Episode 7: Pete Williams

AI Transcription:

 

 [00:00:00] Ben Culora: Hi. This podcast is brought to you
by Practicus. Practicus is a recruitment, consulting and advisory business
specializing in change and transformation. We hope you enjoy the podcast.

 

[00:00:13] Barry Panayi: Hello, Welcome to another
episode of The Data Files. You’ve got me, Barry,

[00:00:17] Tony Cassin-Scott: and me Tony.

And we are joined today by what I’m gonna describe as a data
celebrity.

Pete Williams, Thank you for joining us.

[00:00:26] Pete Williams: Ah, my pleasure. Exciting.

[00:00:27] Barry Panayi: Now, those of you that don’t
know Pete, he’s had a very varied career in data. Currently at Penguin Random
House has worked in retail in M&S amongst other places. It is just too long
for me to go into now, but absolutely wanna get straight into it.

[00:00:42] Tony Cassin-Scott: So, Pete, we ask the same
question to everybody, almost everybody.

[00:00:46] Barry Panayi: The ones we like,

[00:00:47] Tony Cassin-Scott: Yes, only the ones we like.
Uh, you’ll be thankful we’ll get to the other ones we don’t like later,
depending onour answers and the first one. But what makes a great data leader?

[00:00:56] Pete Williams: Okay. So I think there are
three core experiences or attributes that a data leader has to have.

One, I use this phrase, data competent rather than data.
Excellent. And what I mean by that is being across the concepts of data, being
across the opportunities of data, not necessarily sitting at the code line and
bashing out your Python scripts, you know, during the week. Don’t think that’s
the job of the data leader.

But I do think you need to be competent in the conversations to
establish a bit of leadership and to be able to influence, you know, the people
that you as a data leader are trying to influence, particularly the C-suite and
then you know, further down the organization. So one thing, data competency.
The second would be commercially aware.

So commercial acumen, not quite sure which phrase I prefer, but
you have to be plugged into what drives the organization you’re in. And you
know, as Barry very kindly said, I’ve worked in a number of organizations,
which I think gives me not a unique insight cuz other people have done the
same. But it was an eyeopener for me when I first moved out of retail because
I’d spent so long in, in retail, 17, 20 years by then that seeing an
organization ,that wasn’t necessarily driven for profit was quite interesting
when I did some work for our local council and our data strategy where outcomes
were very different. Uh, also moving from retail to fmcg, you know, where all
of a sudden you are. B2B rather than B2C.

Totally different world. And you have to understand exactly
what is, that drives the organization because you need as a data leader to be
able to pitch what you are trying to sell into the language of the place you’re
trying to sell it into. And if you can’t do that, you’re not gonna get
anywhere. So, you know, that’s the second one for me is that sort of commercial
awareness.

And the third thing that I think is really important is change
experience. Anybody at top of organization is a change leader, but
traditionally you might have been in a function that’s more well known, you
know, so cmo, cfo, marketing, finance, uh, those sorts of areas. We are
striking a new industry here.

You know, we’re about 10 years in maybe I personally, you know,
in this sort of data leadership sphere, maybe six or seven, but we’re trying to
take people on our journeys to help them understand. Their business might have
been incredibly successful when they’ve been going for a hundred, 150 years.
That doesn’t mean they’re still gonna be there next year.

And it doesn’t mean that there are new ways of doing this sort
of business that’s gonna come from the outside world that you need to be aware
of. So change experience gives you the ability to understand messaging,
understand implications, structure, communication, plan, manage stakeholders,
get hold of that, sort of with them, you know, what’s in it for me, type
messaging.

The, the importance of boarding down to like three key points
and constantly hammering those points home so that people understand the
consistency of your message. And so they don’t try and repitch your message for
you and you end up with a very disaggregated strategy. So for me, um, the data
leader rolling back, it feels like 15 minutes I’ve been talking, uh, rolling
back.

