For this month’s Influential Voices Roundtable, the American Society for Quality (ASQ) asks: “In today’s current climate, transformation is a common term and transformative efforts are a regular occurrence. Although these efforts are common, according to Harvard Business Review two-thirds of large-scale transformation efforts fail. Research has proven that effective leadership is crucial for a change initiative to be successful. How can an individual become a successful Change Leader?“
Change is hard only because maintaining status quo is easy. Doing things even a little differently requires cognitive energy! Because most people are pretty busy, there has to be a clear payoff to invest that extra energy in changing, even if the change is simple.
Becoming a successful change leader means helping people find the reasons to invest that energy on their own. First, find the source of resistance (if there is one) and do what you can to remove it. Second, try co-creation instead of feedback to build solutions. Here’s what I mean.
Find Sources of Resistance
In 1983, information systems researcher M. Lynne Markus wanted to figure out why certain software implementations, “designed at great cost of time and money, are abandoned or excessively overhauled because they were unenthusiastically received by their intended users.” Nearly 40 years later, enterprises still occasionally run into the same issue, even though Software as a Service (SaaS) models can (to some extent) reduce this risk.
Before her research started, she found these themes associated with resistance (they will probably feel familiar to you even today):
By studying failed software implementations in finance, she uncovered three main sources for the resistance. So as a change leader, start out by figuring out if they resonate, and then apply one of the remedies on the right:
As you might imagine, this third category (the “political version of interaction theory”) is the most difficult to solve. If a new process or system threatens someone’s power or position, they are unlikely to admit it, it may be difficult to detect, and it will take some deep counseling to get to the root cause and solve it.
Co-Creation Over Feedback
Imagine this: a process in your organization is about to change, and someone comes to you with a step-by-step outline of the new proposed process. “I’d like to get your feedback on this,” he says.
That’s nice, right? Isn’t that exactly what’s needed to ensure smooth management of change? You’ll give your feedback, and then when it’s time to adopt the process, it will go great – right?
In short, NO.
For change to be smooth and effective, people have to feel like they’re part of the process of developing the solution. Although people might feel slightly more comfortable if they’re asked for their thoughts on a proposal, the resultant solution is not theirs — in fact, their feedback might not even be incorporated into it. There’s no “skin in the game.”
In contrast, think about a scenario where you get an email or an invitation to a meeting. “We need to create a new process to decide which of our leads we’ll follow up on, and evaluate whether we made the right decision. We’d like it to achieve [the following goals]. We have to deal with [X, Y and Z] boundary conditions, which we can’t change due to [some factors that are well articulated and understandable].”
You go to the meeting, and two hours later all the stakeholders in the room have co-created a solution. What’s going to happen when it’s time for that process to be implemented? That’s right — little or no resistance. Why would anyone resist a change that they thought up themselves?
Find the resistance, cast it out, and co-create solutions. But don’t forget the most important step: recognizing that perfection is not always perfect. (For quality professionals, this one can be kind of tough to accept at times.)
What this means is: in situations where change is needed, sometimes it’s better to adopt processes or practices that are easier or more accessible for the people who do them. Processes that are less efficient can sometimes be better than processes that are more efficient, if the difference has to do with ease of learning or ease of execution. Following these tips will help you help others take some of the pain out of change.
The Minimum Viable Product (MVP) concept has taken off over the past few years. Indeed, its heart is in the right place. MVP encourages product managers to scope features and functionality carefully so that customer needs are satisfied at every stage of development — not just in a sweeping finale at the end of development.
Unfortunately, adhering to MVP won’t guarantee success thanks to one critical caveat. And that is: if your product already exists, you have to consider your product’s base state. What can your customers do right now with your product? Failure to take this into consideration can be disastrous.
An Example: Your Web Site
Here’s what I mean: let’s say the product is your company’s web site. If you’re starting from scratch, a perfectly suitable MVP would be a splash page with one or two sentences about what you do. Maybe you’d add some contact information. Customers will be able to find you and communicate with you, and you’ll be providing greater value than without a web presence.
But if you already have a 5000-page site online, that solution is not going to fly. Customers and prospects returning to your site will wonder why it vaporized. If they’re relying on the content or functionality you previously provided, chances are they will not be happy. Confused, they may choose to go elsewhere.
