Hello there, tech-heads!
Friday the 13th is unusually lucky for you, because it’s another action-packed special this week.
We start with the second half of the video interview with Tom Goodwin. ☝️
There is a transcript of the interview down there, too. 👇
🤔 Some of the points I found very interesting:
“Innovation is much less collaborative than we like to think.”
“Innovation so often is about the theater of innovation.”
“Data can only ever reflect the current reality.”
And as a result:
“a culture of data is particularly destructive to anything that's wild and unexpected and new.”
“What data have you not found?”
We are swayed by spurious data points, without considering the counter-argument. The slavish devotion to “data” makes it very easy to manipulate people with a few percentages from a survey.
“We'll probably find out that we don't need that much data.”
As Tom notes, to sell someone a Porsche, you don’t really need to know what breakfast cereal they prefer. There are more direct routes to address the target market.
There’s lots more in the interview too, so hopefully you find it thought-provoking.
And if you liked those video specials, why not…
Also this week, the hi, tech. TikTok went live! I’m releasing a series on data visualization on there at the moment. Join the party here:
Let’s take in a few Tech Bites from another busy week.
Tech Bites
🍲 Google I/O: As you’d expect, I was most intrigued by multisearch (but you can watch a 12 minute summary of the announcements here).
They showed a demo where you could search using a picture of some food, then Google would find you restaurants that serve the dish nearby. All you have to do is add the label “near me”:
A lot of these splashy announcements stay as just that, but we can at last tell Google’s ambitions for search through this intention.
📉 Softbank’s Vision Fund loses $27bn in value - CNBC
In what we could diplomatically call a “difficult” economic climate, investors are getting defensive. That means high-growth, high-loss tech stocks are a lot less attractive.
⚖️ Ex-Facebook moderator in Kenya sues over working conditions - Guardian
🤚 Binance halts Luna trading following meltdown - TechCrunch
🤨 Sports cryptocurrency firm and Rangers and Hibernian sponsor SportemonGo ceases trading - SportsProMedia
I do not understand why sports clubs accept cash from companies that scam their own fans. A bit of DD, maybe even a chat with some know-it-all like me, and they’d know these business models are built out of twigs. (Yes I do know why they accept the cash - the answer’s in the very premise…)
🔭 This is the first image of the black hole at the center of our galaxy - MIT
🗣 hi, tech. Talks with Tom Goodwin Part II:
The Role of Data & Technology in Innovation
Clark Boyd: So in the second half of our discussion here, I'd like to move on and talk a bit about technology and data when it comes to innovation. And I think we've left that on purpose because this people part is so important that we can talk about it all day. But first of all, do you think that the new ways of working over the last couple of years have affected innovation? Or is everyone just in a rush to get back to the same old?
Tom Goodwin: I don't think the lack of progress over the last few years comes entirely from the way that we have been working. I think the last few years are a little unfair effectively around the world, we sort of pressed an emergency bell, told everyone to get out of the office and run for their lives, not to take anything heavy with them. And then depending on where you are in the world for three months to two years, people then sort of retreated into survival mode. Really. It's fascinating to talk to people about their workload during the pandemic because there's everything from people who are working harder than ever on more urgent solutions, never all the way through to people that had almost nothing to do. But they sort of looked busy. And it's been a very unfair experiment because most companies suddenly went to a sort of coping strategy. It's almost as if people were just trying to keep alive. They were just trying to sort of keep a salary coming in, they were just trying to not get fired. And therefore the main problem I think for the last two years has just been a lack of sort of ambitious Proactive future thinking progress, rather than a sort of reactive step. I don't know. I'm a huge believer.
The way to think about these tools is not to say we've got Zoom, how should we use it, but much more. What is this company trying to accomplish? What process do we need it? What sort of strategic vision do we need to work towards? What are the steps to do that?
And then in each of those cases to figure out the best way to do that. And for some of those things, actually, automation is the way forward. There are way too many jobs being done by 25 year olds in a Soho office getting $75,000 a year. That could actually be done by a sort of macro written by somebody in about ten minutes. Far too many jobs like that.
So automation is key. Figure out the things that are best done by individuals. Again, we talk a lot about where people work, but it's much more interesting to think about how in a holistic way, we have this love of collaboration.
Innovation, for me so often is about the theater of innovation, where you rent out a room with colorful sharp sofas, pull up sort of five mood whiteboards, put up lots of post it notes.
