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Hey there, welcome to Data Democracy.

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This is a podcast where we explore ways to make data and AI more accessible to everyone.

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We do this by interviewing experts across industries, asking them how they think about

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data, what are some of the challenges they face when it comes to data, what's their dream

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state for AI and data, if they had a magic wand and time and resources were not constrained,

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what kind of intelligence and models would they wish to have?

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We've got a great guest today, James Chance.

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James is an entrepreneur, investor, and a leader in product, data, and strategy.

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James is the founder of Yourself Online, an AI-driven reputation management software.

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Yourself Online was acquired by Legal Shield in 2021, which is where I met James.

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James has also worked at Google and Accenture in the past.

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James is an alumnus of London Business School and UT Austin.

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James runs an independent product consulting practice now.

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If you need help with product, reputation management, small business solutions, or strategy,

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James is your guy.

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Give him a shout.

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James is not only a great leader, he's also a great person.

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He easily passes the, hey, I'd love to grab a beer with this guy test.

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So let's welcome James.

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Hi, Mitzi.

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Thanks very much for having me.

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Thank you.

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Thanks, James.

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How are you?

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Yeah, doing very well this morning.

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Doing very well.

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Looking forward to the holidays.

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Awesome.

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Me too.

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Yeah, let's start with your story.

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Can you tell us about your journey so far, your background and product and entrepreneurship?

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Oh, where to start?

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That's a good one.

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So I've had a sort of fairly traditional and fairly untraditional career at the same time.

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So my background in undergrad was mechanical engineering.

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I kind of came out of my undergrads and discovered I actually wanted, you know, I was always,

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you know, more interested in the business side of things.

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And ever since I was a teenager, I'd really had an interest in entrepreneurship.

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And you know, pretty much since I can remember, I've always had little business ideas on the

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go and things like that.

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So when I was, you know, I kind of after I came out of doing my undergrad, really wanted

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to go into something a little bit more entrepreneurial to and kind of business sort of in the context

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of undergrad choices.

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So I ended up going to be a consultant management consultant at Accenture.

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I took this view because it gave me the opportunity to see lots of different types of businesses

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in different industries and kind of learn them and really, you know, be able to use

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some of my skills in engineering from more of a quantitative side and, you know, on these

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different projects.

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But at the same time, be able to see different businesses in different industries and kind

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of get more of an idea of where I wanted to go from there.

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So I was at Accenture for about four years.

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Then after that, I sort of over my time, I'd specialized in retail and I spent some time

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primarily looking with quite a few of the old big box retailers.

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And it was in the mid 2010s and I kind of thought, hang on a second, I've spent quite

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a lot of time working with retailers that aren't really doing so well.

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What happens if, where's the exciting stuff in retail?

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And it was all really at that time around e-commerce and looking at, you know, digital

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marketing to grow e-commerce brands and retail brands as well.

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And was fortunate to, one of my sort of, one of my friends was working at Google and he

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knew their retail team and they were looking for somebody who was, they're looking for

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somebody to join that team in the role of an analytical consultant.

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And what that role was doing was it was working really as the kind of liaison between these

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big retailers.

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So the likes of kind of Amazon, eBay and Google and being a mix of a partnerships person and

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also a salesperson and sort of helping to maintain and grow that relationship.

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And in my role, I was working alongside, you know, working alongside the sales folks as

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an analyst, as effectively like a data analyst.

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And I was looking at some of the data that Google has and saying, how can we, you know,

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use some of the things that Google, the data that Google has across its products to help

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our clients grow their businesses and then at the same time help Google grow their business.

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So this was really one of the things I was doing was kind of taking my knowledge of retail

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for my consulting days and actually applying kind of more skills in data and sort of data

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science and looking at these quite big data sets that Google gathers across things like

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search, across maps, things like that and asking, you know, answering, using it to answer

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questions of what types of customers are interested into what types of products and different

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geographies, how are those different customer markets growing, contracting, all of those

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things.

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And I got over my time, I got a whole load of different kind of different assignments

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of areas that we were trying to Google and these retailers were trying to grow.

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I got things like, you know, trying to understand the, you know, even sort of questions like

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what's the market size of women's plus size fashion in Germany?

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And it's like, okay, if you use all the searches, how much of the searches translates into people

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buying things and you can kind of roughly market size.

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So there were some questions like that.

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It was good fun.

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And then I sort of felt after a while, I was sitting in meetings and realizing that there

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were, my knowledge was kind of, I was sort of running out of knowledge and I was going,

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sitting in meetings with Google, some of the Google management team and some of the clients

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and they were talking about stuff I just really didn't understand, like, you know, ROI on

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advertising, you know, looking at sort of setting a three year marketing strategy, looking

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at setting a three year growth strategy.

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And it went, hang on a second, I think I need to take myself, you know, how's the time to

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go and do an MBA just so I could really get that knowledge and kind of level up a bit

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more around some of these bigger strategic sort of questions.

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So I went to do an MBA for two years at London Business School and the University of Texas.

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Focused on entrepreneurship and growth businesses.

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Whilst I was doing the MBA and was fortunate when I was there, I came up with the idea

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for my business, which was yourself online and was fortunate to be kind of hanging out

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with a good friend of mine who was also then became the co-founder of my business who I've

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met whilst I was at Google.

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Demetrios, shout out to Demi if you're watching the podcast.

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And he, you know, we were sort of talking about some of the things we were seeing and

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we were seeing this kind of rise of cancel culture on social media.

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And also having a few of my friends from the MBA, but also a few of my friends from undergrad

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that were in serious jobs like lawyers and bankers getting really concerned about, you

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know, the image and their online reputation based on their social media posts that were

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often years old.

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And we were on a stage sort of towards the late 2010s, like 2017, 2018, where people

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were being increasingly judged more and more by what they posted a long time ago.

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Even if those things are not illegal or anything like that, it's just, you know, things that

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were bad, bad moments of judgment coming back to haunt people and they're not getting jobs

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or they're not getting, you know, they're not getting, you know, sales opportunities or jobs or promotions

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just purely by the basis of what they posted online, you know, 10 years ago before then.

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And so that was, we then thought, hang on, there's a problem here.

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Let's sort of work together and try and see if we can come up with something to solve

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it.

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And we came up with the idea for Yourself Online and Yourself Online was our vision

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for Yourself Online was to really help people manage their online reputation or all their

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online persona and to make and to start off to doing that, making people aware of what

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their online persona really is.

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So being able to kind of, and our first version of the product was really focused around a

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almost a report card of your online reputation, showing the things that are good, showing

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the things that are bad, highlighting any areas of improvement.

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And that was, and then from that, then we then developed the product over time to be

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a more holistic service that helped people to, you know, to clean up the bad, but then

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also to grow their online reputation in a way that's positive so that they could, you

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know, access opportunities more easily, you know, have a better LinkedIn presence, have

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guidance around how to build a professional online reputation in a way that gives them

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access to opportunities.

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So it's more of an asset rather than a liability.

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Yeah, absolutely.

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I have always thought about Yourself Online like a friend having your back, you know.

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It's really, it's a really solid product.

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I've definitely used that.

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What are some of the challenges you faced when, you know, building company and scaling

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it, I know it was a really new idea.

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What did you have to solve for and were there any challenges?

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I think there were definitely a lot along the way.

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I mean, with any kind of entrepreneurial venture, there's always things that you think

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you're going to get and then there are the things that you don't know you're going to

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get, you know, the sort of the known unknowns and the unknown unknowns.

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So we, you know, we had a, some of the challenges really were that we found over the course

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of the company.

