The problem with sticking a prototype in front of users without any idea of the success criteria is that there is a very good chance the prototype evaluation will be a success. Why? Because you invite people to participate in the activity, probably pay them for their time, put them in front of something that you’ve probably worked on (or they assume you have) and they’re more than likely to say that they like it, that it’s good and yes they would probably use the actual product.

Then six months down the track you’ve invested hundreds of thousands in building, deploying and marketing the product plus all the operational costs and can’t understand why no one is using it. But they told you they would use it! Were they lying?

You become disillusioned with design thinking and prototyping and vow to never use it again. Problem is, you didn’t actually run a proper prototype evaluation. How did you choose the technology to build the prototype in if you didn’t know what you wanted to get out if it? How did you decide how much time to invest in it and what level of detail to go into?

Sometimes you don’t need a prototype at all. Sometimes you decide to test just one specific feature. Sometimes the product is so complex and risky that you spend months on the prototype because that’s the level of detail and functionality required in order to answer the questions and prove the concept; and yes I have spent months working on a single prototype – anything less than that would have been inadequate.

There’s a bunch of data you can get out of prototype evaluation, both self-reported and observed, quantitative and qualitative, through passive testing or through interactive moderation/facilitation and debriefing. But you need to have a good idea of what data is important so you can use the right techniques with an appropriately-developed prototype to get reliable data to inform the next steps.

Did people notice this particular feature? No? Ok, what about after we told them about it, how did they interact with it? How did the way we explained the feature inadvertently bias how they interacted with it? How can we determine whether simply making the feature more visible will address that, or is it that people simply won’t use it … even though they did after we told them about it? Do we even really care about the usefulness of individual features? What about the product as a whole? Are we on-target?

Is there a market for this? How and when might people use it? In what situations? How we can simulate those situations in order to get a more realistic evaluation of the prototype? Should we use paper prototyping or put interactive HTML pages on a mobile device? Whose mobile device? One the user is familiar with, or doesn’t that matter so much?

What design assumptions did we make and how can we use the prototype evaluation to challenge those assumptions? How can we run this evaluation so that we can be more sensitive to new information arising from user feedback or behaviour or contextual clues? Does that matter?

These are the sorts of questions you need to be thinking about before you decide whether you should develop a prototype, how it should be designed and tested with users and how you can cancel out or at least counterweight the various cognitive, social and behavioural biases of putting something that doesn’t really work in front of people who don’t really need to use that thing right then and there, would never use it in that environment and certainly not with someone looking over their shoulder!

The typical example of “desire paths” are footpaths worn on school campuses where the consequence is merely ruined landscaping and lawns.

But here we have a similar thing happening where drivers want to go somewhere that the planners have chosen not to accommodate, but instead of just worn grass we have people slowing down from the speed limit in the space of 10-20 metres, pulling off onto gravel and then accelerating again on the other side off gravel onto a busy road.

The failure of planners to provide a place for drivers to do this safely does not take away the need of drivers to perform the U-turn. It wouldn’t matter if there was signage telling drivers not to perform U-turns or if it was illegal; if drivers want to change direction, they will do so, even though it’s damaging the land and creating a traffic hazard.

And now instead of having one properly-designed, paved and safe section of road to perform U-turns we now have half a dozen worn patches of gravel:

Attempting to shape behaviour in adults by disallowing certain actions, imposing rules and not enabling them to do what they need to do is not a great strategy. Might work for toddlers when you don’t want them to eat the laundry powder, but not for intelligent, wilful and busy adults who blow straight past your rules and warning signs.

This is why people walk their dogs off-leash in a clearly signposted on-leash area or speed through roadworks. A warning sign and even the risk of fine or worse is not enough to persuade many people to do the “right” thing.

And that’s one of the reasons I love design.

Design is not some airy fairy artistic profession where we sit around waiting for inspiration to pop into our heads. Design has more in common with engineering than painting or poetry. Design can be a hard slog. Design is methodical and has well-defined techniques, activities and outputs that we select and apply as we see fit.

But that doesn’t diminish the fact that at the start of a new project especially in a new domain or industry we don’t know where to start, and that’s both terrifying and exciting.

At the start of a new project with a new client I don’t yet know what I don’t know, I don’t know what I should know and need to find out, and I don’t know if what I know is credible.

I have to throw it all up in the air if I’m to have any hope of getting to the core problem and properly scope out the design challenge space. I have to assume that my assumptions are wrong, that the information I have does not accurately represent the priorities and objectives of my client.