And the more people in the meeting, the more innovative it is, the further people have flown in, the more important it is. Actually, I think most innovation happens on an individual level. I think innovation is a much less collaborative thing than you might think.
And again, these are unpleasant, perhaps truths that I’m muttering to figure out ways where people can come together, figuring out ways that people can work independently.
Figure out ways that people can get inspiration. Maybe it means just going to your local shitty shopping mall. Maybe it means picking your kids up from school and having to poke around the buildings if you're allowed to. Maybe it means going to a hospital and walking the corridors. Maybe it means going on a long road trip, whatever it takes to sort of experience the world. And within that structure, I think it will probably become quite obvious that it's very hard to collaboratively come up with ideas at the same time. But it's probably easier to to refine them. It's easier to sell them in, perhaps online as well.
Clark Boyd: Yeah, because there was a big study, actually, a colleague of mine was involved in it from Columbia about the role of Zoom in creative collaboration. And the headline wasn't that surprising, which was that essentially in person, a room of people will generate more ideas than if those people were situated on Zoom all over the place.
But then they found actually there was no difference in the quality of ideas selected between either of them. And in part one of this chat, we were talking about the role of charisma within innovation. And actually, that strikes me as something that comes up in rooms. I'm sure you've seen it. I know what I have experienced, and I'm sure I've been guilty of it dominating the conversation. But I think I've got a really good idea here. And I set off with that idea. And everyone in the room, I think, is so keen to come out of there, having felt that this has been worthwhile, this has been “innovative”. Something has come out of it. And this guy is standing up and he's got the pen and he's going to talk about his ideas. There's a deep down sense of … let's run with it.
And people don't do that quite as much on Zoom if they can just vote on the ideas and bring things up. So there's a blend there, isn't there, where you maybe have that selection process happening differently, or you have people working in isolation before they come to the innovation session where they at least turn up with ideas and they aren't swayed by one person standing up with the pen and telling everyone else what to do.
And another thing they said at the end of that research report and I'll share a link to it when I post this video out was we haven't tested it, but we've never lost the sense of kinetic creativity, if you want to call it that. So if you're in a room, you're with everybody, you can move and so on. On Zoom like us right now, you're quite fixed, you're looking at the camera, that's what you're doing. What about if you had Zoom with the cameras off and it was just audio and people could walk around the room while they were at home, would they be more inclined or less inclined to come up with new creative ideas than in a room? So it's like most research, it just opens up 20 more potential research reports.
Tom Goodwin: One thing to know is I often feel like we're judging working from home and other mechanisms today, a bit like a plane mid flight without the realization that I had to take off because we can have sessions where we can build on reputations that we've established, we can build on trust that we've established, we can build on relationships and knowledge that we have only been able to build in a preponderamic world. And therefore it's very different for an 18 year old or a 21 year old going into work today, where you learn how to present by watching other people present and not on Zoom, you learn how to dissent by listening to small talk in the background of meetings. You learn how to give people bad news by gossip. And all of those informal but vital mechanisms are sort of stripped away, I think in this world.
Clark Boyd: Yeah, it is a good point. And I guess a lot of the takes on what this new world has meant have come from people who have had the benefit of that where they're really trading on everything that happened in the real world. Well, yeah, it's fine to work from home because, well, I'm pretty comfortable if everything stays the same, if it stagnates at home here, I'm fine with that because it's going okay. What about people just starting out who need the benefit of all of that? And I think that I wanted to ask you about in this time, actually, because I end up talking about this and I'm blue in the face and I'm not sure I enlightened anyone along the way. But the role of data within innovation, we all agree there's lots and lots of data out there. And yes, it's a good thing, better than just a gut instinct. And we talk about this potentially someone leading the charge and persuading everyone of their idea with charisma, but maybe not too much behind it, and the data can maybe be that substance. But what do you think, generally speaking, from your experience and what you see at the moment of the a role of data in the innovation process, is it an enabler or is it sometimes a blocker?
Tom Goodwin: I mean, everything really is about the precise circumstances. And depending on innovation within a pharmaceutical company, in particular, is a very data oriented thing where you're just trying serendipitous combinations of drugs and seeing which one works. And then when it works, that's a good idea. But that's a bit unhelpful to be so niche.
Generally speaking, I think we've sort of moved to a new paradigm where we're in love with data and where data gives us insurance. And I think everything about this culture is completely counter generation.
The particular killer point is the data can only reflect the current reality. If you were Tesla doing studies on the EV market before Tesla, obviously all of the data would show you that people don't really want to buy an EV, and EB cars aren't really a good idea and they're going to be expensive and they're not going to go very far. And then you create the reality against all the data. The data is particularly unhelpful, and a culture of data is particularly destructive to anything that's sort of wild and unexpected and new.
