For a decade, I supervised undergrads and grad students as they were completing writing projects: term papers, semester projects, and of course — capstone projects and thesis work. Today, I’m responsible for editing the work of (and mentoring) junior colleagues. The main lesson I’ve learned over this time is: writing is really hard for most people. So I’m here to help you.
If I had a dollar for every time this scenario happened, I’d… well, you get my point:
ME (reading their “final draft”): [Voice in Head] Huh? Wow, that sentence is long. OK, start it again. I don’t understand what they’re saying. What are they trying to say? This doesn’t make any sense. It could mean… no, that’s not it. Maybe they mean… nope, that can’t be it.
ME: So this sentence here, the one that says “Start by commutating and telling the story of what the purpose of the company’s quality management software is, the implementation plans and the impact to the current state of quality roles and responsibilities for everyone involved.”
THEM (laughing): Oh! Commutating isn’t a word. I meant communicating.
ME: Have you tried reading this sentence out loud?
THEM (still laughing, trying to read it): Yeah, that doesn’t really make sense.
ME: What were you trying to say?
THEM: I was trying to say “Start by explaining how quality management software will impact everyone’s roles and responsibilities.”
ME: Well, why don’t you say that?
THEM: You mean I can just say that? Don’t I need to make it sound good?
ME: You did just make it sound good when you said what you were trying to say.
By trying to “make it sound good” — it’s more likely that you’ll mess it up. People think speaking and writing are two different practices, but when you write, it’s really important that when you speak it out loud, it sounds like you’re a human talking to another human. If you wouldn’t say what you wrote to someone in your target audience in exactly the way that you wrote it, then you need to revise it to something you would say.
Why?Because people read text using the voice in their heads. It’s a speaking voice! So give it good, easy, flowing sentences to speak to itself with.
There are two ways you can start improving your writing today:
Read your writing out loud (preferably to someone else who’s not familiar with your topic, or a collaborator). If it doesn’t sound right, it’s not right.
Use a storyboard. (What does that mean?)
There are many storyboard templates available online, but the storyboard attached to this post is geared towards developing the skills needed for technical writing. (That is, writing where it’s important to support your statements with citations that can be validated.) Not only does citing sources add credibility, but it also gives your reader more material to read if they want to go deeper.
The process is simple: start by outlining your main message. That means:
Figure out meaningful section headers that are meaningful on their own.
Within each section, write a complete phrase or sentence to describe the main point of each paragraph or small group of paragraphs
For each phrase or sentence that forms your story, cut and paste material from your references that supports your point, and list the citation (I prefer APA style) so you don’t forget it.
Read the list of section headers and main points out loud. If this story, spoken, hangs together and is logical and complete — there’s a good chance your fully written story will as well.
Not all elements of your story need citations, but many of them will.
When the storyboard is complete, what should you do next? Sometimes, I hand it to a collaborator to flesh it out. Other times, I’ll put it aside for a few days or weeks, and then pick it up later when my mind is fresh. Whatever approach you use, this will help you organize your thoughts and citations, and help you form a story line that’s complete and understandable. Hope this helps get you started!
When I was younger, I felt like I was pretty smart. Then I turned 23, was thrown into the fast-faced world of helping CxOs try to straighten out their wayward enterprise software implementations, and realized just how little I knew. My turning point came around 6pm on a hot, sticky, smelly evening on Staten Island in a conference room where a director named Mike Davis was yelling at a bunch of us youngster consultants. I thought he was mad at us, but in retrospect, it’s pretty clear that he just wanted something simple, and no matter how clearly he explained it, no one could hear him. Not even me, not even when I was being smart.
The customer was asking for some kind of functionality that didn’t make sense to me. It seemed excessive and unwieldy. I knew a better way to do it. So when Mike asked us to tell him, step by step, what user scenario we would be implementing… I told him THE RIGHT WAY. After about five attempts, he blew up. He didn’t want “the right way” — he wanted “the way that would work.” The way that would draw the most potential out of those people working on those processes. The way that would make people feel the most engaged, the most in control of their own destiny, the way that they were used to doing (with maybe a couple of small tweaks to lead them in a direction of greater efficiency). He knew them, and he knew that. He was being a leader.