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And when I look back over the total journey from initial kind of concept in about 2018,

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early 2019 through to the kind of sale of the business to Legal Shield at the end of

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2021, we had what almost could be described as three different companies, because we went

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through the initial stage of trying to find initial product market fits and which had

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some challenges around the, which features we were introducing, trying to make sure that

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we were really trying to solve for the right thing and that we weren't over complicating

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our initial product offering.

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So that was the first one.

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And then after that, you know, I think then it became more, once we had initial product

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market fit, it was really around scaling and some of the scaling challenges, both from

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a technological perspective as more people use the product and from everything from sort

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of compute through to product quality as you get more, as the product saw more things,

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we found we had challenges that way through to kind of organisational, you know, organisational

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challenges around how we kind of organise our self and our people perspective for growth.

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One of the big takeaways, just thinking back to that early stage that I think might be

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interesting to some of your listeners was we, when we first started with Yourself Online,

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we had this big vision and we're kind of like, we want to be the online guardian for professionals

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and we want to be the service that helps people to, that has their back online and, you know,

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help them to be their best self online, which was this really big vision and pitched as

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big vision.

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But then when we came to executing it, we tried to do too much from the beginning and

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we got into a bit of a challenge, which I think sometimes you can be when you're in

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the first version of your product, you're at MVP stage, where you're testing too many

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things at once and you just don't have the resources to deliver the product to be able

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to do that.

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So you end up with like a, you know, a product experience that's, you know, 50% of every

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feature.

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So you end up with almost nothing by the end of it because you can't win on any feature

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because you're trying to do too much.

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And we were trying to, you know, we, at the same time, we were struggling to deliver those

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features and we were also confusing our initial early customers because we were presenting

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them with what could have been probably three separate products around, you know, your social

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media reputation.

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We were talking to people about their privacy.

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We were talking to people about their password breaches, which could have been a very good

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holistic product over time, but we were probably about five years to three or three years too

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early on that when we ended up doing.

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And one of the things that was a real, was probably one of the big, you know, aha moments

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on our journey was actually killing two thirds of the product, the early product and just

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focusing on that reputation part, focusing on helping people to clean up and improve

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their social media and professional profiles.

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And then coming back to over time, the privacy angle, but you know, and the other, the other,

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the kind of more online general holistic online pieces.

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And one of the things that we, that really helped us with this was that we were really,

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we kind of were looking at like, what was that?

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Those product features that sort of got that aha response, you know, from, from, from customers

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where they wanted to really take, you know, get their wallet out of their pocket and go,

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hang on, where do I pay for this?

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And versus the other things that were features that people are kind of look at and they go,

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oh yeah, that's a sort of, that's interesting.

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Like it's good to know that, but, but they're not really, it didn't, it didn't really evoke

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much of a reaction.

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The world of products is kind of new to me.

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I guess I've always been in companies with sort of established products where we focused

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on, you know, sales, marketing and tech.

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I recently attended a product workshop and it kind of opened up a whole new world to

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me is like, okay, there's, there's a lot here that I need to learn.

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Could you please demystify the world of product for us?

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I know there's a lot of phrases, you know, product, what is product, product development,

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product management, design.

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Can you, can you tell us a little bit about it?

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It's a good one.

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And I'm sort of trying to find an easy way to, to sum it up.

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But I think the historical way is a good way of putting it and a good way of kind of seeing

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how, how this product has evolved because typically you're exactly right.

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You know, in, in some organizations, you know, generally across all organizations historically

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and in some organizations now you do have this world of, you know, you have a very traditional

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world of, you know, sales, marketing, IT operations, et cetera. And what was happening that was

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that across those different organizations, there wasn't one person that was really representing

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what is the voice of the, what is the voice of the customer?

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Who is the, who is representing the customer across these different functions and who is

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that in between, between the customer and the business strategy that, you know, the

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overall business strategy for the company and product functions have evolved really

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to knit together the different, you know, knit together the different functions in a

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way that enhances collaboration to, you know, be more focused on achieving business results

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and to also be more focused on, on the customer and meeting the needs of the customer across

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the product life cycle from right from sales and marketing, right the way through to, to

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operations and even in kind of post, post customer journey retention as well.

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Because typically these have been, you know, joined in some cases, isolated efforts that,

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that meant that there wasn't a holistic kind of person that joining to get together. And

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that has been the role of, you know, that's, that's kind of how the product manager role

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evolved was in, certainly in technology organizations, being the person to join together and represent

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the, you know, join together these different group of stakeholders in a way that delivers

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the strategic goals for that product line and also bears in mind the customer, the customer

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needs and represents the customer throughout those different parts of the, the organization.

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Probably the, but then within, you know, within the world of product, you know, you then have

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a product manager, product strategy, you might have product development, product design that

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sit across different parts of the, in the way that the product function goes to market.

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So, so you have, you know, product strategy that will be very much at the kind of the

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tip of the arm around thinking about the interface between the wider business and corporate and

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product. And then, you know, within underneath that you have a product leadership structure

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of a product leader and product managers that, and as they move down that structure, it becomes

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more operational, you know, operationalizing the strategy and then interfacing with those,

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those product managers, you know, you'd have people like a product designer who would be

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working with the product strategy and the voice of the customer and doing those kinds

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of insights and the customer discovery and, and also, and then on the other hand, on the

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other end of the life cycle, you'd have people like product marketing that would say, okay,

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how do we take the things that we know about the product and take the things we know about

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the customer and package that in a way that the marketing organization can embrace to

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execute and kind of be that connect between the two.

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Yeah, it's, it's so interesting. And I think it's really something that every company needs,

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a mature product. So I think there needs to be a time where we look into the product and

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say, okay, can we evolve? Can the product evolve and help the customer's current needs?

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Exactly. And also the progress of organizations towards being more product centric is kind

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of aligned with their journey to being more customer centric. And when you're looking

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at, you know, as organizations have kind of moved over time to be more customer centric

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and showing that that focus on the customer achieves better business results in a longer

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period of over time, the product organization was sort of brought in as a way to kind of

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execute that and to really keep that focus, keep that focus in mind. And it is only natural

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that companies that have been around for a while, previously maybe were operating in

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an operationally focused or sales and marketing focused type environment. And it's natural

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that there isn't, you know, every company will go through an evolution.

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Absolutely. Yeah, it's, yeah, I think the world of product is, like I said, new to me

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and it's super interesting and I'm learning a lot. How does data come into play in product?

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You know, everything from product discovery to product delivery?

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Super important. I think there's the favorite, you know, the sort of commonly known anecdote

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of data, data trumps opinion. And it's super important for a product organization to be

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making data powered decisions throughout that product lifecycle. And, you know, right from

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almost starting to engage with customers on a product and right from the very early days,

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right the way through to customers leaving a product, there's got to be data measuring,

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you know, there's got to be measured. You've got to be measuring the right things in a

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way that is joined up and cohesive across the lifecycle. Some really good examples are

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that, you know, in terms of if we go back to thinking about being more focused on the

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customer, how do we know we're more focused to the customer and what are the things that

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are going to tell us whether we're getting this right? It's data. And so it's, you know,

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it's super, super critical.

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Absolutely. You talked about data being critical for to understand what the customers want.

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I learned that there's like a consensus among the product leaders that customer feedback

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is often skewed or biased. Concepts such as vocal minority selection bias, expectation

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fallacies are cited. Can you provide some color to this? I've always thought about customer

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feedback as the end all be all, hey, the customer said this, so let's go, I do a survey and

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then customer said this, let's go do that. So it kind of changed my perspective that,

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you know, maybe customers only customer feedback is only as good as the questions you're asking

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or the survey you're putting out. So can you talk about how to get customer feedback without

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really asking them for fee feedback, I guess?