I have to assume that what my client thinks they know about their customers and users is flawed, obsolete or just plain wrong. To go along with flawed assumptions and just work with the information your given without further inquiry is unprofessional and will likely lead to a poor quality outcome, an irrelevant product or a missed niche.

Designers have to feel comfortable working with abstract concepts; empty boxes the contents of which they’ll figure out later. Designers have to get up to speed with new domains of knowledge overnight in order to ask intelligent, informed and profound questions and to avoid being dismissed as ignorant … although there is a valid argument to asking stupid questions.

Design can be philosophical as you question and challenge the boundaries of knowledge, what people tell you needs to be done, how people think something needs to be designed. Professional designers must dig deep until the real problem is uncovered, the real opportunity, the actual start of the path rather than some detour or distraction.

Designers – who often might say “It depends” and “How long is a piece of string?” – must be able to provide clear direction and information without appearing to be flailing around aimlessly, even though we don’t want to commit to a solution prematurely.

Design is not for the faint of heart. It requires confidence tempered with humility, leadership that is always listening out for where to go next, an ability to navigate oceans of new information and use the tools of research, analysis, synthesis and design to arrive at the right port. Which port? You probably won’t know for sure until you’ve finished the journey.

As a user experience designer, I’m more concerned with customers … and for this example I’m going to pick on banks – and specifically Automatic Teller Machines (ATM) – because a) banks are often big and mature enough to know better; and b) nearly everyone can relate to this:

• Wherever there is a shopping mall or public place where there are ATMs for all the major banks except yours, that negatively affects your brand.
• If your ATM is poorly designed and hard to use, comprehend or interact with, that negatively affects your brand.
• Removing an ATM from a place negatively affects your brand differently to if you never had an ATM there.
• Every time an ATM can’t provide the cash in the denominations you requested because it’s low on notes negatively affects your brand.
• Pizza sauce smeared over the ATM keypad negatively affects your brand (as happened to me in Christchurch a few months ago) – in fact any sort of graffiti or damage makes you look bad.
• Every ATM that is out of order is negatively affecting your brand.
• An ATM that is out of order for several days consecutively affects your brand negatively differently to several days out of order non-consecutively; “What, it’s still out of order? Don’t they care? Are they lazy?”
• An out of order ATM that has a hand-written sign scribbled with a Sharpie and sticky-taped over the screen negatively affects your brand more than having a proper printed sign or a digital message displayed on screen (every time I see this I imagine their corporate branding team screaming).

Negative brand perception means your brand is worth less than it could and should. Can you justify throwing away possibly millions of dollars of lost or missed brand equity because you don’t have adequate procedures in place for handling scenarios like in the above example?

Also, while I’m at it … you might think those multicoloured LED digital blackboard signs are cool and all … but please understand how tacky they look … and that’s affecting your brand (I’m looking at your Jamison Turkish Pide House). I don’t want to see another one of those again in anything other than an ice-creamery or a bargain $2 shop.

It can be hard for some people to wrap their heads around. “Why does it matter so much?” or “But I don’t want it to look like something else – I know exactly what I want it to look like!”. I’ve been learning GIS and mapping over the last year and it provides a good example of the importance of separating data from presentation, so I thought I might share.

OpenStreetMap contributors are reminded that they should ignore the renderer, which is the component of the website that translates the geographic data into the visual map that you see when you visit openstreetmap.org. But the renderer is only one way of presenting that data, and it has been configured to put emphasis on some elements like highways and less emphasis on elements like trees.

But what if you want more detail about the trees?

Well, if you designed for the renderer then you might draw shapes, such as green circles. But they don’t mean anything, and if someone wanted to change how trees are rendered for them then, what, they try and identify all the green circles?

Here is an example of how a renderer like OpenStreetMap might typically present trees on a map, because frankly most people don’t care about the details, and with all the other symbology and colour-coding it would just be a mess:

The rule with OpenStreetMap is that only “significant” trees should be added to the map, so if you wanted to do a tree survey and add dozens or hundreds of trees for an area then you might use an extract of OpenStreetMap as a basemap and add your own layer for private use.

But what if you are interested in the trees and want to see more info? Well if the map designer has added the features as trees with metadata rather than as dots on the map, then you might be able to colour-code the trees by type:

The OpenStreetMap tag taxonomy provides for tree type with a number of tags including species, genus, taxon and type (broad leaf, conifer, palm). You might also want to style the rendering of trees based on size, if the map designer has included that data (for OpenStreetMap the correct tag key is ‘girth’):

Tick a box, and you can add labels for all your trees too:

The above example maps were made with the free open source software QGIS. It can be a bit daunting at first, but the concepts are similar to those in other applications as far as vector and raster layers, features, nodes or points, ways (open), polygons (closed ways), boolean operations, map projections etc so once you’ve learned the basics the knowledge is transferable between different software packages.

