Deep diving into strange worlds

Posted 11 December 2017 by Ryan Tracey
Categories: blogging, e-learning

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2017 was a whirlwind for me. I started a new job in a new sector, made loads of new friends there, learned heaps, and finished said job 10 months later before starting another one back in financial services.

I haven’t had much of a chance to scratch myself!

As a consequence, I haven’t blogged as frequently this year as I have done in previous years. However, while my posts may have been fewer, I dove deeper into a couple of topics of interest.

Pulp magazine cover entitled Enormous Stories

Data science was one such topic that captured my attention, not only because it’s white hot, but also because I believe it will inform our practice like never before.

I also focused my mind on capability frameworks. Not the most exciting topic, I know, but in my opinion a driver of business performance.

Somehow I also stole enough time to share my thoughts on virtual reality, journals, games, conferences, and the employee lifecycle.

Data science

Capability frameworks


I invite you to review my posts and leave a comment on any that elicit a response. I’d love to hear your thoughts.

In the meantime, here’s to a great 2018!


Louder than words

Posted 13 November 2017 by Ryan Tracey
Categories: capability framework

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My last couple of blog posts have argued in favour of extracting value out of organisational capabilities. Due to the nature of my role I have posited these arguments in terms of employee development.

However, I further advocate the use of organisational capabilities across all parts of the employee lifecycle.

Using the 4+4 Part Employee Lifecycle as my guide, I will share my thoughts on some of the ways in which your capability framework can add value to your organisation’s activities in terms of recruitment, onboarding, performance, and offboarding.

The 4+4 Part Employee Lifecycle: (1) Recruitment; (2) Onboarding; (3) Performance; and (4) Offboarding; plus (1) Performance Management; (2) Development; (3) Health & Wellbeing; and (4) Retention.


Everyone knows that change management is hard. Culture eats strategy for breakfast; an organisation’s culture doesn’t change over night; something about herding cats; the change curve; etc. etc.

We’ve heard it all before, and yes it’s probably true.

But there’s a big elephant in the room: the power of recruitment to accelerate cultural change. That is to say, bring in from the outside the people whose capabilities you desperately need on the inside.

Which begs the question… what capabilities? Well, organisations that focus like an eagle know precisely the capabilities to assess each candidate against, because they are the ones that align to their strategic imperatives.

If your organisation needs to become more collaborative, recruit collaborative people. If it needs to become more innovative, recruit innovative people. And if it needs to become more digitally literate, recruit digitally literate people.

This approach may seem too obvious to mention, yet I dare you to examine your organisation’s current recruitment practices.


Onboarding is one of those pies that everyone wants to stick their fingers into, but nobody wants to own. Yet it is crucial for setting up the new recruit for success.

From an organisational capability perspective, a gold-plated opportunity arises during this phase in the employee’s lifecycle to draw their attention to the capability framework and the riches therein. The new recruit is motivated, keen to prove themselves, and hungry to learn.

Highlight the resources that are available to them to develop their capabilities now. This is important because the first few weeks of their experience in the organisation colours their remaining tenure.

Ensure they start their journey the way you’d like them to continue it: productively.


Capability powers performance, so the capability framework is a tool you can use to improve all four subparts of Performance in the 4+4 Part Employee Lifecycle.

Performance Management

Effective performance management complements development planning to provide the employee with guidance on improving said performance.

When seen through the lens of the capability framework, an employee’s performance appraisal can identify meaningful development opportunities. Performance weak spots may be (at least partly) attributable to gaps in specific capabilities; while a strengths-based approach might also be adopted, whereby an already strong capability is enhanced to drive higher performance.

To inform these decisions with data, I’d be keen to correlate capability assessments against individual performances and observe the relationship between the variables over time.


It’s all very well to have a poetic capability framework, but if learning opportunities aren’t mapped to it, then its value is inherently limited.

If the framework’s capabilities align to leadership stages, I suggest the following question be put to the user: Do you want to excel in your current role or prepare for your next role?