Those are the three key attributes. I think you’ve also got to
just put some experience under your will. You know, get some, get some rubber
on the road because until you’ve got to that point, you’re not credible, you
know? And so often you’ll see like a , I don’t know name, any names, but you’ll
see large consultancy organizations rock up to your organization and tell you
had to do a data strategy from people who could barely spell the word data.

and all they’re doing is reading scripts and reading PowerPoint
slides other people have done, and I think the thing that that you really have
to be able to do is take those three attributes I’ve just mentioned and meld
them to the situation they’re in, in order to give yourself credibility and,
and also see at the table.

[00:04:15] Barry Panayi: Thanks for that. You’ve
definitely eaten your own dog food or whatever that saying is there. You gave
us three key points, which I think I’d be able to re pitch. If I go back to the
first one, it brings up an interesting. point that we’ve had some differing
views on on the pod, and that is does a chief data officer (CDO), director of data, whatever you
wanna call them, the data, big cheese, have to have had their hands dirty doing
data?

I wanna separate that from being on the keys. In the current
role, do they have to have had, in your opinion, Or is it leadership change
data? Two of the three aren’t bad. What? Where are you on that?

[00:04:55] Pete Williams: I have tried to answer this one
once before and I’ll try and repeat the same answer so as not to be a
hypocrite.

I think it’s hard to be a data leader without some data
experience, but I think that’s the same at the top of any large organization or
any successful business where you’re trying to, you know, eventually the whole
business operation boils down to like eight to 10 people sitting in a room on a
Monday morning.

And they’ve got to cover a wide set of briefs that they may not
be over the whole lot. So I mean, I’m a, like, I use the phrase data conciliary
where, you know, I always talk about the person who can whisper in the ear of
the CEO and and may help them be credible on data. And I think if you can’t do
that, you’re really struggling.

I’m gonna now adapt that message to say. If you’ve got really
strong commercial awareness and your data literate, as in you understand the
power of data, I think if you structure the right team underneath you (organisational design), you
stand a good chance of being reasonably successful in that space. I don’t wanna
do myself out the job, obviously, you know, this is important, but I, I really
do think that.

It’s best to have data experience, but if the CEOs look left or
right around the table and pointed the finger and said, You are now leading on
data, I think you can do it. If you’re data literate and if you are open enough
to, what every leader has to be is to structure the team of strengths underneath
you that compensate for your weaknesses.

And so I think if you’ve got yourself a really strong team, the
trouble is you then have to take them into the room with you, and that makes it
hard for you individually to be credible, and you have to manage that situation
or learn very, very quickly. So I would prefer. That my data leader had data
experience.

I think it’s suboptimal, but it is possible to lead on data if
you haven’t had deep data experience. But I think the outcome will not be as
successful as getting a proper data leader featuring the three attributes I
said earlier. Do you think this is a bit of a superhero type? Asked them? I
think there’s a lot of us out there, actually.

I think the hard part is breaking in to an outside
organization. I think it’s quite hard to structure your message. Depends
whereabouts on the data leadership journey You. Um, if I use myself as a
example, just to be purely selfish, a lot of what I enjoy and what I specialize
in is knocking down the first set of balls.

So it’s, it’s the Gen one cdo. If you wanna go to the language
we’ve been using for the last couple years, I, I get most excited by the
transformation. You know, the light coming on in somebody’s eyes when they get
an insightful piece of data that they couldn’t have understood before, but
gives them a unique opportunity in the business.

That’s what I really live for and that’s the journey I really
enjoy it. When you’re doing that, the benefits that you’re putting forward are
much harder to quantify because I think you’re adapting the organization,
you’re, you’re saving hours, you’re making efficiencies. You haven’t yet got to
the place where you’ve crushed that incredible data science model that’s
churning enormous margin.

So, you know, people always want you to come with a cash figure
when you’re going for interviews. But you haven’t always got that, but I think
you have to be able to tell the transformation story in that case, You know,
we’ve just a branch out slightly. We all sit on LinkedIn and look at those
conversations that say we want the data leader, who’s the superhero you’re
describing there, Tony, Those sets of skills that just do not exist.

