Hey there, welcome to Data Democracy.
This is a podcast where we explore ways to make data and AI more accessible to everyone.
We do this by interviewing experts across industries, asking them how they think about
data, what are some of the challenges they face when it comes to data, what's their dream
state for AI and data, if they had a magic wand and time and resources were not constrained,
what kind of intelligence and models would they wish to have?
We've got a great guest today, James Chance.
James is an entrepreneur, investor, and a leader in product, data, and strategy.
James is the founder of Yourself Online, an AI-driven reputation management software.
Yourself Online was acquired by Legal Shield in 2021, which is where I met James.
James has also worked at Google and Accenture in the past.
James is an alumnus of London Business School and UT Austin.
James runs an independent product consulting practice now.
If you need help with product, reputation management, small business solutions, or strategy,
James is your guy.
Give him a shout.
James is not only a great leader, he's also a great person.
He easily passes the, hey, I'd love to grab a beer with this guy test.
So let's welcome James.
Hi, Mitzi.
Thanks very much for having me.
Thank you.
Thanks, James.
How are you?
Yeah, doing very well this morning.
Doing very well.
Looking forward to the holidays.
Awesome.
Me too.
Yeah, let's start with your story.
Can you tell us about your journey so far, your background and product and entrepreneurship?
Oh, where to start?
That's a good one.
So I've had a sort of fairly traditional and fairly untraditional career at the same time.
So my background in undergrad was mechanical engineering.
I kind of came out of my undergrads and discovered I actually wanted, you know, I was always,
you know, more interested in the business side of things.
And ever since I was a teenager, I'd really had an interest in entrepreneurship.
And you know, pretty much since I can remember, I've always had little business ideas on the
go and things like that.
So when I was, you know, I kind of after I came out of doing my undergrad, really wanted
to go into something a little bit more entrepreneurial to and kind of business sort of in the context
of undergrad choices.
So I ended up going to be a consultant management consultant at Accenture.
I took this view because it gave me the opportunity to see lots of different types of businesses
in different industries and kind of learn them and really, you know, be able to use
some of my skills in engineering from more of a quantitative side and, you know, on these
different projects.
But at the same time, be able to see different businesses in different industries and kind
of get more of an idea of where I wanted to go from there.
So I was at Accenture for about four years.
Then after that, I sort of over my time, I'd specialized in retail and I spent some time
primarily looking with quite a few of the old big box retailers.
And it was in the mid 2010s and I kind of thought, hang on a second, I've spent quite
a lot of time working with retailers that aren't really doing so well.
What happens if, where's the exciting stuff in retail?
And it was all really at that time around e-commerce and looking at, you know, digital
marketing to grow e-commerce brands and retail brands as well.
And was fortunate to, one of my sort of, one of my friends was working at Google and he
knew their retail team and they were looking for somebody who was, they're looking for
somebody to join that team in the role of an analytical consultant.
And what that role was doing was it was working really as the kind of liaison between these
big retailers.
So the likes of kind of Amazon, eBay and Google and being a mix of a partnerships person and
also a salesperson and sort of helping to maintain and grow that relationship.
And in my role, I was working alongside, you know, working alongside the sales folks as
an analyst, as effectively like a data analyst.
And I was looking at some of the data that Google has and saying, how can we, you know,
use some of the things that Google, the data that Google has across its products to help
our clients grow their businesses and then at the same time help Google grow their business.
So this was really one of the things I was doing was kind of taking my knowledge of retail
for my consulting days and actually applying kind of more skills in data and sort of data
science and looking at these quite big data sets that Google gathers across things like
search, across maps, things like that and asking, you know, answering, using it to answer
questions of what types of customers are interested into what types of products and different
geographies, how are those different customer markets growing, contracting, all of those
things.
And I got over my time, I got a whole load of different kind of different assignments
of areas that we were trying to Google and these retailers were trying to grow.
I got things like, you know, trying to understand the, you know, even sort of questions like
what's the market size of women's plus size fashion in Germany?
And it's like, okay, if you use all the searches, how much of the searches translates into people
buying things and you can kind of roughly market size.
So there were some questions like that.
It was good fun.
And then I sort of felt after a while, I was sitting in meetings and realizing that there
were, my knowledge was kind of, I was sort of running out of knowledge and I was going,
sitting in meetings with Google, some of the Google management team and some of the clients
and they were talking about stuff I just really didn't understand, like, you know, ROI on
advertising, you know, looking at sort of setting a three year marketing strategy, looking
at setting a three year growth strategy.
And it went, hang on a second, I think I need to take myself, you know, how's the time to
go and do an MBA just so I could really get that knowledge and kind of level up a bit
more around some of these bigger strategic sort of questions.
So I went to do an MBA for two years at London Business School and the University of Texas.
Focused on entrepreneurship and growth businesses.
Whilst I was doing the MBA and was fortunate when I was there, I came up with the idea
for my business, which was yourself online and was fortunate to be kind of hanging out
with a good friend of mine who was also then became the co-founder of my business who I've
met whilst I was at Google.
Demetrios, shout out to Demi if you're watching the podcast.
And he, you know, we were sort of talking about some of the things we were seeing and
we were seeing this kind of rise of cancel culture on social media.
And also having a few of my friends from the MBA, but also a few of my friends from undergrad
that were in serious jobs like lawyers and bankers getting really concerned about, you
know, the image and their online reputation based on their social media posts that were
often years old.