The moral of the story is: in defining the scope of your MVP, take into consideration what your customers can already do, and don’t dare give them less in your next release.
Today is Cinco de Mayo! It’s also the 10th Anniversary of my PhD defense…. something I carefully timed for late afternoon on this day in 2009. (I wanted to make sure I could celebrate the joyful occasion — or drown my sorrows — with 2-for-1 margaritas. Fortunately, the situation was liquid joy; unfortunately, I still got a hangover.)
I’m writing this post to share what I’ve learned about the value of getting a PhD (is there value?) and the applicability of PhD-level work to industry. If you’re considering more education, maybe this will help you decide whether it’s the right choice. If you’re in industry and trying to figure out whether to hire PhDs, some of what I write here might help. But first, some background!
I never even thought I’d get a PhD — it certainly didn’t happen out of intent or design. My family was poor and I studied ridiculously hard so I could “escape it.” I didn’t think I was smart enough for a PhD, even though I started college at 16 taking half undergrad classes and half grad classes in meteorology. I aced my grad classes and very maturely ignored my required classes, so I got kicked out. (At the same time, I wasn’t really fitting in with people… my roommate called me “Nerdcole”.) When I was let back in the department head wouldn’t let me take any grad classes so I got bored and burned out… not surprising since I was supporting myself, and working three jobs to make that happen. I quit school to work at an e-commerce startup when I was 18. A few months later, thanks to (good) peer pressure, I took 3 credit by exams to see if it would get me over the finish line, and thanks to some side skills I had picked up in vector calculus and statistics, it worked and I got the BS. But I was still left with a pretty bad GPA, and even worse self esteem, and I was convinced no one would ever let me into grad school.
I figured I’d focus on industry and help companies grow. There was no other choice.
The Back Story
After spending a couple years building web sites and storefronts (a huge feat in 1995 and 1996!) I took a job at a national lab as a systems analyst, supporting older scientists and engineers and helping them get work done. The main lesson I learned during this time was: Alignment between strategy and objectives doesn’t come for free (teams of people have to spend dedicated time on it), and most people are really disorganized. There had to be a better way to get work done.
A few years later, I was a traveling Solutions Architect, parachuted once or twice a month into CRM software implementation fiascos around the globe. My job was to figure out what to do to turn these jobs around — was it a people problem? An architecture problem? A training problem? A systems thinking problem? A little of everything? I had a couple weeks to make a recommendation, and then I was on to the next project (results were usually pretty good). But since this required evaluating technology decisions in the context of business and financial constraints, my boss suggested that I use the tuition benefits offered by my job to get an MBA. I had taken 9 credits of science and industrial engineering classes since I’d graduated, so I contacted two of the local MBA schools to see if they’d accept me and my credits. Sure enough, one of them did! I took evening classes for a year and a half, and eventually ended up with an MBA. But I never thought I could (or would) go farther — I’m not that smart, I’d tell myself. Also, it’s expensive. Also, a PhD would probably make me less marketable. (All lies, spoken by a lack of confidence.)
Shortly thereafter, the travel started to get to me (I was flying at least three days a week), so I looked for an opportunity to grow and cultivate a software development organization. (That’s how I ended up in Data Management at NRAO.) A little management led to a lot of management. A few years later one of the organization’s leaders said it was “too bad I didn’t have a PhD” — because in a highly scientific and technical organization, it would give me more credibility and make me a better leader.
“Will you pay for it?” I asked. “Sure,” they said. I just had to find a suitable program that wouldn’t require me to go full time. I’ve always loved learning, and I couldn’t resist the temptation of free education — even if it meant I’d have to balance the demands of a challenging full-time job and a first-time baby at the same time. That’s how much I love learning, just for learning’s sake! I still didn’t think a PhD had that much value, unless you were studying to be a lab scientist or you were dead set on becoming a historian and teaching for the rest of your life. None of these personas was me, but the free education thing sold me, and I didn’t really think about how relevant this step was to my career direction until much later.
The next few years were pretty rough, and by the time I got my PhD, I was in my 14th year of post-college professional employment. First lesson learned: it’s probably not the best move to start PhD coursework when you have a three-month old. I have no idea how I made it through.