Now I’m in my 40s and I have a much better view of everything I don’t know. (A lot of that used to be invisible to me.) It makes me both happier (for the perspective it brings) and unhappier (because I can see so many of the intellectual greenfields and curiosities that I’ll never get to spend time in — and know that more will crop up every year). I’m limited by the expiration date on this body I’m in, something that never used to cross my mind.
One of the things I’ve learned is that the best things emerge when groups of people with diverse skills (and maybe complementary interests) get together, drive out fear, and drive out preconceived notions about what’s “right” or “best”. When something amazing sprouts up, it’s not because it was your idea (or because it turned out “right”). It’s because the ground was tilled in such a way that a group of people felt comfortable bringing their own ideas into the light, making them better together, and being open to their own emergent truths.
I used to think leadership was about coming up with the BEST, RIGHT IDEA — and then pushing for it. This week, I got to see someone else pushing really hard for her “best, most right, more right than anyone else’s” idea. But it’s only hers. She’s intent on steamrolling over everyone around her to get what she wants. She’s going to be really lonely when the time comes to implement it… because even if someone starts out with her, they’ll leave when they realize there’s no creative expression in it for them, no room for them to explore their own interests and boundaries. I feel sorry for her, but I’m not in a position to point it out. Especially since she’s older than me. Hasn’t she seen this kind of thing fail before? Probably, but she’s about to try again. Maybe she thinks she didn’t push hard enough last time.
Leadership is about creating spaces where other people can find purpose and meaning. No pushing required.
Thanks to @maryconger who posted the image on Twitter earlier today. Also thanks to Mike Davis, wherever you are. If you stumble across this on the web one day, thanks for waking me up in 2000. It’s made the 18 years thereafter much more productive.
We’re teaching a class on blockchain and cryptocurrencies this semester, and since the field is so new and changing rapidly, we’ve asked our students to make finding and reviewing articles part of their learning practice this semester. This is a particularly challenging topic for this task because there’s so much hype, marketing, and fluff around these topics. We want to slice through that, and improve the signal-to-noise for people new to learning about blockchain and cryptocurrencies. Here are some tips I just prepared for our students — they may be helpful to anyone writing article reviews, especially for technology-related areas.
0 – Type of Source. Reviews or articles from from arXiv, Google Scholar were strong; reviews from Coindesk, CNN were weak; reviews from WSJ and Hacker Noon went both ways. Here are two submissions that were publishable with only minor edits:
1 – Spelling & Grammar. Most of you are college seniors, and the few who aren’t… are juniors. Please use complete sentences that make sense, with words that are spelled correctly! If this is hard for you, remember that every one of you has spell check. One way to remember this is to draft your posts in Word, and then perform spell check before you copy and paste what you wrote into WordPress.
1 – Your job is to create the TL;DR. What’s the essential substance of the source you’re reviewing? What are the main lessons or findings? If you were taking notes for an exam, what elements would you capture? (Using this perspective, commentary about how good or bad you think the article was, or what it didn’t cover well, would not help you on an exam.)
2 – Choose solid source material — primary sources, e.g. research papers, if possible. If the article is less than ~400-500 words, it’s probably not detailed enough to write a 250-300 word summary/analysis. Your job in this class is to break down complex topics & help people understand them. If your article is short and already very easy to understand, there’s nothing for you to do.
3 – Avoid “weasel words” (phrases or sentences that sound like marketing or clickbait but actually say nothing) and words/sentences that sound like you’re writing a Yelp or Amazon review rather than a critical academic review. Here are a couple weaselly examples drawn from this week’s draft posts (see if you can spot what’s wrong):
It is clear how beneficial blockchain can be to smaller businesses.
Blockchain has the potential to change the world.
Each other the topics covered in the article deserve their own piece and could be augmented upon greatly.
There is a degree of uncertainty that comes with an emerging technology.
Blockchain can bring them into the 21st century to compete with larger corporations.
Many people are scared of the changes, and governments will seek to regulate it.
4 – Answer the “so what” question. Why is this topic interesting or compelling?