And we were on a stage sort of towards the late 2010s, like 2017, 2018, where people
were being increasingly judged more and more by what they posted a long time ago.
Even if those things are not illegal or anything like that, it's just, you know, things that
were bad, bad moments of judgment coming back to haunt people and they're not getting jobs
or they're not getting, you know, they're not getting, you know, sales opportunities or jobs or promotions
just purely by the basis of what they posted online, you know, 10 years ago before then.
And so that was, we then thought, hang on, there's a problem here.
Let's sort of work together and try and see if we can come up with something to solve
it.
And we came up with the idea for Yourself Online and Yourself Online was our vision
for Yourself Online was to really help people manage their online reputation or all their
online persona and to make and to start off to doing that, making people aware of what
their online persona really is.
So being able to kind of, and our first version of the product was really focused around a
almost a report card of your online reputation, showing the things that are good, showing
the things that are bad, highlighting any areas of improvement.
And that was, and then from that, then we then developed the product over time to be
a more holistic service that helped people to, you know, to clean up the bad, but then
also to grow their online reputation in a way that's positive so that they could, you
know, access opportunities more easily, you know, have a better LinkedIn presence, have
guidance around how to build a professional online reputation in a way that gives them
access to opportunities.
So it's more of an asset rather than a liability.
Yeah, absolutely.
I have always thought about Yourself Online like a friend having your back, you know.
It's really, it's a really solid product.
I've definitely used that.
What are some of the challenges you faced when, you know, building company and scaling
it, I know it was a really new idea.
What did you have to solve for and were there any challenges?
I think there were definitely a lot along the way.
I mean, with any kind of entrepreneurial venture, there's always things that you think
you're going to get and then there are the things that you don't know you're going to
get, you know, the sort of the known unknowns and the unknown unknowns.
So we, you know, we had a, some of the challenges really were that we found over the course
of the company.
And when I look back over the total journey from initial kind of concept in about 2018,
early 2019 through to the kind of sale of the business to Legal Shield at the end of
2021, we had what almost could be described as three different companies, because we went
through the initial stage of trying to find initial product market fits and which had
some challenges around the, which features we were introducing, trying to make sure that
we were really trying to solve for the right thing and that we weren't over complicating
our initial product offering.
So that was the first one.
And then after that, you know, I think then it became more, once we had initial product
market fit, it was really around scaling and some of the scaling challenges, both from
a technological perspective as more people use the product and from everything from sort
of compute through to product quality as you get more, as the product saw more things,
we found we had challenges that way through to kind of organisational, you know, organisational
challenges around how we kind of organise our self and our people perspective for growth.
One of the big takeaways, just thinking back to that early stage that I think might be
interesting to some of your listeners was we, when we first started with Yourself Online,
we had this big vision and we're kind of like, we want to be the online guardian for professionals
and we want to be the service that helps people to, that has their back online and, you know,
help them to be their best self online, which was this really big vision and pitched as
big vision.
But then when we came to executing it, we tried to do too much from the beginning and
we got into a bit of a challenge, which I think sometimes you can be when you're in
the first version of your product, you're at MVP stage, where you're testing too many
things at once and you just don't have the resources to deliver the product to be able
to do that.
So you end up with like a, you know, a product experience that's, you know, 50% of every
feature.
So you end up with almost nothing by the end of it because you can't win on any feature
because you're trying to do too much.
And we were trying to, you know, we, at the same time, we were struggling to deliver those
features and we were also confusing our initial early customers because we were presenting
them with what could have been probably three separate products around, you know, your social
media reputation.
We were talking to people about their privacy.
We were talking to people about their password breaches, which could have been a very good
holistic product over time, but we were probably about five years to three or three years too
early on that when we ended up doing.
And one of the things that was a real, was probably one of the big, you know, aha moments
on our journey was actually killing two thirds of the product, the early product and just
focusing on that reputation part, focusing on helping people to clean up and improve
their social media and professional profiles.
And then coming back to over time, the privacy angle, but you know, and the other, the other,
the kind of more online general holistic online pieces.
And one of the things that we, that really helped us with this was that we were really,
we kind of were looking at like, what was that?
Those product features that sort of got that aha response, you know, from, from, from customers
where they wanted to really take, you know, get their wallet out of their pocket and go,
hang on, where do I pay for this?
And versus the other things that were features that people are kind of look at and they go,
oh yeah, that's a sort of, that's interesting.
Like it's good to know that, but, but they're not really, it didn't, it didn't really evoke
much of a reaction.
The world of products is kind of new to me.
I guess I've always been in companies with sort of established products where we focused
on, you know, sales, marketing and tech.
I recently attended a product workshop and it kind of opened up a whole new world to
me is like, okay, there's, there's a lot here that I need to learn.
Could you please demystify the world of product for us?
I know there's a lot of phrases, you know, product, what is product, product development,
product management, design.
Can you, can you tell us a little bit about it?
It's a good one.
And I'm sort of trying to find an easy way to, to sum it up.
But I think the historical way is a good way of putting it and a good way of kind of seeing
how, how this product has evolved because typically you're exactly right.
You know, in, in some organizations, you know, generally across all organizations historically
and in some organizations now you do have this world of, you know, you have a very traditional
world of, you know, sales, marketing, IT operations, et cetera. And what was happening that was
that across those different organizations, there wasn't one person that was really representing
what is the voice of the, what is the voice of the customer?