The venue was rather classy, hosted in one of the Senate rooms at Parliament House courtesy of Senator Kate Lundy, but the event was lo-fi with no projector – just the presenters addressing the reasonable sized group without props or slide decks.

Stuart Coleman is the Commercial Director at the Open Data Institute in the UK. Exposing data for free public consumption is not enough. It’s not enough to publish some data and put it out there, to tick that box and be done with it. It’s not about transparency for transparency’s sake. Sure, there’ll be some developers who might be able to code something up and make use of that data, but government needs to share the right data and it needs to ensure it is useful.

That’s a point that Keitha Booth from the New Zealand Government made also, and in fact government departments and agencies in NZ are required to identify “high value” data for opening up. Most importantly, they are not permitted to make the call as to what is “high value” but rather must consult with the potential consumers of that data to identify the economic, social and other benefits of opening that data.

Stuart highlighted some of the cases where businesses have grown around open public data as part of the open data initiatives in the UK, like Mastodon C and the Prescribing Analytics portal which has saved (or is expected to save) the NHS around £200 million by highlighting the under-adoption of generic medications.

I enjoyed both presentations and it was great to have international guests come and share their stories and experiences, and also good to get an update from Pia Waugh, Director for Gov 2.0 in the Technology and Procurement Division of Finance, on Data.gov.au which will soon be getting a tech refresh with an update to CKAN – you can read more in Pia’s recent blog post about the roadmap.

Transcript of Stuart Coleman’s presentation

Listen to the audio of Stuart’s presentation on Soundcloud *

Thanks Pia and thank you everyone for giving up your lunch time – it’s not quite lunch time here – but to listen to me for 20 minutes. I’d very much like questions throughout the 20 minutes; if I can’t answer them and I need a bit more time to think on my feet I may push it back and answer it at the end.

Speaking to Pia before this visit I think there was a really number of questions that kept coming up as to what she would like me to talk through. The Open Data Institute is a very new initiative from the UK and I think there was a lot of questions about what are we actually here to do, so hopefully I can answer that today.

I’ve tried to get up to date with some of the activity going on down here; there was a paper published in February around the the open data strategy in Australia by your Information Commissioner. I’ve had a great chat with Keitha this morning, beforehand, and I think what I’d like to do is summarise quite succinctly what the Open Data Institute is (going) to do, how we were set up and the learnings we’ve had so far.

We were initially launched last year with government commitment, government funding in the UK. We’re really the brainchild of Sir Tim Berners-Lee and Professor Nigel Shadbolt. I’m sure most of you know who Tim Berners-Lee is; for those of you who don’t know Professor Nigel Shadbolt he is a luminary, really, in the data movement in the UK; he’s co-chaired with Tim down in Southampton University, he’s done a lot of great, early ground-breaking work with open data with successive governments.

I think it’s important to say that the initiative is not just a UK conservative government initiative, it’s spawned from successive government activity. The most notable piece that originally got the attention of government was forcing UK hospitals to publish all their data on MRSA about seven or eight years ago, which was particularly problematic in the UK. That got the rates of infection down massively and actually wiped off £s;18 million from the NHS’ budget (having) to deal with that, so it saved a lot of money as well as improved (patient) health care.

Audience: What did you say they published?

They published their performance rates on MRSA – it’s a particular challenge around maintaining cleanliness in hospitals, and infections that spread …

That was the first piece, the next piece was really around postcode address data which is still a challenge in the UK – we just had a recent pushback on data in that area – but just to give you a quick summary on the Open Data Institute as an organisation: we’re set up as not-for-profit company. We have £10 million of government investment over five years, so that’s £2 million a year. We have a commitment to match that investment with private sector investment, and we’ve been going six months. We officially launched on the first of October. We’ve already secured just under a million pounds of private sector investment; some internationally but quite a lot from European corporates who are seeing open data as both an opportunity for them to make use of new information, but also as potentially a chance for them for them to start opening up some of their data.