Not only does this question focus the user’s development goal, it also identifies the relevant leadership stage so the capabilities can be presented in the right context.

A follow-up question may then be posed: Would you like to browse all the capabilities – useful for those who want to explore, or already know which capability to develop – focus on our strategic imperatives – useful for those who are time poor – or assess your capabilities – useful for those who seek a personal diagnosis.

The answers to these questions lead to a selection of capabilities which, beyond the provision of clear descriptions, outline the opportunities for development.

Resist the urge to dump masses of resources into their respective buckets. Instead, curate them. I suggest the following approaches:

KASAB is an esoteric extension of the KSA heuristic in teaching circles, and I like it because it includes “B” for “Behaviour”.

For example, help your colleagues move beyond the consumption of teamwork videos, design thinking workshops, and moocs on digital business; by encouraging them to contribute to communities of practice, submit ideas to the enterprise idea management system, and participate in the company’s social media campaign.

Health & Wellbeing

I see organisational capabilities applying to health & wellbeing in two ways.

The first way concerns the impact of employee development on mental health. Given the satisfaction and pride of building mastery drives engagement, the capability framework presents opportunities to improve mental health across the enterprise.

The second way concerns the composition of the capability framework. Given a healthy employee is a productive employee, why isn’t Wellness itself an organisational capability?


I’ve seen with my own eyes the impact of employee development (or lack thereof) on retention.

Given the sense of support and growth that the investment in people’s learning brings, the capability framework presents opportunities to retain talent across the enterprise.


Capabilities that align to leadership stages are useful for succession planning. Not only do they identify the capabilities that someone needs to succeed in their current role, but also the capabilities they need to succeed in their next role. Assessment of the latter informs the readiness of the employee for promotion.

Conversely, when the employee leaves the team (or exits the organisation) the capability framework can be used to assess the skills gap that remains.

Girl with home-made wings

In 7 tips for custodians of capability frameworks I declared a capability framework that remains unused is merely a bunch of words. But it’s worse than that. It is unrealised value across the employee lifecycle.

So use your capability framework to improve the organisation’s recruitment, onboarding, performance, and offboarding.

Actions speak louder than words.

7 tips for custodians of capability frameworks

Posted 18 September 2017 by Ryan Tracey
Categories: capability framework

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Wow, my previous blog post elicited some rich comments from my peers in the L&D profession.

Reframing the capability framework was my first foray into publishing my thoughts on the subject, in which I argued in favour of using the oft-ignored resource as a tool to be proactive and add value to the business.

To everyone who contributed a comment, not only via my blog but also on Twitter and LinkedIn… thank you. Your insights have helped me shape my subsequent thoughts about capability frameworks and their implementation in an organisation.

I will now articulate these thoughts in the tried and tested form of a listicle.

Metallic blue building blocks, two golden.

If you are building, launching or managing your organisation’s capabilities, I invite you to consider my 7 tips for custodians of capability frameworks…

1. Leverage like a banker.

At the organisational level, the capabilities that drive success are strikingly similar across companies, sectors and industries. Unless you have incredibly unique needs, you probably don’t need to build a bespoke capability framework from the ground up.

Instead, consider buying a box set of capabilities from the experts in this sort of thing, or draw inspiration *ahem* from someone else who has shared theirs. (Hint: Search for a “leadership” capability framework.)

2. Refine like a sculptor.

No framework will perfectly model your organisation’s needs from the get-go.

Tweak the capabilities to better match the nature of the business, its values and its goals.

3. Release the dove.

I’ve witnessed a capability framework go through literally years of wordsmithing prior to launch, in spite of rapidly diminishing returns.

Lexiconic squabbles are a poor substitute for action. So be agile: Launch the not-yet-finished-but-still-quite-useful framework (MVP) now.

Then continuously improve it.

4. Evolve or die.

Consider your capability framework an organic document. It is never finished.

As the needs of the business change, so too must your people’s capabilities to remain relevant.