Who is the, who is representing the customer across these different functions and who is
that in between, between the customer and the business strategy that, you know, the
overall business strategy for the company and product functions have evolved really
to knit together the different, you know, knit together the different functions in a
way that enhances collaboration to, you know, be more focused on achieving business results
and to also be more focused on, on the customer and meeting the needs of the customer across
the product life cycle from right from sales and marketing, right the way through to, to
operations and even in kind of post, post customer journey retention as well.
Because typically these have been, you know, joined in some cases, isolated efforts that,
that meant that there wasn't a holistic kind of person that joining to get together. And
that has been the role of, you know, that's, that's kind of how the product manager role
evolved was in, certainly in technology organizations, being the person to join together and represent
the, you know, join together these different group of stakeholders in a way that delivers
the strategic goals for that product line and also bears in mind the customer, the customer
needs and represents the customer throughout those different parts of the, the organization.
Probably the, but then within, you know, within the world of product, you know, you then have
a product manager, product strategy, you might have product development, product design that
sit across different parts of the, in the way that the product function goes to market.
So, so you have, you know, product strategy that will be very much at the kind of the
tip of the arm around thinking about the interface between the wider business and corporate and
product. And then, you know, within underneath that you have a product leadership structure
of a product leader and product managers that, and as they move down that structure, it becomes
more operational, you know, operationalizing the strategy and then interfacing with those,
those product managers, you know, you'd have people like a product designer who would be
working with the product strategy and the voice of the customer and doing those kinds
of insights and the customer discovery and, and also, and then on the other hand, on the
other end of the life cycle, you'd have people like product marketing that would say, okay,
how do we take the things that we know about the product and take the things we know about
the customer and package that in a way that the marketing organization can embrace to
execute and kind of be that connect between the two.
Yeah, it's, it's so interesting. And I think it's really something that every company needs,
a mature product. So I think there needs to be a time where we look into the product and
say, okay, can we evolve? Can the product evolve and help the customer's current needs?
Exactly. And also the progress of organizations towards being more product centric is kind
of aligned with their journey to being more customer centric. And when you're looking
at, you know, as organizations have kind of moved over time to be more customer centric
and showing that that focus on the customer achieves better business results in a longer
period of over time, the product organization was sort of brought in as a way to kind of
execute that and to really keep that focus, keep that focus in mind. And it is only natural
that companies that have been around for a while, previously maybe were operating in
an operationally focused or sales and marketing focused type environment. And it's natural
that there isn't, you know, every company will go through an evolution.
Absolutely. Yeah, it's, yeah, I think the world of product is, like I said, new to me
and it's super interesting and I'm learning a lot. How does data come into play in product?
You know, everything from product discovery to product delivery?
Super important. I think there's the favorite, you know, the sort of commonly known anecdote
of data, data trumps opinion. And it's super important for a product organization to be
making data powered decisions throughout that product lifecycle. And, you know, right from
almost starting to engage with customers on a product and right from the very early days,
right the way through to customers leaving a product, there's got to be data measuring,
you know, there's got to be measured. You've got to be measuring the right things in a
way that is joined up and cohesive across the lifecycle. Some really good examples are
that, you know, in terms of if we go back to thinking about being more focused on the
customer, how do we know we're more focused to the customer and what are the things that
are going to tell us whether we're getting this right? It's data. And so it's, you know,
it's super, super critical.
Absolutely. You talked about data being critical for to understand what the customers want.
I learned that there's like a consensus among the product leaders that customer feedback
is often skewed or biased. Concepts such as vocal minority selection bias, expectation
fallacies are cited. Can you provide some color to this? I've always thought about customer
feedback as the end all be all, hey, the customer said this, so let's go, I do a survey and
then customer said this, let's go do that. So it kind of changed my perspective that,
you know, maybe customers only customer feedback is only as good as the questions you're asking
or the survey you're putting out. So can you talk about how to get customer feedback without
really asking them for fee feedback, I guess?
That's a really good one. And I think we all feel now that we're in a world that, you know,
as soon as we do something, whether it's, you know, we stay at a hotel, we go to a restaurant,
or we go out to see some culture or something, we always get hit up with feedback surveys.
It seems to be a recurring thing that, you know, almost every day I delete the feedback
from whatever thing I did yesterday, whether it was going to the grocery store or, you
know, that we get asked for feedback so many times with these surveys that we are moving
to a point where the people who probably fill those out are more extreme outliers where,
you know, you either generally you have something bad to say, or, you know, you may be the other
camper, you've had such a great experience that it's amazing. And you're really focusing
on that. That explicit feedback that you're gathering is kind of focusing on those tales
of your customers. And you don't really know where the middle group is. So although they
feedback surveys do provide some indicators, they can't be seen as gospel because of, as
you said, you know, there's the challenge around collection, there's the challenge around
the survey bias, there's challenges around, you know, making sure that you're getting
a representative sample. I think one of the things that is important when you're gathering
data from customers is making sure you're gathering both explicit and implicit feedback.