5 – Choose information-rich tags. For example, in our class, don’t include blockchain as a tag… pretty much everything we do will be related to blockchain, and everyone will tend to use it, so there won’t be much information contained in the tag.
You may wonder why I’m reviewing a book written by the creator of the Occupy movement for an audience of academics and practitioners who care about quality and continuous improvement in organizations, many of which are trying to not only sustain themselves but also (in many cases) to make a profit. The answer is simple: by understanding how modern social movements are catalyzed by decentralized (and often autonomous) interactive media, we will be better able to achieve some goals we are very familiar with. These include 1) capturing the rapidly changing “Voice of the Customer” and, in particular, gaining access to its silent or hidden aspects, 2) promoting deep engagement, not just in work but in the human spirit, and 3) gaining insights into how innovation can be catalyzed and sustained in a truly democratic organization.
This book is packed with meticulously researched cases, and deeply reflective analysis. As a result, is not an easy read, but experiencing its modern insights in terms of the historical context it presents is highly rewarding. Organized into three sections, it starts by describing the events leading up to the Occupy movement, the experience of being a part of it, and why the author feels Occupy fell short of its objectives. The second section covers several examples of protests, from ancient history to modern times, and extracts the most important strategic insight from each event. Next, a unified theory of revolution is presented that reconciles the unexpected, the emotional, and the systematic aspects of large-scale change.
The third section speaks directly to innovation. Some of the book’s most powerful messages, the principles of revolution, are presented in Chapter 14. “Understanding the principles behind revolution,” this chapter begins, “allows for unending tactical innovation that shifts the paradigms of activism, creates new forms of protest, and gives the people a sudden power over their rulers.” If we consider that we are often “ruled” by the status quo, then these principles provide insight into how we can break free: short sprints, breaking patterns, emphasizing spirit, presenting constraints, breaking scripts, transposing known tactics to new environmental contexts, and proposing ideas from the edge. The end result is a masterful work that describes how to hear, and mobilize, the collective will.
James Siegal (picture from his Twitter profile, @jsiegal at http://twitter.com/jsiegal)
Last weekend, I had the opportunity to talk to James Siegal, the President of KaBOOM! – a non-profit whose mission is lighthearted, but certainly not frivolous: to bring balanced and active play into the daily lives of all kids! James is another newBusiness Innovation Factory (BIF) storyteller for 2015… and I wanted to find out how I could learn from his experiences to bring a sense of play into the work environment. (For me, that’s at a university, interacting with students on a daily basis.)
Over the past 20 years, KaBOOM! has built thousands of playgrounds, focusing on children growing up in poverty. By enlisting the help of over a million volunteers, James and his organization have mobilized communities using a model that starts with kids designing their dream playgrounds. It’s a form of crowdsourced placemaking.
Now, KaBOOM! is thinking about a vision that’s a little broader: driving social change at the city level. Doing this, they’ve found, requires answering one key question: How can you integrate play into the daily routine for kids and families? If play is a destination, there are “hassle factors” that must be overcome: safety, travel time, good lighting, and restroom facilities, for starters. So, in addition to building playgrounds, KaBOOM! is challenging cities to think about integrating play everywhere — on the sidewalk, at the bus stop, and beyond.
How can this same logic apply to organizations integrating play into their cultures? Although KaBOOM! focuses on kids, he had some more generalizable advice:
The desire for play has to be authentic, not forced. “We truly value kids, and we truly value families. Our policies and our culture strive to reflect that.” What does your organization value at its core? Seek to amplify the enjoyment of that.
“We take our work really seriously,” he said. “We don’t take ourselves too seriously. You have to leave your ego at the door.” Can your organization engage in more playful collaboration?
We drive creativity out of kids as they grow older, he noted. “Kids expect to play everywhere,” and so even ordinary elements like sidewalks can turn into experiences. (This reminded me of how people decorate the Porta-Potties at Burning Man with lights and music… although I wouldn’t necessarily do the same thing to the restrooms at my university, it did make me think about how we might make ordinary places or situations more fun for our students.)