You know, in, out there, in the real world. So, you know, I
think there’s a lot of maturation to go in the, in the recruitment industry.
There are some bright sparks out there that I think we all recognize and
hopefully they’re helping organizations picture those jobs better and, and
better understand what it is they’re buying in.

[00:08:07] Tony Cassin-Scott: Is there a need for
education then at board level to say actually what is possible and what. isn’t?

[00:08:13] Pete Williams: I think board people generally
are smart. Mm-hmm. , you know, , it is tempting to slate them when you’re
inside the organization and you’re frustrated. But typically, I don’t think at
the top of the organization, unless you’re smart, they have to be open, I think
is what they need to be.

They’re all smart enough to understand what you’re talking
about, and every single one of them is using data. You know, I always say that
every decision people make is data. And whether they understand it because
they’ve collated the data or whether they’re just basically on experience
they’ve had in the past, you know, or something their mates told ’em down the
pub, you’re still using something to inform the decision you’re making.

C-Suites are all using data to make decisions, and I think they
just need to be a bit more open to understand that even if somebody’s offering
you a piece of insight that’s outside of your typical chain of command and
hasn’t done your job, it doesn’t mean they haven’t got something useful to add.

And if you work together, You can probably multiply your
opportunities to drive the company forward.

[00:09:04] Tony Cassin-Scott: Right? Similarly, there are
poor data leaders. People give data a bad name. Have you come across them? Are
they tell, tell them so much? No. No. You’d have to name not in this room. No,
but, but just, I’ll just watch your eyes though.

[00:09:17] Pete Williams: Um, I think there are poor data
leaders. Um, and for me, the, the poor ones are the ones who maybe have come
from do two deeper data expertise. This is a watch out. I always have. You
know, um, when you dunno what you’re hiring somebody with data in there, title
sounds like a data leader. You know, I, I don’t wanna, um, run down certain
professions or anything like that in the data world because we’ve all got value
to add.

But if you spent your entire life at the code line, or you
spent your entire life as a BA in a data team or something like that, or a data
engineer, That doesn’t mean you’ve developed those things that I talked about
earlier. The ability to structure the matches, the ability to understand the
business strategy and break that down, disaggregate it into questions that you
pose to the business, and then build forward into a data strategy.

So for me, the, the poorest examples of data leaders are the
ones who have been two data obsessed or two data embedded in the past and have
been given the job because they had data in their title or they did something
technical than nobody else.

[00:10:14] Barry Panayi: Excellent. Uh, that you used a
phrase, uh, data consigliere, , which I like.

[00:10:19] Pete Williams: Thank you.

[00:10:19] Barry Panayi: Um, and to extend the Godfather
reference further, the wartime and peace time consigliere argument, Tom Hagen,
for example, being the peace time consigliere. For those of you that haven’t
watch your Godfather . Um, apologies. I’ll stop talking about it now. , um,
would you describe yourself then as a wartime consigliere?

So you like the. Having to sort the whole thing out and bash
down the walls and then the peace time CDO would come in to run it. Uh, the
reason, the question behind the question is the tenure of data leaders, whether
we’re CDOs, group directors of data, whatever we are, is quite low. Yeah. And
there are a number of theories as to why that is, and I was just wondering, is
it as simple in your mind as we like doing the things we like doing, either
setting up or running.

Is there something else behind it? Is there just a high level
of organ rejection or do you not even observe that there is a short tenure of
cdo? It’s a terribly structured question. That’s why I don’t do, this is my
full-time job, . But I was very interested in your cdo one point nor bash down
the walls light bulb’s come on.

That’s all exciting. And then you adrenaline still going, but
you’ve kind of done it. Is that it? Is that why it’s short?

[00:11:35] Pete Williams: For myself, I can see something
in that because, um, it’s quite dangerous. I, I have been at this stage, and
sometimes I am even right now, where you win a number of battles. You know, you
undertake that trench warfare and all of a sudden you’ve achieved the thing
that you tried to achieve.

