Data as Star/Data as Author pt.1

This is a two-part post with some ideas about the stories that data tells (author) and using data to tell a story (star).

Data as Star

When I was running strategy for iZS, Zurich Scudder’s VC attempt during the first dot-com run, I read an HBR article (the physical, ridiculously priced magazine) talking about companies producing two things; a product and a data stream. Today’s environment has sensitized those of us who are paying attention to the fact that companies know a s*%$ ton about us. So with all this data flying around why are we still married to the old-school, direct data collection.  Or limited by the lack thereof. We need to get smarter to solve for two problems:

  • What we are asking our measures to measure has changed but the measure has not.
  • Measures that don’t exist.

In order to use data as the star of our story, it must exist and it has to be believable (source, methodology, red-face test). Other than that, data in support of a pre-constructed narrative too often a creative endeavor.  We use L&D spend benchmarks as a way of showing how our company is leading the pack in our industry.  we use the same metric but compare it to the highest performers in the world (where the company is a bit more middle of the pack)  when we submit our annual budget. While data authored narratives can also be fraught with biases, assumptions, and intent at least it starts with and is anchored by the data.

Two data scientists delivering a talk to a group of startups in Delaware remind the audience that data is like a bikini.  Very intriguing but revealing nothing. Loved those guys.

I have a job, kinda.

I worked with a company that did a large volume of formation documents.  These are the documents that need to be completed and filed with states and the Federal government when you start a company.  They reached out to me to look for additional revenue streams, partnerships, or any other ideas that would help strengthen their already leading position.

Of the many ideas that my team and the internal team developed the one I loved was a data play. At the time, ADP has just begun releasing their employment data index.  A set of publicly announced numbers that suggested the current state of employment.  You may ask, “don’t we already have a number from the Bureau of Labor Statistics (BLS) for that?” We do. It sucks. If you go underneath the BLS numbers you realize the number’s number is up.  Don’t take my word just google it.  One of the many voices you will come across is Dr. Robert Shapiro’s from EconVue, 

Dr. Shapira is no slouch having served as Under Secretary of Commerce for Economic Affairs for four years.  Here is how he describes the reason why people at the outset of the pandemic were deemed not “available” for work and therefore excluded from the unemployment calculation.

“In fact, millions of Americans were not “available” for work in April because they were caring for children whose schools were closed—and millions of people didn’t look for new jobs because the avalanche of layoffs made a job search pointless. They fit a textbook definition of individuals whom the BLS excludes from the ranks of the labor force, and so do not count as unemployed.”

So tell me again why we don’t need a data point that simply counts the number of paychecks it issues for its clients every two weeks. Seems closer to the source.  Still not perfect, but important.

So our data play for the formation king was just as simple as ADP’s.  We already knew how many new businesses were being formed.  We knew what percentage of the overall formations it represented (more than representative).  Hell, we even knew what type of business was being formed (barbershop, restaurant, welding.) So why not create an entrepreneurial index that showed how active the startup spirit was and in which sectors they were seeking their fortunes.  This information could be useful for commercial real estate, business banking, tax incentives, and more.  Again, imperfect but important. They chose other options but this post is also me pitching the idea to them again (call me).

How will the rapidly growing gig economy make the current measures even less calibrated to reality? We have to look at new and better measures for important things that must be measured properly.  If we are to use the unemployment data as a proxy for how well we are doing, as workers and as an economy, then it better be as close to right as possible.

The economy goes as pickup sales go 

This proxy is real and has been around for a long time. DataTrek is a believer that, “pickup truck sales can be a helpful indicator since the trucks are used in a vast array of businesses, and are typically a discretionary purchase.” More here. To paraphrase Forum Corporation co-founder, Richard Whitely, quoting some ancient wisdom from someone…

“We cannot see the wind.  We know it exists by watching the leaves.” – Unknown

“True dat.” – Me

So the pickup proxy may be a bit of a stretch but it has a history (relatively accurate directionally) and broad nodding acceptance. While these correlations may seem silly finding one that works can provide insight, and even advantage.  Say for example you are the first to see the relationships between two data sets, Home Depot revenue and Housing Sales. Whenever housing sales go up revenue goes down implying that people are buying rather than building. Or revenues rises when home sales go down implying more fixing up less moving.  Either way (or both) there is an insight into consumer behavior and as long as there is a time lag between dataset results there is time to exploit it.

So in the case of the health of the economy what other leaves could we be watching with our all-access pass to the data-sphere. So how about a healthy neighborhood index? It could consist of:

  • Credit card receipts for all the businesses in the ‘hood (local business revenue)
  • Utility late payments (resident disposable cash)
  • Major crime statistics (gun violence, home invasions, assaults)
  • Building permits issued (new development)
  • Employment by local businesses (growth, stability)
  • Aggregate savings growth
  • Property maintenance complaints filed
  • Utility events in the last time period (power outage, water quality, etc)
  • Solar installations (environmentally consciousness)

None of these ask residents how healthy is your neighborhood.  NPS is a great proxy but redemption of referral codes is better.  Leaves don’t lie.  People can say that being green or putting money away for retirement is very important to them but counting solar panels, recycling bins and 401k balances are the truth.

L&D some days feels paralyzed by not having the direct answer to the question, “did the learning make a difference?” Too many levels removed. Too many variables in motion. So we say we don’t have the data.  No data, no credit. It is a binary directional question.  Precision is not required just a solid proxy that meets our requirements, especially the red-face one. Look for meaningful correlations.  Not the “umbrella sales go up every time it rains,” kind.