KaBOOM! is such a unique organization that I had to ask James: what’s the most amazing thing you’ve ever observed in your role as President? He says it’s something that hasn’t just happened once… but happens every time KaBOOM! organizes a new playground build. When people from diverse backgrounds come together with a strong shared mission, vision, and purpose, you foster intense community engagement that yields powerful, tangible results — and this is something that so many organizations strive to achieve.
I believe that the data scientist “unicorn” is hidden right in front of our faces; the purpose of this post is to help you find it.First, we’ll take a look at some models, and then I’ll present my version of what a data scientist is (and how this person can become “great”).
#1 Drew Conway’s popular “Data Science Venn Diagram” — created in 2010 — characterizes the data scientist as a person with some combination of skills and expertise in three categories (and preferably, depth in all of them): 1) Hacking, 2) Math and Statistics, and 3) Substantive Expertise (also called “domain knowledge”).
Later, he added that there was a critical missing element in the diagram: that effective storytelling with data is fundamental. The real value-add, he says, is being able to construct actionable knowledge that facilitates effective decision making. How to get the “actionable” part? Be able to communicate well with the people who have the responsibility and authority to act.
“To me, data plus math and statistics only gets you machine learning, which is great if that is what you are interested in, but not if you are doing data science. Science is about discovery and building knowledge, which requires some motivating questions about the world and hypotheses that can be brought to data and tested with statistical methods. On the flip-side, substantive expertise plus math and statistics knowledge is where most traditional researcher falls. Doctoral level researchers spend most of their time acquiring expertise in these areas, but very little time learning about technology. Part of this is the culture of academia, which does not reward researchers for understanding technology. That said, I have met many young academics and graduate students that are eager to bucking that tradition.” — Drew Conway, March 26, 2013
#2 In 2013, Harlan Harris (along with his two colleagues, Sean Patrick Murphy and Marck Vaisman) published a fantastic study where they surveyed approximately 250 professionals who self-identified with the “data science” label. Each person was asked to rank their proficiency in each of 22 skills (for example, Back-End Programming, Machine Learning, and Unstructured Data). Using clustering, they identified four distinct “personality types” among data scientists:
Data Businesspeople who are most focused on the information itself and how it is applied to business decisions. (These people were least likely to identify with the “data scientist” label.)
Data Developers, the wizards of the technical aspects of data management (accessing it, moving it around, archiving it, curating it), and
Data Researchers, those deeply familiar with the mathematical and statistical underpinnings of the work, who can develop new techniques as necessary (in addition to correctly selecting from available techniques).
As a manager, you might try to cut corners by hiring all Data Creatives(*). But then, you won’t benefit from the ultra-awareness that theorists provide. They can help you avoid choosing techniques that are inappropriate, if (say) your data violates the assumptions of the methods. This is a big deal! You can generate completely bogus conclusions by using the wrong tool for the job. You would not benefit from the stress relief that the Data Developers will provide to the rest of the data science team. You would not benefit from the deep domain knowledge that the Data Businessperson can provide… that critical tacit and explicit knowledge that can save you from making a potentially disastrous decision.
“The data scientist’s skills – advanced analytics, data integration, software development, creativity, good communications skills and business acumen – often already exist in an organisation. Just not in a single person… likely to be spread over different roles, such as statisticians, bio-chemists, programmers, computer scientists and business analysts. And they’re easier to find and hire than data scientists.”
They cite British Airways as an exemplar:
“[British Airways] believes that data scientists are more effective and bring more value to the business when they work within teams. Innovation has usually been found to occur within team environments where there are multiple skills, rather than because someone working in isolation has a brilliant idea, as often portrayed in TV dramas.”
Their position is you can’t get all those skills in one person, so don’t look for it. Just yesterday I realized that if I learn one new amazing thing in R every single day of my life, by the time I die, I will probably be an expert in about 2% of the package (assuming it’s still around).
#4 Others have chimed in on this question and provided outlines of skill sets, such as:
The Udacity blog: basic tools (R, Python), software engineering, statistics, machine learning, multivariate calculus, linear algebra, data munging, data visualization and communication, and the ultimately nebulous “thinking like a data scientist”
IBM: “part analyst, part artist” skilled in “computer science and applications, modeling, statistics, analytics and math… [and] strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge.”