It's a little bit more helpful when it comes to smaller, more incremental innovation. You can look to see so the trending flavors of ice cream and then decide to make a smoothie of that flavor. You can kind of look towards the sales of meatless burgers and then decide you're going to do a meatless chicken or something, so you can use it to go with the flow. And of course, you can use it to substantiate your arguments. So I think 1more healthily, the best role with data is to do something that you know is great in your heart.
And then when because it's always when required by a company to make a business case, when required by a company to sell it in, that's when you have to use data, because it does appear it's really hard within a bigger entity to win an argument on charisma. It's a lot easier in a smaller company, obviously, but, yeah, it's very hard for someone to stand up and say, I just believe biscuits made out of candy floss are going to be great. You have to sort of say candy floss is trending in South Korea. Biscuits are the future, according to IBM. And even completely spurious, laughable non robust data will not normally allow any argument to succeed because people don't really understand data enough to challenge it.
Clark Boyd: Yeah, that's kind of where my question was headed was that. I certainly understand the importance of data. I actually quite like, whether it's apocryphal or not, the Amazon approach of if you're going to bring data to the meeting, you need to bring data that disproves your point as well. It encourages people to see the other side and display two sides of the argument.
“So I've done my research. These are two things I'm seeing, and this is the reason I believe A to be the case. And I believe that B is not strong enough to hold us back.”
Well, at least that gives people some structure and a reason to bring another argument to the table, because often I've seen it's that charismatic front runner who turns up with the spurious data that they may or may not have made up. And it goes up of course, the line just goes straight up into the heavens, and then by the time the results don't happen, they've moved on to the next client or the next gig or whatever. How would you really describe that? I've given away my point of view, but the lack of data literacy really when it comes to organizations. Given how important it is, do you think that the level of data literacy is where it should be? And if not, how might that be improved?
Tom Goodwin: I am staggered by how poor the level of basic statistics or maths or even logic. I'm staggered by the lack of it that I see both in advertising and market marketing, even in consulting and even in business more broadly. I don't really understand it. I was quite a normal student. I was quite good at maths. I feel like I can feel maths. I didn't really have to do any work of it, and I did quite well. I got a Masters in structural engineering, and maths, to me is a bit like learning French. If you are French, you can probably get better at it, but it doesn't feel that hard. And maybe it's the choice of sort of meetings I've sat in.
Maybe it's the roles I've done in that. Generally speaking, people don't seem to have any sort of instinctive ability to understand it. If you sort of said 53% of people think it's a great idea, no one seems to turn around and say, well, look, 47% of people also don't agree with this. That means it's not a strong point. The fact you've got a number there no one's asking questions about the methodology, no one's asking questions about what other data you found that disproves that point. It's completely extraordinary to me.
Again, I'm not a statistician like I'm not very good at the more advanced concepts. But if you were asked to put together a business case to make these candy floss biscuits, if you can find social media analysis in South Korea that shows that people like it, the obvious question is there's like 180 plus other countries. Did you try to see it? There the fact that you didn't say here at 19 countries where people aren’t talking about candy floss at all, that means quite a lot as well. What data have you not found? Like you said before, from Amazon, what date refutes that point? There seems to be no thoroughness. It's almost like a weird game show where you can just hold up a paddle that says, here's a stat from Gartner, we've won the argument.
Most of these data points are actually wrong as well. The number of them, which sort of come from methodology no one understands. A lot of the thinking behind Plastic Straws was based on an eight year old doing an assignment in school once that somehow went viral. The knowledge of doing 10,000 steps comes from the fact that it's a funny word in Japanese or something like that. We live this whole world stuck completely wrong data, but as long as it helps us refine it.
Clark Boyd: Yeah, that is the thing. If it is genuinely helpful. It's like the five fruit and vegetables a day in the UK. People were staggered when they found out that that was just a number because someone was under pressure, I don't know, five? Parents for slavishly trying to hit the five a day, but it does give you something to aim for and it's better than getting two. So it's not a bad thing.
I just find it strange in businesses with such big decisions to make, and it's not always the case. I've worked with some that are very rigorous about this and that demand a lot of this from their staff, and they tend to be quite successful. Also tend to be in finance, which would make a lot of sense. And then you get a lot of people who work in, say, fashion, where there's so much data that you could be using about the trends that are coming up. There's a lot of global things, supply chain issues at the moment that could affect everything. And there are people in the business who are doing that and doing it in detail in a way I'll never understand, to be completely honest.