Longer sales calls could be an indication of better customer engagement skills. Lower T&E expenses could indicate stronger adoption of Zoom.  Increased sticky note purchases could be the basis for a corporate innovation index by including cross-department meetings (pulled from the attendee lists in our work calendars) and Ted.com views from the work internet (IT has this just like they have your entire browser history.)

If you want or need data to be the star of your show, it is out there.  Sometimes you just need to not look directly at it. Some people say that selling is not convincing but rather helping a customer do the right thing.  Data can do both.  It can convince and help. How it is used is up to those of us who use it.

“Use your powers for good, you will.” – Yoda?

Part 2 – What’s My Backstory? [coming soon]

Catching Lightning on the Back-of-an-Envelope

“How far away is it?” Depending on the reason for the question, the precision of the answer has a different value.  “No more than a mile” may be specific enough for you to make the decision between walking and grabbing an Uber.  Every tenth of a mile may make a huge difference if you are wondering if you have enough gas to get to the next service station.  Different uses for the results of a query help to define the valuable level of precision. I love the hacks, shortcuts, and rules-of-thumb that relieve me from spending energy on precision that is not valued.

One Mississippi… Two Mississippi…

I will let the National Weather Service explain one of the most well-known guesstimates.  It is also one where the level of precision in the answer matches the question being sought to be answered.

“Since you see lightning immediately and it takes the sound of thunder about 5 seconds to travel a mile, you can calculate the distance between you and the lightning. If you count the number of seconds between the flash of lightning and the sound of thunder, and then divide by 5, you’ll get the distance in miles to the lightning: 5 seconds = 1 mile, 15 seconds = 3 miles, 0 seconds = very close.”

So it is not a terribly precise measure.  It actually only takes a little over 4.8 seconds for sound to travel a mile,  Based on that difference alone a perfect count would still be off by half a football field for every “mile” counted. And that is not even including the many variations of “Mississippi” in spoken timekeeping. But who cares? No one cares!

The questions that this guesstimate seeks to provide input to are equally imprecise.  In answering, “how far away is the storm?” fifty yards is hardly relevant.  The actions taken to prepare for a storm that is 3 miles away are identical to those for one that is only two and one-half miles away. “Is the storm moving towards us?”  the other question frequently informed by this data, is equally imprecise.  It is simply a directional measure.  Did I count fewer Mississippis this time versus the time before? Having more precision adds little value to answering the question.

The ROI Misalignment

I am thinking about this alignment as I read yet another article promising to deliver a straight forward method for capturing ROI.  I appreciate the noble quest but wonder if it is really needed.  What are the questions seeking input?  What precision is valuable to these questions?

Binary, one-time questions are what come to mind first. “Is this a worthwhile investment?” This is a simple yes or no question which does not value a highly specific percentage to be calculated. Confidence that I am going to at least get my money’s worth may be the hurdle to be cleared. The difference between 125% and 140% is negligible for confidence building.

There is a slide that used to be part of the standard startup pitch deck that made me cringe.  The slide’s objective was to reduce the perceived risk associated with competition, prove the size of the market, and get potential investors excited about the startup’s potential.  We called it the 1% slide.   Often it was little more than a large pie chart showing the billion-dollar market size with a small 1% slice.  The slide’s commentary always included some variation of, “and if we are only able to capture 1% of the market we are still a $400 million business.” Translated, “even if we suck, you win.”

So what if, rather than saying a certain learning initiative has a certain return perhaps all we need to do is show that the return clears some hurdle.  The equivalent of the 1% slide. A blog on what is called the kaizen method sums it up this way.

“It might not seem like much, but those 1% improvements start compounding on each other.”

What L&D needs is a simple back-of-the-envelope calculation that allows it to confidently say to our business sponsors, “even if the initiative only moves the [profit/revenue] needle 1% we still show a positive return on your investment.”  In my next blog I will lay out my back-of-the-envelope (BOTE) calculation. Spoiler alert: even if we suck the business wins.  I look forward to your feedback and suggestions.

Bruce Lee on L&D Data

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“I am but a finger pointing to the moon. Don’t look at me; look at the moon.”

Love me some Bruce Lee.

I began this weekend’s mental wanderings with a thought that maybe, just maybe, when it comes to discussions on data the issue may be a “finger/moon” issue. For those not schooled in the ways of Enter the Dragon allow me to bring you up to speed.  In the movie, Sensei Lee is instructing his pupil on kicking.  He is actually exhorting his student for “more emotional content.” Maybe there are future Bruce Lee blog posts coming. Maybe ATD should re-issue this movie with associated CPEs. When he sits down to hammer home the lessons of the day with his student he explains to the student that,

“the finger is useful because of what it points us toward, not as an object of study for its own sake.”

Thanks to FakeBuddhaQuotes.com for the perfect summary. Upon reflection, I am now very convinced we have a finger/moon situation going on. And here is why we should care. The peaceful Essence of Buddhism Blog gives readers the big three reasons not to just look at the finger.

  1. You’ll miss the moon
  2. You think the finger is the moon
  3. You don’t know what is naturally bright (has enlightenment) vs what is naturally dark (lacks enlightenment)

We will leave #3 to others to ponder.  But #1 and #2 need some more time in the dohyō.

The moon is beautiful. Don’t miss it!

How do you know how fast you are going in your car?

How does your car know? Sensor on hub? Sensor on axle? GPS movement? Transferred on a tension wire? Onboard calculation?

Without knowing about “the moon” you can’t validate/invalidate a reading. You can’t know the impact changing out the axle for a thicker one or getting the big rimless tires on the odometer, speedometer and other measures.   And by understanding the moon you are able to draw the connections and queries that lead to actionable insight.