Just a short summary on sort of the core activities that we’re focused on: the first is what we term “Unlocking data supply”, so we don’t think it’s enough for government to just publish data. We’re not of the view that if they publish it people will come and use it. There are examples of where people do and there’s some great leadership there often from the developer communities where good things get done, but we need more of that and we need a focus on selecting the right data, so we’ve seen particular success stories in certain sectors; transport is one, healthcare is another one. We’re starting to see more around land use information which from what I’m seeing in this part of the world is potentially a very compelling area particularly given the geographical size of Australia; I think it’s a fantastic use case for opportunities.

So unlocking data supply is a part of our mission. It doesn’t mean we’re there to do the work for government but where we see an opportunity to help government accelerate opening up their supply our goal is to step in and help them accelerate the opportunity and enable them, so we’re not wanting to become a huge organisation doing loads of professional services or consultant work; our objective is to enable others and help them develop the skills.

The second piece is really around unlocking the demand, so how do we connect the different data sets that are available; I think you’ve got just over 1,100 datasets here in Australia at the moment, how do you know which ones are the most useful? And (when) I see on your front page you’ve got some statistics around what is being used, but have you actually drilled down and identified what that use translates to? So, connecting individuals, companies, sectors, leadership (in) innovation to the use of that data is critical, and that’s another area where we’ve struggled.

Our engagement with the startup community in the UK has been great; there’s been a great burgeoning growth of startup businesses around an initiative generally know as “Tech City” centred out of London but also with satellite representation in places like Manchester and Bristol and other cities, where there have been a number of new startup businesses starting to emerge and use in large part or in some part open data to bolster their business models, and I can talk some more of examples in a second.

The final pillar – so after we’ve improved and helped a lot more supply and connected that to demand – is around communicating value. So, I’m repeatedly asked “Hang on, how do you actually prove that this open data stuff is making a difference? Is it just driving a transparency agenda for government?” Well our fundamental belief is no, it is not. A part of our mission is to incubate and demonstrate the value of open data by working with innovative young companies who do things with that data that is valued.

What I’ll quickly do is just talk about a particular example that we’ve worked on in our first six weeks; if you are online here at the moment and you can go to prescribinganalytics.com you will see a interactive website that shows you a map of the UK and in particular it hones down on regions of England and parts of Wales called Primary Care Trusts. These are geographical regions controlled by general practitioners in the UK, so doctors providing public services back to the community. One of the challenges to government for some time that no one’s been really able to prove in the UK is been the is issues of lack of knowledge and bad practice around prescribing certain drugs to patients in the UK. One particular area is around the drug class called ‘statins’ which are traditionally prescribed for cardiovascular conditions of cholesterol and those types of things. There’s this cluster I think of three or four different brand name drugs you can have prescribed for such a condition, made by all the usual suspects – Glaxo, Phizer – the cost of those brand name drugs is sometimes twenty or thirty times the price of the generic alternative and the drugs are identical in their delivery agents. So, we knew, we felt, we believed there was an opportunity to use some sets of open data to try and highlight opportunities for savings in this area and the case where the malpractice was happening. So working with one of our startups, a company called Mastodon C – a slightly quirky name, named themselves after an extinct animal – but they are experts on working with big data using open source technologies like Hadoop and using rigorous statistical and data science techniques to drill into that data, they took a bunch of datasets primarily what’s called the Prescriptions Dataset UK.

So, each month the government publishes every single patient prescription written and it’s an interesting set of data because the public at large often jump on (statements) like that and say “Well, hang on, isn’t that our personal data?” and the answer is well yes, but it’s an asset that you can derive value from; it’s published as anonymised data so we don’t see that I, Stuart Coleman took a particular type of drug. What we do see is what was prescribed, where it was prescribed and which Primary Care Trust it fell in to. If you look at that map on the page that I’ve brought you to, you can actually drill in and see, by Primary Care Trust, the trends around prescribing brand name and generic alternatives. And rolling this data up and with engaging specific medical expertise we identified £200 million of savings for the NHS, and the National Health Service in the UK has a target to save a billion and this is just for one drug class remember; this data’s released every month and is updated.

So we have a very small company work with us to achieve this. They were pre-revenue – they had no revenue, these people – they’d left their day jobs at Google and a bank to set up this company and this piece of work not only helped highlight to a huge audience – they got publications in the Economist, the Financial Times and in various government press the opportunity around open data – it also showcased their skills. And as a result in the last three months they’ve secured over £300,000 of contracts and have grown their team. And they’re working with other open datasets; they’ve done some work in the energy space.