SAS: “a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved. They’re part mathematician, part computer scientist and part trend-spotter.” (Doesn’t that sound exciting?)
DataJobs.Com: well, these guys just took Drew Conway’s Venn diagram and relabeled it.
#5 My Answer to “What is a Data Scientist?”:A data scientist is a sociotechnical boundary spanner who helps convert data and information into actionable knowledge.
Based on all of the perspectives above, I’d like to add that the data scientist must have an awareness of the context of the problems being solved: social, cultural, economic, political, and technological. Who are the stakeholders? What’s important to them? How are they likely to respond to the actions we take in response to the new knowledge data science brings our way? What’s best for everyone involved so that we can achieve sustainability and the effective use of our resources? And what’s with the word “helps” in the definition above? This is intended to reflect that in my opinion, a single person can’t address the needs of a complex data science challenge. We need each other to be “great” at it.
A data scientist is someone who can effectively span the boundaries between
1) understanding social+ context,
2) correctly selecting and applying techniques from math and statistics,
3) leveraging hacking skills wherever necessary,
4) applying domain knowledge, and
5) creating compelling and actionable stories and connections that help decision-makers achieve their goals. This person has a depth of knowledge and technical expertise in at least one of these five areas, and a high level of familiarity with each of the other areas (commensurate with Harris’ T-model). They are able to work productively within a small team whose deep skills span all five areas.
It’s data-driven decision making embedded in a rich social, cultural, economic, political, and technological context… where the challenges may be complex, and the stakes (and ultimately, the benefits) may be high.
(*) Disclosure: I am a Data Creative!
(**)Quality professionals (like Six Sigma Black Belts) have been doing this for decades. How can we enhance, expand, and leverage our skills to address the growing need for data scientists?
So much innovation in STEM is fueled by imagination and exploration, and in my opinion, we don’t communicate that very well to younger people. A great gateway drug for this purpose is art. There’s even a movement underway to expand out vision of STEM, and more tightly and more essentially integrate aesthetics, form, design, and fun into what we do via STEAM (Science, Technology, Engineering, Art, and Math).
STEAM doesn’t advocate just doing the arts alongside more traditional science and engineering. It actually requires that we look towards how we can use STEAM to create meaning for ourselves and our communities. In other words, it can help us get our mind off of science and engineering to understand and control the world around us – and focus more on how beautiful and intriguing things are that we can learn in those domains.
The picture above is the interactive zonohedral dome (or “zome”) that our students created specifically to engage others in the fun of integrated science and engineering. Here’s how they summarize their project:
As our communities expand rapidly, both physically and digitally, we can lose our sense of connection and togetherness. Interactive and participatory art interventions cultivate community by provoking engagement in unexpected areas. In this project, the prototype for an interactive zonohedral dome (or “zome”) was constructed as a proof of concept for an art intervention to engage students in collaborative STEM (Science, Technology, Engineering, and Math ) learning, by creating feelings of connection with the technology and with each other. Consequently, it demonstrates the values of the STEAM (Science, Technology, Engineering, Art, and Math) movement in education. Design elements (and an assessment approach) were selected based on a comprehensive literature review which focused on the aspects of engagement that would boost participants’ interest in and proficiency with STEM subjects.
A zome is a structure that supports itself solely due to its geometry. No nails or glue are used in the construction. The interactive nature of the structure emerges from sensors that detect occupancy, with music and lights automatically responding to the pattern of people entering and leaving the zome. Many technologies were combined to create this experience, including SketchUp (to design the components), Makerbot Replicator II (to build the structure), Arduino (to detect occupancy via phototransistors), LightShowPi (to generate Fast Fourier transforms of music files and control the frequency and amplitude of audio communicated via LEDs), and RaspberryPi (a microcomputer to run LightShowPi and translate the signals from the Arduino to play audio at pre-designated decibel levels).
We’ll post a video of the zome in action very soon. It’s so fun to look at, and play with… and what better way to learn programming than to make a structure respond to the presence and motion of the people around it?