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That's a really good one. And I think we all feel now that we're in a world that, you know,

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as soon as we do something, whether it's, you know, we stay at a hotel, we go to a restaurant,

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or we go out to see some culture or something, we always get hit up with feedback surveys.

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It seems to be a recurring thing that, you know, almost every day I delete the feedback

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from whatever thing I did yesterday, whether it was going to the grocery store or, you

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know, that we get asked for feedback so many times with these surveys that we are moving

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to a point where the people who probably fill those out are more extreme outliers where,

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you know, you either generally you have something bad to say, or, you know, you may be the other

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camper, you've had such a great experience that it's amazing. And you're really focusing

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on that. That explicit feedback that you're gathering is kind of focusing on those tales

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of your customers. And you don't really know where the middle group is. So although they

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feedback surveys do provide some indicators, they can't be seen as gospel because of, as

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you said, you know, there's the challenge around collection, there's the challenge around

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the survey bias, there's challenges around, you know, making sure that you're getting

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a representative sample. I think one of the things that is important when you're gathering

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data from customers is making sure you're gathering both explicit and implicit feedback.

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So combining those qualitative surveys and all the kind of qualitative data gathering,

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it is very useful to get a pulse check on your customers. It's very useful to see what

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they're calling out. And if they're calling out specific things that you need to take

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action on in a survey, but making sure you're combining that qualitative feedback with quantitative

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feedback through measuring the customer journey and measuring the interactions of the customer

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with your product and really saying, okay, what are the things that I want my, the action

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I want my customers to take or I need my customers to take to see whether my business is forming

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successfully and the business is doing successfully and how, what proportion of my customer base

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is actually doing those things. And looking at the customer journey end to end in a way

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that you can understand which, which those customers, how are those customers interacting

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with my business and what across which touch points and how are they using the product

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and then, you know, correlating that information with the, you know, customer, customer life,

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lifetimes over time and customer outcomes in more of a much bigger picture to really

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understand, you know, are we doing the right things that make our customers stay longer

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and improve the health of the business. I think you, you were doing some fantastic work

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on that legal shields around really understanding, you know, what are the interactions that customers

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are doing with the, with the product and, and, and, and, you know, how are they interacting

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with the service and how does that actually affect their, their life, their, their customer

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lifetime? And one of the cool things is that when you get into more quantitative data,

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as I'm sure you, you, you found that is that you really start to understand what are the,

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how do the different types of customers interact with the business and how can I make sure I'm

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serving properly the different customer groups I'm getting in the door? And, and, and, and

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some of that may mean that you have to, you know, embrace some customer groups more, some

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customer groups, you may say, actually, we're not going to do a good job. We're not going

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to be able to do the best job serving these customers. So we're going to move away from

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marketing to them and actually move into other customer segments or, or we're going to improve

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the customer, the product to meet the customer's needs.

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Yeah, that's a really good point. Oh yeah. Our CEO says, Hey, do we want to focus on

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a customer who's born or made kind of like what you said, do we want to focus on people

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who already we know would be good for us or do we want to change people minds? Sometimes

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changing people minds and trying to get to change their behavior, maybe harder, maybe

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focusing on people who already we know that are a really good fit, maybe a good start

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and then evolve into expanding it out depending on what kind of the goals that you have at

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the moment.

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Totally. And we found a very good example of that with, with yourself online and when

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we were building that building the business that we found very early on, there were some

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groups of customers that even though the product was, you know, the product we were developing

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a product and improving the product in time, it was still relatively, it was very early,

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you know, relatively early on. We found there were some groups of products, some groups

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of customers that were like, yes, take my money right now. Like I need this because

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I'm one good example is people who are professionals who were applying for jobs in certain industries

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where they know they're going to get looked up and that they know that the HR department,

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the company they're applying for is know they're going to check them out with either a background

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screen or checking just out their social media. They were kind of like, right, take my money.

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And then we knew there were some, some other customers that maybe could have found the

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service useful, but they just, they just weren't there at that particular stage in their life

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and life stage where they were, where they were, you know, that they, they either would

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have had to be at a closer life stage to needing the product or they may have actually maybe

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needed more education and more maybe, you know, more education and a broader conversation

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around why they needed it. And given our resources at the time, we just, we just were thinking,

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okay, we just got to, we just got to really focus on the customers that are the types

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of customers that are really putting their hands up and wanting us right now.

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That's, yeah, that's a great point. We, we just have to maybe focus on people who are

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looking for the exact product we're selling sometimes. And then, you know, when you have

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resources, go, go in and go all in on brand awareness and all the other things that needs

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to happen to bring them. Yeah, really good point. What is some of the advice you have

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for somebody who was wanting to get into product, product first, and then we can go into entrepreneurship?

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Maybe it goes hand in hand, product and entrepreneurship.

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I think a little bit of hand in hand, but I'll talk a little bit about product first.

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I think understanding, you know, for really understanding product, I think spending a

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bit of time reading about the role of a product manager. And it's quite a unique role because

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one of the things that product management managers do is, is they have, they don't generally

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have very many direct reports, but they have to influence a lot of different parts of an

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organization and understanding that, you know, spending some time looking at, there are a

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number of different books that I will, I've got in the bookshelf behind me, but I'm just

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thinking off the top of my head that I will, that I will, I'll kind of, I'll send you some

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links and you can put the maybe in your chat afterwards. But really understanding the role

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of a product manager and kind of what makes a good product manager, what makes a bad product

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manager. Really thinking about the entire product, you know, doing, reading on what

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that product life cycle looks like, where, you know, the different from, right from what's

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involved between starting a product, starting thinking about a product through to running

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it and operating it. And then thinking, that's the sort of more on the functional side, but

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then on the other sort of soft skill side, thinking a little bit about, you know, influencing

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people because a lot of the success and the role, and this kind of also applies with entrepreneurship

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as well, is thinking about how you can, how you can kind of influence people and really,

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you know, be able to affect change, but without having authority over the top of it. And that

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kind of applies a little bit with entrepreneurship as well in terms of you don't have a huge

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amount of resources, you've got to make things happen. And how can you try and make that

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change happen with limited resources and also being able to kind of sell yourself to both,

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you know, your team, your investors, your customers, to sort of make things happen.

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On the entrepreneurship side, I think, you know, there is so much stuff out there. There's

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so many, so many things written by so many phenomenal entrepreneurs. They're just, you

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know, that really the sort of interesting ones that I've read recently or I'm reading

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that could be of interest to your readers. I've been reading this one, which is, you

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know, fall in love with the problem, not the solution by Uri Levine, who was the founder

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of co-founder of Waze that was, you know, acquired by Google. He's a serial entrepreneur

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really focusing on understanding the customer and understanding customer needs. And the

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idea that, you know, you're better off understanding the customer and the problem than having a

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solution that's looking for a problem or a hammer that's looking for a nut. You're better

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off making sure you start with being customer centric. On top of that are the other recommendations.

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You know, there's the classic Eric Rees lean startup, but one that's particularly relevant

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for the data is a book called Lean Analytics. And it really describes the data that you

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should measure in a product and both from it's sort of from an entrepreneur, it's useful

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for entrepreneurship, but also for product in general of how you can take more data,

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database decisions, what the typical things to measure are for different types of businesses,

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how you can kind of take an experiment led approach. I think one of the things that we

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discussed a lot in yourself online is people talking in products and in digital around

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AB testing and all we're just going to AB test, AB test. It's hard to AB test until you think

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you have an A. And it's when you when you have a decent A, you can start AB testing.