It’s just like project delivery from when I was a project
manager like 20 years ago. The, that you set out the battle, you achieved the
battle, and then there’s a massive calm down, isn’t there? Because you’ve,
you’ve achieved what you, and then you’ve gotta get yourself built up again in
order to take the next one on.

So I think, I think there is something around that if, if your
specialism and, and the thing you enjoy is reasonably singular. Um, you know, a
particular type of offensive CDO like you’re describing there. As I said, I
pitch myself in that category and I don’t think I’m the same person who would
then get enormous benefit or enormous enjoyment out of the continuous
refinement of, it’s almost like the leadership stroke, uh, versus manager
compet, you know, um, conversation that we used to have quite often, not you
and I, but in the industry, you know, the manager who’s kind of making the team
better and better and better, and the leader who’s setting their vision and
running over the top and taking people with them.

I’ve found myself drifting towards leadership. That’s what I
prefer. And I think that’s where my personality is. And I think for me as a CDO
or a director of data or whatever you wanna choose, that’s the thing that I
enjoy. And I think if I got to stage two and there was, um, generic competency
in the organization, I don’t know whether I’d found that as exciting.

However, um, I think a lot of this in the industry comes from
two things. One is the. There are many data jobs. We are actively trying to
create more data jobs through things like this and, and raise people’s
awareness. And I think I read that 20% of companies have got our CDO in
position now, which for me leaves 80% where we need to like get some good jobs
out there and start taking some of those rewards.

So there are more jobs being created, which means that more
people are moving on because the opportunities are there that weren’t there
before. I think that’s one. So there’s a great deal of hype. I think the other
is kind of caveat em. You know, has an organization bought what they thought
they were buying?

Is data the thing they thought? Is the data leader the thing
they were, were they ready for it? You know, I think we probably all recognize
that data projects and data transformations fail for a number of quite simple
but generic reasons. And a lot of them is the organization isn’t ready to take
it in.

Whether through investment or creating the right organizational
roles to allow it to embed and to carry forward, whether it’s putting the right
sort of effort into data governance and being able to do those things, whether
it wants to hear a different message when you’re a commercial leader and you’ve
got the the data guy walk in the room and tell to do your job.

So I think the organizations can buy something that they think
is better than it is. They don’t recognize the hard yards that have to come to
make value from data because I think they see data every single day when
they’re looking at their BI report and staring back into the past. So they
think there’s just gonna land like a big ATM machine is not like that.

[00:14:20] Tony Cassin-Scott: At beginning, at one of the
traits as a change agent. altering the words, but it’s in effect, effect what
you said. So by definition it’s got a short tenure. Yeah. Change, Change
happens. Yeah. Ab. Ab, absolutely. So, and you said also it’s a relatively new
industry in terms of its maturity within organization’s about 10 years old.

So you could argue it’s new. Yeah. At what point is it not new?
At what point is it bau? At what point is, is their level of maturity that it
becomes second? And what does it take?

[00:14:51] Pete Williams: I think there are steps to the
BAU nature of it for me, and the first one is when the organization is, I
guess, data enabled. So I’m gonna pitch you the three E’s now.

I’ve come up with this communication vehicle for the data
strategy that I call the three E. The first E is establishment. So I need to
create the environment in which data can be successful. And for me, that’s been
about migration into suitable tools, a creation of a suitable team, getting the
business case and the budget and the strategy signed off to allow those things
to happen.

And preparing, uh, a foundation that the organization can see
as robust, reliable, and can use confidently. So some elements of data
governance as well.

With that in place, the second E is enablement. So how do I now
tune the organization to use the foundation that I’ve just. This is where data
literacy starts to come in really heavily.

So literacy, every role relevant to the role in the
organization. And for me, awakening curiosity. The biggest pitch I use for this
in a number of presentations is moving from statements to questions.
Organizations are not yet data enabled. Look at the past and find it easy to
invest in ever better, more high definition rear view mirrors that don’t in any
way help them to go forward.