5. Sing from the same song sheet.

Apply the same capabilities to everyone across the organisation.

While technical capabilities will necessarily be different for the myriad job roles throughout your business, the organisational capabilities should be representative of the whole organisation’s commitment to performance.

For example, while Customer Focus is obviously relevant to the contact centre operator, is it any less so for the CEO? Conversely, while Innovation is obviously relevant to the CEO, is it any less so for the contact centre operator?

Having said that, the nature of a capability will necessarily be different across levels or leadership stages. For example, while the Customer Focus I and Innovation I capabilities that apply to the contact centre operator will be thematically similar to Customer Focus V and Innovation V that apply to the CEO, their pitches will differ in relation to their respective contexts.

6. Focus like an eagle.

Frameworks that comprise dozens of capabilities are unwieldy, overwhelming, and ultimately useless.

Not only do I suggest your framework comprise fewer rather than extra capabilities, but also that one or two are earmarked for special attention. These should align to the strategic imperatives of the business.

7. Use it or lose it.

A capability framework that remains unused is merely a bunch of words.

In my next blog post I will examine ways in which it can be used to add value at each stage of the employee lifecycle.

Reframing the capability framework

Posted 28 August 2017 by Ryan Tracey
Categories: capability framework

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There once was a time when I didn’t respect the capability framework. I saw it as yet another example of HR fluff.

You want me to be innovative? No kidding. And collaborative? What a great idea! And you want me to focus on our customers? Crikey, why didn’t I think of that?!

But that was then, and this is now.

Now I realise that I severely underestimated the level of support that my colleagues seek in relation to their learning and development. As a digitally savvy L&D professional, I’ve had the temperament to recognise the capabilities I need – nay, want – to develop, the knowledge of how and where to develop them, and crucially the motivation to go ahead and do it.

But our target audience is not like us. While we live and breathe learning, they don’t. Far too many imho wait to be trained, and our boring, time-guzzling and ultimately useless offerings haven’t helped change their minds.

Yet even those who are motivated to learn struggle to do so effectively.

A businessman thinking

Sure, we’ve read about those intrepid millennials who circumnavigate the languid L&D department to develop their own skills via YouTube, MOOCs, user forums, meet-ups and the like; but for every one wunderkind is several hundred others scratching their heads once a year while they ponder what to put in their Individual Development Plan, before finally settling on “presentation skills”.

This is unacceptable!

While it’s admirable for L&D to be responsive to the business’s relentless requests for training, it’s time for us to break out of the cycle of reactivity. I put it to you that a capability framework can help us do that. It’s a tool we can use to be proactive.

If we inform the organisation of the capabilities that will improve our performance, enable individuals to assess these capabilities to identify those that are most relevant for their own development, and map meaningful learning opportunities against each one, we add value to the business.

In an era in which the ROI of the L&D department is being put under ever-increasing scrutiny, I suggest a value-added approach is long overdue.

The good life

Posted 26 July 2017 by Ryan Tracey
Categories: human resources

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In a previous role I had cause to draw up an employee lifecycle. Despite my years in HR up until that point, it wasn’t something that had ever occurred to me to do.

The driving force was an idea to support managers through the various people-related matters to which they needed to attend. The employee lifecycle would provide the structure for a platform containing information and resources that our managers could draw upon on demand.

After a bit of googlising, it struck me that there is no one standard model of the employee lifecycle. I found this surprising as the basics of the employee experience – and the HR functions that correspond to them – are arguably similar across jobs, organisations and industries.

Moreover, some of the models I found were either overly complicated (in my opinion) or they were presented in an illogical manner. In any case they didn’t quite suit my needs, so I decided to draw up my own.

After much thinking and reflection, I realised the employee lifecycle can be distilled into just four main parts: (1) Recruitment; (2) Onboarding; (3) Performance; and (4) Offboarding. Of course the employee experience is more complex than that, but it is within these four parts that the complexities reside.

I call this model the 4 Part Employee Lifecycle.