Shortly after graduating, the impacts of the financial crisis hit our federally funded organization and I was able to segue into a second career as a college professor, teaching data science and manufacturing/EHSQ classes. For the past year, I’ve been back in industry (maybe permanently; we’ll see) and have a better sense of the value of PhDs in industry.
Value of Getting a PhD
There are lots of reasons I’m happy with the time I spent getting a PhD, other than the fact that it helped me get an entirely new job when the economy was down:
First and foremost, I’m a better critical thinker. It’s now my nature to look at all parts of a problem, examine the interactions between them, and make sure I have all the required background information needed to start working on a problem.
I’m a better writer too. I look at reports and presentations I wrote years ago, and can see all the holes and places where I made assumptions that weren’t valid.
I developed a new appreciation for clarity. Researchers want to make sure their messages, methodologies, and models are clear and unambiguous… through the contrast, I was able to recognize that in industry, there’s often pressure to skip due diligence and move fast to perform. This pressure leads to ambiguity, which tends towards what I call “intellectual waste” – people assuming that they see a problem or a project in the same way because they haven’t taken the time to guarantee clarity.
It’s easier for me to quickly determine whether information might be true or false, or whether there are gaps that need to be closed before moving forward. (It’s possible that this skill is more from grading and evaluating student work… something that’s orders of magnitude harder than it seems.)
I realized that words matter. Really thinking about how one person will respond to a word or phrase, and whether it conveys the meaning that you intend, is a craft — that’s enhanced by working with collaborators.
And although I knew this one prior to the PhD, I found that data matters. Where did your data come from? Can you access the original? What kind of people (or instruments) gathered it? Can you trust them? The quality of your data — and the suitability of the methods you choose — will impact the quality and integrity of the conclusions you generate from it. Awareness of these factors is essential.
Value of Caution
One of the biggest lessons was the most surprising. Early on in the PhD program I was told that my opinion didn’t count — regardless of how many years of experience I had. Every statement I made had to be backed up and cited, preferably using material that had been peer-reviewed by other qualified people. At first I was kind of offended by this… didn’t these academics have any sense of the value of actual real-world employment? Apparently not.
But something funny happened as I developed the habit of looking for solid references, distilling their messages, and citing them accurately: I became more careful. And in the evolution of my caution and attention to detail, the quality of my work — ANY work — improved tremendously. I was able to learn from what other people had discovered, and anticipate (and resolve) problems in advance. I learned that “standing on the shoulders of giants” actually means figuring out when solved problems already exist so you don’t waste time reinventing wheels.
Something else funny happened as soon as I graduated: all of a sudden, people were asking me for my opinion. But the habit of due diligence was so ingrained that I couldn’t express my opinion… I was compelled to back it up with facts!
(I think this was the point all along. Go figure.)
The beauty of going through the entire messy process of PhD coursework and comps and research and defense and editing — the entire end-to-end process, not cutting out in the middle anywhere — it gave me the discipline and process to root out accurate and complete answers to problems. Or at the very least, to be able to call out the gaps to get there.
There’s a lot of pressure in industry to move fast, but due diligence is still critical for accurate self-assessment and effective cross-functional communication. Slowing down and figuring out how you know what you know — and making sure everyone is literally on the same page — can help your organization achieve its goals more quickly.
Value of PhDs to Industry
So employers (especially in tech) — should you hire PhDs? Yes. Here’s why:
PhDs are trained to find gaps in knowledge and understanding. Is your strategic plan grounded in reality, or is it just wishful thinking? Are your Project Charters well scoped, budgeted, and planned out? Is your workforce prepared to carry out your strategic initiatives?
Many PhDs with experience teaching undergrads are great at making complex topics accessible to other audiences. This is fantastic for training, cross-training, and marketing.
PhDs love research and writing, and can help you with gathering and interpreting data and content marketing.
PhDs love learning. Want to be on the cutting edge? They’re great in R&D… they can help you distill new insights from research papers and interpret and apply them accurately.
If you want to do AI or machine learning, or anything that uses Big Data, make sure you have at least one PhD statistician with practical analytical experience. They can prevent you from spending millions on dead ends and help you apply Occam’s Razor to avoid unnecessary complexity (the kind that can lead to technical debt later).