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But until you have a decent A, you have to like experiment and just do these kind of

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on off tests where, you know, you don't necessarily AB test it, you just sort of make it better.

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Look what happens after a week and go, okay, did that make it work? Okay, no, we go back

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again, we're going to use it. You're in much more. It's not quite a or B. It's a sort of

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on or off type approach to experimentation and getting data.

399
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Yeah, that's a really good point, especially in startup environment where the product is

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no so new that you're still evolving. Hey, did I break something? No, keep going.

401
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All good, all good. Exactly. What are some of the skills that are transferable across

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the things that you've experienced like in product entrepreneurship, you've also been

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corporate executive. So are there any common skills that if people are trying to move from

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one to the other, are they transferable skills? Can you talk about that?

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On a so I started a little bit on the kind of functional side, then a bit more on some

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soft skills as well. On the functional side, doing data, being comfortable with data, data

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led decision making is something that I've used all throughout and getting really comfortable

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with gathering, understanding what data to measure, finding that data as much as you

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can and then using that to then make a decision and saying to yourself, okay, I think this

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is a situation, this is the hypothesis I want to try and approve or this is the question

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I want to answer to know whether I should do X or Y. Let's go gather the data or let's

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look at data to see if it supports that or disproves it. That's something I use a lot

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during time in consulting, time in tech, time into entrepreneurship is just that ability

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to kind of really know, have a feeling of what data you should be using data to solve

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a problem and kind of answer questions and sort of being informed by data. On top of

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that, I think having a good understanding of kind of broader strategic problem solving

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as well. So understanding how companies work was always a useful one and kind of how you

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can then based on that, you know, on structure, have a structured way to approach solving

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a problem. Then sort of jumping around a little bit, but building on the data piece, I think

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a basic understanding of just kind of basic stats tools like the sort of stuff that you

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can do in Excel is super useful to get a feeling of where your data is going. And then on top

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on then sort of above that is that piece I mentioned a little bit before around sort

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00:38:11,680 --> 00:38:18,080
of influencing, understanding. One of the things I found really, really valuable on

424
00:38:18,080 --> 00:38:25,200
the journey was understanding this concept called social styles, which is understanding,

425
00:38:25,200 --> 00:38:30,320
you know, your particular social style and how you interact with other people and how

426
00:38:30,320 --> 00:38:40,360
you approach things versus other people's. One, there's a sort of, we used Google and

427
00:38:40,360 --> 00:38:46,960
also in my consulting days, we used a model called, it was a sort of four quadrant model.

428
00:38:46,960 --> 00:38:51,440
Myers-Briggs is similar, but it's a little bit more detailed. And it was one of the things

429
00:38:51,440 --> 00:38:55,640
I found really important in terms of thinking about how you interact with other people and

430
00:38:55,640 --> 00:38:59,960
how you can make sure that you're, you know, putting yourself across in the best way to

431
00:38:59,960 --> 00:39:05,060
other people was understanding your own social style and then understanding other people's

432
00:39:05,060 --> 00:39:11,720
social styles as well. Because then you really understand how you can work most effectively

433
00:39:11,720 --> 00:39:17,180
with other people and kind of do that piece where you can kind of say, you can clearly

434
00:39:17,180 --> 00:39:21,360
communicate in a way that other people will relate to, what's in it for them, what's

435
00:39:21,360 --> 00:39:24,760
in it for you, why they should do something that you're suggesting, why you should do

436
00:39:24,760 --> 00:39:30,120
something that they're suggesting and kind of just generally be more effective.

437
00:39:30,120 --> 00:39:38,440
Absolutely. I think social style can be really effective in any role that you're in. Especially

438
00:39:38,440 --> 00:39:45,520
I feel that it really applies in the data world because you are, I mean, I can build

439
00:39:45,520 --> 00:39:52,400
all the models, all the intelligence. You know, I can, but if it's not implemented,

440
00:39:52,400 --> 00:39:54,920
then I'm just doing it for myself.

441
00:39:54,920 --> 00:40:03,640
100%. And understanding other people's point of view and their style really helps communicating.

442
00:40:03,640 --> 00:40:08,120
One example is understanding a little bit whether your audience are more detail oriented

443
00:40:08,120 --> 00:40:15,120
people or they're people who are really just interested in the top line executive summary

444
00:40:15,120 --> 00:40:20,920
and you know, what the results versus the kind of the analysis to get to the results

445
00:40:20,920 --> 00:40:26,200
and tailoring the message to get that so that that analysis actually lands with the audience

446
00:40:26,200 --> 00:40:29,160
and kind of makes change happen.

447
00:40:29,160 --> 00:40:37,200
Absolutely. Yeah. I really like that you talk about understanding of data as one of the

448
00:40:37,200 --> 00:40:44,080
transferable skills across the different things, different roles. How does your relationship

449
00:40:44,080 --> 00:40:52,200
with data change with those roles as a product executive versus an entrepreneur? Do you go

450
00:40:52,200 --> 00:41:01,680
more into storytelling when you're an entrepreneur, having to pitch an idea, backed up with data

451
00:41:01,680 --> 00:41:05,840
and sort of things?

452
00:41:05,840 --> 00:41:10,960
Very good question. My, when I was thinking a little bit about this beforehand, I think,

453
00:41:10,960 --> 00:41:18,480
you know, that the biggest one is that you don't, when you're an entrepreneur, you know

454
00:41:18,480 --> 00:41:25,120
the data you don't know. And in some cases, you're able because of that, you're able to

455
00:41:25,120 --> 00:41:34,960
make slightly more of a feel more comfortable making a gut in tunes decision. And so really,

456
00:41:34,960 --> 00:41:41,200
you're happy to operate with a slightly lower confidence, you know, lower confidence in

457
00:41:41,200 --> 00:41:50,000
your data than maybe in a corporate setting. Because you understand the in-depth parts

458
00:41:50,000 --> 00:41:54,680
of the product, you understand the in-depth part of the customer and you know, sort of

459
00:41:54,680 --> 00:41:58,800
a quote, you're a lot closer to it. So you're willing to deal with a little, you have a

460
00:41:58,800 --> 00:42:03,920
higher level of willingness to deal with uncertainty. And when you're in it, when I was in a corporate

461
00:42:03,920 --> 00:42:10,880
role, you know, you generally would would come under potentially more scrutiny in your

462
00:42:10,880 --> 00:42:18,080
decision making from, you know, your stakeholders, your management. So actually getting closer

463
00:42:18,080 --> 00:42:27,840
to all the numbers you can and using less kind of intuitive decision making is certainly

464
00:42:27,840 --> 00:42:36,760
one of those things. I think the one of the things that I, you know, one of the learnings

465
00:42:36,760 --> 00:42:44,000
that between the differences is you're more like, you know, it's a little thinking a lot

466
00:42:44,000 --> 00:42:52,120
about audience. When we're in a, when I was in doing the doing yourself online, we were

467
00:42:52,120 --> 00:42:57,440
a small team, we all had an in-depth understanding of the product. We then, you know, could communicate

468
00:42:57,440 --> 00:43:02,160
data quite freely without necessarily having to understand, you know, get, bring people

469
00:43:02,160 --> 00:43:05,440
on board of what it was because they all knew the different stages of products and they

470
00:43:05,440 --> 00:43:08,680
knew the things that we were measuring. And it was like, okay, cool. This is, this is