So combining those qualitative surveys and all the kind of qualitative data gathering,
it is very useful to get a pulse check on your customers. It's very useful to see what
they're calling out. And if they're calling out specific things that you need to take
action on in a survey, but making sure you're combining that qualitative feedback with quantitative
feedback through measuring the customer journey and measuring the interactions of the customer
with your product and really saying, okay, what are the things that I want my, the action
I want my customers to take or I need my customers to take to see whether my business is forming
successfully and the business is doing successfully and how, what proportion of my customer base
is actually doing those things. And looking at the customer journey end to end in a way
that you can understand which, which those customers, how are those customers interacting
with my business and what across which touch points and how are they using the product
and then, you know, correlating that information with the, you know, customer, customer life,
lifetimes over time and customer outcomes in more of a much bigger picture to really
understand, you know, are we doing the right things that make our customers stay longer
and improve the health of the business. I think you, you were doing some fantastic work
on that legal shields around really understanding, you know, what are the interactions that customers
are doing with the, with the product and, and, and, and, you know, how are they interacting
with the service and how does that actually affect their, their life, their, their customer
lifetime? And one of the cool things is that when you get into more quantitative data,
as I'm sure you, you, you found that is that you really start to understand what are the,
how do the different types of customers interact with the business and how can I make sure I'm
serving properly the different customer groups I'm getting in the door? And, and, and, and
some of that may mean that you have to, you know, embrace some customer groups more, some
customer groups, you may say, actually, we're not going to do a good job. We're not going
to be able to do the best job serving these customers. So we're going to move away from
marketing to them and actually move into other customer segments or, or we're going to improve
the customer, the product to meet the customer's needs.
Yeah, that's a really good point. Oh yeah. Our CEO says, Hey, do we want to focus on
a customer who's born or made kind of like what you said, do we want to focus on people
who already we know would be good for us or do we want to change people minds? Sometimes
changing people minds and trying to get to change their behavior, maybe harder, maybe
focusing on people who already we know that are a really good fit, maybe a good start
and then evolve into expanding it out depending on what kind of the goals that you have at
the moment.
Totally. And we found a very good example of that with, with yourself online and when
we were building that building the business that we found very early on, there were some
groups of customers that even though the product was, you know, the product we were developing
a product and improving the product in time, it was still relatively, it was very early,
you know, relatively early on. We found there were some groups of products, some groups
of customers that were like, yes, take my money right now. Like I need this because
I'm one good example is people who are professionals who were applying for jobs in certain industries
where they know they're going to get looked up and that they know that the HR department,
the company they're applying for is know they're going to check them out with either a background
screen or checking just out their social media. They were kind of like, right, take my money.
And then we knew there were some, some other customers that maybe could have found the
service useful, but they just, they just weren't there at that particular stage in their life
and life stage where they were, where they were, you know, that they, they either would
have had to be at a closer life stage to needing the product or they may have actually maybe
needed more education and more maybe, you know, more education and a broader conversation
around why they needed it. And given our resources at the time, we just, we just were thinking,
okay, we just got to, we just got to really focus on the customers that are the types
of customers that are really putting their hands up and wanting us right now.
That's, yeah, that's a great point. We, we just have to maybe focus on people who are
looking for the exact product we're selling sometimes. And then, you know, when you have
resources, go, go in and go all in on brand awareness and all the other things that needs
to happen to bring them. Yeah, really good point. What is some of the advice you have
for somebody who was wanting to get into product, product first, and then we can go into entrepreneurship?
Maybe it goes hand in hand, product and entrepreneurship.
I think a little bit of hand in hand, but I'll talk a little bit about product first.
I think understanding, you know, for really understanding product, I think spending a
bit of time reading about the role of a product manager. And it's quite a unique role because
one of the things that product management managers do is, is they have, they don't generally
have very many direct reports, but they have to influence a lot of different parts of an
organization and understanding that, you know, spending some time looking at, there are a
number of different books that I will, I've got in the bookshelf behind me, but I'm just
thinking off the top of my head that I will, that I will, I'll kind of, I'll send you some
links and you can put the maybe in your chat afterwards. But really understanding the role
of a product manager and kind of what makes a good product manager, what makes a bad product
manager. Really thinking about the entire product, you know, doing, reading on what
that product life cycle looks like, where, you know, the different from, right from what's
involved between starting a product, starting thinking about a product through to running
it and operating it. And then thinking, that's the sort of more on the functional side, but
then on the other sort of soft skill side, thinking a little bit about, you know, influencing
people because a lot of the success and the role, and this kind of also applies with entrepreneurship
as well, is thinking about how you can, how you can kind of influence people and really,
you know, be able to affect change, but without having authority over the top of it. And that
kind of applies a little bit with entrepreneurship as well in terms of you don't have a huge
amount of resources, you've got to make things happen. And how can you try and make that
change happen with limited resources and also being able to kind of sell yourself to both,
you know, your team, your investors, your customers, to sort of make things happen.
On the entrepreneurship side, I think, you know, there is so much stuff out there. There's
so many, so many things written by so many phenomenal entrepreneurs. They're just, you
know, that really the sort of interesting ones that I've read recently or I'm reading
that could be of interest to your readers. I've been reading this one, which is, you
know, fall in love with the problem, not the solution by Uri Levine, who was the founder
of co-founder of Waze that was, you know, acquired by Google. He's a serial entrepreneur
really focusing on understanding the customer and understanding customer needs. And the
idea that, you know, you're better off understanding the customer and the problem than having a
solution that's looking for a problem or a hammer that's looking for a nut. You're better
off making sure you start with being customer centric. On top of that are the other recommendations.
You know, there's the classic Eric Rees lean startup, but one that's particularly relevant
for the data is a book called Lean Analytics. And it really describes the data that you
should measure in a product and both from it's sort of from an entrepreneur, it's useful
for entrepreneurship, but also for product in general of how you can take more data,
database decisions, what the typical things to measure are for different types of businesses,
how you can kind of take an experiment led approach. I think one of the things that we
discussed a lot in yourself online is people talking in products and in digital around
AB testing and all we're just going to AB test, AB test. It's hard to AB test until you think
you have an A. And it's when you when you have a decent A, you can start AB testing.