Bottom line… don’t be afraid of PhDs! We are mere mortals who just happen to have spent several years trying to figure out how to get to the core — the fundamental truth — of a complex problem. As a result we know how to approach problems like this — problems that many businesses have lots of. (We are not overqualified at all… we just have an extra skill set in something you desperately need, but may not realize you need it.)
Getting a PhD was challenging, frustrating, and maddening at times (especially the final part of getting your camera-ready text ready for ProQuest). I never planned to do it, but I’d totally do it again. I think my only regret is that I got a PhD in a hybrid business/industrial engineering discipline… it allowed me the freedom to pursue my interests, but if I was at the same crossroads now, I’d get a PhD in statistics to complement my MBA. Overall, this is a pretty tiny regret.
One of the heuristics we use at Intelex to guide decision making is former US President Truman’s advice that “imperfect action is better than perfect inaction.” What it means is — don’t wait too long to take action, because you don’t want to miss opportunities. Good advice, right?
When I share this with colleagues, I often hear a response like: “that’s dangerous!” To which my answer is “well sure, sometimes, but it can be really valuable depending on how you apply it!” The trick is: knowing how and when.
Here’s how it can be dangerous. For example, statistical process control (SPC) exists to keep us from tampering with processes — from taking imperfect action based on random variation, which will not only get us nowhere, but can exacerbate the problem we were trying to solve. The secret is to apply Truman’s heuristic based on an understanding of exactly how imperfect is OK with your organization, based on your risk appetite. And this is where loss functions can help.
Along the way, we’ll demonstrate how to do a few important things related to plotting with the ggplot package in R, gradually adding in new elements to the plot so you can see how it’s layered, including:
Plot a function based on its equation
Add text annotations to specific locations on a ggplot
Draw horizontal and vertical lines on a ggplot
Draw arrows on a ggplot
Add extra dots to a ggplot
Eliminate axis text and axis tick marks
What is a Loss Function?
A loss function quantifies how unhappy you’ll be based on the accuracy or effectiveness of a prediction or decision. In the simplest case, you control one variable (x) which leads to some cost or loss (y). For the case we’ll examine in this post, the variables are:
How much time and effort you put in to scoping and characterizing the problem (x); we assume that time+effort invested leads to real understanding
How much it will cost you (y); can be expressed in terms of direct costs (e.g. capex + opex) as well as opportunity costs or intangible costs (e.g. damage to reputation)
Here is an example of what this might look like, if you have a situation where overestimating (putting in too much x) OR underestimating (putting in too little x) are both equally bad. In this case, x=10 is the best (least costly) decision or prediction:
# describe the equation we want to plot
parabola <- function(x) ((x-10)^2)+10
# initialize ggplot with a dummy dataset
p <- ggplot(data = data.frame(x=0), mapping = aes(x=x))
p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) +
xlab("x = the variable you can control") +
ylab("y = cost of loss ($$)")
In regression (and other techniques where you’re trying to build a model to predict a quantitative dependent variable), mean square error is a squared loss function that helps you quantify error. It captures two facts: the farther away you are from the correct answer the worse the error is — and both overestimating and underestimating is bad (which is why you square the values). Across this and related techniques, the loss function captures these characteristics:
Not all loss functions have that general shape. For classification, for example, the 0-1 loss function tells the story that if you get a classification wrong (x < 0) you incur all the penalty or loss (y=1), whereas if you get it right (x > 0) there is no penalty or loss (y=0):
# set up data frame of red points
d.step <- data.frame(x=c(-3,0,0,3), y=c(1,1,0,0))
# note that the loss function really extends to x=-Inf and x=+Inf
ggplot(d.step) + geom_step(mapping=aes(x=x, y=y), direction="hv") +
geom_point(mapping=aes(x=x, y=y), color="red") +
xlab("y* f(x)") + ylab("Loss (Cost)") +
ggtitle("0-1 Loss Function for Classification")
Use the Loss Function to Make Strategic Decisions
So let’s get back to Truman’s advice. Ideally, we want to choose the x (the amount of time and effort to invest into project planning) that results in the lowest possible cost or loss. That’s the green point at the nadir of the parabola:
What this means is — if we don’t plan, or we plan just a little bit, we incur high costs. We might make the wrong decision! Or miss critical opportunities! But if we plan too much — we’re going to spend too much time, money, and/or effort compared to the benefit of the solution we provide.