So what I really wanted to do was talk about one of the businesses we work with, because that’s part of our mission at the Open Data Institute, is to stimulate economic growth through the application and demonstration of the value in open data. And there’s a great example of one company; there’s actually five other companies in our incubation program, one of them has done some great stuff – a company called Placr – with transport data. They’ve now secured half a million pounds of venture capital. But that’s really in the ‘communicating value’ pillar of what we do.

If we look back into the demand and supply side we see a fascinating opportunity and challenge around enabling people to actually work with data and maximise the opportunity of open data. And if you look at most institutes around the world, that is where I’d say our business focus is, so our mission is to become self-sustaining and not depend on government funding as soon as we can. To that end we are engaging both private sector and public sector in training courses and activities, so addressing data literacy which is a theme we see as being a real challenge. So, how do we support senior civil servants to, for example, in the UK we’re (–) engage the procurement community in government to help work with them to embed open data into procurement principles so that when they write a contract with a supplier they can be guaranteed the data that’s going to result from those activities is something they retain as an asset (to) government and it doesn’t get owned by a supplier who can then charge an inordinate amount of money to change a particular asset.

So we are developing some specific training materials to that end. We have also been running some training courses around open data licensing and the (law) and that’s had a significant uptake in the private sector. So we’re very much listening to the community both in the developer side of the world; we’ve got a great leadership team, so very diverse team. Our CEO is an ex-entrepreneur whose last business was based on open data. He raised over £10 million in VC for that business. My background is partly in the dark side, so proprietary technology companies who’ve existed for a while, but also more recently with (the) venture capital community and open data.

And I guess the real practitioner in our leadership team is Jeni Tennison and she is a leadership figure in the open data community, has been behind legislation.gov.uk which is a significantly successful initiative to take the whole of the UK’s law and digitise it as open data. And that’s a big, big feat. The data and documentation dates back to 1267. Jeni’s architected that, and with a small team has achieved great things. Not only has she helped save a lot of money for government; she’s helped make legal information available to a much broader audience and we’re seeing some early signs of businesses using that data to create things like student-configurable texts on open data, and Jeni architected that over the last four years.

We’re never planning to be a large massive organisation; we’re around 15 people. Most of the people are actually delivering things. We have membership model for the private sector and just to mention that, why are the private sector interested? Well, we’ve got a company like Telefonica – which I guess would be similar to Telstra over here – they are starting to use open data, to combine it with some of their own proprietary data; they’re starting to sell new services based on network events, to (have them) measure the footfall of people in urban areas based on one of (our) network activity and combine that with census open data and then sell that to industry. Basically that’s an opportunity.

We’re working with Virgin Media who are the second largest Internet Service Provider. They’re looking to publish open data on their users’ behaviour which is potentially quite controversial. We’re going to anonymise it, but they’re going to help create what they see as hyperlocal communities of Internet users in areas that are perhaps disjointed across the UK and help share activity on browsing the web (to) provide new services and improve customer services. So they’re experimenting, and we’re providing a (breeding) ground for that experimentation.

I don’t really have a lot more to add – I’d quite like to open up for some question. Just to give you a bit more of our international flavour, again I’ve been asked by a number of people “What are the ODI’s international ambitions?” and first of all we do have them; we’re tempering our international appetite with the requirement to execute in the UK all the funding we’ve been given in the first year or so. We would like to set up what we’re seeing as ‘nodes’, so specific focussed communities around open data and supporting our mission and the summary of our mission is to capitalise on open data culture. I’m using the word ‘culture’ quite carefully.

We see a requirement to build on some of the fantastic work done by the likes of the Open Knowledge Foundation, MySociety, some of these leading, pioneering organisations who are trying to take open data to the mass. When we launched, we launched with a data-driven art exhibition, which really engaged a much broader community than would have been possible purely from (–). And I’ve had a great time wandering around (–) exhibitions you’ve got here; I’ve only seen a slight portion of, but I see you’ve got a piece of the Magna Carta here, which I think is quite inspiring, and ahead of ANZAC Day on the 25th, it’s been a real pleasure to be here.

I hope that slightly congealed message was of interest. I do have some content here I would happily make available so I will give that to Pia and she can stick that up online and you’re very welcome to use it and redistribute it and do whatever you want with it.

* Unfortunately Keitha spoke too softly and I couldn’t even transcribe the audio recording, so sorry folks and my apologies to Keitha for not getting her talk online – I tried.

If he can, the business executive [when selecting a designer] should resist the impulse to make what has become almost an inevitable remark, “Of source, our product is different from any you’ve ever tackled.” This may be true, but an industrial designer’s work, by its very nature, is one of continually approaching and solving new problems. Moreover, the techniques he develops in working out a variety of problems qualify him to come to grips with each one that is “different.”