This is where most data conversations get awkward.  Most people don’t know the source data and so the conversation starts to sound like an interrogation.  But it is just genuine curiosity. There is a lot of recent talk about the importance of curiosity. Feel free to get curious and go find some of these great articles.  Scorecards are great but if you don’t trust where the numbers are coming from or don’t understand the calculations used you can’t understand why an initiative may or may not move the needle. The definitions of key data points are often undefined and it is only through this curiosity that the questions that need answers can get needed attention?

Let’s look at a simple question like, “How many FTE did your company have last year?” Answering this question is not as straightforward as it may seem. For example, how does your FTE answer treat elements such as working days per year? (220? Less due to vacation policy?) Hours per day? (8? 6.5?)  Answers to these questions open a range of >23%. in a 10,000 person company that is 2,300 jobs that can make a big impact on any metric.  Now the big moonbeam here is that FTE is part of the calculation of a ton of numbers.  Wherever you see the lovely phrase “per employee” there FTE is, somewhere in the Excel spreadsheet…giving you the finger.

The key to avoiding #1 is simple.  Get curious. Ask questions.  You will be rewarded. And if you are wondering what the answer to the speedometer question is.  Click here.  Warning, the answer is really cool and while it feels a little bit overcomplicated (showing off?) it is still awesome.

“Don’t look at me; look at the moon.”

Ok so let me start this round out by assuming the following:

  • You are not/no longer suffering from #1
  • You are “nice”
  • Your boss is only watching the finger (all she has time for? understanding of?)

Taking #1 off the table saves us a bunch of time. Many New Orleans restaurants/bars have a sign somewhere in their establishment that simply says, “Be nice or leave.”  I agree.  If you want to game the numbers go ahead.  Most good thieves have a deep understanding of the numbers so a slight hat tip to them. But since embezzlement and theft are not nice, they are out.

The last one…I will simply say this.  I get it.  If the finger is my $scorecard$ then yes I will look at the finger. Ignoring this dynamic is not going to help. We are all grown-ups and can talk about this stuff right? I wish business execs all wore their scorecards like handkerchiefs.  I could instantly find business alignment and have an idea of the economics on the business leader’s side.  A high impact learning event delivered in an area that moves a business sponsor’s personal scorecard is more valuable as the one for a non-scorecard business unit. Eye of the beholder and all, it just is.

And then there is 70/20/10

Please reconsider the value of this metric today. Blindly measuring Blend (delivered, available) is not valuable.  Like Malcolm Gladwell’s 100,000 hours, we love clear finish lines.   However, this part of the finger is my nomination for the most gamed stat in the L&D organization. What likely started as a slide to justify the costs of a digital library conversion became an industry gold-standard for a hot minute is pretty amazing.  Someone should do a map of the acceptance of the 70/20/10 concept (google search?) along with Skillsoft stock price. We all get lazy and when everyone is yelling 70/20/10 you know where your safe place is. Sometimes we need to remember that there is a moon out there.

As for re-imagining the stat, with the moon on my mind, here are my thoughts.  It should still be blend but from a learner’s perspective. So typical employee persona (please tell me you have these for your org) is seeing learning from all these channels at this %. The right mix is the one that drive results, just be prepared to defend your mix. By starting from the learner, not the media, we can now follow a valuable path of questioning:

  • How is this mix impacting employee experience?
  • How can mix be improved through scheduling?
  • How does this media mix compare with non-business related learner behaviors?
  • Every channel (online, in flow, etc) should have a channel objectives quant and qual. How are we doing against those channel objectives?

Sensei Lee would say to stay curious about the moon and remember to ask why of the finger.  Crazy uncle Elon would ask if we are prepared for Mars.  I would say that all we need to do is to get the boss curious about the moon.

Goofus Data

Goofus image

When I was a kid, one of the only exciting things about going to the dentist was the chance to catch up on my Highlights magazine reading.  The childrens’ magazine is famous for a monthly feature titled “Goofus and Gallant” which showed the behaviors of good children versus those of not-so-good kids.

I was reminded of these cartoons as I sat, frustrated once again, listening to the media and politicians discuss Covid data. If you wanted to put together some real life Goofus examples for dealing with data you don’t have to look any further than the local or network news. From “garbage in, garbage out” to mistaking the data as the end and not an input to a deeper insight, Goofus seems to be hard at work daily.

Don’t have unclear/inconsistent reporting standards.

What is the definition of a Covid death? When do numbers get reported (even on Sunday?)

Don’t focus on the wrong data.  

Infection count is only useful or important in the context of audience size or tests conducted.

Don’t look at daily data if the system operates on a different time scale.

We know there is a lag between action and impact with Covid.  Would a rolling 14 day average be more useful for planning and trend analysis?

Don’t lose the message in averages.

Pull out a few early states, or remove the elephant that is New York and watch how the chart of the country’s battle changes.

Don’t use the wrong units.

Percentages can be a marketers friend (100% growth of a small number sounds better than the actual number) but sometimes it is also the best way to understand the data. Percentage (%) of beds in use versus number (#) of hospitalizations is more readily understandable when ICU beds are a key capacity constraint.

Those are just some of my daily irritants.  And don’t get me started on false positive % or how an exponential function works (just watch this old shampoo commercial.) https://youtu.be/mcskckuosxQ

Do you work with data?  What would you add?

Project Dragonfly

Last fall a client asked me to write a quick brief on the book Range. With permission from the client, I am sharing it here.  You can read it below and download it here Dragonfly Concept Design

Enjoy. – j.

Dragonfly

named for an insect whose eye contains thousands of “eyes”, giving it a wide range of perspectives from which its brain constructs its reality and actions.