But until you have a decent A, you have to like experiment and just do these kind of
on off tests where, you know, you don't necessarily AB test it, you just sort of make it better.
Look what happens after a week and go, okay, did that make it work? Okay, no, we go back
again, we're going to use it. You're in much more. It's not quite a or B. It's a sort of
on or off type approach to experimentation and getting data.
Yeah, that's a really good point, especially in startup environment where the product is
no so new that you're still evolving. Hey, did I break something? No, keep going.
All good, all good. Exactly. What are some of the skills that are transferable across
the things that you've experienced like in product entrepreneurship, you've also been
corporate executive. So are there any common skills that if people are trying to move from
one to the other, are they transferable skills? Can you talk about that?
On a so I started a little bit on the kind of functional side, then a bit more on some
soft skills as well. On the functional side, doing data, being comfortable with data, data
led decision making is something that I've used all throughout and getting really comfortable
with gathering, understanding what data to measure, finding that data as much as you
can and then using that to then make a decision and saying to yourself, okay, I think this
is a situation, this is the hypothesis I want to try and approve or this is the question
I want to answer to know whether I should do X or Y. Let's go gather the data or let's
look at data to see if it supports that or disproves it. That's something I use a lot
during time in consulting, time in tech, time into entrepreneurship is just that ability
to kind of really know, have a feeling of what data you should be using data to solve
a problem and kind of answer questions and sort of being informed by data. On top of
that, I think having a good understanding of kind of broader strategic problem solving
as well. So understanding how companies work was always a useful one and kind of how you
can then based on that, you know, on structure, have a structured way to approach solving
a problem. Then sort of jumping around a little bit, but building on the data piece, I think
a basic understanding of just kind of basic stats tools like the sort of stuff that you
can do in Excel is super useful to get a feeling of where your data is going. And then on top
on then sort of above that is that piece I mentioned a little bit before around sort
of influencing, understanding. One of the things I found really, really valuable on
the journey was understanding this concept called social styles, which is understanding,
you know, your particular social style and how you interact with other people and how
you approach things versus other people's. One, there's a sort of, we used Google and
also in my consulting days, we used a model called, it was a sort of four quadrant model.
Myers-Briggs is similar, but it's a little bit more detailed. And it was one of the things
I found really important in terms of thinking about how you interact with other people and
how you can make sure that you're, you know, putting yourself across in the best way to
other people was understanding your own social style and then understanding other people's
social styles as well. Because then you really understand how you can work most effectively
with other people and kind of do that piece where you can kind of say, you can clearly
communicate in a way that other people will relate to, what's in it for them, what's
in it for you, why they should do something that you're suggesting, why you should do
something that they're suggesting and kind of just generally be more effective.
Absolutely. I think social style can be really effective in any role that you're in. Especially
I feel that it really applies in the data world because you are, I mean, I can build
all the models, all the intelligence. You know, I can, but if it's not implemented,
then I'm just doing it for myself.
100%. And understanding other people's point of view and their style really helps communicating.
One example is understanding a little bit whether your audience are more detail oriented
people or they're people who are really just interested in the top line executive summary
and you know, what the results versus the kind of the analysis to get to the results
and tailoring the message to get that so that that analysis actually lands with the audience
and kind of makes change happen.
Absolutely. Yeah. I really like that you talk about understanding of data as one of the
transferable skills across the different things, different roles. How does your relationship
with data change with those roles as a product executive versus an entrepreneur? Do you go
more into storytelling when you're an entrepreneur, having to pitch an idea, backed up with data
and sort of things?
Very good question. My, when I was thinking a little bit about this beforehand, I think,
you know, that the biggest one is that you don't, when you're an entrepreneur, you know
the data you don't know. And in some cases, you're able because of that, you're able to
make slightly more of a feel more comfortable making a gut in tunes decision. And so really,
you're happy to operate with a slightly lower confidence, you know, lower confidence in
your data than maybe in a corporate setting. Because you understand the in-depth parts
of the product, you understand the in-depth part of the customer and you know, sort of
a quote, you're a lot closer to it. So you're willing to deal with a little, you have a
higher level of willingness to deal with uncertainty. And when you're in it, when I was in a corporate
role, you know, you generally would would come under potentially more scrutiny in your
decision making from, you know, your stakeholders, your management. So actually getting closer
to all the numbers you can and using less kind of intuitive decision making is certainly
one of those things. I think the one of the things that I, you know, one of the learnings
that between the differences is you're more like, you know, it's a little thinking a lot
about audience. When we're in a, when I was in doing the doing yourself online, we were
a small team, we all had an in-depth understanding of the product. We then, you know, could communicate
data quite freely without necessarily having to understand, you know, get, bring people
on board of what it was because they all knew the different stages of products and they
knew the things that we were measuring. And it was like, okay, cool. This is, this is
where we are now. This is what we're trying to go to. And when you move to a more corporate
setting, you know, you're working with stakeholders that maybe work across a bunch of different
products and they don't know what those metrics are, where they've come from or where they
were. So you have to really bring people in a corporate setting, you have to bring people
along for the journey and also communicate in different ways so that a broad range of
people can understand because, you know, you might be presenting something that will have
somebody from, you know, I think there's some stereotypes there, but you know, you're going
to have everyone from people who can, who can really understand the data and they want
more detail and more detail right through to people who, who really switch off when
you, you show them numbers and they just want the high level kind of high level thing. And
that was the thing that, that was a learning as well is really thinking a bit about, you
know, thinking, understanding the numbers, but also understanding who your audience is.
