Welcome to the APM podcast. APM is the childhood body for
the project profession. My name is Emma David and I'm
the editor of Project at APM's Quarterly Journal and your host.
The subject of this podcast is data literacy, how to improve
your own, what data-driven decision making looks like in
real life, and how it can be used to make the delivery of
projects more successful. The new APM Data Literacy Skills
Framework was published earlier this year and is a practical
toolkit for project professionals which has five
competencies you need to get a handle on.
These include managing project information correctly,
possessing a foundation knowledge of data concepts,
interpreting and influencing with data, data visualisation
and storytelling, and decision making with data.
Corporate members of APM can find a full copy of the report,
while everyone else can find a shorter bridged version and
other helpful resources on thewhatispage@apm.org.uk/resources.
An adequate data literacy is a massive barrier to adoption of
optimised data use in projects. Without it, project teams risk
misusing data and AI tools or failing to recognise their
value. It's a missed opportunity for
all. So in this podcast, we're going
to demystify data and give you a bit more confidence in
navigating this brave new world. So let me introduce you to our
two guests. First is Rob Lord, Director of
Major Programmes at BT International and 2nd is
architect of the APM Data Literacy Skills Framework,
Gareth Parks, who's Head of Data and Analytics at Sir Robert
McAlpine and one of the founding members of the Project Data
Analytics Task Force. Why don't we start with each of
you telling us a bit about your role and where your interest in
data stems from? So first, why don't we go to
you, Rob? Yeah, sure thing.
Ever see? So, Mike, my name's Rob.
Lord, last four years I've been director of major programmes at
BT International. So I lead all of our most
complex technology change digital transformation
programmes into our our, our multi multinational kind of
customers that really translates into accountability around
governance, delivery, insurance, management of risk.
And I guess, you know, my interest in data has really been
kind of fine-tuned and amplified by the the needs I have for it
to discharge my role successfully.
Can you give us an idea of the types of programmes maybe if you
can't give company owns, but it's the sorts of things so we
can get more of a handle on what it is you're you're working on.
They're very kind of impactful technology change programmes.
They often relate to kind of mission critical functions and
services. They can include critical
national infrastructure, but air traffic control, for example, or
leading multinational global banks, Oregon national financial
institutions, global logistic kind of providers and also
foreign governments. So we'll provide secure
telecommunication services for foreign governments as well as
UK government. Right.
So I mean the pressure's on, right?
You can't mess around with these kind of critical infrastructure
programmes, can you? Exactly.
And I mean, coming back to your, your points around kind of, you
know, data, I mean decisions without evidence in that type of
environment has a tendency to fail pretty quickly and it
becomes quite public and quite messy pretty quickly.
And without kind of naming and shaming between the three of us,
we could probably come up with a quite a list of of of things
that kind of have met that condition.
Bit of a left field question, but what what do you enjoy about
your job? What is it about it that you
that that kind of really go gets you going?
I really relish the the leadership aspect of it, but I
also really appreciate the fact it is definitely not boring and
it is definitely not kind of repetitive.
Because whilst there are many kind of I guess common factors
in how one manages complex major programme, indeed runs a
portfolio programme programmes, how one manages risks and
issues. Actually, there's always kind of
variation in the kind of in the context, in the nuance in the in
the customer perspective that brings kind of daily challenges
and all those things in terms of how I conquer that really
motivates me. Gareth, tell us a bit about what
you do and a little about a little bit too about the APM
Data literacy Skills framework, which is quite a mouthful to
say. Yeah, it is, yes.
Well, so I've been living and breathing data for, for several
years now. But I, I guess it's something
that I've been interested in my whole career.
It just took me a little while to realise that that's what I
was interested in. Have you always been in project
management? No, no, I've, I've, I guess I've
been adjacent to project management throughout my career
and then have steadily moved closer and closer to it and
taken on a more of a sort of hands on an operational role as
as time's gone on. So I think that the data is all
around us all of the time. And the way that I see this,
almost anything and everything that people get excited about,
data's involved in it somewhere. It's just that nobody names it
or recognises that it is data. And because of that, we miss so
many opportunities to build bridges and make connections
between different things. And for me, what data does is it
gives just an insight into the this incredible complexity that
surrounds us in everything that we do.