471
00:43:08,680 --> 00:43:13,560
where we are now. This is what we're trying to go to. And when you move to a more corporate

472
00:43:13,560 --> 00:43:17,480
setting, you know, you're working with stakeholders that maybe work across a bunch of different

473
00:43:17,480 --> 00:43:22,800
products and they don't know what those metrics are, where they've come from or where they

474
00:43:22,800 --> 00:43:28,160
were. So you have to really bring people in a corporate setting, you have to bring people

475
00:43:28,160 --> 00:43:32,960
along for the journey and also communicate in different ways so that a broad range of

476
00:43:32,960 --> 00:43:39,120
people can understand because, you know, you might be presenting something that will have

477
00:43:39,120 --> 00:43:42,960
somebody from, you know, I think there's some stereotypes there, but you know, you're going

478
00:43:42,960 --> 00:43:47,920
to have everyone from people who can, who can really understand the data and they want

479
00:43:47,920 --> 00:43:54,400
more detail and more detail right through to people who, who really switch off when

480
00:43:54,400 --> 00:44:00,800
you, you show them numbers and they just want the high level kind of high level thing. And

481
00:44:00,800 --> 00:44:05,400
that was the thing that, that was a learning as well is really thinking a bit about, you

482
00:44:05,400 --> 00:44:11,760
know, thinking, understanding the numbers, but also understanding who your audience is.

483
00:44:11,760 --> 00:44:16,640
And these are trade-offs as well because generally because of that in an, in an entrepreneurial

484
00:44:16,640 --> 00:44:20,400
environment, because you're, you have a much closer team that's much closer to the product

485
00:44:20,400 --> 00:44:24,680
and also you're more comfortable with ambiguity, you can move faster and feel confident in

486
00:44:24,680 --> 00:44:29,640
that. Whilst in a corporate setting, you just naturally, by the broader range of people

487
00:44:29,640 --> 00:44:34,520
you're working with potentially and their knowledge and their styles, you have to move

488
00:44:34,520 --> 00:44:39,320
slightly more slowly. You have to be slightly, you know, and you have to have the detail

489
00:44:39,320 --> 00:44:45,360
and have the thinking to be able to, to, to, to kind of compensate for that scenario.

490
00:44:45,360 --> 00:44:54,360
Yeah. Like you said, you have to get people to buy in and that takes time. So it's, it's

491
00:44:54,360 --> 00:45:01,520
like moving a large ship versus a boat. I feel like we touched on storytelling a little

492
00:45:01,520 --> 00:45:07,720
bit there. It's, I feel it's very important part of, you know, being a leader. What are

493
00:45:07,720 --> 00:45:14,980
your thoughts about it? What's your advice for someone who wants to get better at it?

494
00:45:14,980 --> 00:45:21,960
Is there anything apart from, yeah, some good.

495
00:45:21,960 --> 00:45:27,520
I think less is, so two, two things from probably my experience of pitching yourself online

496
00:45:27,520 --> 00:45:36,400
and that was, that was the most, probably the most, the most relevant of having to take

497
00:45:36,400 --> 00:45:43,240
a story that has data points in it and be able to have this different versions of these

498
00:45:43,240 --> 00:45:48,400
stories, whether it's the, you know, 62nd version, the three minute pitch in front of

499
00:45:48,400 --> 00:45:52,600
a room of strangers or the half an hour pitch in front of a room of educated investors,

500
00:45:52,600 --> 00:45:59,360
you have different versions of this story. I think the first one is, is less is more.

501
00:45:59,360 --> 00:46:05,360
I think it's very easy to try and put lots of information in there and be like facts,

502
00:46:05,360 --> 00:46:11,680
facts, facts, facts. And the reality is that, that certainly most people, they just, they

503
00:46:11,680 --> 00:46:16,920
just tune out after a certain point. And one of the things also is that when you, you have

504
00:46:16,920 --> 00:46:23,400
a point of, you've got a point of diminishing returns on information where if you, there'll

505
00:46:23,400 --> 00:46:27,920
be a point in the story you're trying to tell or the point you're trying to put across

506
00:46:27,920 --> 00:46:33,100
or the pitch where if you cram more detail into it, you actually get to a point where

507
00:46:33,100 --> 00:46:37,640
people don't want to engage more because it's starting to hurt their head, hurt their head.

508
00:46:37,640 --> 00:46:42,240
And so their instinctive thing for their brain is just kind of go, okay, like I want to make

509
00:46:42,240 --> 00:46:46,560
sense of people's brains, want to make sense of this information. And they'll get to the

510
00:46:46,560 --> 00:46:49,960
point where it's just too much for them. And so their natural thing is just to sort of

511
00:46:49,960 --> 00:46:55,760
withdraw. So less is more. And then the other one is making the data as much as you can

512
00:46:55,760 --> 00:47:04,720
in the story relatable in a way that sometimes when people, you know, will tell a story,

513
00:47:04,720 --> 00:47:13,960
tell with data, they will put up, you know, put up big numbers, but often, often, you

514
00:47:13,960 --> 00:47:17,640
know, individuals, the people at the receiving end of that story can't make sense of what

515
00:47:17,640 --> 00:47:27,440
it means. So being able to put a, you know, being able to give a key point and being able

516
00:47:27,440 --> 00:47:32,760
to have some sort of reference point of what that means. So it's a kind of, you know, one

517
00:47:32,760 --> 00:47:42,640
example could be, you know, this year, our organization, you know, lost 5,000 liters

518
00:47:42,640 --> 00:47:49,640
of water in our, in our facility. You know, the people might go, I don't know if that's

519
00:47:49,640 --> 00:47:53,600
a lot or this or that, but then you might say, well, they lost 500, 5,000 liters of

520
00:47:53,600 --> 00:47:57,520
water, which is the same as two Olympic sized swimming pools. And people go, oh, wow, that's

521
00:47:57,520 --> 00:48:03,080
a lot of water. So being able to give people things that they relate to in that story will

522
00:48:03,080 --> 00:48:09,840
mean that they can, it just makes it more impactful and makes it more relatable. And

523
00:48:09,840 --> 00:48:14,720
then less is more and really thinking about what's the hierarchy of information you're

524
00:48:14,720 --> 00:48:20,080
kind of trying to, trying to communicate. And there's a really interesting principle

525
00:48:20,080 --> 00:48:25,280
called the Minto principle, which is a pyramid. It's a sort of pyramid, the idea that you

526
00:48:25,280 --> 00:48:30,280
take structure information in a pyramid where you have one main point that has supporting

527
00:48:30,280 --> 00:48:35,240
points underneath, and then they have supporting points underneath those. And that's a, that's

528
00:48:35,240 --> 00:48:40,040
a very good way to be able to make sure you're not putting too much into it and to make sure

529
00:48:40,040 --> 00:48:41,880
it all stacks together.

530
00:48:41,880 --> 00:48:47,400
Yeah, definitely. Less is more is something that I've learned over the years and Minto

531
00:48:47,400 --> 00:48:57,240
principle is great. In my experience, I think I've learned that, you know, I've got so many

532
00:48:57,240 --> 00:49:05,240
things I want to say of I'm in the data, I have so much information that I used to just

533
00:49:05,240 --> 00:49:11,080
go and blabber all of it and then kind of lose people midway because like you said,

534
00:49:11,080 --> 00:49:18,640
there's information overload and people are tuning out. So how do you have that one single

535
00:49:18,640 --> 00:49:25,560
point in a conversation? That's my main point I want to drive home. And then you don't have

536
00:49:25,560 --> 00:49:33,720
to necessarily convey everything all at once. You know, you may have to take it slow, but

537
00:49:33,720 --> 00:49:40,760
make sure that the point is effectively communicated. I think it's more for the data folks and

538
00:49:40,760 --> 00:49:50,240
listening, right? That's really important. And yeah, less is more. And it's, it's definitely

539
00:49:50,240 --> 00:49:56,000
something that I've learned over the years.