And these are trade-offs as well because generally because of that in an, in an entrepreneurial
environment, because you're, you have a much closer team that's much closer to the product
and also you're more comfortable with ambiguity, you can move faster and feel confident in
that. Whilst in a corporate setting, you just naturally, by the broader range of people
you're working with potentially and their knowledge and their styles, you have to move
slightly more slowly. You have to be slightly, you know, and you have to have the detail
and have the thinking to be able to, to, to, to kind of compensate for that scenario.
Yeah. Like you said, you have to get people to buy in and that takes time. So it's, it's
like moving a large ship versus a boat. I feel like we touched on storytelling a little
bit there. It's, I feel it's very important part of, you know, being a leader. What are
your thoughts about it? What's your advice for someone who wants to get better at it?
Is there anything apart from, yeah, some good.
I think less is, so two, two things from probably my experience of pitching yourself online
and that was, that was the most, probably the most, the most relevant of having to take
a story that has data points in it and be able to have this different versions of these
stories, whether it's the, you know, 62nd version, the three minute pitch in front of
a room of strangers or the half an hour pitch in front of a room of educated investors,
you have different versions of this story. I think the first one is, is less is more.
I think it's very easy to try and put lots of information in there and be like facts,
facts, facts, facts. And the reality is that, that certainly most people, they just, they
just tune out after a certain point. And one of the things also is that when you, you have
a point of, you've got a point of diminishing returns on information where if you, there'll
be a point in the story you're trying to tell or the point you're trying to put across
or the pitch where if you cram more detail into it, you actually get to a point where
people don't want to engage more because it's starting to hurt their head, hurt their head.
And so their instinctive thing for their brain is just kind of go, okay, like I want to make
sense of people's brains, want to make sense of this information. And they'll get to the
point where it's just too much for them. And so their natural thing is just to sort of
withdraw. So less is more. And then the other one is making the data as much as you can
in the story relatable in a way that sometimes when people, you know, will tell a story,
tell with data, they will put up, you know, put up big numbers, but often, often, you
know, individuals, the people at the receiving end of that story can't make sense of what
it means. So being able to put a, you know, being able to give a key point and being able
to have some sort of reference point of what that means. So it's a kind of, you know, one
example could be, you know, this year, our organization, you know, lost 5,000 liters
of water in our, in our facility. You know, the people might go, I don't know if that's
a lot or this or that, but then you might say, well, they lost 500, 5,000 liters of
water, which is the same as two Olympic sized swimming pools. And people go, oh, wow, that's
a lot of water. So being able to give people things that they relate to in that story will
mean that they can, it just makes it more impactful and makes it more relatable. And
then less is more and really thinking about what's the hierarchy of information you're
kind of trying to, trying to communicate. And there's a really interesting principle
called the Minto principle, which is a pyramid. It's a sort of pyramid, the idea that you
take structure information in a pyramid where you have one main point that has supporting
points underneath, and then they have supporting points underneath those. And that's a, that's
a very good way to be able to make sure you're not putting too much into it and to make sure
it all stacks together.
Yeah, definitely. Less is more is something that I've learned over the years and Minto
principle is great. In my experience, I think I've learned that, you know, I've got so many
things I want to say of I'm in the data, I have so much information that I used to just
go and blabber all of it and then kind of lose people midway because like you said,
there's information overload and people are tuning out. So how do you have that one single
point in a conversation? That's my main point I want to drive home. And then you don't have
to necessarily convey everything all at once. You know, you may have to take it slow, but
make sure that the point is effectively communicated. I think it's more for the data folks and
listening, right? That's really important. And yeah, less is more. And it's, it's definitely
something that I've learned over the years.
Speaking of, you know, we were talking about data and how it's being used. You worked at
Google, you mentioned as an analytics consultant. Google is one of the best companies in the
AI and data space. What can you tell us about Google's approach to AI and data? And what
can we learn from it?
I have to caveat this response by saying I left in 2017. So it's a little bit out of
date now. But I think the ethos of the company, you know, the ethos of the company is probably
still remains. And that was the, they had a really, really strong preference in the
organization for self-serve, of actually self-serving your data. And everyone from, you know, whether
you're a product manager or you're a sales analyst or you're a sales manager, would be
encouraged to run their own data to the best of their abilities. And there would be, you
know, there would be tools, you know, from, you know, there'd be tools and controls from
everyone being able to, you know, that would protect that data from it. You know, some
people would have access to product logs and some people had access to rolled up tables
in a kind of self-serve UI type thing. But the effect of that meant that it, it democratized
access for data across the organization. It meant that there wasn't necessarily silos,
data sitting in silos or, you know, people who were looking to try and, you know, or
data being a kind of bottleneck in the process of making decisions in the organization. Because
the, you know, for the things that most people would need to do in their job, they'd have
access to tools that would be able to give it to them and would be expected to do that
regardless of kind of what level you're at. Admittedly, that there would be, you know,
at a director level or a senior director on MD level, they would have their own dashboards
that would be built for them. But, you know, they wouldn't get a report emailed to them
with numbers, they'd go on and they'd look at it and they'd, you know, they'd do that.