Project Genesis

Sparked by the release of David Epstein’s book Range, the question that launched the quest was crafted as follows:

“Is there a way to predict employee high performance, and therefore company high performance, by examining the degree to which their path to performance (P2P) is in alignment with the principles outlined in Range?”

Contributing questions include:

  • What performance does business need from its employees?
  • What is the traditional/current/accepted P2P for employees?
  • What are the principles that define the Range P2P?
  • How can we objectively measure the alignment of a company’s current P2P?
  • What are the barriers to organizations adopting a “Range-driven” approach to talent management?

What performance does business need from its employees?

Today’s business environment has been termed and widely accepted as VUCA (volatile, uncertain, complex, ambiguous.)  This environment has placed a premium on organizations that are:

  • Agile – able to quickly deploy and redeploy human capital to emerging needs and opportunities.
  • Experimental – have a culture and resources able to conceive, test and iterate of new hypotheses.
  • Innovative – ability to generate and execute new ideas in order to capture business opportunities.
  • Data powered – human capital is augmented by data for management (ie. performance tracking, succession planning) and the delivery of the above capabilities.

Today’s business environment is as being what Robin Hogarth calls a “wicked domain.”  Wicked domains are defined as having problems that are not readily computable and have feedback loops that are long and may not provide accurate feedback. The answers to wicked problems are unknown at the outset and need to be created. Examples of wicked domains include improvisational jazz, and cancer research.

Net: Today’s business environment requires a P2P that makes human capital “wicked smart” (Boston joke)

 

What is the traditional/current/accepted P2P for employees?

Today’s development paths generally follow two paths depending n the employee.  Designated high potential employees are often provided a wide ranging development path early on in their careers. Business units rotations, projects and even mentorship expose this employee to all aspects of the company as a means of preparing them for future leadership positions. This cohort is typically extremely small relative to the employee base.  

The second path serves the typical employee.  On this path employes, who have been hired for a specific domain experience are funneled into deeper knowledge of that domain.  Sales people receive sales and product training, an operations employee may receive task management and process training wile operators might attend equipment and safety courses.  These trainings, limited to a single domain are associated with the assumption of a kind domain and problems.  

Kind problems are domain-constrained with tight and accurate feedback loops. Unlike the wicked variety, answers to kind problems are known and simply need to be found. Kind doesn’t mean easy, a sport can be kind because you quickly know if you executed the correct stroke. Examples of kind domains include classical music, hernia surgery and chess. Kind domains are are often the best targets for automation. 

Range principles, where existing, are often found in the initial hiring process. In 1991 David Guest introduced the concept of t-shaped skills. The vertical bar of the T refers to expert knowledge and experience in a particular area, while the top of the T refers to an ability to collaborate with experts in other disciplines and a willingness to use the knowledge gained from this collaboration. This concept was further popularised by Tim Brown, CEO of design firm IDEO. While the concept was seen to have value  and gained momentum with HR the concept of developing t-shaped employees never took hold. The priority for most recruiting and promotions involves an emphasis on the vertical domain of the individual.

T-shaped employees provide companies with increased talent agility and mobility. Having an agile workforce can spell the difference between being an industry leader or falling behind. PwC reports that when businesses have development programs that increase agility, 86 percent respond rapidly to changes in the business environment. Without these kinds of programs, only about half do. A Forbes article from earlier this year stated  that talent mobility enables organizations to rapidly adapt to changing environments, with the ability to deploy and move key skills across projects, across the business and across borders when needed. Mobility provides avenues for staff to progress and evolve within an organization, and can lead to 30% better processes and 23% more productivity.

Epstein captures it this way. “Facing uncertain environments and wicked problems, breadth of experience is invaluable.  Facing kind problems, narrow specialization can be remarkably efficient. The problem is that we often expect the hyperspecialist, because of their expertise in a narrow area, to magically be able to extend their skills to wicked problems.  The results can be disastrous.”

Net: Today’s P2P does not fit business to a “T” (I am on a roll)

 

What are the principles that define the Range P2P?

First it is important to acknowledge, as Epstein does multiple times in the book, that while Range-enabled generalists are critical to business success the value of specialists is not diminished.  A multitude of examples are provided showing how generalists draw on the deep expertise of specialists in order to achieve the results delivered. Distilling the book’s insights and translating them to corporate talent management creates three levels of guidance.  The first contains the characteristics of a Rangey(?) path-to-performance (rP2P). The second, is guidance on keys to rangey teams. The final level is focused on the organizations itself.

 

rP2P

The goal of the rP2P is to build polymaths.  Polymaths differ from T-shaped employees in that the emphasis is on the horizontal dimension. A polymath’s breadth is greater than T-shaped human capital while their vertical depth may be less than traditional T-shapes. The polymath’s superpower comes from a range of transferable thinking skills (conceptual, computational, lateral, and ambidextrous for example) that allow for innovative problem solving across multiple domains.  It also includes more tangible skills such as communication, collaboration and anticipatory competencies that drive higher value solutions. The final element of a range-y employee is attitudinal with value-adding polymaths displaying active open mindedness and scientific curiosity. 

The first two principles associated with a Range-aligned P2P focus on the structure and focus of the developmental path. The rP2P contains:

  • A sampling period – This is characterized by what experts often call “unstructured play.”  This feature of the developmental path allows for individuals to experiment in domains other than their own.  In the corporate environment this may be a rotational program or project-based. The unstructured element forces participants to improvise first before learning existing rules.  This is analogous to the way in which humans learn language. We learn the sounds first before we learn the rules of grammar. This element should be designed to provide the participant with experiences that allow them to better understand alternative domains giving them knowledge of resources that may be valuable when facing later challenges as well as exposure and personal knowledge of their interest and proclivity for other areas. 
  • Mechanisms for improving “match quality” – The rP2P should include opportunities for individuals and organizations to re-deploy individuals to domains better suited to their skills, interests, and proclivities. Short-term assignments, internal internships and project participation can be used to serve this purpose.  Up or out development paths serve neither the individual or organization.