The 4 Part Employee Lifecycle: (1) Recruitment; (2) Onboarding; (3) Performance; and (4) Offboarding.

While some other models of the employee lifecycle start with “Attraction”, I consider this a subset of recruitment, along with other activities such as interviewing and selection. Diversity may also reside in this part.

Onboarding concerns the bringing up to speed of the new recruit, and it may include a combination of pre-boarding, orientation and/or induction.

Performance is the raison d’etre of recruitment and onboarding. It is the productivity of the employee. In other words, are they doing what they are paid to do, and how well are they doing it?

Offboarding is probably the most under-leveraged of all the employee experiences. While exiting resides here – voluntary or otherwise – so too does succession planning and promotion. An organisation that neglects this part of the lifecycle shoots itself in the proverbial foot.

While the 4 Part Employee Lifecycle is purposefully simple, for many it may be a little too simple in terms of “Performance”. So I propose the subdivision of this part into its own four subparts: (1) Performance Management; (2) Development; (3) Health & Wellbeing; and (4) Retention.

Hence I call this model the 4+4 Part Employee Lifecycle.

The 4+4 Part Employee Lifecycle: (1) Recruitment; (2) Onboarding; (3) Performance; and (4) Offboarding; plus (1) Performance Management; (2) Development; (3) Health & Wellbeing; and (4) Retention.

Performance management would include probation, along with goal setting – KPI’s and behavioural markers – and the dreaded performance appraisal. While performance management has attracted a lot of heat in recent years, my view is that rather than dispensing with it altogether (to the organisation’s detriment), change its nature. For example, I suggest performance appraisals be frequent, short, and feedback rich. There should be no nasty surprises at the end of the year!

Development is complex in its own right; indeed this blog is almost entirely devoted to it. Suffice it to say that in this context, it’s probably best to think of an employee’s development as the totality of their formal development – including training, development planning, leadership programs, career development and talent management – and their informal development – comprising learning (as opposed to training) and performance support.

Health & wellbeing enjoys ever-increasing interest among HR folks, and rightly so as beyond the ethical imperative, an employee who is healthy in body and mind is also productive. I see the usual suspects – inclusion, bullying & harassment, WH&S – in this space, along with personal health initiatives such as pedometer challenges and flu jabs.

And finally, retention concerns the obvious – remuneration and benefits – and the less obvious such as opportunities for growth and career prospects. Engagement may also reside here.

White collar workers communicating in office against window with their colleagues walking around.

A smart man once declared all models are wrong, but some are useful; and I find the 4+4 Part Employee Lifecycle useful because it identifies key parts of the employee experience which we HR folks need to support.

If we look at the model through the lens of L&D, for example, it prompts us to ask questions that are critical to the success of the business:

  • Recruitment – What capabilities do we need to buy into the organisation? Which attitudes do we need to inject to shift our culture? Who can we develop into a future leader or SME?

  • Onboarding – What do we need our new recruits to know and do as soon as possible? How do we support this process?

  • Performance Management – Where are the performance gaps? Why do these gaps exist? Are they due to deficiencies in capability?

  • Development – Which capabilities do our people need to develop? What training should we push? How do we enable our people to drive their own learning? How do we support their performance on the job?

  • Health & Wellbeing – Are our people in tune with their physical and mental health? Are our managers capable of supporting them in this space? How do we shift our culture from one of rules and regulation to one of care and collaboration?

  • Retention – Are our people aware of the wonderful benefits that are available to them? What kinds of work experiences do they seek? Do they have a career development plan?

  • Offboarding – What capabilities do our people need to equip them for the future?

In a similar manner we can look at the model through other lenses, such as technology, process improvement, innovation, or analytics, to ensure they add value across the gamut of HR functions.

The sum of us

Posted 10 July 2017 by Ryan Tracey
Categories: analysis

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What is the definition of the term “data scientist”…?