And I think when you’ve done your job as a data change agent,
like we are describing here, you’re looking out the windscreen. And you’re
understanding what you could and you should be doing and trying to work out why
you didn’t get there. So from statements to questions is the change. So that’s
what I’m trying to bring in the second E and for me, this is year two of my
data strategy and that’s what I’m currently in, uh, in my current job is
bringing that enablement and already I’m starting to see a different type of
question coming back to my team, which is forcing me to challenge my team
members to deliver in a different way. And, you now, opening the eyes of the
organization. I was really exciting.

And the third E is exploitation, which , I think when I
mentioned this to my, my comms colleague, they were like, Oh, you can’t use
exploitation. But I couldn’t think of a better E for it because having built a
great foundation and enabled the organization and awakened curiosity, now
everything’s in place.

To do the third thing, which is to really push on and, you
know, make efficiencies, drive commercial benefits, go for market share, and
then use more innovation to be able to understand your market and pitch new
ideas. That’s, that’s my three E strategy, so,

[00:17:05] Barry Panayi: And it’s a good one. It’s an
excellent one. A fourth E, , on your, on your first, first E you, you spoke
about establishing the data strategy.

Yeah. My view is it’s fraught with. danger, jargon. There’s
been too many crap ones. Yeah. By, by consultants or by people in the
organization. I’m interested, how do you balance the need for a very specific
plan versus data vision that you then translate into a strategy which sticks,
uh, and you can hold people to account on?

How do you get that embedded?

[00:17:40] Pete Williams: I, I think thick skin
persistence is the answer to that. Uh, you have to be able to go. A lot and
refresh the same message often with the same people until they understand the
opportunities that you’re trying to describe to them. So I, I’m being slightly
flippant, but absolutely being persistent, thick skinned.

Courageous might be a word for it, cuz you’ve gotta go back
constantly into the den and, and tempt the organization with what you’re trying
to offer. But it, this is why it goes back for me into the link between
commercial awareness and change knowledge. You know, you can ignore the data
part to a certain degree here because when I’m trying to start these
conversations, I try to ban the use of the word data.

That is not interesting and not useful, and I don’t wanna get
stuck into the conversation. Are the quality’s bad? Can’t use that. Or, you
know, we tried that before. It didn’t work. The questions are, do you know your
customer? What price should you be selling that? Where are they buying these
things from? Do people know about you in the market?

Are you making this effectively? Are you buying and precuring
successfully to make this product? Have you got the right people in your
organization? These are the sort of questions that the C-suite understands, and
if you pose them like that, they share with you what they need to do. And if
you are smart, and you should be, when you’re trying to do this data leadership
job, you take those questions and you turn them into data, opportunities.

So did you know. We could find out X, Y, and Z at about the
customer. And if I could give you that, what would you then be able to do and
what would that turn into? So the data strategy for me is a lot about surfacing
those questions. And then finding the answers to them. But I think specifically
finding one particular area, cuz if you try and solve the whole problem at
once, and I think you’re in a world of pain and I always think that starts
small, but be ready to scale quickly.

So find one particular burning question that’s really gonna
help for you and structure your strategy around answering that one. And then
the others follow on very, very quickly. I won’t explain what our one is, at
penguin random house, but having engaged the right part of the rganization and
awaken the appetite and being able to generate the investment case.

Then all of the rest is just more, you know, data is one of
those things that’s additive cuz your templates and your processes kind of
follow when you go to the other parts of the organization. So I think to try
and answer your question, Barry, rather than waffle all afternoon, it’s about
understanding the questions the organization should be answeringwwhen they’re a
leader of the organization, ignoring data, your jobs to go away and turn that
into the data based answers and then what you present to them in terms of a
shiny data strategy and what you need to achieve in terms of a detailed plan
that you’re gonna try and deliver. They don’t both need to be shared, but there
needs to be a link between them.

[00:20:09] Barry Panayi: I’m gonna go slightly off to the
side here, but you work at Penguin Random House now. It’s a, it’s an amazing
brand that is used to sell physical stuff, books, and you’ve worked in other
industries that have had to digitize, grocery retail and so on. Without giving
away any family secrets. How has it been at Penguin Random House in terms of
getting traction for that data strategy?