The final two principles associated with a Range-aligned P2P focus on the content and presentation of that content on the developmental path. The rP2P contains:

  • Flexible content – Flexible content is content that is both sticky (retained over the long-term) and broadly applicable. Retention of rP2P content is driven by two key factors.  The first is the use of testing. rP2P test questions are connection making in nature versus procedural. Testing should focus on cross domain application, pattern recognition, categorization and decision making. Procedural questions such as “what are the four steps in handling a customer objection?” are minimized in far of questions such as, “what other uses for the customer objection handling process are there?” Stickiness is also enhanced by the use of spacing. Delaying the testing process forces the knowledge to be placed into long-term memory.  End of class assessments are less indicative of future performance than follow up assessments given at a later date.
  • Difficult learning experiences – Learning should include what Nate Kornell calls “desirable difficulties.”  These productive difficulties include creating a generational effect, where participants produce their own answers exclusive of guidance.  Learners benefit greatly from this self-reliant process even if the answer generated is incorrect. Instructors should also be creating environments in which learners struggle. This may include problems beyond learners’ capabilities, mixing multiple areas of new knowledge together to prohibit “block” learning (aka memorization) and assessments in which few learners achieve a passing score.  While this often results in lower instructor/session ratings from participants it has been shown to have significant long-term benefits in retention and performance.

Rangey Groups

While the book focuses primarily on the individual, Epstein highlights two characteristics of high performing groups.

  • Diverse –  High performing groups included a wide variety of participants.  This includes range in:
    • Geography/World view
    • Demographic
    • Experience in domain from novice to expert
    • Domain, but still polymaths not a collection of specialists  
  • Porous boundaries – Groups that performed well were also not walled off from the rest of the organization.  Groups frequently showed improved value when they were able to reach out to specialists across the organization and even outside the organization. 

Rangey Organizations

Example such as 3M are cited in the book as examples of an organization that supports its range-y individuals and teams.  From creating an internal award for innovation to the ways it allows individuals to follow their passions (increased match quality) 3M regularly produces significant innovations across a wide range of domains.  Epstein touches on a few organizational keys.

  • Culture –  3M’s internal award and its talent management approach are operational examples of a culture that sees the value of supporting its rangey employees. By celebrating, facilitating and empowering range, 3M has created a culture that makes it values more than just an annual statement cliche.  
  • Long-term focus – Because range often does not show immediate results the organizations that embrace it must have a longer view.  Think of the innovations that emerged from Amazon and the newly approved LTSE (long-term stock exchange) that clearly states that “Companies that operate with a long-term mindset tend to outperform their peers over time. But going public can pressure even the most visionary founder into a short-term mindset.” 
  • Risk tolerant – Creating new solutions that work often means finding many more that don’t.  Acceptance, even encouragement of failure, and the adoption of an experimental scientific mindset are cornerstones of organizations that deliver higher performance over the long-term.

 

Net: The principles of Range have implications for a number of areas in talent management including; candidate selection, onboarding, development, succession planning and leadership. In order to drive success in today’s wicked environment organizations, and talent management functions that support them, must become an integrated farm (egg-to-soup) for free-range talent. (the roll continues)

 

How can we objectively measure the alignment of a company’s current P2P?

Standard measures for HR practices are in limited supply and often not publicly available. The table below captures some initial thoughts regarding potential metrics/proxies for the various elements of range. Primary research in collaboration with one or more of the partner listed at the end of this section and/or analyst-like interviews focused on HR leaders may also prove useful in the development and testing of a quantitative range “score”. 

 

Dimension Metric/Area of Inquiry
rP2P Sampling
  • Onboarding process
  • Use of project assignments
  • Cross domain rotations 
Match Quality
  • Internal lateral transfers versus upward promotions
  • Use of project assignments
  • Cross domain rotations
  • Former employees now working in another domain
  • Employee satisfaction
  • Employee retention
  • Employee engagement
  • Employee development plans
Flexible Content
  • Learning experience (LXP) design
  • LXP satisfaction scores
  • Cross functional applicability of LXP (multi-audience)
  • Layoffs
Difficult Learning XP
  • Learning experience (LXP) design
  • LXP assessment timing
  • LXP pre/post assessments
  • Instructor ratings
  • Post-LXP performance reviews  
rGroup Diversity
  • Employee census
  • Recruitment procedures
  • Job requirements (narrow/broad)
Porous Boundaries
  • Resource sharing policies
  • Use of outside experts/consultants
rOrg Culture
  • Employee survey
  • Leadership characteristics
Long-term Focus
  • Strategic plan
  • R&D spend

What are the barriers to organizations adopting a “Range-driven” approach to talent management?

While the value of rangey practices are widely documented, adoption of the proven practices face a number of challenges.  Some of these challenges are documented below.  This list is not meant to be all-inclusive as it excludes any number of organizational structure, compensation and process barriers.