And being able to sort of see that and tap into that and
understand it a little bit just makes everything so much more
vibrant to me and so much more interesting.
Well, that so cleverly segues into my next question, which is
what is data literacy and why does it matter?
So you're already highlighting that I guess every project is a
data project now, and is that how we should think?
It's just part and parcel of everything that that you're
working on. So why what is what does data
literacy mean? And why, if you're listening to
this, should it be something you should be interested in and
wanting to improve? So for me, it's, it's your
ability to speak confidently about data and that then becomes
a universal language that enables you to have all sorts of
very kind of detailed cultured conversations as well as being
able to zoom all the way out to, to see the big picture.
And being able to tap into that and have that kind of, yeah,
have that literacy breeds a confidence and a curiosity.
And as I say, it makes everything in more interesting
and richer and it helps people in in all sorts of different
roles to, to achieve their goals and do their jobs and reach
objectives much quicker and easier.
So I think absolutely every project is a data project.
It's just that most of us haven't realised that realised
that yet. What I want to, whilst I've got
you, Gareth, is the, you've kind of led the APM data literacy
Skills framework, which is trying to give advice or it's,
it's kind of like a practical toolkit for project managers,
project leaders about the kind of skills they need when it
comes to data. And I can only think that this
is going to grow in importance. This is not going to go away.
This is going to be a kind of core competency for project
professionals going forward. So perhaps for those who have
thought data is for someone else, that that data won't be
part of everyone's role when it comes to kind of delivering
projects. Could you just very briefly
describe what those kind of, I think there were five, weren't
there kind of? Five skills.
Five skills and what that why it is that you need to think about
these things and try and figure out how far along the kind of
scale you are. It comes when it comes to each
of those skills. Yes, yeah, of course.
So the one of the reasons that I wanted to be involved in in this
project from the APM was that I've been, so I've been doing a
lot of work in this space with project delivery organisations
across, across the project delivery sector, not just within
our own company. There's various other things
that I've been involved in across the, yeah, across the
sort of the industry. And I say that we are still at a
state of unconscious incompetence when it comes to
data. What do you mean by unconscious
incompetence? In so much as there's so much to
know about it and lots of people either as you, as you said,
think it's somebody else's responsibility to, to deal with
this or to think about this or it's seen as the, the, the role
of, of technologists or the IT department or it's, you know,
it's somebody else's responsibility.
And, and I think it's, it's very easy to yeah, to, to not think
about it because it is it is complex and it is a, is a
learning a new language. It's difficult to do.
And I think whilst lots of organisations have invested very
heavily in tools and visualisations and all sorts of
other things, there is this unnamed issue that most people
don't use many of those tools. They still rely on their gut and
they still rely on other. They won't rely on the company
spreadsheet. They'll rely or the, yeah, the
company's platform they'll have had.
They'll have their own spreadsheet.
That's their version of of truth for for the things that they
need to focus on And and that well, yeah, that starts then get
into the the five skills that that we've introduced through
this framework. So the first of those is
managing project information. And I, I was challenged on why a
project leader needs to know about this.
Surely that's somebody else's job.
But, but the fact is that if you as a project leader don't know
where your information is, you don't have a handle on the, the
methodology or the approach that you and your team are taking to,
to truth, to trust to the evidence that you're going to be
using for, for your project. Then it it, it makes life harder
for everybody in your team to be able to do their jobs because
everybody has their own version of things.
They have to check, they have to search, they spend a lot of time
trying to discover what the right, yeah, the the right
answer is to things. And, and it is a it's a whilst
it's a fundamental thing for many projects.
We don't necessarily broadcast that as leaders to say actually
information management is really important.
It's, it's certainly not the most exciting topic, but
actually once you've got that right, that gives you a really
strong platform to, to unlock lots and lots of other
capabilities. And that applies all the way
through to artificial intelligence.
Because if you're not putting in trustworthy data, then the
outcomes that you're getting out from those tools become a lot
less trustworthy, a lot harder to interpret.
So that's the fair skill. Skill number 2 is then the sort
of foundations of data. And really this is about the,
the language of data and being able to, to distinguish between
the, you know, the different averages, mode, median, mean,
understanding the, the names of different charts that you might
be looking at, understanding that in different situations,
there are going to be different biases that will be introduced.