Were they waiting for something? They didn’t know what it was.
Was it difficult? It just said it feels like with no evidence at all, a tough
sell to, to tell a publisher what it needs to do with data. I mean, was it?

[00:20:53] Pete Williams: Uh, yes.

[00:20:54] Barry Panayi: Thank you very much, Tony. Next
question, ,

[00:20:57] Tony Cassin-Scott: I suppose it’s about, it’s
about how do you, how do you measure success?

So it seems to me you, you’re kind of in push mode, but
ultimately you want to be in pull mode, as in people are asking you to do
things because they understand what’s possible. So how do you go from push to
pull to deliver that success?

[00:21:14] Pete Williams: Let, let me just come back a
bit on Barry, cause I’m slightly flippant and my organization might listen to
this.

You never know. No, they will. Uh, yeah. in my world where I’m
in an industry that, um, hasn’t particularly needed to take too much account of
data so far, you know, having come from, as you mentioned, retail where the
awakening a long time ago about knowing your customer, customer 360 loyalty
schemes, you’re getting all of that data around what people are buying in a
basket.

The sort of basket associations, what are you gonna push
promotional offers? All that sort of stuff is well established. So when you’re
talking to an organization or an industry like that, actually they’re quite
often a little bit ahead of you in terms of what they want to do. Cuz there are
so many people who have done it before and they’ve seen it.

So it’s easier there. I think in, in the media industry, the
publishing industry that I’m part of that hasn’t been necessary in the past.
That’s not to say people are ignorant or incompetent. This is. And another
model that I’m gonna share with you before I answer your question, Tony, which
I call unconscious incompetence, but I think could be more nicely phrases,
conscious competence.

But I always come from the negative and I think this underpins
a lot of data strategy work, and I really like it for that reason. It’s when
you go to an organization and you explain to them what’s going on. They’re
unconsciously incompetent. They don’t know that they don’t know something. They
don’t know they could be doing it differently and therefore they’re existing
and they’re being successful.

And this is not to, you know, put the knife in the back of my
current employer. It’s just to recognize that there are many organizations that
have been here for a long time selling great products to people who really want
them. But there are different ways that you can optimize those organizations
and go from more.

And when you haven’t experienced those or seen them, or you’ve
got. An organization which is composed of deeply loyal people who have been
there for a very long time and haven’t seen things in other organizations that
make them just say, Is there a different way of doing this? Then I think you’re
unconsciously incompetent.

My job is to make you consciously incompetent. So I rock up and
I, without trying to be an arrogant, Or stupid. I try and be empathetic. I
listen to their problems and challenges and what they want to do, and I try and
pitch the data work that could change that, and I think that’s where the data
strategy work starts, is that moving from unconscious to conscious competence.

What I then mean to do then is getting to the three Es. Is the
third phase of this is conscious competence, where now you’ve learned the
lessons, you’ve taken those to heart, and you’re starting to run the
organization. And you know that in my world, data is the way and generating
data insights and using those rather than killing data, bringing it out into
spreadsheet and PowerPoint, et cetera, is the way forward.

And everything is a, a data enabled decision. And from there
you move into the unconscious competence where almost like driving a car, You
don’t think about changing gear operating a clutch or if certain manufacturers
using the indicators as you go down the road because all your muscle memory is.
And then you have the loop completes when you become, you know, almost too
stagnant and unconsciously incompetent again.

But for me, that really nicely sums up the world of the data
strategy when you have to illuminate the organization as the data conciliary,
though way jamming this together, it’s really nice on it. And then that’s the
loop you have to take them on. , Can you repeat your question now? Cause I got
so excited after.

[00:24:18] Tony Cassin-Scott: No, no. It’s, well it’s
related to that is it’s, it’s about maturity at the moment. You’re a, you’re an
agent of change. Yeah. And you’re pushing the art of the possible. At what
point, and I think this is a measure of success to some extent, at what point
does that push become a pull? Yeah. On your, on your skills, your team skills?