  • Slow thinking requires a longer payback period – As is the case in spacing of testing where immediate results may be poor while results produced further out exceed the current P2P so it is with much of the range principles.  Organizations and other stakeholders (HR, Management) may find it difficult to “stay the course” without near-term ROI.
  • Spacing assessments is less rewarding to learners – Individuals often do not receive the immediate gratification of progress and success when participating in a rP2P. Without buy-in from individuals to the range approach and the safety of knowing that their immediate performance will not be seen as a negative by the broader organization individuals may not actively engage in the path.
  • The result of increased match quality (job switching) can make individuals feel like they are falling behind – Job switching, often the result of seeking improved match quality may leave individuals feeling behind their peers.  The concept of sunk cost, time and energy committed to a pre-switch domain, may make individuals and organizations reluctant to follow through with match optimization. 
  • Misaligned metrics (learner satisfaction, post-assessment scoring) reward non-rangey principles – Current P2P metrics are short-term oriented and in many cases run contrary to effective implementation of range principles. Changes to how rP2P facilitators (recruitment, development, management) are necessary in order to properly measure range-y progress.
  • Lack of obvious linkage to near-term business results – Rangey performance often delivers value in unexpected areas/was.  Measurement and management of impact on wicked problems must be different than on kind ones.  New product development (connected/wicked) should have a set of metrics distinct from new store openings (procedural/kind).  
  • Short-term perception of poor performance – Public company cadence (quarterly) may inhibit the longer term investment in a rP2P.

Net: Lots of fences between the herd of sheep and the range. “Just let me know if you wanna go to that home out on the range. They got a lot of nice girls.” – ZZ Top

 

Final Thoughts

After reviewing the publicly available research used for the compilation of this document I believe the following to be true:

  • “Wicked” is an accurate description for an ever increasing portion of the business environments and the “kind” portion will increasing face the pressures of automation and commoditization.
  • The companies that win in a wicked world win exhibit a significantly higher degree of “range”.
  • Adoption of range positive dimensions require significant change for both the individual seeking to increase their personal range and companies seeking to provide a range-enabling organization.
  • The current structure for talent development and management is designed for the creation of specialists and ill-prepared for a shift to polymath focused human capital.
  • With additional work there exists an opportunity to capture or create qualitative and quantitative metrics for assessing range on both the individual and organization levels.

 

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The Tail Will Wag the Dog

David Vance recently did a webinar regarding the pending legislation requiring the reporting of human capital metrics for public companies. I cannot state strongly enough the potential implications of this move. A move which I feel equally strongly is being largely ignored by my L&D colleagues. I do not have a crystal ball but simply applying the dynamics of other publicly reported numbers may help to clarify.

Publicly Reported Numbers Get C-Suite Attention

L&D has long asked for it but is it ready for it’s close up? While L&D’s current data reporting may make the industry feel good but will it stand up to the scrutiny given to financial data reporting. As a CLO can you sit with your CFO and defend the numbers, the methodology of collection and the actions taken as a result of them. Financial numbers (margins, expense, key ratios) are never good enough and always include the plan for improvement.

Transparency

Having the numbers out there without context is going to create some interesting dynamics. The L&D metrics for a company are highly contextual. This is something that I have long argued as a mitigating force to the use of benchmarks. Most companies are a cohort of one. The growth goals, competitive environment, geographic challenges and legacy paradigms are just a few a few of the things that can make a company’s metrics right for them and them alone. Without this context L&D may face pressure from new external and ill-informed senior internal sources.

Teaching the Test

If the metrics are what becomes the face of L&D the natural response is to game them. This is no different than sales organizations that pull sales forward to make a quarter look better or an operations department that delays a purchase to manage costs. When what gets delivered is in pursuit of two masters (performance and metrics) and one is highly visible, which one do you think wins.

Short-term Thinking

There are many who decry the behavior of public companies driven by a quarter-by-quarter mentality. We know that performance development occurs over time. How will our approaches to leadership, diversity, and upskilling change when they are held to the 90 day window of reporting. This is not to mention the fact that if the metrics are wrong. We all know that vanity metrics are a constant, although comforting, threat to true performance development.

What do you think will happen when L&D goes from opt-in self-reported numbers to a friendly industry organization to a federal requirement? Is your organization ready?

Time for L&D to Bear Down?

“Some confluence of events at some point in the future will cause a recession. I don’t know what those are, nobody knows what those are, and nobody will ever know what they are.” – Jamie Dimon, CEO JPMorgan

So I am as uncertain about a coming recession as a guy on track to make $30 million in salary this year and whose business it is to see them coming. But it is an honest answer and it is important to watch and see if, “at some point,” is this point in time.

  • American central bankers have grown more fearful, models gauging recession probabilities had “increased notably in recent months.” Meanwhile, indicators tracking manufacturing and services industries, as well as business and consumer confidence — so-called “soft” indicators often seen as a harbinger for weakness in hiring and spending — continued to worsen. – Livemint · October 10, 2019
  • In a recent Forbes article Sergei Klebnikov reported that Harvard University professor and former treasury secretary Larry Summers placed the odds of a recession before 2021 at nearly 50%. This supports Summers’ interview with the Wall Street Journal, In which Summers delivered a grim outlook for U.S. economic conditions—“I haven’t been this alarmed since the financial crisis,” he admitted.
  • Even our prescient friends at SHRM, in a 2017 article entitled, “How HR can prepare for the next recession,” plainly stated, “Economic downturns are inevitable, so HR should act now to prepare for the next one.”

L&D Under the Macroscope

While the L&D industry (IMHO) can get caught upin the micro (is. learning solutions, platforms, evaluation) I prefer to focus my attention on the macro because these become the undercurrents that drive the broader industry. Judging by my LinkedIn connections there are a number of L&D leaders that were not in place in 2009. So let’s begin with a quick L&D recession primer.

