Years ago I consulted for an organization that had an enticing mission, a dynamic and highly qualified workforce of around 200 people, and an innovative roadmap that was poised to make an impact — estimated to be ~$350-500M (yes really, that big). But there was one huge problem.
As engineers, the leadership could readily provide information about uptime and Service Level Agreements (SLAs). But they had no idea whether they were on track to meet strategic goals — or even whether they would be able to deliver key operations projects — at all! We recommended that they focus on developing metrics, and provided some guidelines for the types of metrics that might help them deliver their products and services — and satisfy their demanding customers.
Unfortunately, we made a critical mistake.
They were overachievers. When we came back six months later, they had nearly a thousand metrics. (A couple of the guys, beaming with pride, didn’t quite know how to interpret our non-smiling faces.)
“So tell us… what are your top three goals for the year, and are you on track to meet them?” we asked.
They looked at each other… then they looked at us. They looked down at their papers. They looked at each other again. It was in that moment they realized the difference between KPIs and metrics.
KPIs are KEY Performance Indicators. They have meaning. They are important. They are significant. And they relate to the overall goals of your business.
One KPI is associated with one or moremetrics. Metrics are numbers, counts, percentages, or other values that provide insight about what’s happened in the past (descriptive metrics), what is happening right now (diagnostic metrics), what will happen (predictive metrics or forecasts), or what should happen (prescriptive metrics or recommendations).
For the human brain to be able to detect and respond to patterns in organizational performance, limit the number of KPIs!
A good rule of thumb is to select 3-5 KPIs (but never more than 8 or 9!) per logical division of your organization. A logical division can be a functional area (finance, IT, call center), a product line, a program or collection of projects, or a collection of strategic initiatives.
Or, use KPIs and metrics to describe product performance, process performance, customer satisfaction, customer engagement, workforce capability, workforce capacity, leadership performance, governance performance, financial performance, market performance, and how well you are executing on the action plans that drive your strategic initiatives (strategy performance). These logical divisions come from the Baldrige Excellence Framework.
Similarly, try to limit the number of projects and initiatives in each functional area — and across your organization. Work gets done more easily when people understand how all the parts of your organization relate to one another.
What happened to the organization from the story, you might ask? Within a year, they had boiled down their metrics into 8 functional areas, were working on 4 strategic initiatives, and had no more than 5 KPIs per functional area.They found it really easy to monitor the state of their business, and respond in an agile and capable way. (They were still collecting lots more metrics, but they only had to dig into them on occasion.)
Remember… metrics are helpful, but:
KPIs are KEY!!
You don’t have thousands of keys to your house… and you don’t want thousands of KPIs. Take a critical look at what’s most important to your business, and organize that information in a way that’s accessible. You’ll find it easier to manage everything — strategic initiatives, projects, and operations.
(special shout-out to those of you who saw the typo the 30 sec it existed!)
In college, to meet my phys ed requirement, I chose a class where I wouldn’t have to exert much physical energy: golf. Almost three decades later, I still can’t play golf, but I did learn one thing in that class that has helped me through life.
When you’re trying to reach a goal, figure out a process to help you reach that goal, then focus on the process instead of the goal. I used this approach to improve my putting. Here’s how it worked: to get the ball in the hole, don’t aim for the hole… aim for a point along the line that goes to the hole, which should be easier to hit. If your ball hits that midpoint, it’s more likely that your putt will go in.
For example, if you’re at the white dot, aim for the Red X, not the hole:
This approach centers you on the process of making the putt. Getting your mind off the pressure of the goal results in the freedom to focus on what’s most important: developing the discipline and habit that will lead to success.
Bryan Cranston, the actor who played Walter White in Breaking Bad, had a similar experience until he was in his mid-40s. Although he had landed many roles in films and television series, none were the kind of long-lived and memorable performance Cranston was aiming for. So he made a conscious effort to shift his perspective.
Author Scott Mautz, citing Cranston’s 2016 memoir, describes the process:
Early in Cranston’s career he was an auditioning machine for commercials or guest-starring roles, a bevy of high-pressure stabs that might serve as at least a step up to the big time. But he was walking into a slew of rooms where he felt he had no power. All that changed when a mentor suggested a new outlook, and it led to an honest-to-goodness six-word secret to his success.
Focus on process rather than outcome.
Suddenly, Cranston felt free. He approached each audition as not going to get something, but to give something–a performance. And giving a great performance requires staying obsessively focused on the process of preparing to be able to give a great performance. He learned that if he overly focused on the outcome (will he get that part?) it set him up for disappointment and left him yearning for validation. Focusing solely on the outcome had also kept him from taking risks as he didn’t want to give a potential gig away with a mis-step.
But this mindset shift, of falling in love with and staying laser-focused on the process, changed everything for him. Soon after he adopted it, he got the role in Malcolm in the Middle, and then the career-changing Breaking Bad starring role.
When you have a challenging or aspirational goal in your sights, like when your organization is starting a lean transformation or digital transformation, it can seem overwhelming. The heavy feeling can actually prevent you from getting where you want to go.
The solution is to identify your intermediary goals — the ones you can achieve by developing and tuning an operational process. Let go of the aspirations, and focus on the daily work, creating the habits that will make you and your organization successful.