The trick is to FIND THAT CRITICAL LEVEL OF TIME and EFFORT invested to gain information and understanding about your problem… and then if you’re going to err, make sure you err towards the left — if you’re going to make a mistake, make the mistake that costs less and takes less time to make:
The moral of the story is… imperfect action can be expensive, but perfect action is ALWAYS expensive. Spend less to make mistakes and learn from them, if you can! This is one of the value drivers for agile methodologies… agile practices can help improve communication and coordination so that the loss function is minimized.
## FULL CODE FOR THE COMPLETELY ANNOTATED CHART ##
# If you change the equation for the parabola, annotations may shift and be in the wrong place.
parabola <- function(x) ((x-10)^2)+10
my.title <- expression(paste("Imperfect Action Can Be Expensive. But Perfect Action is ", italic("Always"), " Expensive."))
arrow.x <- c(10, 10, 10, 10)
arrow.y <- c(35, 50, 65, 80)
arrow.x.end <- c(6, 6, 6, 6)
arrow.y.end <- arrow.y
d <- data.frame(arrow.x, arrow.y, arrow.x.end, arrow.y.end)
p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) +
xlab("Time Spent and Information Gained (e.g. person-weeks)") + ylab("$$ COST $$") +
annotate(geom="text", x=10, y=5, label="Some Effort, Lowest Cost!!", color="darkgreen") +
geom_point(aes(x=10, y=10), colour="darkgreen") +
annotate(geom="text", x=0, y=100, label="$$$$$", color="green") +
annotate(geom="text", x=0, y=75, label="$$$$", color="green") +
annotate(geom="text", x=0, y=50, label="$$$", color="green") +
annotate(geom="text", x=0, y=25, label="$$", color="green") +
annotate(geom="text", x=0, y=0, label="$ 0", color="green") +
annotate(geom="text", x=2, y=0, label="minutes\nof effort", size=3) +
annotate(geom="text", x=20, y=0, label="months\nof effort", size=3) +
annotate(geom="text",x=3, y=85, label="Little (or no) Planning\nHIGH COST", color="red") +
annotate(geom="text", x=18, y=85, label="Paralysis by Planning\nHIGH COST", color="red") +
geom_vline(xintercept=0, linetype="dotted") +
geom_hline(yintercept=0, linetype="dotted") +
geom_segment(data=d, mapping=aes(x=arrow.x, y=arrow.y, xend=arrow.x.end, yend=arrow.y.end),
arrow=arrow(), color="blue", size=2) +
annotate(geom="text", x=8, y=95, size=2.3, color="blue",
label="we prefer to be\non this side of the\nloss function") +
Now sometimes you need to make this investment! (Think nuclear power plants, or constructing aircraft carriers or submarines.) Don’t get caught up in getting your planning investment perfectly optimized — but do be aware of the trade-offs, and go into the decision deliberately, based on the risk level (and regulatory nature) of your industry, and your company’s risk appetite.
As Industry 4.0 and Digital Transformation efforts bear their first fruits, capabilities, business models, and the organizations that embody them are transforming. A century ago, we thought of organizations as machines to be rigidly designed and controlled. In the latter part of the 20th century, organizations were thought of as knowledge to be cultivated, shared, and expanded. But “as intelligent systems gain traction, we are once again at a crossroads – where organizations must create complete and meaningful experiences” for their customers, stakeholders, and employees.
“Participatory Art” doesn’t just mean creating things that are pretty to look at in your office lobby or tradeshow booth. It means finding ways to connect with your audience in ways that help them find meaning, purpose, and self-awareness – the ultimate ingredient for authentic engagement.
Designing experiences to make this happen is challenging, but totally within reach. Learn more in today’s new article!
Like a champion rowing team, your organization needs to make sure everyone is working together, engaged in synchronized work and active collaboration, and not working at cross-purposes.
But like risk management, working on alignment can seem like a luxury. No one really has time to slow down and make sure everyone’s moving in the same direction. And besides, alignment just happens naturally if each functional area knows what they’re supposed to be working on… right?
