4DXing Your Procrastination with Lead Measures

When it comes to productivity, there’s a whole self-help industry, culture, etc., that’s established itself. No wonder, more than ever, many of us really do need ‘the help’ … especially when it comes to ‘regulars’ like procrastination – “… the avoidance of doing a task that needs to be accomplished.” From the highly academic to the exceedingly practical, procrastination rightly serves as the target for research investigations to hacks, respectively. That being the case, there is hardly the need to add to this mountainous and at times unexpected terrain that today characterizes the landscape that is procrastination – except, to share what might be a serendipitous intersection between procrastination and lead measures. For those seeking more-comprehensive resources on procrastination, you’ll need to look elsewhere (via Google for example), as our focus here needs to be squarely on this intersection.

Before making the procrastination connection, we need to ensure we’re on the same page when it comes to lead measures.

Lead Measures

As I understand them, based upon Cal Newport’s introduction in Deep Work, the notion of lead measures derives from McChesney et al.’s The 4 Disciplines of Execution. Briefly, and in my own words, the approach requires you shift your emphasis onto lead measures when chasing your Wildly Important Goals (WIGs) – i.e., it requires you to emphasize those behaviors that’ll allow you to ensure the success of your WIGs.

Suppose your WIG, a lag measure, is to find a job. A fine example of a lead measure then is networking. In other words, if you ‘merely’ ensure you network with three people per week for example, you’ll be executing a behavior that will contribute towards your WIG of finding a job. In fact, your lead measures with respect to networking could be even-more granular – for example:

Reach out to (say) 15 people in my LinkedIn network to secure introductions to people at employers of interest

The act of reaching out to those you know, via the enabling means provided by LinkedIn (for example), serves as an effective behavior for securing the introductions you require for networking. As an effective lead measure, it has an embarrassingly low activation energy. Whereas I’m not purporting to be an expert here on job seeking, networking, etc., I’m sure you get the gist of lead measures through this example. (In the case of networking, for example, one cannot overemphasize the importance of meeting in person – a behavior that has an even greater potential to serve as a lead measure, I’m sure many might argue.)

I shared another concrete example of a very different nature recently. At its core, the idea was to chase the WIG of propping up math skills in probability and statistics, for the purpose of engaging in the field of Deep Learning. Amongst the lead measures suggested, was that of tutoring prob & stats to high-school students as a vehicle for acquiring valuable knowledge and skills in this mathematical cornerstone for Deep Learning.

Whereas even a SMART’ly crafted WIG whose core objective is “getting a job” or “learning some math” has the potential to be overwhelming, teasing out lead measures results in things that are readily actionable … and that allows us to return to the matter of procrastination …

Turfing Procrastination

By employing the execution-discipline of acting on lead measures then, I believe we have herein the potential for a very different take on addressing the challenges and opportunities rendered through procrastination. With its execution focus that emphasizes the ‘right behaviors’, teasing out easily actionable lead measures is so much more than merely breaking down WIGs into digestible chunks – it’s a strategic approach that, when executed, has a much greater likelihood for success. In some respects then, lead measures serve as a foil for those lag measures corresponding to your WIG.

As the examples above illustrate, effective lead measures are the means through which lag measures that encapsulate your WIGs are achieved. Since we are concerned here specifically with the matter of procrastination, successful application of the approach described here rests upon striving for appropriately teased out lead measures. Stated simply, you’ll know you’ve ‘arrived’ at the right articulation of your lead measures when you can honestly state: “I can do that, right now”. In the examples used here, that would mean reaching out to LinkedIn contacts and booking math-tutoring sessions.

The process of ‘extracting’ those lead measures that best ensure the success of your WIG takes some concentrated effort and practice. For example, networking has been deemed by qualified others to be of value to job seekers at certain stages of their career; it may not be appropriate to others whose circumstances are quite different. Tutoring math to learn math will only be useful to those who have some experience both as a tutor and in math; it may be almost useless to others. In other words, the need for personalization is also critical in teasing out lead measures – and this is especially so when it comes to procrastination. So important is the matter of personalization to effectively crafted lead measures that I suspect the need for additional posts, courses/workshops, coaching, etc. Of course, this’ll demand an even deeper level of appreciation of the very nature of procrastination. For example, many have alluded to the emotional angles of procrastination. Owing to the inherently behavioral foundation of execution mediated via lead measures then, there exists an approach here that has the potential for mounting a targeted attack on procrastination at an emotional level! I remain optimistic then, that lead measures could become our superpower for addressing procrastination – as they have the potential to collectively serve as the foil for the lag measures that encapsulate our WIGs.