And they could be able to double click and go into things if they wanted to. So that
was, I think one of the learnings was when you are able to make sure that data is not
siloed and data is not a, you know, we've just, oh, we'll make a request to the analytics
department and they come back. It's actually being able to say that everyone is expected
to, regardless of their job, be able to get this stuff, get their hands, you know, get
their hands on the stuff and actually use it. And was it was a was to change the organization.
And that also came with transparency as well, which was which was both from the product
data. Naturally, there are controls on this around privacy and around GDPR and also around
some of the data, which is just is just very sensitive that Google handles. But being able
more broadly to within an organization, be able to see the data that a particular team
is measuring, to be able to see their objectives against that information, to be able to see
their, you know, that their KPIs to be able to see their OKRs as a team means that you're
able to when you're interfacing with different teams, you're able to go, OK, I've I'm working
with this person in search. OK, I know that I can look that person's objectives up. I
can look at their team's objectives up. I can understand how they're tracking against
it and I can go to them and say, hey, like, I need to land this project. You're working
on the same thing in a different market. How about we work together and then we'll both
help meet our objectives on it, because you can see what they're working on and you can
see how they're motivated and you see you can see what how how you know how the things
they're responsible for, how they're performing, but also how that, you know, the team is performing.
It's kind of cool to see that transparency as well as being able to solve. And that also
comes with lastly, I think, an education. And with education, you shouldn't necessarily
force you've got to realize that your different team members will have different skills. And
you know, people that write marketing narratives are not going to be able to necessarily query
SQL or do regression models or anything like that. But, you know, there's different people.
But at the same time, there's people that are going to want to go deeper than that.
But for people's level, they should have appropriate access to training that helps them build skills
that help make their job more effective in, you know, up to the limit of their willingness
to learn and their willingness to sort of take it in. Was one thing and having a training
curriculum and access to training that will take, you know, people everywhere from people
who are very, you know, who are not, you know, whose jobs are not super data heavy, but they
do need to, you know, they do need to look at data and they're more, you know, maybe
more emotive people to be able to understand the concepts of data right the way through
to training for people that want to do, you know, PhD, PhD level stuff or like deep machine
learning and all of that kind of those really, really deep concepts of being able to offer
training across that spectrum, I think is important.
Yeah, that's one of the biggest challenges as a data person I have, right? How can we
make it? How can we democratize data for everybody of all skill levels and data? It's a really
solid challenge. And you made a really good point on having transparency on other people's
OKRs or other teams objectives. I think that's really a great point on getting alignment.
How can I, you know, how can I understand what the other team is doing so that I can
align myself to their goals, you know, kind of both of our goals can be aligned and kind
of work together. That's really a solid point.
100% and both the products or the potentially areas of business that the team is responsible
for and the team's metrics themselves, it's important to be able to do both because you
can see sometimes how they relate as well. And having that transparency, I think really
helps people to work together and helps people, you know, be aligned when they're working
on these kind of multi stakeholder, multi departmental projects.
Absolutely. Again, I think it comes back to your earlier point on how can people work
across different teams and not work in silo and, you know, all the aspects of storytelling,
less is more. Having greater transparency all of that plays into this.
Let's switch gears to reputation management and AI for a second. I feel that in the era
of AI, it's so easy to just ask a question, type a question into Chad GPT and then it
gives you everything. So if I type in, hey, tell me something about James Chance, it'll
do the research for me and then pick out all of your quotes from 10 years ago, 20 years
ago. And then kind of, I can actually, I, full transparency actually tried this and
then it gave me some really good quotes of yours and kind of we can also, I could also
ask it, what does, what is James's thought about data or product management and things
like that. And like it gave me some good answers. I don't know if it was making it up or if
it's your, you know, article somewhere or not. But the point is it's so maybe reputation
management is more relevant than ever because you have the best research tools in history
access by almost everybody. So it's, it's really becomes important to manage your own
reputation in the web versus in the past where people may, if you just kept your social media
clean, it may have worked, but going forward, I think with these AI tools, it may be more
relevant and more important to have better reputation management. What are your thoughts?
100%. And firstly, I hope, I hope chat GPT hallucinated some, some good things about
me on there. It sounds like it might've done. But I totally agree with you. Previously,
we were in an age where if you kept your social media clean and you sort of managed and curated
your first page of Google, you could not really worry about anything that's behind that.
And now we're in a world where these tools have the processing power and these models
have the complexity that is it's almost like going through, you know, doing a Google search
for somebody and reading and synthesizing every single result or close to every single
single result. I mean, I didn't want every single one yet, but I'm a hundred percent
and, and, and then it just becomes, you know, it becomes a slightly different challenge
at scale. Because then it's really a bit about saying, okay, you know, what am I, what do
I say now? And what is my online presence now? And what's my online persona now? And
does that really align with who I am? And then working back and trying to understand,
okay, where is this information coming from? And then what, what power do I have over it?