Understanding a whole raft of other concepts that don't take
long, and many of which are, are, are very simple and, and we
use all the time. It's just that we, we don't, we
aren't explicit about it. And what that skill 2 does is it
starts to make some of those things more explicit so that
everyone can relate to each other.
And it, it means it makes life a lot easier, makes it a lot
easier to communicate between leaders and, and technologists
or technical experts. So the the third skill is then
about interpreting and influencing with data.
So actually being able to spot trends, being able to, to
understand that, yeah, you may not be looking at all of the
data, starting to question where that data is coming from,
starting to, to spot that some data, if data is missing or if
data is incorrect, then it might be throwing out your results or
introducing anomalies. And, and it's that curiosity of
asking why and, and then being able to, to, to know what to do
about it. The, the 4th skill is then the
one that I guess lots of people jump on and, and it's all about
storytelling and visualising with data.
And, and again, from a project leader's perspective, it's not
necessarily going to be the case that you are going to be the,
the, the expert that's able to produce incredibly detailed
visualisations using some specialist tools.
But as a leader, you need to, need to be able to articulate
the, the story or talk about the storyboard and, and ultimately
ask direct somebody to, to be able to create that for you.
And if you've not got that understanding of some of the,
of, of how those things get made-up, then it makes it much
more difficult for you to be able to do that.
And then the final skill, which is I think the one that most
naturally sits with project leaders is then making decisions
with data. How do you make those decisions?
How do you and, and for, for so many of us, that's then making
decisions under under uncertainty.
And, and often people then do rely on their guts to to make
decisions. And, and, and many leaders will
say, well, yeah, I don't have the information at my
fingertips, so I have to rely on my gut for these things.
It's not, it's not available to me.
And in many situations, that's fine.
That's borne out of lots of different experience.
But then those experiences, as project delivery gets ever more
complex, our own experiences can only ever provide a sliver of of
the answer in a sliver of the understanding.
And without tapping into these other skills and these other
tools, then you are limiting your your ability to continue to
be able to make good decisions. So thanks, Gareth, that was a
really, really concise way of summing these skills up.
Rob, what is there anything you'd like to add about how you
perceive data literacy and why it matters and also how what's
it like within your own organisation?
I want to ask both of you bring it back to the profession and
how far on the what the profession needs, you know, to
do to get up to speed on this. But yeah, Rob, I'd like to hear
your thoughts on that. Yeah.
So, so, so just in answer to your first question, I guess
it's probably quite a lot of crossover with my thoughts and,
and Gareth's thoughts. But I guess to me, kind of data
and literacy is about, it's about the ability to ask the
right questions of the data, which is something that Gareth's
answers kind of explored. It's also understanding its
limits. So again, kind of Gareth talked
about incomplete data sets and I guess having 100% information is
almost utopia if you like that you'll never quite reach.
And it's absolutely about using it to make better decisions.
It's quite easy for some folks to fall into the trap of being
obsessed around perhaps data inputs and the process
management around that. But much more important is the
ability to answer the So what question.
So what's the, what's the output or impact that's going to have a
result, a positive result for you, for your customer, for your
own organisation or for your supply chain, whichever
stakeholders you're serving. So that's that's why it's so
kind of important to me and I think it, it really is an aid to
improve predictability assurance, especially when
you're dealing with large portfolios of of work and to
build trust through objectivity. What What do you both think
about where the profession is along when it comes to data
literacy? Like, is there a lot of room for
improvement? What really needs to shift
across the profession to to make this happen?
I would say, I mean, I'm going to, I'm going to kind of base my
answer closer to home, so to speak in terms of what I see
across my own kind of organisation.
I think it's fair to say it's a pretty uneven kind of landscape.
So what is absolutely true is we've definitely made big bounce
forwards in terms of common tooling and column common
systems and common dashboards when it comes to data analysis
and management information. But there's definitely, there's
definitely a kind of gap perhaps in in how that's kind of
interpreted and how we kind of pivot from analysis to action.
I mean, some people are kind of masters in that, in that domain,
but I think there's a lot of variation in, in skills and
performance in, in, in that regard.