[00:24:36] Pete Williams: I’m finding that is pull now in
the second E of my strategy. Because you know, a lot of the foundational work
is happening in the background. It’s trying to, is trying to build what you
hope is gonna serve the demand that will come. Now I’m in the second phase. In
the enablement phase, the pull is coming because people are curious and they’re
starting to ask different questions and now they’re trusting, they’re starting
to demand more.

My team, and this is where, this is another key challenge, I
think, for any data. It’s the tipping point between raising expectations,
teasing the organization, getting them more excited and then realizing it’s
gonna cost a bit more than they ever expected. You need more people and unless
you give me more both of those things, I can’t do everything that you’re
excited about me delivering, but they don’t wanna spend on you anymore.

Cause they’ve felt like I’ve done enough already. So there’s
that really tricky tipping point here and I think I am now in that phase, I
think, and data leaders will find self in that phase where once people are
curious. And they know the data’s there and they know they can use it
confidently, and you’ve done enough to help them understand the questions. They
could start to answer. That’s where it moves into pu and I am, I am consciously
in pull mode now, so I’m now trying to spoon stuff out so that people can feed
it, you know, cuz data culture and information culture, if you answer one
question 10 more, come and, and it’s really exciting when you can see that
start to happen.

And I, and I’m at that phase now.

[00:25:54] Barry Panayi: So you’re, you’re two Es in
Yeah. Yeah. On the pull yeah. , as it were And uh, you my next question’s
about, Okay, you’ve, you’ve done that. It’s hard. You do that again somewhere
else, one could do that again somewhere else. Yeah. And you could do it again
and again. The career path to the data leader, whether it be CDO or whatever it
is, is still getting worked out.

Yeah. We talked about, you probably have to know roughly what
data is and you know what it smells like and what it can do. What then? I’ve
been thinking a bit about this. We’ve asked some of our guests on previous
podcasts about it, and some of the answers have been your cdo just get big CDO
jobs. Some are what, one day the CEO will be a cdo, and other ones are, Well,
maybe you put in technology and product and you create a new thing.

What, what do you think the career path for data leaders is? I
mean, CFOs maybe become a cfo, a small place than a slightly bigger place than
a massive place. Is it the same for a CDO

[00:26:55] Pete Williams: On the first part of your
question on the journey to becoming CDO or data leader? I think it’s important
to have worked in both sides of the organization, and I think one of the
benefits I’ve had is having spent a long time in technology at the start of my
career, I then moved into the, what we still call the business side.

I hate the phraseology, but you want me too, the commercial
side of the organization, but even people object to that. But moving out the
service side into the commercial, You get a whole different sense of how you’re
perceived when you’ve been in the other half, which is why I’m sensitive to
that language.

I think get a better understanding of the skills are on both
sides, and you start to understand what drives the organization. Too often
technology tries to wag the dog and drive the strategy, and I think
technology’s an enabler for it, but it should never replace the business strategy
and the two need to work more together.

So I think on the journey to getting. Having tried both sides I
think is really important and that helps with the commercial acumen and the
change experience once we’re there. I think that the data gives you the DNA of
the organization. You uniquely understand more than I think almost anybody in
the organization, how things are plumed together, that sort of nervous system
that connects all of the operations and all of the departments, and therefore,
because you’ve got this commercial awareness and you’ve got this sort of change
in transformation.

I think you’re really primed to move out of the data function
again and into a leading commercial role, Which one that is, I dunno, I’m a big
fan of the CDO to CEO journey. I don’t see why a CDO couldn’t do that. I think
the biggest challenge there would be have you, I guess, run the significant
organization from the people and strategy aspects as opposed to driving a, a
singular instance of something, you know, but data like this and,

[00:28:35] Barry Panayi: And what about the P&L
experience. Yeah. Because we manage large budgets. Yeah. As data leaders, but,
we often don’t have a, unless you’re in a, a data tech business, we often don’t
have a p and l to look after. Do you think that that goes against us?

[00:28:50] Pete Williams: I, I think it goes against you,
but it shouldn’t count you out because lots of other people with p and l
experience, also ignorant of some other parts of the organizational capability.