Accountable Scoreboards

The most-comprehensive template I’ve ever run across for ensuring the success of habits-oriented goals can be found in Michael Hyatt’s Your Best Year Ever. A less-comprehensive, yet effective ‘template’ is provided by your calendar – a fine tool for tracking your streaks. Popularized by comedian Jerry Seinfeld, streak tracking addresses the final two of four execution disciplines head on – namely the need to keep a compelling scoreboard, as well as a cadence of accountability.

To make use of our two examples above for one final time, tracking might translate to:

  • Logging weekly the number of times you’ve reached out to LinkedIn contacts for networking referrals, and then weekly logging the referrals acquired, and finally the (monthly) conversations held.
  • Logging weekly the number of math students tutored, and for each time spent, over some period of time (e.g., a term, semester, session, course).

Such quantitative measures render your actions visible, without ambiguity. If your outcomes aren’t matching your expectations, you have at your fingertips the data to validate your hypotheses – e.g.:

Is reaching out to 15 LinkedIn contacts weekly producing the number (3) of referrals I need?

With evidence then, you can always re-examine your lead measures to ensure they are appropriately aligned with your encapsulation of your WIG via your lag measure.

It’s a no-brainer that professional athletes engage intensely in the habit of daily practice – and the best, NFL football’s Tom Brady for example, never stop! Even though along with his teammates and the coaching staff of the New England Patriots they have collectively won five Super Bowl championships, Brady remains a ‘student of the game’, practising with the utmost intensity in a effort to repeat this feat for a record-setting sixth time. The same could be shared with respect to world-class musicians irrespective of musical genre.

Actions quantified, through streak tracking on calendars to thorough templates, allows each of us to confront procrastination through the discipline of execution – thus elevating our game, our level of play, to the level of a professional. Stated differently, armed with the right lead measures, these final two steps ensure that failure is not an option – or that we’ll, at the very least, land a whole-lot closer to our WIGs.

Key Takeaways

Having WIGs is great. However, achieving WIGs is better. The difference is in execution. By focusing on lead measures, your prospects for actually achieving your WIGs will be significantly enhanced in practice (literally). Appropriately teased out lead measures have the potential to significantly inhibit procrastination – by focusing on behaviors that you can implement immediately … behaviors you can make use of to evaluate your progress in an objective way. Net-net, the four disciplines of execution can be especially valuable to those of us ‘prone’ towards procrastination; they can become a highly effective mitigation strategy.

Bonus Takeaway

Commiting to a WIG, or some aspect of a WIG, that can be achieved in about a month’s time can be extremely appealing. Whereas the focus of a 30-day effort may reduce symptoms of procrastination in some of us, interweaving lead measures into the context of an Agile Sprint is even more likely to drive most of us to the next level of achievement. For a concrete example, based upon the teaching-math-to-learn-math ‘use case’ above, please have a look at the Agile Sprints section in this previous post.

Demonstrating Your Machine Learning Expertise: Optimizing Breadth vs. Depth

Developing Expertise

When it comes to developing your expertise in Machine Learning, there seem to be two schools of thought:

  • Exemplified by articles that purport to have listed, for example, the 10-most important methods you need to know to ace a Machine Learning interview, the School of Breadth emphasizes content-oriented objectives. By amping up with courses/workshops to programs (e.g., certificates, degrees) then, the justification for broadening your knowledge of Machine Learning is self-evident.
  • Find data that interests you, and work with it using a single approach for Machine Learning. Thus the School of Depth emphasizes skills-oriented objectives that are progressively mastered as you delve into data, or better yet, a problem of interest.