And we definitely need to be a more challenging organisation in
terms of the exploitation of that data management information
don't kind of fall into track just consuming it kind of almost
passively and make sure in programme management we're we're
challenging kind of assumptions. We're really clear on
definitions and we're really trying to drive quality from it.
Thank you. That's probably going to be a
common situation across many organisations that people work
for who are listening to this. Gareth, what would you like to
say on that? Well, so I mean that everything
that Rob said I think resonates across our organisation and
many, many other organisations that and, and for a lot of
people, the first step is making sure that there is common
tooling and that and that the inputs are starting to be
standardised to a degree. And that's hard.
That's, that's not an easy task in and of itself.
But as, as Rob said, the So what can often get lost either either
in terms of people aren't quite sure what they're asking for or
what the insight is that they're looking for off the back of it.
And we we spend so much time kind of doing the plumbing,
putting together the pipes and the wires, Everyone forgets
about the the water or the, you know, the fluid that's coming
out the other end. What and what are we trying to
do with it? How are we going to use it?
And yeah, so we end up kind of spending lots of time trying to
perfect things that that actually don't need to be
perfected. It can be good enough.
And I think often, yeah, the perfection side of things gets
in the way of of just having good enough data or a good
enough set of visuals to be able to answer the immediate question
and then make the decision and move on to the next thing.
So and and I think a lot of this is then amplified by AAI at the
moment. There's there's lots of noise
about it and there's so many positive things, but my fear is
that without some of the foundations, we're at risk of
trying to run before we can walk and that then that just sets us
up for a four if we're not careful.
Yeah, Yeah. You're not the first person to
raise that issue. I think many organisations are
worried about this. But it's really interesting what
you're both saying about there kind of being pockets of
probably higher expertise and understanding and, and other
areas across an organisation where it's not that much
interest or doing the bare minimum or misunderstanding of
what it could do or like under appreciation of how it can help
you in your work. What how so how should one go
about improving data literacy? I guess within, I guess within 3
parameters, there's an organisation who works with on
projects as a project team and then as an individual.
So the, the framework is brilliant across all of those,
particularly for as an individual to understand.
But honestly, like where do you, how do you go about improving
this? Someone might be listening who
is project manager, project leader realises the importance
of this, perhaps not receiving that much support from the
organisation. So how do you go about at those
3 levels, team, organisation, individual to kind of really
getting to grips with this and and learning about how to do it
right? So weren't, yeah, as you say,
that's that's exactly how we designed the the framework as a
as a conversation starter as much as anything else at at
those 3 levels. So it's, it's easy enough to
pick it up and, and just run through and the, the, the
categories there are sort of five levels of of skill and they
tie back to the, the competencies from awareness
through to expert in in aligned to APM competence frameworks.
So there's, it's easy enough for somebody to pick it up and just
take a look and, and give themselves 5-10 minutes to
process and work out where they see themselves across each of
the five levels. And, and from that
self-assessment, work out which of those we have where they may
have some blind spots. And there there's so many.
I mean, it's so easy now to start Googling once you've done
that, once you've got that. So hopefully that at the an
individual level, what the framework does is it gives you
that that. So it's like a checklist, isn't
it? Like, what do I know?
What don't I know? What do I need to find out more
about? Yeah, exactly.
And then? And then what, what you can do
with it at the team level is use it at the beginning of a team
meeting as a just taking five questions, one on each of them.
Get get each of each of your team members to score where they
see themselves against each of the five metrics.
It's simple enough and, and there's a there's, it's easy
enough to almost cherry pick the things that are then relevant to
your situation, your project to be able to to put those into
right. OK, well, for, for the level of
competence, actually, that's what this means for us.
And so make it quite specific to your particular project.
And then is everyone reached that level of competence or
actually are still people moving towards it and proficient but
not yet practised or yeah, there's so there's, there's
various different ways then that a team can do that.
And and then from that very quick 5-10 minute exercise as a
team, being able to to identify where there may well be some
blind spots, where there may be some untapped, untapped
expertise may well be that you have a somebody that's
incredibly passionate about visualising data that that
hasn't necessarily been yet or is is being underutilised it.
It seems like an obvious thing to me that if you have a team
where you decide that you say you have someone who's expert
has more expertise or understanding one thing that you
can quickly have a session, you know, like even a lunchtime
session where people come together and say, I can show you
this. I, you know, it's that sharing
that it doesn't have to be a formal thing.