Neither of these statements are, of course, true. Synchronizing people and processes – and making sure they’re aware of the needs and desires of real customers instead of cardboard personas – takes dedicated effort and a commitment from senior leaders. There are other critical impacts too: lack of alignment negatively impacts not only project outcomes – but also professional relationships and the bottom line.
An Example of Diagnosing Misalignment
Although alignment is a many-to-many problem, and requires you to look at relationships between people in all your functional areas, a January 2018 survey from Altify examined one part of the organizational puzzle: alignment between sales and marketing. This is a big one, because sales teams use marketing materials to understand and sell the product or service your company offers. Their survey of 422 enterprise-level executives and sales leaders showed that:
74% of marketers think they understood customer needs, but only 44% of sales people in their organizations agreed
71% of marketers think sales and marketing are aligned, but only 59% of sales people in their organizations agreed
These differences may seem small, but they reveal a lack of alignment between sales and marketing. One group thinks they “get it” – while people in the other group are just shaking their heads.
Symptoms of Misalignment
…include things like:
of Fear. Your organization has a strategic plan (knows WHAT it wants to do),
but there is little to no coordination regarding HOW people across the
organization will accomplish strategic objectives. You know what KPIs you’re
supposed to deliver on, but you don’t know how exactly you’re supposed to work
with anything in your power or control to “move the needle.”
Tower Syndrome. You’re in a meeting and get the visceral sense that things
aren’t clear, or that different people have different expectations for a
project or initiative. But you’re too nervous or uncertain to ask for clarification
– or maybe you do ask, but you get an equally unclear answer.
Naturally, you assume that everyone in the room is smarter than you (particularly
the managers) so you shut up and hope that it makes sense later. The reality is
that you may be picking up on a legitimate problem that’s going to be problematic
for the organization later on.
A department committed you to a task, but you weren’t part of that decision. Once
you find out about it, the task just may not get done. Alternatively, you’ll
have to adjust your workload and reset expectations with the stakeholders who
will now be disappointed that you can’t meet their needs according to the
original schedule. Or maybe work evenings and weekends to get the job done on
time. Either way, it’s not pleasant for anyone.
How often are you called on to respond to something that’s absolutely needed by close of
business today? How often are you expected to drop everything and take care
of it? How often do you have to work nights and weekends to make sure you don’t
In this scenario, key stakeholders are called into projects at the 11th
hour, when they are unable to guide or influence the direction of an
initiative. The initiative becomes a “dead man walking” that’s doomed to an
untimely end, but since the organization has sunk time and effort into it, people
will push ahead anyway.
Cut Off at
the Pass. Have you ever been working on a project and find out – somewhere in
the middle of doing it – that some other
person or team has been working on the same
thing? Or maybe they’ve been working on a different project, but it’s ultimately
at cross purposes with yours. Whatever way this situation works out, your
organization ends up with a pile of waste and potential rework.
That’s the subject for more blog posts that will be coming this spring – as well as what causes misalignment in the first place (hint: it’s individual behaviors on an organizational scale). The good news is – misalignment can be fixed, and the degree of alignment can be measured and continuously improved. Sign up to follow this blog so you don’t miss the rest of the story.
What other symptoms of misalignment have you experienced?
It can be difficult to focus on strategy when your organization has to comply with standards and regulations. Tracking and auditing can be tedious! If you’re a medical device manufacturer, you may need to maintain ISO 13485 compliance to participate in the supply chain. At the same time, you’ve got to meet all the requirements of 21 CFR 820. You’ve also got to remember other regulations that govern production and postmarket. (To read more about the challenges, check out Wienholt’s 2016 post.) There’s a lot to keep track of!
I have not shared all the commonalities of or differences between ISO 9001:2015 and the Baldrige Excellence Framework. Instead, I have tried to show the organizational possibilities of building on conformity assessment to establish a holistic approach for achieving excellence in every dimension of organizational performance today, with a look to the strategic imperatives and opportunities for the future. Baldrige helps an organization take this journey with a focus on process (55% of the scoring rubric) and results (45% of the rubric), recognizing that great processes are only valuable if they yield the complete set of results that lead to organizational sustainability… I encourage organizations that have not gone beyond conformity to take the next step in securing your future.