540
00:49:56,000 --> 00:50:01,400
Speaking of, you know, we were talking about data and how it's being used. You worked at

541
00:50:01,400 --> 00:50:08,120
Google, you mentioned as an analytics consultant. Google is one of the best companies in the

542
00:50:08,120 --> 00:50:14,880
AI and data space. What can you tell us about Google's approach to AI and data? And what

543
00:50:14,880 --> 00:50:18,480
can we learn from it?

544
00:50:18,480 --> 00:50:24,520
I have to caveat this response by saying I left in 2017. So it's a little bit out of

545
00:50:24,520 --> 00:50:30,920
date now. But I think the ethos of the company, you know, the ethos of the company is probably

546
00:50:30,920 --> 00:50:38,320
still remains. And that was the, they had a really, really strong preference in the

547
00:50:38,320 --> 00:50:46,160
organization for self-serve, of actually self-serving your data. And everyone from, you know, whether

548
00:50:46,160 --> 00:50:51,320
you're a product manager or you're a sales analyst or you're a sales manager, would be

549
00:50:51,320 --> 00:50:57,040
encouraged to run their own data to the best of their abilities. And there would be, you

550
00:50:57,040 --> 00:51:03,440
know, there would be tools, you know, from, you know, there'd be tools and controls from

551
00:51:03,440 --> 00:51:09,080
everyone being able to, you know, that would protect that data from it. You know, some

552
00:51:09,080 --> 00:51:13,000
people would have access to product logs and some people had access to rolled up tables

553
00:51:13,000 --> 00:51:23,440
in a kind of self-serve UI type thing. But the effect of that meant that it, it democratized

554
00:51:23,440 --> 00:51:29,520
access for data across the organization. It meant that there wasn't necessarily silos,

555
00:51:29,520 --> 00:51:36,640
data sitting in silos or, you know, people who were looking to try and, you know, or

556
00:51:36,640 --> 00:51:41,960
data being a kind of bottleneck in the process of making decisions in the organization. Because

557
00:51:41,960 --> 00:51:48,260
the, you know, for the things that most people would need to do in their job, they'd have

558
00:51:48,260 --> 00:51:52,720
access to tools that would be able to give it to them and would be expected to do that

559
00:51:52,720 --> 00:51:57,840
regardless of kind of what level you're at. Admittedly, that there would be, you know,

560
00:51:57,840 --> 00:52:01,840
at a director level or a senior director on MD level, they would have their own dashboards

561
00:52:01,840 --> 00:52:06,840
that would be built for them. But, you know, they wouldn't get a report emailed to them

562
00:52:06,840 --> 00:52:11,000
with numbers, they'd go on and they'd look at it and they'd, you know, they'd do that.

563
00:52:11,000 --> 00:52:15,760
And they could be able to double click and go into things if they wanted to. So that

564
00:52:15,760 --> 00:52:21,280
was, I think one of the learnings was when you are able to make sure that data is not

565
00:52:21,280 --> 00:52:25,720
siloed and data is not a, you know, we've just, oh, we'll make a request to the analytics

566
00:52:25,720 --> 00:52:30,400
department and they come back. It's actually being able to say that everyone is expected

567
00:52:30,400 --> 00:52:35,920
to, regardless of their job, be able to get this stuff, get their hands, you know, get

568
00:52:35,920 --> 00:52:41,680
their hands on the stuff and actually use it. And was it was a was to change the organization.

569
00:52:41,680 --> 00:52:48,400
And that also came with transparency as well, which was which was both from the product

570
00:52:48,400 --> 00:52:55,240
data. Naturally, there are controls on this around privacy and around GDPR and also around

571
00:52:55,240 --> 00:53:00,800
some of the data, which is just is just very sensitive that Google handles. But being able

572
00:53:00,800 --> 00:53:07,000
more broadly to within an organization, be able to see the data that a particular team

573
00:53:07,000 --> 00:53:12,240
is measuring, to be able to see their objectives against that information, to be able to see

574
00:53:12,240 --> 00:53:18,000
their, you know, that their KPIs to be able to see their OKRs as a team means that you're

575
00:53:18,000 --> 00:53:23,960
able to when you're interfacing with different teams, you're able to go, OK, I've I'm working

576
00:53:23,960 --> 00:53:29,920
with this person in search. OK, I know that I can look that person's objectives up. I

577
00:53:29,920 --> 00:53:33,560
can look at their team's objectives up. I can understand how they're tracking against

578
00:53:33,560 --> 00:53:38,260
it and I can go to them and say, hey, like, I need to land this project. You're working

579
00:53:38,260 --> 00:53:43,440
on the same thing in a different market. How about we work together and then we'll both

580
00:53:43,440 --> 00:53:46,760
help meet our objectives on it, because you can see what they're working on and you can

581
00:53:46,760 --> 00:53:50,880
see how they're motivated and you see you can see what how how you know how the things

582
00:53:50,880 --> 00:53:54,880
they're responsible for, how they're performing, but also how that, you know, the team is performing.

583
00:53:54,880 --> 00:53:59,560
It's kind of cool to see that transparency as well as being able to solve. And that also

584
00:53:59,560 --> 00:54:08,300
comes with lastly, I think, an education. And with education, you shouldn't necessarily

585
00:54:08,300 --> 00:54:13,200
force you've got to realize that your different team members will have different skills. And

586
00:54:13,200 --> 00:54:18,080
you know, people that write marketing narratives are not going to be able to necessarily query

587
00:54:18,080 --> 00:54:24,200
SQL or do regression models or anything like that. But, you know, there's different people.

588
00:54:24,200 --> 00:54:27,200
But at the same time, there's people that are going to want to go deeper than that.

589
00:54:27,200 --> 00:54:34,400
But for people's level, they should have appropriate access to training that helps them build skills

590
00:54:34,400 --> 00:54:38,680
that help make their job more effective in, you know, up to the limit of their willingness

591
00:54:38,680 --> 00:54:45,920
to learn and their willingness to sort of take it in. Was one thing and having a training

592
00:54:45,920 --> 00:54:50,560
curriculum and access to training that will take, you know, people everywhere from people

593
00:54:50,560 --> 00:54:58,360
who are very, you know, who are not, you know, whose jobs are not super data heavy, but they

594
00:54:58,360 --> 00:55:01,720
do need to, you know, they do need to look at data and they're more, you know, maybe

595
00:55:01,720 --> 00:55:06,000
more emotive people to be able to understand the concepts of data right the way through

596
00:55:06,000 --> 00:55:11,880
to training for people that want to do, you know, PhD, PhD level stuff or like deep machine

597
00:55:11,880 --> 00:55:17,640
learning and all of that kind of those really, really deep concepts of being able to offer

598
00:55:17,640 --> 00:55:21,360
training across that spectrum, I think is important.

599
00:55:21,360 --> 00:55:27,280
Yeah, that's one of the biggest challenges as a data person I have, right? How can we

600
00:55:27,280 --> 00:55:38,240
make it? How can we democratize data for everybody of all skill levels and data? It's a really

601
00:55:38,240 --> 00:55:50,480
solid challenge. And you made a really good point on having transparency on other people's

602
00:55:50,480 --> 00:55:59,600
OKRs or other teams objectives. I think that's really a great point on getting alignment.