Depending upon whichever factors you currently have under consideration then (e.g., career stage, employment status, desired employment trajectory, …), breadth versus depth may result in an existential crisis when it comes to developing and ultimately demonstrating your expertise in Machine Learning – with a modicum of apologies if that strikes you as a tad melodramatic.

Demonstrating Expertise

Somewhat conflicted, at least, is in all honesty how I feel at the moment myself.

On Breadth

Even a rapid perusal of the Machine Learning specific artifacts I’ve self-curated into my online, multimedia Data Science Portfolio makes one thing glaringly evident: The breadth of my exposure to Machine Learning has been somewhat limited. Specifically, I have direct experience with classification and Natural Language Processing in Machine Learning contexts from the practitioner’s perspective. The more-astute reviewer, however, might look beyond the ‘pure ML’ sections of my portfolio and afford me additional merit for (say) my mathematical and/or physical sciences background, plus my exposure to concepts directly or indirectly applicable to Machine Learning – e.g., my experience as a scientist with least-squares modeling counting as exposure at a conceptual level to regression (just to keep this focused on breadth, for the moment).

True confession: I’ve started more than one course in Machine Learning in a blunt-instrument attempt to address this known gap in my knowledge of relevant methods. Started is, unfortunately, the operative word, as (thus far) any attempt I’ve made has not been followed through – even when there are options for community, accountability, etc. to better-ensure success. (Though ‘life got in the way’ of me participating fully in the fast.ai study group facilitated by the wonderful team that delivers the This Week in Machine Learning & AI Podcast, such approaches to learning Machine Learning are appealing in principle – even though my own engagement was grossly inconsistent.)

On Depth

What then about depth? Taking the self-serving but increasingly concrete example of my own Portfolio, it’s clear that (at times) I’ve demonstrated depth. Driven by an interesting problem aimed at improving tsunami alerting by processing data extracted from Twitter, for example, the deepening progression with co-author Jim Freemantle has been as follows:

  1. Attempt to apply existing knowledge-representation framework to the problem by extending it (the framework) to include graph analytics
  2. Introduce tweet classification via Machine Learning
  3. Address the absence of semantics in the classification-based approach through the introduction of Natural Language Processing (NLP) in general, and embedded word vectors in particular
  4. Next steps …

(Again, please refer to my Portfolio for content relating to this use case.) Going deeper, in this case, is not a demonstration of a linear progression; rather, it is a sequence of outcomes realized through experimentation, collaboration, consultation, etc. For example, the seed to introduce Machine Learning into this tsunami-alerting initiative was planted on the basis of informal discussions at an oil and gas conference … and later, the introduction of embedded word vectors, was similarly the outcome of informal discussions at a GPU technology conference.

Whereas these latter examples are intended primarily to demonstrate the School of Depth, it is clear that the two schools of thought aren’t mutually exclusive. For example, in delving into a problem of interest Jim and I may have deepened our mastery of specific skills within NLP, however we have also broadened our knowledge within this important subdomain of Machine Learning.

One last thought here on depth. At the outset, neither Jim nor I had as an objective any innate desire to explore NLP. Rather, the problem, and more importantly the demands of the problem, caused us to ‘gravitate’ towards NLP. In other words, we are wedded more to making scientific progress (on tsunami alerting) than a specific method for Machine Learning (e.g., NLP).

Next Steps

Net-net then, it appears to be that which motivates us that dominates in practice – in spite, perhaps, of our best intentions. In my own case, my existential crisis derives from being driven by problems into depth, while at the same time seeking to demonstrate a broader portfolio of expertise with Machine Learning. To be more specific, there’s a part of me that wants to apply LSTMs (foe example) to the tsunami-alerting use case, whereas another part knows I must broaden (at least a little!) my portfolio when it comes to methods applicable to Machine Learning.

Finally then, how do I plan to address this crisis? For me, it’ll likely manifest itself as a two-pronged approach:

  1. Enrol and follow through on a course (at least!) that exposes me to one or more methods of Machine Learning that compliments my existing exposure to classification and NLP.
  2. Identify a problem, or problems of interest, that allow me to deepen my mastery of one or more of these ‘newly introduced’ methods of Machine Learning.