It's like sharing the knowledge that people have already.
Is that a good idea? I'm just thinking of how people
could do something quite quickly.
Yeah, absolutely. It's, it's and a lot of it
because this is a, it's a soft skill.
It's not a technical skill. And it's, it's not as hard as
people think. It's because there are so many
practical examples when you start thinking about it.
And I think the challenge is that often data is seen as this
sort of ephemeral conceptual thing.
But it, it's, it's document management, our, our monthly
reporting, our, yeah, feedback mechanisms.
It's, it's, it's embedded within everything that we're doing.
And then so you can have a 10 minute conversation or a, yeah,
a lunchtime session to have somebody just show what they've
been doing or what they've what they've learnt and, and speak
about it. That's really great.
And then so at the, I think so we've covered kind of individual
project. What about an organisation?
Because then surely I'm jumping in here.
But if you have one team that's doing interesting things, it's
about sharing the knowledge across the whole organisation.
Exactly. Yeah, yeah.
And, and then, and then there's different ways that it can start
to apply between, yeah, inter team conversations and, and two
teams picking up and comparing where they are all the way up
to. It's, it's when we designed it,
we were thinking that actually you could build out, build out a
much more comprehensive sort of benchmarking exercise to, to
assess the, the whole organisation or large swathes of
an organisation or particular capabilities within an
organisation to, to benchmark where they are and then
introduce some specific measures to improve in, in certain areas
and and then re rebaseline or reassess.
Thanks, Gareth. Rob, how do you working within a
massive organisation like EED? How, how do you go about
assessing where people are on a kind of data literacy journey?
And any advice or things that you've seen work when it comes
to improving data literacy, understanding and skills and
competencies at individual team and and kind of leadership level
particularly interesting for you as a leader, like how one, how
one makes it known that this is something people need to care
about. I think there's so many sources
of information, learning and knowledge out there that are
available through open sources that there's no real excuse for
ignorance on anybody's kind of part.
I think even if you're, even if your company perhaps isn't as
supportive as they can be, you know, folks can use obviously
leverage the APM framework or LinkedIn training or other
mechanisms to, to, to kind of build the kind of foundation of
their knowledge. Then I think with that kind of
foundation, you know, established, I think there's a
lot to be said through kind of learning through lived
experience. So try and put that learning
into direct practise. I think some of that is almost
about forming habits in terms of how we use data management
discipline to drive good outcomes.
So a couple of practical examples might be to establish a
contract with yourself that you always triangulate multiple data
sources. You don't fall foul of being,
you know, biassed towards one data source.
You use multiple sources and take a 360 perspective around
the problem or the requirement that you're trying to kind of
solve. You might also make a contract
with yourself that you're always going to use data to inform
your, you know, decisions, whether that be a decision
internal to your team or a decision that's paramount to
your kind of customer. So I think you know, kind of
being a self learner, utilising those open sources, I think
building on those foundations through learning by lived
experience and then establishing some habits and principles that
you contract to work by. I think all of those things
would have a positive multiplying kind of effect to
then a bigger kind of organisational level.
I think it's really important that we see data info management
as being very much kind of, you know, core to our business, core
to how we operate. It's not something that sits on
the periphery. It's not an adjacency that's
only done by kind of specialist. It should be informing
everybody. Now there's an onus that flows
from that around the company establishing a strategy for for
how that should be done because as Gareth said earlier, data is
all around us. It kind of informs everything
we've done. And actually if we don't have a
strategy, we'll kind of lack structure and actually this
thing will kind of grow very organically and could actually
grow in quite a disorganised way that can't then be exploited to
best effect. So, and also if you've got a
strategy and you're clear on the North Star, what you're trying
to achieve and how data management contribute,
contribute towards that, it does make kind of investment
decisions and also investment dilemmas perhaps easier to
grapple with. And who's who does that sit
with? At which level of an
organisation should should this, who, who should be setting the
strategy? I mean, this is going right to
the top of the business. Yeah, right.
I guess it differs business by business in terms of how the
data and insight is represented, you know, at board level or
perhaps one level down or or or two level down.
And we, we're a technology company.