What the business executive is looking for is a man of vision who is not a visionary, a practical merchant who is something of an artist, a fellow who lives in an ivory tower but has one foot on the ground, a personable diplomat who is equally at home in a high-level conference with the president and in a technical discussion with the operator of an eight-thousand ton press. Curiously enough, that’s just about what he gets.

It’s a great book and Henry Drefuss was one of the leaders of design in that era in the United States during World War II and the economic boom that followed. It’s great to see that ‘design’ was recognised as broader and more useful back then before it was hijacked by visual/graphic design to be all about colours and logos, or at best “look and feel”.

I liked this passage in the introductory pages; I’ve stripped out the anecdote from Moneyball that Cass uses, but I think the message stands on its own just fine.

As someone who has spent a few years working for government I am all for more evidence-based decision making and less knee-jerk hysteria:

Here is a possible approach to regulatory issues, one that can be tempting to some government officials: Ask which groups favour or oppose a proposed rule, who would be satisfied and who distressed, and whether a particular approach could be chosen that would please some without displeasing others.

These questions are not exactly irrelevant; public officials need to answer them. But they are far from the most important matters.

The plea for empirical foundations may seem obvious, a little like a plea for sense rather than nonsense, or for a day of sunshine rather than brutal cold [but] those seeking or resisting regulation say, “The public is very worried,” or “Polls show that the majority of people strongly favour protection against air pollution,” or “The industry has strong views,” or “The environmental groups will go nuts,” or “A powerful senator is very upset,” or “If an accident occurs, there will be hell to pay.” In government, I heard one or more of these claims every week.

None of these points addresses the right question, which is what policies and regulations will actually achieve.

In other words… Requirements should be customer jobs to [be] done and desired outcomes. Couldn’t agree more

Wim Rampen observed:

still huge gap for many ux/interaction designers.

This is where Graham threw in the “guessing” word:

While #design is based on abduction =guessing it will always struggle to optimise outcomes. Period

But that doesn’t mean abduction has no role. See @snowded for insights bit.ly/MVpICI

I shared that tweet for all my followers, and my friend Steve Baty took the bait, arguing against Graham’s assertion:

Abduction is not guessing and you should know that. Design doesn’t narrowly focus on one concept, and you know that.

So either you’re choosing to ignore those things, or not doing your research.

Graham cited a reference for claiming that design ⊃ abduction = guessing:

According to Peirce who coined the abductive logic term in the early c20, abduction IS guessing bit.ly/15hXXh

Then there was this gem of a generalisation:

Many designers start with abduction and never quite progress to falsifiable hypothesis testing required of induction

And then finally lops the head off design with this:

The innovation landscape is never searched properly, the optimum solution is never found and prototyping is biased

I don’t know Graham well and we only started following each other on Twitter a few days ago, but from someone who claims to know design thinking process I thought this conversation was quite interesting and controversial.

Design is bloody hard. We start with very little in the way of concrete credible requirements and reliable data. Our job is to identify and collect the data we need, test the assumptions that fed into the requirements, understand the root problem and explore the space to fully understand the challenge.

Because we’re often dealing with known unknowns and unknown unknowns it can feel like we’re stabbing in the dark and sometimes even guessing, but when we have the time and resources I absolutely believe that design is a rigorous and methodical process of research, analysis, synthesis, ideation, evaluation and delivery.

So no, design is not guessing, and the “innovative landscape” is searched properly (though perhaps not exhaustively as there are always constraints on time and resources). Anything less than that is not design.

I’ve now created a new version of the poster (download link below) without the hideous illustrations and some alternations to the wording of the questions, including dropping one so now it’s just twelve.

I recommend you download and print this poster and stick it next to your desk. I have found these questions incredibly useful over the years to make sure I don’t just plough ahead with my work without stopping to think about that why and how.

Download free poster (34 Kb)

Don’t be a mere cog or an automaton. Think about what you’re doing, why you’re doing it, the best way to do it and know what it should look like when it’s finished. Don’t isolate yourself in your own little world, but think about the bigger implications and the actual intent. Sometimes people ask you to do things a certain way because they’re thinking within a limited knowledge framework. Challenge assumptions, understand the purpose, write your own requirements if need be.

The work is licensed under a CC-BY-SA Creative Commons license so feel free to modify this work as long as you also share it, and attribute the original concept to me.