And the, one of the things that is a challenge about providing reputation management at scale
is a lot of, a lot of there, you know, some, if there is something that is illegal, it's
about you, you have powers to take it down. And if it's something that is a deflamatory
or, or, you know, that is untrue, you, you do, you have potential part, you know, abilities
to, to court, you know, have to make requests to Google, write requests to courts, all of
that sort of stuff. But that's a pretty hard process to do at scale. So then the question
then comes about saying, okay, well, how can we make sure that we're putting the right
things about ourselves out there, that we're, you know, that we're really reflecting, you
know, we're, we're, we are making sure that we're reflecting what we want to be seen,
you know, our true version of ourselves online. So there isn't anything that necessarily is
going to start confusing things. So it has made the process of reputation, you know,
management is now, it's a big, it's got more new mentally larger as a challenge. And it's
interesting to see, you know, how, how things will evolve over time, especially when you
think about how easy it is to generate this information from these AI tools to put out
there. And also that the unfortunate ability that you're now able to do things like deep
fakes as well. And how that affects people's reputation and being able to call out actually
is this, is this thing, you know, whether it's text or it's images or video, it has
this actually been generated by a person or has this actually been auto generated and
just dropped out there as potentially some sort of defamation campaign. That's when it
starts getting really scary. And there is a couple of startups or actually now they're
growing very fast. And was one business called Reality Defender. And it's looking at deep
fakes around reputation. And it's looking around deep fakes primarily for companies
and companies and also kind of high net worth individuals, media personalities, and looking
at things that are out there on people and actually saying, you're trying to reverse
engineer the image or the video and try and understand is this being generated by a person
or an AI and it's going to be an interesting space to watch for sure.
For sure. It's, it's rapidly evolving and kind of scary at times. What's your advice
for individuals to better manage their reputations and digital footprints in the era of AI?
I think the first one is, is having a real, having an understanding yourself of what's
out there. And so taking the time to Google yourself, taking the time to go back a few
pages and also taking the time to look at the, the, the privacy settings on the different
web pages that you know, you come up in. So for example, you know, the thing I'd really
say to somebody is, you know, one, do a Google audit, two, go back to your social media and
post your social media platforms and make sure that your privacy settings are locked
down. Also potentially think about what's your, do you still need that information up
there? So one of the things I've done, and you could say that maybe this is because I've
worked in reputation management, is actually say, actually, I don't need that social media
page up there. If I don't use Instagram, actually, I'm going to take it down. I'm going to delete
it because it just helps really managing that, that footprint and making sure that you're
aware of the footprint, you do have the privacy controls on things that, that you can, that,
you know, you have potentially deleted old profiles, you know, maybe if you had a Flickr
account, a Pinterest account, you know, all of these old services, making sure that, you
know, that, that, that, that if you have deleted them, there are a number of different services
that help you get an understanding of what that online footprint is. The, the, the, the
what yourself online is, which is now part of ID Shield will help people give you an
understanding of where you appear publicly. So understanding those sites is important.
And then beyond that, my last recommendation is having, having done an audit, done a lockdown,
done a cleanup, is think about, are you projecting yourself in the best way? And, and it doesn't
have to be a PR campaign, but just making sure that, you know, you've got your LinkedIn
updated. It's a clear bio for yourself. It's putting it across where you are right now.
If you do something that's interesting in your spare time that you think people might
learn about, maybe having a blog page or having, you know, a bio page and just being thoughtful
about what you share to making sure that, you know, that you're communicating what you
want to share on your terms rather than what you might've shared unwillingly, unwittingly
over the last decade or two that's being picked up.
Absolutely. Yeah. I think there is some responsibility on us to kind of go back and, you know, see
what we've done in the past and probably clear it out. And like the services you mentioned
are definitely helpful. One of our questions is what's the dream state for you?
In terms of AI and data, if you had a magic wand and time and resources weren't constrained,
what kind of intelligence tools and models would you have?
Very good. Very, very good question. I think that the, for me, where I'm thinking about
data and where AI tools now are is having more, firstly, having more understanding of
where things are coming from in the responses you're getting from certainly generative AI
tools just to be able to, because it's one of the things that makes me feel a little
bit uncomfortable in having certainty to be able to use some of these tools more, is just
having that uncertainty around exactly where the stuff is coming from. I know OpenAI is
starting to do more referencing, but I think there's still a long way to go in terms of
having accountability in AI. And as these models get used more widely to do more decision
making, having, making sure that these models are not black boxes is really key to making
sure that we're implementing AI in a sustainable way and a way that doesn't disadvantage particular
groups of people or, you know, confirm biases. And one of the big, this is one of the big
challenges of the learning corpuses that these LLMs are feeding on is that there's, you know,
that some of the material they're feeding from is going to be biased in some way and
making sure that we're not just, you know, feeding the machine this content, but then
it's going to generate more content at the same. So we're just in this kind of, you know,
never ending loop of bad stuff is kind of scary. That I think would be my magic one's
one number one. It's just that accountability and being able to not be in a black box is
really that I think that would be so valuable for the rollout of these tools, the certainty,
the general certainty of these tools and the kind of wide rollout across humanity.
Yeah, that's a solid answer. Yeah. It's a, again, super rapidly evolving technology and
we don't know how they work exactly. In a lot of ways, nobody knows. So it's kind of
important to have those guardrails and understand, you know, how it's doing its thing. Awesome.
This was a great conversation. I learned a lot, James. Appreciate your time so much.
Thank you. Any last thoughts for our viewers or myself?
I think one of the biggest things I learned on my kind of journey with entrepreneurship
and generally in my kind of business life is just experimenting. Just go out and experiment
and, you know, think about how you can de-risk decisions for yourself, decisions for starting
a business. And I think using data as part of that experiment is really, has the key
to really deciding what you're going to do next and how you're going to develop your
career or how you're going to develop a business or how you're going to grow a business is
really key. And not being afraid to experiment is really critical and one of the big things
I've learned over my career.