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

Tags: , , , , , , , , , , ,

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.

How to make the most out of a conference

Posted 15 May 2017 by Ryan Tracey
Categories: conference

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When I was invited to kick off last week’s AITD National Conference by hosting a breakfast session about Personal Knowledge Management, the last thing I wanted to do was deliver a traditional presentation.

Given the massive scope of PKM, I needed to narrow my focus. And given the contemporary thrust of this event, I needed to do something fresh.

After agonising over the problem for almost a full minute, it dawned on me that the immediate relevance of PKM to the conference attendees lay in how they were going to make the most out of said conference.

But who was I to teach my peers in the industry how to suck eggs? So I ditched the typical instructivist approach in favour of the andragogic. In other words, I crowdsourced the content.

From this fruitful exercise I’m pleased to share with you 8 co‑created tips for making the most out of a conference.

Ryan Tracey at AITD2017 with Michelle Ockers documenting the crowd's ideas.

1. Attend all the sessions.

This one seems too obvious to mention, but a 1 or 2 day conference can be mentally exhausting. You may be tempted to wag a session here or there to relax and recharge, but don’t do it out of sheer laziness.

I’ve lost count of the number of times an apparently unattractive session has turned out to be excellent, or it’s sparked a useful tangential idea.

Remember you’ve invested time and money into these days. They won’t be back until next year, so extract every drop of goodness while you can.

2. Read the blurbs.

Conference organisers are getting a lot better at ensuring the content of the blurb bears some resemblance to the content of the session.

Read the blurb to get your mindset in order, and to consider how the content will help you in your role. Also consider what questions you might want to have answered. Which leads me to…

3. Ask questions.

Some presenters welcome questions during the session, while others prefer you wait until the end. In either case, be brave and ask your questions because by doing so you are personalising your learning experience.

4. Take notes.

The fire hydrant of ideas is too much for the human brain to handle, so you need to distribute your cognition.

You might want to go old-school by jotting your notes down on paper, or type them into a mobile device. Alternatively you could take photos, draw pictures, produce mind maps, or record videos.

I like to tweet my notes because the character limit forces me to zero in on the essence of the message. After the conference, I’ll look up my profile on Twitter to review my list.

5. Use social media.

If you do use Twitter, include the official hashtag in your tweets. Not only does this feed the backchannel, but you too can follow the tweets of your fellow attendees. I find it fascinating to learn their thoughts about the session I’m watching.

If you don’t blog, I suggest you reconsider. Even if you don’t publish your work, blogging is an excellent vehicle for reflection. After the conference, expand on the notes you’ve taken by deep diving into aspects that take your fancy. And if you publish your blog, you’ll be sharing something useful with the wider community.

6. Extend your network.

In the AITD’s discussion forum on LinkedIn, I asked my fellow members whether conferences were obsolete in the digital age. Each of the 20-odd replies I received was a resounding “no”, citing the rich networking opportunities that in-person events offer.

I love catching up with old friends as well as meeting new people at conferences. I used to be too shy to introduce myself to strangers, until I realised I was doing my professional development a disservice.

I also consider it a professional courtesy to speak to the vendor reps at the expo. They financially support the running of the conference, so the least we can do is say hello. I know from first-hand experience how awful it feels to be ignored by attendees. So class up and have a chat. Besides, you might find something helpful.

7. Share your wisdom.

There’s no point hiding your notes in a drawer or keeping them locked inside your head. Share your new-found knowledge with your colleagues, adding your own insights for local context.

In fact, if your employer paid for your ticket, I’d argue you have an ethical obligation to do this.

8. Transform your business.

Don’t stop now!

Review your notes with the intent of converting each one into action. What can you do to make it happen? Even if it’s something tiny, do it to get the ball rolling.

Crowdsourced tips for how to make the most out of this conference.

The overarching theme of these tips is: BE ACTIVE!

When you attend your next conference, you’ll get out of it what you put into it.

5 games every e-learning professional should play

Posted 3 April 2017 by Ryan Tracey
Categories: game-based learning, games

Tags: , , , , , , , , , , , , , , , ,

You can narrow down someone’s age by whether they include spaces in their file names. If they do, they’re under 40.

That is a sweeping declaration, and quite possibly true.