603
00:55:59,600 --> 00:56:06,140
How can I, you know, how can I understand what the other team is doing so that I can

604
00:56:06,140 --> 00:56:11,040
align myself to their goals, you know, kind of both of our goals can be aligned and kind

605
00:56:11,040 --> 00:56:14,040
of work together. That's really a solid point.

606
00:56:14,040 --> 00:56:21,920
100% and both the products or the potentially areas of business that the team is responsible

607
00:56:21,920 --> 00:56:26,840
for and the team's metrics themselves, it's important to be able to do both because you

608
00:56:26,840 --> 00:56:33,700
can see sometimes how they relate as well. And having that transparency, I think really

609
00:56:33,700 --> 00:56:39,200
helps people to work together and helps people, you know, be aligned when they're working

610
00:56:39,200 --> 00:56:44,720
on these kind of multi stakeholder, multi departmental projects.

611
00:56:44,720 --> 00:56:52,000
Absolutely. Again, I think it comes back to your earlier point on how can people work

612
00:56:52,000 --> 00:56:59,680
across different teams and not work in silo and, you know, all the aspects of storytelling,

613
00:56:59,680 --> 00:57:08,400
less is more. Having greater transparency all of that plays into this.

614
00:57:08,400 --> 00:57:21,400
Let's switch gears to reputation management and AI for a second. I feel that in the era

615
00:57:21,400 --> 00:57:31,840
of AI, it's so easy to just ask a question, type a question into Chad GPT and then it

616
00:57:31,840 --> 00:57:37,400
gives you everything. So if I type in, hey, tell me something about James Chance, it'll

617
00:57:37,400 --> 00:57:43,640
do the research for me and then pick out all of your quotes from 10 years ago, 20 years

618
00:57:43,640 --> 00:57:49,520
ago. And then kind of, I can actually, I, full transparency actually tried this and

619
00:57:49,520 --> 00:57:55,400
then it gave me some really good quotes of yours and kind of we can also, I could also

620
00:57:55,400 --> 00:58:04,400
ask it, what does, what is James's thought about data or product management and things

621
00:58:04,400 --> 00:58:09,840
like that. And like it gave me some good answers. I don't know if it was making it up or if

622
00:58:09,840 --> 00:58:17,940
it's your, you know, article somewhere or not. But the point is it's so maybe reputation

623
00:58:17,940 --> 00:58:29,480
management is more relevant than ever because you have the best research tools in history

624
00:58:29,480 --> 00:58:38,040
access by almost everybody. So it's, it's really becomes important to manage your own

625
00:58:38,040 --> 00:58:44,840
reputation in the web versus in the past where people may, if you just kept your social media

626
00:58:44,840 --> 00:58:51,520
clean, it may have worked, but going forward, I think with these AI tools, it may be more

627
00:58:51,520 --> 00:58:58,640
relevant and more important to have better reputation management. What are your thoughts?

628
00:58:58,640 --> 00:59:04,800
100%. And firstly, I hope, I hope chat GPT hallucinated some, some good things about

629
00:59:04,800 --> 00:59:12,760
me on there. It sounds like it might've done. But I totally agree with you. Previously,

630
00:59:12,760 --> 00:59:20,840
we were in an age where if you kept your social media clean and you sort of managed and curated

631
00:59:20,840 --> 00:59:28,560
your first page of Google, you could not really worry about anything that's behind that.

632
00:59:28,560 --> 00:59:34,680
And now we're in a world where these tools have the processing power and these models

633
00:59:34,680 --> 00:59:39,240
have the complexity that is it's almost like going through, you know, doing a Google search

634
00:59:39,240 --> 00:59:43,940
for somebody and reading and synthesizing every single result or close to every single

635
00:59:43,940 --> 00:59:49,040
single result. I mean, I didn't want every single one yet, but I'm a hundred percent

636
00:59:49,040 --> 00:59:54,080
and, and, and then it just becomes, you know, it becomes a slightly different challenge

637
00:59:54,080 --> 01:00:00,560
at scale. Because then it's really a bit about saying, okay, you know, what am I, what do

638
01:00:00,560 --> 01:00:05,960
I say now? And what is my online presence now? And what's my online persona now? And

639
01:00:05,960 --> 01:00:10,760
does that really align with who I am? And then working back and trying to understand,

640
01:00:10,760 --> 01:00:16,280
okay, where is this information coming from? And then what, what power do I have over it?

641
01:00:16,280 --> 01:00:21,680
And the, one of the things that is a challenge about providing reputation management at scale

642
01:00:21,680 --> 01:00:27,120
is a lot of, a lot of there, you know, some, if there is something that is illegal, it's

643
01:00:27,120 --> 01:00:33,000
about you, you have powers to take it down. And if it's something that is a deflamatory

644
01:00:33,000 --> 01:00:38,240
or, or, you know, that is untrue, you, you do, you have potential part, you know, abilities

645
01:00:38,240 --> 01:00:43,080
to, to court, you know, have to make requests to Google, write requests to courts, all of

646
01:00:43,080 --> 01:00:49,000
that sort of stuff. But that's a pretty hard process to do at scale. So then the question

647
01:00:49,000 --> 01:00:54,200
then comes about saying, okay, well, how can we make sure that we're putting the right

648
01:00:54,200 --> 01:01:00,960
things about ourselves out there, that we're, you know, that we're really reflecting, you

649
01:01:00,960 --> 01:01:05,840
know, we're, we're, we are making sure that we're reflecting what we want to be seen,

650
01:01:05,840 --> 01:01:10,520
you know, our true version of ourselves online. So there isn't anything that necessarily is

651
01:01:10,520 --> 01:01:16,280
going to start confusing things. So it has made the process of reputation, you know,

652
01:01:16,280 --> 01:01:23,520
management is now, it's a big, it's got more new mentally larger as a challenge. And it's

653
01:01:23,520 --> 01:01:30,440
interesting to see, you know, how, how things will evolve over time, especially when you

654
01:01:30,440 --> 01:01:35,200
think about how easy it is to generate this information from these AI tools to put out

655
01:01:35,200 --> 01:01:40,640
there. And also that the unfortunate ability that you're now able to do things like deep

656
01:01:40,640 --> 01:01:47,320
fakes as well. And how that affects people's reputation and being able to call out actually

657
01:01:47,320 --> 01:01:53,640
is this, is this thing, you know, whether it's text or it's images or video, it has

658
01:01:53,640 --> 01:01:57,720
this actually been generated by a person or has this actually been auto generated and

659
01:01:57,720 --> 01:02:02,240
just dropped out there as potentially some sort of defamation campaign. That's when it

660
01:02:02,240 --> 01:02:07,760
starts getting really scary. And there is a couple of startups or actually now they're

661
01:02:07,760 --> 01:02:13,440
growing very fast. And was one business called Reality Defender. And it's looking at deep

662
01:02:13,440 --> 01:02:18,080
fakes around reputation. And it's looking around deep fakes primarily for companies

663
01:02:18,080 --> 01:02:26,400
and companies and also kind of high net worth individuals, media personalities, and looking

664
01:02:26,400 --> 01:02:31,360
at things that are out there on people and actually saying, you're trying to reverse

665
01:02:31,360 --> 01:02:35,560
engineer the image or the video and try and understand is this being generated by a person

666
01:02:35,560 --> 01:02:40,640
or an AI and it's going to be an interesting space to watch for sure.

667
01:02:40,640 --> 01:02:52,240
For sure. It's, it's rapidly evolving and kind of scary at times. What's your advice

668
01:02:52,240 --> 01:03:06,440
for individuals to better manage their reputations and digital footprints in the era of AI?