In my previous post, Painting by numbers, I offered a shorthand definition of data science based on what I could synthesise from the interwebs. Namely, it is the combination of statistics, computer programming, and domain expertise to generate insight. It follows, then, that the definition of data scientist is someone who has those skill sets.

Fat chance!

In this post I intended to articulate my observation that in the real world, incredibly few people could be considered masters of all three disciplines. I was then going to suggest that rather than seeking out these unicorns, employers should build data science teams comprising experts with complementary talents. I say “was” because I subsequently read this CIO article by Thor Olavsrud in which he quotes Bob Rogers saying, well… that.

Given Thor and Bob have stolen my thunder (18 months ago!) I think the only value I can add now is to draw a parallel with pop culture. So I will do so with the geeky HBO sitcom Silicon Valley.

The cast of Silicon Valley: Dinesh, Gilfoyle, Richard, Jared and Erlich.

If you aren’t familiar with this series, the plot revolves around the trials and tribulations of a start-up called Pied Piper. Richard is the awkward brainiac behind a revolutionary data compression algorithm, and he employs a sardonic network engineer, Gilfoyle, and another nerdy coder, Dinesh, to help bring it to market. The other team members are the ostentatious Erlich – in whose incubator (house) the group can work rent-free in exchange for a 10% stake – and Jared, a mild-mannered economics graduate who could have been plucked from the set of Leave It to Beaver.

The three code monkeys are gifted computer scientists, but they have zero business acumen. They are entirely dependent on Jared to write up their budgets and forecasts and all the other tickets required to play in the big end of town. Gilfoyle and Dinesh’s one attempt at a SWOT analysis is self-serving and, to be generous, NSFW.

Conversely, Jared would struggle to spell HTML.

Arguably the court jester, Erlich, is the smartest guy in the room. Despite his OTT bravado and general buffoonery, he proves his programming ability when he rolls up his sleeves and smashes out code to rescue the start-up from imploding, and he repeatedly uses his savvy to shepherd the fledgling business through the corporate jungle.

Despite the problems and challenges the start-up encounters throughout the series, it succeeds not because it is a team of unicorns, but because it comprises specialists and a generalist who work together as a team.

Unicorn silhouette

And so the art of Silicon Valley shows us how unlikely we would be in real-life to recruit an expert statistician / computer programmer / business strategist. Each is a career in its own right that demands years of education and practice to develop. A jack-of-all-trades will inevitably be a master of none.

That is not to say a statistician can’t code, or a programmer will be clueless about the business. My point is, a statistician will excel at statistics, a computer programmer will excel at coding, while a business strategist will excel at business strategy. And I’m not suggesting the jack-of-all-trades is useless; on the contrary, he or she will be the glue that holds the specialists together.

So that begs the question… which one is the data scientist?

Since each is using data to inform business decisions, I say they all are.

Painting by numbers

Posted 3 June 2017 by Ryan Tracey
Categories: analysis

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A lifetime ago I graduated as an environmental biologist.

I was one of those kids who did well in school, but had no idea what his vocation was. As a pimply teenager with minimal life experience, how was I to know even half the jobs that existed?

After much dilly dallying, I eventually drew upon my nerdy interest in science and my idealistic zeal for conservation and applied for a BSc. And while I eventually left the science industry, I consider myself extremely fortunate to have studied the discipline because it has been the backbone of my career.

Science taught me to think about the world in a logical, systematic manner. It’s a way of thinking that is founded on statistics, and I maintain it should inform the activities we undertake in other sectors of society such as Learning & Development.

The lectures I attended and the exams I crammed for faded into a distant memory, until the emergence of learning analytics rekindled the fire.

Successive realisations have rapidly dawned on me that I love maths and stats, I’ve floated away from them over time, the world is finally waking up to the importance of scientific method, and it is high time I refocused my attention onto it.

So it is in this context that I have started to review the principles of statistics and its contemporary manifestation, analytics. My exploration has been accompanied by several niggling queries: what’s the difference between statistics and analytics? Is the latter just a fancy name for the former? If not, how not?

