and random variates are generated, and shows how they are applied to perform simple simulations.

Level

MEDIUM

Topics covered

Variability in Stochastic Processes

Why Generate Random Variables?

Pseudorandom Number Generation

Linear Congruential Generators

Inverse Transformation Method

Using sample for Discrete Distributions

Is this Sequence Random? Tests for Randomness

Autocorrelation, Frequency, Runs Tests. Using the randtests package

Tests for homogeneity

Simple Simulations with Random Numbers

 

Skills learned

The reader will be able to:

  • Generate pseudorandom numbers that are uniformly distributed
  • Use random numbers to generate random variates from a target distribution
  • Perform simple simulations using streams of random numbers

Chapter 4: Data Generating Processes – 30 pages

Description

To execute a simulation, you must be able to generate random variates that represent the physical process you are trying to emulate. In this chapter, we cover several common statistical distributions that can be used to represent real physical processes, and explain which physical processes are often modeled using those distributions.

Level

MEDIUM

Topics covered

What is a Data Generating Process?

Continuous, Discrete, and Multivariate Distributions

Discrete Distributions

Binomial Distribution

Geometric Distribution

Hypergeometric Distribution

Poisson Distribution

Continuous Distributions

Exponential Distribution

F Distribution

Lognormal Distribution

Normal Distribution

Student’s t Distribution

Uniform Distribution

Weibull Distribution

Chi2 Distribution

Stochastic Processes

Markov. Poisson. Gaussian, Bernoulli. Brownian Motion. Random Walk.

Stationary and Autoregressive Processes.

 

Skills learned

The reader will be able to:

  • Understand the characteristics of several common discrete and continuous data generating processes
  • Use those distributions to generate streams of random variates
  • Describe several common types of stochastic processes

Chapter 5: Distribution Fitting – 30 pages

Description

An effective simulation is driven by data generating processes that accurately reflect real physical populations. This chapter shows how to use a sample of data to determine which statistical distribution best represents the real population. The resulting distribution is used to generate random variates for the simulation.

Level

MEDIUM

Topics covered

Why is Distribution Fitting Essential?

Techniques for Distribution Fitting

Shapiro-Wilk Test for Normality

Anderson-Darling Test

Lillefors Test

Kolmogorov-Smirnov Test

Chi2 Goodness of Fit Test

Other Goodness Of Fit Tests

Transforming Your Data

When There’s No Data, Use Interviews

Skills learned

The reader will be able to:

  • Use a sample of real data to determine which data generating process is required in a simulation
  • Transform data to find a more effective data generating process
  • Estimate appropriate distributions when samples of real data are not available

Chapter 6: Monte Carlo Simulation – 30 pages

Description

This chapter explains how to set up and execute simple Monte Carlo simulations, using data generating processes to represent random inputs.

Level

ADVANCED

Topics covered

Anatomy of a Monte Carlo Project

The Many Flavors of Monte Carlo

The Hit-or-Miss Method

Example: Estimating Pi

Monte Carlo Integration

Example: Numerical Integration of y = x2

Estimating Variables

Monte Carlo Confidence Intervals

Example: Projecting Profits

Sensitivity Analysis

Example: Projecting Variability of Profits

Example: Projecting Yield of a Process

Markov Chain Monte Carlo

Skills learned

The reader will be able to:

  • Plan and execute a Monte Carlo simulation in R
  • Construct confidence intervals using the Monte Carlo method
  • Determine the sensitivity of process outputs and interpret the results

Chapter 7: Discrete Event Simulation – 30 pages

Description

What is this chapter about?

Level

ADVANCED

Topics covered

Anatomy of a DES Project

Entities, Locations, Resources and Events

System Performance Metrics

Queuing Models and Kendall’s Notation

The Event Calendar

Manual Event Calendar Generation

Example: An M/M/1 system in R

Using the queueing package

Using the simmer package

Arrival-Counting Processes with the NHPoisson Package

Survival Analysis with the survival Package

Example: When Will the Bagels Run Out?

Skills learned

The reader will be able to:

  • Plan and execute discrete event simulation in R
  • Choose an appropriate model for a queueing problem
  • Manually generate an event calendar to verify simulation results
  • Use arrival counting processes for practical problem-solving
  • Execute a survival analysis in R and interpret the results

Chapter 8: System Dynamics – 30 pages

Description

This chapter presents system dynamics, a powerful technique for characterizing the effects of multiple nested feedback loops in a dynamical system. This technique helps uncover the large-scale patterns in a complex system where interdependencies and variation are critical.

Level

ADVANCED

Topics covered

Anatomy of a SD Project

The Law of Unintended Consequences and Policy Resistance

Introduction to Differential Equations

Causal Loop Diagrams (CLDs)

Stock and Flow Diagrams (SFDs)

Using the deSolve Package

Example: Lotka-Volterra Equations

Dynamic Archetypes

Linear Growth

Exponential Growth and Collapse

S-Shaped Growth

S-Shaped Growth with Overshoot

Overshoot and Collapse

Delays and Oscillations

Using the stellaR and simecol Packages

Skills learned

The reader will be able to:

  • Plan and execute a system dynamics project
  • Create causal loop diagrams and stock-and-flow diagrams
  • Set up simple systems of differential equations and solve them with deSolve in R
  • Predict the evolution of stocks using dynamic archetypes in CLDs
  • Convert STELLA models to R

Chapter 9: Agent-Based Modeling – 25 pages

Description

Agent-Based Modeling (ABM) provides a unique perspective on simulation, illuminating the emergent behavior of the whole system by simply characterizing the rules by which each participant in the system operates. This chapter provides an overview of ABM, compares and contrasts it with the other simulation techniques, and demonstrates how to set up a simulation using an ABM in R.

Level

ADVANCED

Topics covered

Anatomy of an ABM Project

Emergent Behavior

PAGE (Percepts, Actions, Goals, and Environment)

Turtles and Patches

Using the RNetLogo package

Skills learned

The reader will be able to:

  • Plan and execute an ABM project in R
  • Create specifications for the ABM using PAGE

Chapter 10: Resampling – 25 pages

Description

Resampling methods are related to Monte Carlo simulation, but serve a different purpose: to help us characterize a data generating process or make inferences about the population our data came from when all we have is a small sample. In this chapter, resampling methods (and some practical problems that use them) are explained.

Level

MEDIUM

Topics covered

Anatomy of an Resampling Project

Bootstrapping

Jackknifing

Permutation Tests

Skills learned

The reader will be able to:

  • Plan and execute a resampling project in R
  • Understand how to select and use a resampling technique for real data

Chapter 11: Comparing the Simulation Techniques – 15 pages

Description

In this chapter, the simulation techniques will be compared and contrasted in terms of their strengths, weaknesses, biases, and computational complexity.

Level

ADVANCED

Topics covered

TBD – at least two simulation approaches will be applied

Skills learned

The reader will learn how to:

  • Think about a simulation study holistically
  • Select an appropriate combination of techniques for a real simulation study
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