But then you get to the marketing department and there's none of that coming through. And it is like you say, someone calls up a thing and said, well, I've seen that these dresses are going viral on TikTok. Maybe we should be doing that in three months time. And it's like, have you got data for that? You need data. If it's not data, it's just an opinion. They go, well, it’s 37,000 likes. That's a lot of likes. Okay, let's go for it. And that's kind of it. It's intriguing. I've been discussing this with some of these businesses and ad agencies and places like that. How do you go about bringing that data literacy to a certain level?
Tom Goodwin: Maybe it is made up data points. Like you say, five fruit and vegs a day. You need to bring three data points pro and contra what you're saying in this meeting. And you give people that sort of a number so that they at least are presenting that and you can make your minds up, but then you're trusting people to decode it themselves and make their minds up as well, which is another kettle of fish, I suppose.
Clark Boyd: So when you look at the role of technology and data as we're kind of coming to the close of our lovely conversation, Tom, any final thoughts on how people should be approaching this whole idea of a culture of innovation and where technology and data fit into this alongside the important role of people.
Tom Goodwin: I think technology is, broadly speaking, an amazing tool to get more and better. And I think what's happened is people have tended to go for more rather than better. At some point I was going to try and research this to prove it with data, but I'm pretty sure that more people die of drowning than they do of thirst. And I thought when we talk about sort of data being the new oil like data kind of is the new water in a way where there's so much of it and we're more likely to get in trouble from it and we have to sort of have too much of it. Maybe it's not as good as I thought.
Clark Boyd: I'm always nonplussed by things, Tom. I thought that was a very good idea. Don't judge things by my face.
Tom Goodwin: I think technology, generally, it's an amazing way to get more. The cost of getting data came down to almost nothing. The cost of storing data definitely came down to nothing. The cost of connecting data basically went down to nothing. The cost of processing dates basically went down to nothing.
And then companies ended up with all these servers. And there were servers held by different departments. There were servers that didn't talk to each other. There were servers where data was measured in different ways, but it was recorded in the same way. There are places where it was recorded differently but measured the same way.
And companies ended up with all of this data. It was called big data. And then what do you do with it? And then we sort of invented things like AI as a way to sort of glean insights from that data. And now we have all the shitty data being processed by pretty shitty algorithms giving us stuff which is not that helpful. But because we followed a very advanced process, we assumed it must be helpful.
And we need to do the exact opposite, which is to figure out exactly what decisions are we trying to make. What data types do we need for that? Where do we get that data from that's best? How do we store that data? How do we get that data with permission? How do we ensure that other people around our organization get access to that data? How do we tidy it? How do we refresh it? And I don't know anything about data. It's strange. I'm saying things that appear to be quite clever because this is not my world at all. But it just seems like common sense to me.
And we'll probably find out that we don't need that much data. It’s sort of staggering. If you're trying to sell a Porsche 911 to somebody, you probably don't need to know that much. I mean, you need to know they give a shit about cars. You need to know that they are driving fast. You need to know they've got quite a lot of money. You don't need to know what breakfast cereal they like. You don't need to know what newspaper they read. You can probably guess some of this stuff. You don't need to know where they are. You don't need to know how many steps they've done that day. You don't need to know what TV shows they watch. And we need remarkably little data to make most decisions.
You see all these incredibly fancy things that can be done, but most of it is way more complicated than it needs to be. If you're trying to get a car, launch the mass market, find a TV show about cars and stick a TV ad in it, part of your job is done. You didn't need behavioral targeting. You just needed to be in the right context.
Clark Boyd: Strangely enough, that's where a lot of potentially digital advertising is going to help soon with interest based targeting. And what's more, contextual targeting, as we lose a lot of the data that maybe we didn't even need in the first place for some areas anyway. Well, Tom, thank you so much for joining me today. Where can people keep up to date with your work?
Tom Goodwin: Probably LinkedIn is the best thing I think I've also got a website. I can't remember what it is. Tgoodwin.co I think or tomgoodwin,me. I know people can normally find me.
Clark Boyd: Yeah, they'll find you. They'll find you. Well, thank you very much for joining Tom. Thanks everyone for watching. See you next time!
I found out this week that my phone autocorrects ‘years’ to ‘tears’.
How emo.
✌️ Part II: hi, tech. Talks w/ Tom Goodwin