And there’s nobody who sits there with p and l responsibility
who interprets that p and l and makes all the decisions around it without some
help from the finance function. So I think you can learn. And be supported in
that process to be successful where you are. So I don’t think that should rule
you out.

I think it gives you a unique accountability if you’ve been to
that. And I think that’s part of the data maturity curve we talked about
earlier, um, where the data function moves from being a cost center to a profit
center. And at that point, because it’s generating commercial revenue for the
organization, it should be held accountable for how much it’s costing to do
that.

And I think the data leaders should assume, I’m not sure that
that many of the data roles that have been created are actually at that stage
yet. I think data is still seen, particularly where part of technology as a
cost center and it’s not sitting enough in the commercial side of the
organization. So I agree with you, Barry.

I think it’s really important that you are held to account for
all of the lines on the p and l that drives the function and the outcomes that
you are in charge of as a data leader, or is it another part of the
organization. But I, but I don’t think not having it to that point should count
you out from a seat at the table.

[00:30:03] Tony Cassin-Scott: Do you think that’s a
function of the environment in where you work? So for example, my own career,
I’ve oscillated between IT data and commercial responsibilities, P&L
responsibilities, but that’s because someone took a risk on me. They allow me
to play in an area hadn’t played before. Do you think data people have that
opportunity?

[00:30:20] Pete Williams: I think that’s entirely down to
the organizations, to be honest with you. I don’t think being a data person
gives or negates that. I think the organization has to want to make you
accountable for it. Uh, and some will choose to do so by the accounting
treatments by which they run their company and some won’t.

And I think people in experience specific of the leadership of
those organizations, I don’t think that’s role specific to against data people.
We are essentially all number competent, so we should be able to interpret this
stuff. .

[00:30:47] Barry Panayi: Thank you. Well, we have reached
the end of the formal bit of the podcast, but you’re not off lightly. Uh, we
save our most devious question till the end. We asked all our guests this, What
is the best advice you never took?

[00:31:03] Pete Williams: Uh, that’s quite hard. I could
definitely share advice that I nearly didn’t take and then kind of save myself
at the last minute. And I think it’s about being true to yourself and your
personal brand.

So there was a point in my career. About 10 years ago or so
where I was getting really frustrated. Data leaders weren’t that prevalent at
the time. I’d done a lot of data, I’d done quite a lot of speaking. I thought I
had quite a good industry profile, and I wanted to push that a bit harder, and
I wasn’t getting that in the organization I was in.

So I started speaking to other people about jobs, and I got
offered a really tempting. To represent the software of a company I really
don’t enjoy working with very much. Just be very careful. But I was so seduced
by the money and the opportunity that I, I followed that all the way to getting
the contract and then I, I could never quite reconcile myself to the position
I’d put myself in, you know, if I was on the other side of the table, could not
ever recommend buying this, and I knew that I couldn’t, and that I’d fought
about, fought against it for a very long time.

The happiest moment of my life and the thing that really
spurred me on partly to where I am today, is printing that contract off and
ripping it up and not taking that job, and then kind of getting stuck in a
career dead end for a period of time. But being true to myself and my personal
brand and what I stand for and what I believe in.

And that’s, you know, I do quite a lot of podcasts and quite a
lot presentations where I talk about some of the mistakes I. And what’s brought
me to this position. And I think that was one of the positions, you know,
believe in you, the authenticity of what you create for yourself. And don’t be
afraid to put that out there because there were, there are people looking for
what you are bringing.

Uh, and, and they will find you at some point.

[00:32:41] Barry Panayi: What a wonderful way to end the
podcast. Thank you for being so honest and courageous, which doesn’t begin with
E but you did mention it . Thank you so much.

[00:32:48] Tony Cassin-Scott: I’m just gonna get my
theasuarus out to find a word beginning with E now, but, uh, thank you Peter
Williams.

[00:32:52] Pete Williams: No worries.

[00:32:53] Barry Panayi: Thank you.