  • The effects last longer than you think. a Harvard Business Review article entitled “Roaring out of the Recession,” (published in 2010!) stated, “CEOs continue to combat the myriad challenges thrown up by the Great Recession of 2007.”
  • L&D gets the axe. JB at Bersin & Associates reported that average training expenditures per employee fell 11 percent in the past year, from $1,202 per learner in 2007 to $1,075 per learner in 2008.
  • It is global. The UK Commission for Employment and Skills released a report on the impact of the 2008-2009 recession on training at workIn it authors Alan Felstead, Francis Green and Nick Jewson made it clear that discretionary training got the brunt of it with, “Private sector employers continued to train their workforces because they were faced with a number of ‘training floors’; that is, types of training that are essential, and therefore cannot be abandoned, by functioning businesses or organisations.”
  • It doesn’t go back to the way to was. Bersin’s group also noted that at the time almost all business leaders reluctantly admit that the current crisis also marks an inflection point: The world after it is unlikely to resemble the one before it.

 

Why I am Excited About a Recession

“He said what?!?!” I recently met a out-of-town friend at the New Orleans Air BnB he was staying at. I took an Uber to his place. Why does this matter. Because macros trends like recessions have played in role in both Air BnB’s and Uber’s success. In his “startup land famous” TED talk Bill Gross identified the number one factor in determining startup success. Bill should know. His firm, IdeaLab has been there since the beginning and has seen hundreds If not thousands of startup over that time.

In his talk Gross identified “timing” as the most highly correlated factor. Not quality of idea. Not founder, industry or technology. If the recession had not occurred would the founders of Air BnB needed to rent out thier extra air mattress to make ends meet? If you ever wondered where the “Air” came from, it isn’t from aspirations to sell plane tickets. Would people be willing to offer perfect strangers rides in their personal vehicles if it weren’t for the need for extra income in an economy where it can feel like job skills have the shelf life of an avocado.

In addition to the economy, another macro that caught my attention this was this one showing that at H1B visas are being issued at record LOW levels! Combined with the fear of recession does this accelerate the trend towards remote workers? With budget reductions in the back of executives’ minds is L&D empowered to more effectively implement new ways of doing things (given we can make the cost-saving case) that were resisted prior?

Discomfort enables behavior change and constraints drive innovation. I agreed with JB when he said last time. I agree with it for the recession to come.

“The world after it is unlikely to resemble the one before it.”

And I, for one, think that that is something to get excited about.

 

The Math of Upskilling

The case for learning versus hiring has long been a topic of discussion. With the recent job market as tight as ever the conversation continues.  Just this week Josh Bersin (or as I call him, “JB”, not because I know him that well, just because it sounds cool) released the highlights of a study done with three firms that concluded,

“It can cost as much as 6-times more to hire from the outside than to build from within.” – JB

While I can take issue with the phrase, “as much as”, since I have a dog who can be obedient “as much as” half the time.  Or perhaps the sample size, only three companies in different industries. Or maybe that the study used highly paid jobs +$150k salary to joust at.  But none of that will stop the industry from using this stat widely.  This may be fine at L&D conferences but try it with a CFO and you better be prepared with the math.

So that you know that I am not picking on JB (who I think is the Seth Godin of Human Capital) the issue that I have is with reports that don’t stay loyal to the kind of math that has credibility with finance folks.  While for some, being able to simply cite a case study with a recognizable company may be enough.  For me it is not. And for my own learning this blog is my attempt to take Jane Bozarth’s work out loud approach and show my work.

“And showing what we’re doing—narrating our work in a public way—helps make learning more explicit.” – the other JB

The Case for Upskilling

We start with the simple comparison of costs to determine value.  If the result is positive then reskilling wins.  If not, hire away.

The value of reskilling (V) = Cost of New Hire (CN) – Cost of Reskilling (CR)

Seems simple enough but the devil is in the details.  So lets break it down further.

V= [Cjn+Chn+Cpn+Co+Cs] – [Cjx+Chx+Cpx+Cu]

The cost  of new hire (CN) equals:

  • Cost of job opening (Cjn) plus
  • Cost of new hire (Chn) plus
  • Cost of lost productivity (Cpn) plus
  • Cost of onboarding (Co)
  • Cost of redundancy/severance (Cs)

The cost of reskilling (CR) equals:

  • Cost of job opening (Cjx) plus
  • Cost of transfer hire (Chx) plus
  • Cost of lost productivity (Cpx) plus
  • Cost of upskilling (Cu)

This approach leaves some very real variables out:

  • Calculation does not include fully loaded employee costs (benefits, occupancy, equipment, etc.  This is assumed to be a wash between CR and CN.
  • Does not include quantifiable costs associated with loss of investor confidence due to layoffs  which would likely show up in stock price.
  • Does not include quantifiable costs associated with loss of employee/candidate confidence due to layoffs such as; unplanned attrition, longer time to hire, reduction in candidate quality.
  • Does not include the 2X-3X higher turnover rate for new hires used by JB for his calculation.

Please let me know what I have missed and how this calculation can be more valid and useful.  In my next post I will further breakdown each of these costs, insert some assumptions (cost of onboarding/upskilling, recruiter fees, time to productivity, etc.) and share the excel spreadsheet plus the results it spits out.

 

Learning is the Silver Bullet

Originally posted on LinkedIn 10.16.19

Nelson Mandela said,”Education is the most powerful weapon which you can use to change the world.” Malcolm X called education, “the passport to the future.” And with the World Economic Forum currently estimating that 54% of today’s global workforce will need reskilling in the upcoming years…never were these sentiments more relevant.

Fortune 500 companies alone employs over 65 million people, many of which will need to be reskilled in order for them and the companies they support to thrive in today’s business environment.  Whether you call the times we live in  VUCA, transformative or just batshit crazy, the reality is, we live in a time where learning is needed as much as air to survive.