Overlaying the post-modern notion of data science, what are the differences among the three? Is a data scientist, as Sean Owen jokingly attests, a statistician who lives in San Francisco?

The DIKW Pyramid

My journey of re-discovery started with the DIKW Pyramid. This beguilingly simple triangle models successive orders of epistemology, which is quite a complex concept. Here’s my take on it…

The DIKW Pyramid, with Data at the base, Information a step higher, Knowledge another step higher, and Wisdom at the peak.

At the base of the pyramid, Data is a set of values of qualitative or quantitative variables. In other words, it is the collection of facts or numbers at your disposal that somehow represent your subject of study. For example, your data may be the weights of 10,000 people. While this data may be important, if you were to flick through the reams of numbers you wouldn’t glean much from them.

The next step up in the pyramid is Information. This refers to data that has been processed to make it intelligible. For example, if you were to calculate the average of those ten thousand weights, you’d have a comprehensible number that is inherently meaningful. Now you can do something useful with it.

The next step up in the pyramid is Knowledge. To avoid getting lost in a philosophical labyrinth, I’ll just say that knowledge represents understanding. For example, if you were to compare the average weight against a medical standard, you might determine these people are overweight.

The highest step in the pyramid is Wisdom. I’ll offer an example of wisdom later in my deliberation, but suffice it to say here that wisdom represents higher order thinking that synthesises various knowledge to generate insight. For example, the wise man or woman will not only know these people are overweight, but also recognise they are at risk of disease.

Some folks describe wisdom as future focused, and I like that because I see it being used to inform decisions.


My shorthand definition of statistics is the analysis of numerical data.

In practice, this is done to describe a population or to compare populations – that is to say, infer significant differences between them.

For example, by calculating the average weight of 10,000 people in Town A, we describe the population of that town. And if we were to compare the weights of those 10,000 people with the weights of 10,000 people in Town B, we might infer the people in Town A weigh significantly more than the people in Town B do.

Similarly, if we were to compare the household incomes of the 10,000 people in Town A with the household incomes of the 10,000 people in Town B, we might infer the people in Town A earn significantly less than the people in Town B do.

Then if we were to correlate all the weights against their respective household incomes, we might demonstrate they are inversely proportional to one another.

The DIKW Pyramid, showing statistics converting data into information.

Thus, our statistical tests have used mathematics to convert our data into information. We have climbed a step up the DIKW Pyramid.


My shorthand definition of analytics is the analysis of data to identify meaningful patterns.

So while analytics is often conflated with statistics, it is indeed a broader expression – not only in terms of the nature of the data that may be analysed, but also in terms of what is done with the results.

For example, if we were to analyse the results of our weight-related statistical tests, we might recognise an obesity problem in poor neighbourhoods.

The DIKW Pyramid, showing analytics converting data into knowledge.

Thus, our application of analytics has used statistics to convert our data into information, which we have then translated into knowledge. We have climbed another step higher in the DIKW Pyramid.

Data science

My shorthand definition of data science is the combination of statistics, computer programming, and domain expertise to generate insight. Or so I’m led to believe.

Given the powerful statistical software packages currently available, I don’t see why anyone would need to resort to hand coding in R or Python. At this early stage of my re-discovery, I can only assume the software isn’t sophisticated enough to compute the specific processes that people need.

Nonetheless, if we return to our obesity problem, we can combine our new-found knowledge with existing knowledge to inform strategic decisions. For example, given we know a healthy diet and regular exercise promote weight loss, we might seek to improve the health of our fellow citizens in poor neighbourhoods (and thereby lessen the burden on public healthcare) by building sports facilities there, or by subsidising salad lunches and fruit in school canteens.

The DIKW Pyramid, showing data science converting data into wisdom.

Thus, not only has our application of data science used statistics and analytics to convert data into information and then into knowledge, it has also converted that knowledge into actionable intelligence.

In other words, data science has converted our data into wisdom. We have reached the top of the DIKW Pyramid.