Over the past few years, Agile has gained popularity. This methodology emerged as a solution to manage projects with a number of unknown elements and to counter the typical waterfall method. Quality practitioners have observed the numerous similarities between this new framework and Lean. Some have speculated that Agile is simply the next generation’s version of Lean. These observations have posed the question: Is Agile the new Lean?
ASQ Influential Voices Roundtable for December 2019
The short answer to this question is: NO.
The longer answer is one I’m going to have to hold back some emotions to answer. Why? I have two reasons.
Reason #1: There is No Magic Bullet
First, many managers are on a quest for the silver bullet — a methodology or a tool that they can implement on Monday, and reap benefits no later than Friday. Neither lean nor agile can make this happen. But it’s not uncommon to see organizations try this approach. A workgroup will set up a Kanban board or start doing daily stand-up meetings, and then talk about how they’re “doing agile.” Now that agile is in place, these teams have no reason to go any further.
Reason #2: There is Nothing New Under the Sun
Neither approach is “new” and neither is going away. Lean principles have been around since Toyota pioneered its production system in the 1960s and 1970s. The methods prioritized value and flow, with attention to reducing all types of waste everywhere in the organization. Agile emerged in the 1990s for software development, as a response to waterfall methods that couldn’t respond effectively to changes in customer requirements.
Agile modeling uses some lean principles: for example, why spend hours documenting flow charts in Visio, when you can just write one on a whiteboard, take a photo, and paste it into your documentation? Agile doesn’t have to be perfectly lean, though. It’s acceptable to introduce elements that might seem like waste into processes, as long as you maintain your ability to quickly respond to new information and changes required by customers. (For example, maybe you need to touch base with your customers several times a week. This extra time and effort is OK in agile if it helps you achieve your customer-facing goals.)
Both lean and agile are practices. They
require discipline, time, and monitoring. Teams must continually hone their
practice, and learn about each other as they learn together. There are no magic
Information plays a key role. Effective flow of information from strategy to action is important for lean because confusion (or incomplete communication) and forms of waste. Agile also emphasizes high-value information flows, but for slightly different purposes — that include promoting:
Rapid, targeted, and effective action
The difference is easier to understand if you watch a couple cat videos.
This Cat is A G I L E
This cat is continuously scanning for information about its environment. It’s young and in shape, and it navigates its environment like a pro, whizzing from floor to ceiling. If it’s about to fall off something? No problem! This cat is A G I L E and can quickly adjust. It can easily achieve its goal of scaling any of the cat towers in this video. Agile is also about trying new things to quickly assess whether they will work. You’ll see this cat attempt to climb the wall with an open mind, and upon learning the ineffectiveness of the approach, abandoning that experiment.
This Cat is L E A N
This cat is using as LITTLE energy as possible to achieve its goal of hydration. Although this cat might be considered lazy, it is actually very intelligent, dynamically figuring out how to remove non-value-adding activity from its process at every moment. This cat is working smarter, not harder. This cat is L E A N.
Hope this has been helpful. Business posts definitely need more cat videos.
That’s the way this process works. As a National Examiner, you will be frustrated, you may cry, and you may think your team of examiners will never come to consensus on the right words to say to the applicant! But because there is a structured process and a discipline, it always happens, and everyone learns.
I’ve been working with the Baldrige Excellence Framework (BEF) for almost 20 years. In the beginning, I used it as a template. Need to develop a Workforce Management Plan that’s solid, and integrates well with leadership, governance, and operations? There’s a framework for that (Criterion 5). Need to beef up your strategic planning process so you do the right thing and get it done right? There’s a framework for that (Criterion 2).
Need to develop Standard Work in any area of your organization, and don’t know where to start (or, want to make sure you covered all the bases)? There’s a framework for that.
Once you become a National Examiner (my first year was 2009), you get to look at the Criteria Questions through a completely different lens. You start to see the rich layers of its structure. You begin to appreciate that this guidebook was carefully and iteratively crafted over three decades, drawing from the experiences of executives and senior leaders across a wide swath of industries, faced with both common and unique challenges.
The benefits to companies that are assessed for the award are clear and actionable, but helping others helps examiners, too. Yes, we put in a lot of volunteer hours on evenings and weekends (56 total, for me, this year) — but I got to go deep with one more organization. I got to see how they think of themselves, how they designed their organization to meet their strategic goals, how they act on that design. Our team of examiners got to discuss the strengths we noticed individually, the gaps that concerned us, and we worked together to come to consensus on the most useful and actionable recommendations for the applicant so they can advance to the next stage of quality maturity.
One of the things I learned this year was how well Baldrige complements other frameworks like ISO 9001 and lean. You may have a solid process in place for managing operations, leading continuous improvement events, and sustaining the improvements. You may have a robust strategic planning process, with clear connections between overall objectives and individual actions.
What Baldrige can add to this, even if you’re already a high performance organization, is:
tighten the gaps
call out places where standard work should be defined
identify new breakthrough opportunities for improvement
help everyone in your workforce see and understand the connections between people, processes, and technologies
The whitespace — those connections and seams — are where the greatest opportunities for improvement and innovation are hiding. The Criteria Questions in the Baldrige Excellence Framework (BEF) can help you illuminate them.
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.