So we, we, we tend to establish very, very close relationships
with our CTIO function because quite often they're the
mechanism apparatus, the plumbing, as Gareth said
earlier, which enables this to be done at a kind of
organisational level, a corporate level.
So yeah, we're, we're, we're quite system orientated, CTIO
orientated, but it differs by company.
What can I ask both of you, what you think are the biggest
barriers to data literacy at the moment and how they might be
overcome? Maybe Rob, I think.
That the the the greatest barriers probably learning
knowledge, culture, mindset. I think all the component parts
of this topic, it's not rocket science in isolation.
It's not mega hard things to do, but actually it's how you make
it a organisational wide kind of endeavour and bring some
standardisation to that. Make it business as usual and
put the enablers in place to achieve that kind of outcome.
Be it like in the same way, cybersecurity Once Upon a time
was this peripheral area of expertise that used to take
place in a dark room which nobody really understood
anything about. Whereas actually everybody now
understands that cybersecurity and information security is
everybody's responsibility. There's there's certain kind of
parallels with with data management I would suggest.
Gareth, what would you say are the biggest sort of barriers?
Actually, Joe, what I wanted just to point out the way that
you said that this is about, it's not a tech skill, it's a
soft skill. That is a massive shift in
perception because I imagine pizza, one of the barriers might
be it's just techie. It's not.
I don't want to go down that route.
It's. Not that's really important to
make that distinction. Very much so.
And and I think there so some of the things that Rob touched on
in terms of leadership and culture and habits.
Coming back to something Rob said earlier.
And the way that you almost need to look at this is that it is
it's literacy, it is a language to learn.
And anyone that's tried learning a second language will know that
the hardest thing to do is to actually speak it.
You can, you can be great at all of your yeah, that, that verbs
and, and of being able to read the language.
But until you can have a conversation about it.
And that's, that's when you get the richness.
And that's when your, your yeah, your fluency really starts to
take off. And that's, it's, it's that sort
of fear factor of, of I'm going to, I'm going to stumble over my
words. I'm going to say the wrong
thing. I'm what I'm going to say, I'm
going to use the wrong terminology and yeah, and, and
yeah, I'm going to fail. And so there's a real sense in
and around this that's that's hard to overcome, but just start
talking. And as Rob said, it's it's that
habit of yeah, being able to find and it may well be on a
daily report that you look at. And so today, tomorrow, rather
than just skimming over it because it's what you've always
kind of snow blind to it because you see it everyday.
Stop and just take it, take a breath and look at the details.
How old is it? What's the source of the data
that you're getting that's on the report?
Are there any biases that you can see from it?
What's the story that the reports telling you?
Is it the same story that was being told yesterday or yeah.
Is, is there something that you don't understand or you don't,
you're not quite clear on? And then ask the question,
what's the, what does this mean? Why, why does this and this not
equal each other today when they did yesterday?
It's, it's those kind of things that you then start to see these
examples everywhere. And, and it's that, it's, it's
that individual habit that organisations need to then kind
of, you know, three, leadership and culture and cultural change
to, to be able to sort of embody and harness in the right way.
Otherwise, again, as Rob says it, it grows organically and
which is great, but it doesn't necessarily then align to the
organisational objectives and the organisational priorities.
And then there's a sweet spot to hit in and amongst all of that.
Advertising with the way that people are experimenting with
AI. So lots of companies like
individuals going off and doing their own thing in certain
departments going off and it all gets a little bit chaotic and
messy and people doing stuff because they feel like they
ought to. But what, why are you doing
this? And let's let's share knowledge
and think of it in a kind of more strategic way.
So really the one of the most important questions I haven't
asked you yet, although we have touched on it a bit is why
bother? So what are the benefits that
are kind of So what to, to cracking data literacy?
And can you give me some examples, example or two where
you've used it, you've, you've had good data literacy on
particular projects or programme and it's brought better outcomes
or benefits. So Rob, if you'd like to go
first and then Gareth. So there's a, there's a number
of things that this kind of drives and enables.
I mean, one of the first things is, is kind of confident
executive decisions. I mean what I'm involved in is
always major programmes. So big, big technology change
programmes that have big impact to our customer organisation and
to their, their end users and their end customers.