In a perfect situation, perhaps we’d emphasize breadth and depth. However, when you’re attempting to introduce, pivot, re-position, etc. yourself, a trade off between breadth versus depth appears to be inevitable. An introspective reflection, based upon the substance of a self-curated portfolio, appears to be an effective and efficient means for roadmapping how gaps can be identified and ultimately addressed.


In many settings/environments, Machine Learning and Data Science in general, are team sports. Clearly then, a viable way to address the challenges and opportunities presented by depth versus breadth is to hire accordingly – i.e., hire for depth and breadth in your organization.

Prob & Stats Gaps: Sprinting for Closure

Prob & Stats Gap

When it comes to the mathematical underpinnings for Deep Learning, I’m extremely passionate. In fact, my perspective can be summarized succinctly:

Deep Learning – Deep Math = Deep Gap.

In reflecting upon my own mathematical credentials for Deep Learning, when it came to probability and statistics, I previously stated:

Through a number of courses in Time Series Analysis (TSA), my background affords me an appreciation for prob & stats. In other words, I have enough context to appreciate this need, and through use of quality, targeted resources (e.g., Goodfellow et al.’s textbook), I can close out the gaps sufficiently – in my case, for example, Bayes’ Rule and information theory.

Teaching to Learn

DSC02681Although I can certainly leverage quality, targeted resources, I wanted to share here a complementary approach. One reason for doing this is that resources such as Goodfellow et al.’s textbook may not be readily accessible to everyone – in other words, some homework is required before some of us are ready to crack open this excellent resource, and make sense of the prob & stats summary provided there.

So, in the spirit of progressing towards being able to leverage appropriate references such as Goodfellow et al.’s textbook, please allow me to share here a much-more pragmatic suggestion:

Tutor a few high school students in prob & stats to learn prob & stats.

Just in case the basic premise of this suggestion isn’t evident, it is: By committing to teaching prob & stats, you must be able to understand prob & stats. And as an added bonus, this commitment of tutoring each of a few students (say) once a week, establishes and reinforces a habit – a habit that is quite likely, in this case, to ensure you stick with your objective to broaden and deepen your knowledge/skills when it comes to probability and statistics.

As an added bonus, this is a service for which you could charge a fee – full rate for tutoring math at the high-school level to gratis, depending upon the value you’ll be able to offer your students … of course, a rate you could adjust over time, as your expertise with prob & stats develops.

Agile Sprints

Over recent years, I’ve found it particularly useful to frame initiatives such as this one in the form of Agile Sprints – an approach I’ve adopted and adapted from the pioneering efforts of J D Meier. To try this for yourself, I suggest the following two-step procedure:

  1. Review JD’s blog post on sprints – there’s also an earlier post of his that is both useful and relevant.
  2. Apply the annotated template I’ve prepared here to a sprint of your choosing. Because the sample template I’ve shared is specific to the prob & stats example I’ve been focused on in this post, I’ve also included a blank version of the sprint template here.


Before you go, there’s one final point I’d like to draw your attention to – and that’s lead and lag measures. Whereas lag measures focus on your (wildly) important goal (WIG), lead measures emphasize those behaviors that’ll get you there. To draw from the example I shared for addressing a math gap in prob & stats, the lag measure is:

MUST have enhanced my knowledge/skills in the area of prob & stats such that I am better prepared to review Deep Learning staples such as Goodfellow et al.’s textbook

In contrast, examples of lead measures are each of the following:

SHOULD have sought tutoring positions with local and/or online services

COULD have acquired the textbook relevant for high-school level prob & stats

With appropriately crafted lead measures then, the likelihood that your WIG will be achieved is significantly enhanced. Kudos to Cal Newport for emphasizing the importance of acting on lead measures in his Deep Work book. For all four disciplines of execution, you can have a closer look at Newport’s book, or go to the 4DX source – the book or by simply Googling for resources on “the 4 disciplines of execution”.

Of course, the approach described here can be applied to much more than a gap in your knowledge/skills of prob & stats. And as I continue the process of self-curating my Data Science Portfolio, I expect to unearth additional challenges and opportunities – challenges and opportunities that can be well approached through 4DX’d Agile Sprints.