669
01:03:06,440 --> 01:03:10,320
I think the first one is, is having a real, having an understanding yourself of what's

670
01:03:10,320 --> 01:03:15,640
out there. And so taking the time to Google yourself, taking the time to go back a few

671
01:03:15,640 --> 01:03:24,480
pages and also taking the time to look at the, the, the privacy settings on the different

672
01:03:24,480 --> 01:03:29,600
web pages that you know, you come up in. So for example, you know, the thing I'd really

673
01:03:29,600 --> 01:03:34,600
say to somebody is, you know, one, do a Google audit, two, go back to your social media and

674
01:03:34,600 --> 01:03:39,400
post your social media platforms and make sure that your privacy settings are locked

675
01:03:39,400 --> 01:03:46,480
down. Also potentially think about what's your, do you still need that information up

676
01:03:46,480 --> 01:03:52,280
there? So one of the things I've done, and you could say that maybe this is because I've

677
01:03:52,280 --> 01:03:56,280
worked in reputation management, is actually say, actually, I don't need that social media

678
01:03:56,280 --> 01:03:59,520
page up there. If I don't use Instagram, actually, I'm going to take it down. I'm going to delete

679
01:03:59,520 --> 01:04:05,600
it because it just helps really managing that, that footprint and making sure that you're

680
01:04:05,600 --> 01:04:12,360
aware of the footprint, you do have the privacy controls on things that, that you can, that,

681
01:04:12,360 --> 01:04:17,080
you know, you have potentially deleted old profiles, you know, maybe if you had a Flickr

682
01:04:17,080 --> 01:04:22,440
account, a Pinterest account, you know, all of these old services, making sure that, you

683
01:04:22,440 --> 01:04:28,720
know, that, that, that, that if you have deleted them, there are a number of different services

684
01:04:28,720 --> 01:04:33,400
that help you get an understanding of what that online footprint is. The, the, the, the

685
01:04:33,400 --> 01:04:36,560
what yourself online is, which is now part of ID Shield will help people give you an

686
01:04:36,560 --> 01:04:41,880
understanding of where you appear publicly. So understanding those sites is important.

687
01:04:41,880 --> 01:04:47,760
And then beyond that, my last recommendation is having, having done an audit, done a lockdown,

688
01:04:47,760 --> 01:04:55,240
done a cleanup, is think about, are you projecting yourself in the best way? And, and it doesn't

689
01:04:55,240 --> 01:05:00,400
have to be a PR campaign, but just making sure that, you know, you've got your LinkedIn

690
01:05:00,400 --> 01:05:05,580
updated. It's a clear bio for yourself. It's putting it across where you are right now.

691
01:05:05,580 --> 01:05:08,760
If you do something that's interesting in your spare time that you think people might

692
01:05:08,760 --> 01:05:15,720
learn about, maybe having a blog page or having, you know, a bio page and just being thoughtful

693
01:05:15,720 --> 01:05:21,960
about what you share to making sure that, you know, that you're communicating what you

694
01:05:21,960 --> 01:05:27,920
want to share on your terms rather than what you might've shared unwillingly, unwittingly

695
01:05:27,920 --> 01:05:31,520
over the last decade or two that's being picked up.

696
01:05:31,520 --> 01:05:39,320
Absolutely. Yeah. I think there is some responsibility on us to kind of go back and, you know, see

697
01:05:39,320 --> 01:05:46,840
what we've done in the past and probably clear it out. And like the services you mentioned

698
01:05:46,840 --> 01:05:57,280
are definitely helpful. One of our questions is what's the dream state for you?

699
01:05:57,280 --> 01:06:04,680
In terms of AI and data, if you had a magic wand and time and resources weren't constrained,

700
01:06:04,680 --> 01:06:13,120
what kind of intelligence tools and models would you have?

701
01:06:13,120 --> 01:06:21,440
Very good. Very, very good question. I think that the, for me, where I'm thinking about

702
01:06:21,440 --> 01:06:30,640
data and where AI tools now are is having more, firstly, having more understanding of

703
01:06:30,640 --> 01:06:36,960
where things are coming from in the responses you're getting from certainly generative AI

704
01:06:36,960 --> 01:06:41,720
tools just to be able to, because it's one of the things that makes me feel a little

705
01:06:41,720 --> 01:06:47,320
bit uncomfortable in having certainty to be able to use some of these tools more, is just

706
01:06:47,320 --> 01:06:53,040
having that uncertainty around exactly where the stuff is coming from. I know OpenAI is

707
01:06:53,040 --> 01:06:56,360
starting to do more referencing, but I think there's still a long way to go in terms of

708
01:06:56,360 --> 01:07:04,480
having accountability in AI. And as these models get used more widely to do more decision

709
01:07:04,480 --> 01:07:11,840
making, having, making sure that these models are not black boxes is really key to making

710
01:07:11,840 --> 01:07:20,280
sure that we're implementing AI in a sustainable way and a way that doesn't disadvantage particular

711
01:07:20,280 --> 01:07:26,600
groups of people or, you know, confirm biases. And one of the big, this is one of the big

712
01:07:26,600 --> 01:07:31,280
challenges of the learning corpuses that these LLMs are feeding on is that there's, you know,

713
01:07:31,280 --> 01:07:35,840
that some of the material they're feeding from is going to be biased in some way and

714
01:07:35,840 --> 01:07:40,800
making sure that we're not just, you know, feeding the machine this content, but then

715
01:07:40,800 --> 01:07:44,520
it's going to generate more content at the same. So we're just in this kind of, you know,

716
01:07:44,520 --> 01:07:53,840
never ending loop of bad stuff is kind of scary. That I think would be my magic one's

717
01:07:53,840 --> 01:08:00,760
one number one. It's just that accountability and being able to not be in a black box is

718
01:08:00,760 --> 01:08:05,680
really that I think that would be so valuable for the rollout of these tools, the certainty,

719
01:08:05,680 --> 01:08:09,800
the general certainty of these tools and the kind of wide rollout across humanity.

720
01:08:09,800 --> 01:08:18,080
Yeah, that's a solid answer. Yeah. It's a, again, super rapidly evolving technology and

721
01:08:18,080 --> 01:08:26,640
we don't know how they work exactly. In a lot of ways, nobody knows. So it's kind of

722
01:08:26,640 --> 01:08:35,080
important to have those guardrails and understand, you know, how it's doing its thing. Awesome.

723
01:08:35,080 --> 01:08:39,720
This was a great conversation. I learned a lot, James. Appreciate your time so much.

724
01:08:39,720 --> 01:08:47,240
Thank you. Any last thoughts for our viewers or myself?

725
01:08:47,240 --> 01:08:53,400
I think one of the biggest things I learned on my kind of journey with entrepreneurship

726
01:08:53,400 --> 01:08:58,600
and generally in my kind of business life is just experimenting. Just go out and experiment

727
01:08:58,600 --> 01:09:03,880
and, you know, think about how you can de-risk decisions for yourself, decisions for starting

728
01:09:03,880 --> 01:09:10,120
a business. And I think using data as part of that experiment is really, has the key

729
01:09:10,120 --> 01:09:13,120
to really deciding what you're going to do next and how you're going to develop your

730
01:09:13,120 --> 01:09:17,080
career or how you're going to develop a business or how you're going to grow a business is

731
01:09:17,080 --> 01:09:22,880
really key. And not being afraid to experiment is really critical and one of the big things

732
01:09:22,880 --> 01:09:35,560
I've learned over my career.