So data definitely, definitely, definitely enables and supports
confident executive decisions. And often those confident
executive decisions actually are a fuel and propellant for a
programme. Because actually if you get into
decision kind of paralysis because you haven't got good
data, empirical inputs, that can be really, really dangerous for
programmes. Health and success, perhaps
building on that a little bit, good data management also assist
greatly around risk management. So it's how we use it to drive
early intervention in kind of failed delivery.
And we are actually right now looking at how AI and predictive
analytics can kind of further take us on the journey of kind
of almost, you know, reactive to proactive to predictive and
almost at the planning stage informed by AI and predictive
analytics. Plan for success on day zero
because you've got all of the data benefit of all the failings
of projects and programmes that you've run before and the perils
kind of associated with, with that, which if we can harness
that will be extremely helpful in minimising any variation in
our major programme performance. And probably lastly, just how it
can help us almost in the visualisation and the
articulation of confidence levels.
I mean, I work in a world of IT and network, so kind of
precision and reliability. Is everything we can put a
number on pretty much everything as well.
So being kind of empirically kind of enabled and be able to
embed that in the confidence levels we can communicate for
our storytelling is all quite key to to stakeholder
management. I've been thinking just the
headlines at the moment about a conflict, I guess that's what we
call it, incursion into Iran and the global impact of that on
markets, on energy prices. And every month there's
something else that is disrupting the kind of the
business as usual for many organisations, many
international ones. Does data literacy give you a
sense of kind of greater understanding of or prediction
or stability? Is that something that is
relevant to helping manage the ever greater uncertainty and
volatility that certainly multinational organisations are
are trying to get grapple with at the moment?
As an international company that deals in IT and network services
into multinational companies and we've got good representation in
that region. The current situation is, is
definitely a concern. I mean, I think it's also kind
of Fair to say that we, you know, we are leveraging, you
know, our existing kind of data management methods where it
comes to service assurance. So in that sense how we operate
is is kind of BAU, but what we tend to do is the wrapper around
it is more our business continuity management, crisis
management kind of arrangements. And you know, we have daily
stand ups where we use quite specific data points to kind of
confirm and assure that both our primary services, secondary
services, resilient services are still intact and has no impact
to the customer and is not presenting any further risk or
threat to the to the customer. So this is something we're we're
putting in practise kind of daily through those command and
control arrangements. OK, thank you.
And and Gareth, what why should we bother with data literacy?
What benefits does it bring? I think so, as Rob touched on
many of these things already in terms of the ability to speed up
governance, reduce friction between people and across teams,
win people around with stories that are backed up by evidence
rather than yeah, yeah, leaving things to, to chance, bridging
the gap between technical specialists and senior
leadership, reducing the amount of rework.
These are all things that that data literacy contributes to.
But for me, the, the biggest benefit is that it turns your
data and your information into strategic assets.
And then the benefits and and the achievements that you can,
yeah, that you can make happen with it are literally only
limited by your imagination. I guess this comes all the way
back to your first question as to why this excites me so much
is that it is. And especially now with, with
the the advances and the exponential advances in the, the
quality of AI tooling, all reliant on good quality data,
but you're literally only limited by your imagination.
So risk mitigation, how we can get better at predicting and
preventing risks and identification of variants,
greater cost control, new service lines.
And yeah, so whatever it is that you're trying to achieve, data
literacy, data management and treating your data as a
strategic asset will help you get there faster than you are
doing guaranteed. Before you wrap up, because I'd
like to ask you, each of you, if there's one takeover, one piece
of advice you'd like to pass on what what that might be.
But there's a kind of lot of thought going around at the
moment. We haven't really talked too
much about AI, but it I guess it was thinking about the soft
skills that the skills you need as a project professional to
thrive in the next 5 to 10 years.
And AI is only going to increase in its importance in the way
that projects are managed to run, delivered.
If you are, if someone's listening to this, thinking what
the skills I need to hone, part of that is a critical skill will
be kind of fluency with data and as part of that AI.
Would you agree with that? I would absolutely I would
absolutely kind of agree with that.
And I guess there is some very tight relationship come
crossover between kind of data literacy and the the power and
opportunity around AII mean in the world of project and
programme management. I I don't see AI replacing the
human in terms of project and programme manager.