According to the Bureau of Labor Statistics,  the US currently has over 6 million unemployed. This comes at the same time that there are over 7 million open positions to be filled at our companies. This mismatch of skills to opportunity affects approximately 1 out of every 30 families.  Add to this the almost 2 million jobs impacted by corporate layoffs in the month of July and the need for learning is crystal clear.

Education, at all levels, has the ability to deliver unmistakable value on a multitude of fronts. From  socio economic issues like poverty and classism to global challenges like terrorisim. Malala Yousafzai,  Pakistani Taliban target turned activist for female education and the youngest Nobel Prize laureate said, “With guns you can kill terrorists, with education you can kill terrorism.”  I couldn’t agree more.

As Learning and Development professionals we have an obligation to continuously seek and employ new ways to deliver the ever higher levels of value required by today’s world.  This is why I wrote Running Training Like a Startup. To provide a new way of looking at the work we do and they way we do it.

Over 20 years ago I was lucky enough to be part of the team tasked with taking the concepts and practices described in the seminal book Running Training Like a Business to clients around the world.  The book focused not on the solutions being provided but rather on the L&D engine that provided those solutions. For the last two decades I have worked with companies of all sizes across the globe to explore ways to improve the value their learning delivers.

During this time I also worked as part of the rapidly emerging startup economy.  First as strategist for a VC fund during the first dot com run then as manager of a 50 million dollar fund and advisor to startups, angel investors and startup ecosystems in both Delaware and New Orleans. Then 5 years ago, I saw an opportunity to bring these two domains and the passion I had for each, together.

W. B. Yeats said, “Education is not the filling of a pail, but the lighting of a fire.” This is the same fire sought after by startups as well, in the form of virality and exponential growth. With the support of Ed Trolley and David van Adelsberg, the authors of Running Training Like a Business, I set out to turn the practices of high performing startups into principles for use by L&D organizations. The book includes approaches to speed, team, product, communication, data and even failure. The lessons from early stage companies like Uber, Air BnB, Slack and a multitude of others are captured in my updated look at what it means to run training like a business today.

Taking a page out of the startup toolkit I open sourced the book in January.  The principles were too important and the mission too critical to limit the spread of these ideas. It also allows the book to be a living thing and not a snapshot.  Like an app on your phone I have released 2 updates to the book this year with more to come as we gain more experience with its approach. Running Training Like a Startup is my contribution to addressing the challenges we, as L&D pros, face.

Abraham Lincoln viewed learning, “as the most important subject which we as a people may be engaged in”. As an L&D community we must engage with new ideas and new approaches. Call today’s business environment what you will. But hanging out in the desert with its vibrant and beautiful ecosystem reminds me that even in the harshest environments we can adapt and thrive.

Note: In case you think I am just a sappy romantic about L&D, read this tough love post from a couple weeks back.

L&D is a Master of VR

When Ed and David released Running Training Like a Business (RTLAB) it was clear to many that the industry needed a new way of looking at not just how and what we were training employees but why.  The book aspired to take the industry discussion up a level.  Away from the micro of courses, design methodologies and technology to the macro and meta.  The book encouraged a turn inward away from the course and curricula towards the creator, the L&D organizations itself.  What the factory was designed for, pre-determined what the output was.  Transforming the organization would transform the output and the value it produced.

In 2010 when I started writing the Learning Hacks blog as a way to capture my musings on L&D I began with a blog entitled “The Spark That Started It All”, the working title for this post can still be seen in the URL for the post.  It expressed my disappointment that many of the challenges described in RTLAB, over a decade prior, remained unaddressed.  In my book Running Training Like a Startup I cite one of my favorite Ed Trolley quotes.  A quote that was validated in many of the assessments we did for clients around the world.

“Business leaders have low expectations of training. And they are being met.”

-Ed Trolley

Yesterday, Harvard Business Review released an article entitled. “Where Companies Go Wrong with Learning and Development” that put things in clear perspective. In it Steve Glaveski highlights recent studies that show:

  • 75% of 1,500 managers surveyed from across 50 organizations were dissatisfied with their company’s Learning & Development (L&D) function;
  • 70% of employees report that they don’t have mastery of the skills needed to do their jobs;
  • Only 12% of employees apply new skills learned in L&D programs to their jobs; and
  • Only 25% of respondents to a recent McKinsey survey believe that training measurably improved performance.

Glaveski nets it out this way, “Not only is the majority of training in today’s companies ineffective, but the purpose, timing, and content of training is flawed.”  I don’t disagree.

While the L&D community hold conferences dominated by sessions on how to create compelling Powerpoint title slides, the use of chatbots, and incorporating podcasting into a curriculum, the businesses they support keep moving and changing desperate for employees that can perform.  In the late 90’s I was tasked to lead a project for Microsoft.  At the time they were under intense scrutiny for monopolistic practices.  It was also a time when Fred Reicheld (who would later create the Net Promoter Score) released the “Loyalty Effect” debunking the marketer “top-box” approach to assessing satisfaction.  I won’t go into it here but when retention does not show a drop off as satisfaction goes down there are other market forces at play. High switching costs, tie-ups and lack of alternatives can be some of those drivers. The retention results don’t reflect the satisfaction of customers (it may make it worse because they feel trapped) but it does give the provider an extremely distorted view of how it is performing.

Over 20 years post the release of RTLAB the data on L&D’s customer satisfaction continue to come in.  While the L&D industry focuses on budget amounts, spend per employee and other “vanity metrics”, the HBR article clearly shows it is long overdue for the learning organizations that are delivering leadership training to take a leadership role.  For the L&D groups supporting innovation initiatives to innovate.  For the industry, as a whole, to take off the goggles and stop living in its virtual reality world.