Here’s another one… Gamers are a sub-culture dominated by young men.

This declaration, however, is stone-cold wrong. In fact, 63% of American households are home to someone who plays video games regularly (hardly a sub-culture). Gamers are split 59% male / 41% female (approaching half / half) while 44% of them are over the age of 35 (not the pimply teenagers one might expect). [REF]

In other words, the playing of video games has normalised. As time marches on, not gaming is becoming abnormal.

Woman and man seated on a couch playing a video game.

So what does this trend mean for e-learning professionals? I don’t quite suggest that we start going to bed at 3 a.m.

What I do suggest is that we open our eyes to the immense power of games. As a profession, we need to investigate what is attracting and engaging so many of our colleagues, and consider how we can harness these forces for learning and development purposes.

And the best way to begin this journey of discovery is by playing games. Here are 5 that I contend have something worthwhile to teach us…

1. Lifesaver

Lifesaver immediately impressed me when I first played it.

The interactive film depicts real people in the real world, which maximises the authenticity of the learning environment, while the decision points at each stage gate prompt metacognition – which is geek speak for realising that you’re not quite as clever as you thought you were.

The branched scenario format empowers you to choose your own adventure. You experience the warm glow of wise decisions and the consequences of poor ones, and – importantly – you are prompted to revise your poor decisions so that the learning journey continues.

Some of the multiple-choice questions are unavoidably obvious; for example, do you “Check for danger and then help” or do you “Run to them now!”… Duh. However, the countdown timer at each decision point ramps up the urgency of your response, simulating the pressure cooker situation in which most people I suspect would not check for danger before rushing over to help.

Supplemented by extra content and links to further information, Lifesaver is my go-to example when recommending a game-based learning approach to instructional design.

2. PeaceMaker

Despite this game winning several prestigious awards, I hadn’t heard of PeaceMaker until Stacey Edmonds sang its praises.

This game simulates the Israeli-Palestinian conflict in which you choose to be the Israeli Prime Minister or the Palestinian President, charged with making peace in the troubled region.

While similar to Lifesaver with its branched scenario format, its non-linear pathway reflects the complexity of the situation. Surprisingly quickly, your hipsteresque smugness evaporates as you realise that whatever you decide to do, your decisions will enrage someone.

I found this game impossible to “win”. Insert aha moment here.

3. Diner Dash

This little gem is a sentimental favourite of mine.

The premise of Diner Dash is beguilingly simple. You play the role of a waitress in a busy restaurant, and your job is to serve the customers as they arrive. Of course, simplicity devolves into chaos as the customers pile in and you find yourself desperately trying to serve them all.

Like the two games already mentioned, this one is meant to be a single player experience. However, as I explain in Game-based learning on a shoestring, I recommend it be deployed as a team-building activity.

4. Keep Talking and Nobody Explodes

As its name suggests, Keep Talking and Nobody Explodes is a multi-player hoot. I thank Helen Blunden and David Kelly for drawing it to my attention.

In the virtual reality version of the game, the player wearing the headset is immersed in a room with a bomb. The other player(s) must relay the instructions in their bomb defusal manual to their friend so that he/she can defuse said bomb. The trouble is, the manual appears to have been written by a Bond villain.

It’s the type of thing at which engineers would annoyingly excel, while the rest of us infuriatingly fail. And yet it’s both fun and addictive.

As a corporate e-learning geek, I’m also impressed by the game’s rendition of the room. It underscores for me the potential of using virtual reality to simulate the office environment – which is typically dismissed as an unsuitable subject for this medium.

5. Battlefield 1

I could have listed any of the latest games released for Xbox or PlayStation, but as a history buff I’m drawn to Battlefield 1.

It’s brilliant. The graphics, the sounds, the historical context, the immersive realism, are nothing short of astonishing. We’ve come a long way since Activision’s Tennis.

Activision's Tennis video game on a vintage TV featuring two blocky players on court.

My point here is that the advancement of gaming technology is relentless. While we’ll never have the budget of Microsoft or Sony to build anything as sophisticated as Battlefield 1, it’s important we keep in touch with what’s going on in this space.

Not only can we be inspired by the big end of town and even pick up a few design tips, we need to familiarise ourselves with the world in which our target audience is living.

What other games do you recommend we play… and why?