I see. I see kind of AI and data
literacy as being mechanisms by which we can make the the job of
those individuals easier. Certainly kind of automate some
of the repeatable tasks when it comes to like programme control.
So how we manage risks, how we manage issues, how we deal with
assumptions, how we enable good decision support, so on and and
so forth. And, and really kind of enable
the and project to programme management.
It could be, could have always like pushed up the, the value
chain and give them the time and space to actually spend more
time managing upwards and outwards rather than downwards
and, and, and inwards. And some of the data will give
them the kind of the, the confidence and assurance that
their internal machine is working as expected and they
won't have to spend unnecessary time checking and duplicating
the work of others. So I see, I see all of these
things coming together as a bit of a positive ecosystem.
What do you mean by managing upwards?
Can you just expand on that? Yes, yes, I can.
I mean, I think again kind of reflect on on operations kind of
nearer to home, we can find that our projects and programme
managers spend too much time managing internal functions and
teams and assuring that their performance that's contributing
to the customer outcome you know is on track, is on plan.
It's the right point of quality, it's the right point of cost,
it's the right point of schedule, so on and so forth.
Through kind of data management techniques.
Some of that assurance can be done for them through system
means. I actually just trigger
interventions when strictly necessary rather than having a
big kind of time consumption of ongoing kind of checking and
assurance. And actually far more value can
be given by actually working outwards and upwards into the
client organisation, acting as their trusted advisor, helping
them with their decision making that's going to lead to the
joint success or failure of a programme, so on and so forth.
So that's that's what I meant by that Quit.
Thanks for a really interesting Gareth.
What? What would you like to say?
I'd say that there's not a magic bullet for this, that it's not
something that you can, you can go on a single training course
and all of a sudden you can, you can do all of these things.
You have to practise it, you have to engage with it.
And so I would encourage people to to take a look at the skills
framework that we've produced, get a sense of where you are and
where you might want to improve. And yeah, there are, there are
then so many online sort of Open Access resources for people to
to start Googling, start start making use of.
And AI is a fantastic tool to just yeah, if you, if you don't
want to have a conversation with, with another person, just
start talking to one of the yeah, your AI tool of choice.
And, and, and you will very quickly learn a lot more about
this subject. I guess the, the, in terms of,
as you mentioned, some of those kind of core capabilities for,
for project leaders going forwards.
And, and obviously, yeah, the, the, the piece around AI and AI
is not going to replace the need for data literacy.
What it's going to do is amplify all sorts of capabilities, but
also potentially the mistakes and data literacy is one part of
enabling you to be able to determine the difference between
the two and, and avoid some of those pitfalls.
Make sure that you are in a in a place to be able to understand
the options that are that are being presented to you and and
make the right decisions. Rob, is there one piece of
advice take away you'd like to pass on to listeners?
Yeah, it's probably, I guess it's probably more of a strap
line than one piece, but definitely be self starter, self
learner, be curious and put in practise would be it.
So about mindset, yeah, OK, like that.
Gareth, what would yours be? So I've spent the first half of
my career focusing on unravelling digital pipes and
wires and, and a lot around that kind of plumbing.
And I, I want my second second-half of my career to be
about making sure that the people at the end of those pipes
actually know what they're doing with the insight that's flowing
through them. And so my piece of advice is be
constantly asking So what? And and it's it's the coining
phrase that Rob used earlier. What is the action that this is
going to drive? What's the value that this is
going to drive? How can we make it real?
Rather than, yeah, focusing on capturing more and more data
with with little without that. So what without that purpose.
So I guess, yeah, it's that sense of purpose and and
forethought of what you're going into and why.
Yeah, Know why you need to dip data you're after and what
you're going to do and have that understanding, just being smart
about it. OK, that is really fantastic.
Thank you both. It just leaves me to say thanks
for for joining us on the OPM podcast.
Really appreciated. Appreciate you sparing the time
and what an interesting conversation.
So thanks very much. Thank you.
Thank you very much, Emma. Thanks again to Rob and Gareth
for joining us and to you for listening to the APM Podcast.
If you're interested in finding out more about data literacy,
please go to theresourcessectionoftheapmwebsite@apm.org.uk
and look out for the Spring 2026 issue projects where we go into
the subjects in more depth. Anyway, don't forget to look out
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