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Is data literacy a soft skill? · Project Podcast

0:14
Welcome to the APM podcast. APM is the childhood body for
0:17
the project profession. My name is Emma David and I'm
0:20
the editor of Project at APM's Quarterly Journal and your host.
0:24
The subject of this podcast is data literacy, how to improve
0:28
your own, what data-driven decision making looks like in
0:31
real life, and how it can be used to make the delivery of
0:34
projects more successful. The new APM Data Literacy Skills
0:38
Framework was published earlier this year and is a practical
0:42
toolkit for project professionals which has five
0:44
competencies you need to get a handle on.
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These include managing project information correctly,
0:51
possessing a foundation knowledge of data concepts,
0:54
interpreting and influencing with data, data visualisation
0:58
and storytelling, and decision making with data.
1:01
Corporate members of APM can find a full copy of the report,
1:04
while everyone else can find a shorter bridged version and
1:08
other helpful resources on thewhatispage@apm.org.uk/resources.
1:15
An adequate data literacy is a massive barrier to adoption of
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optimised data use in projects. Without it, project teams risk
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misusing data and AI tools or failing to recognise their
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value. It's a missed opportunity for
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all. So in this podcast, we're going
1:30
to demystify data and give you a bit more confidence in
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navigating this brave new world. So let me introduce you to our
1:37
two guests. First is Rob Lord, Director of
1:40
Major Programmes at BT International and 2nd is
1:44
architect of the APM Data Literacy Skills Framework,
1:47
Gareth Parks, who's Head of Data and Analytics at Sir Robert
1:51
McAlpine and one of the founding members of the Project Data
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Analytics Task Force. Why don't we start with each of
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you telling us a bit about your role and where your interest in
2:02
data stems from? So first, why don't we go to
2:05
you, Rob? Yeah, sure thing.
2:07
Ever see? So, Mike, my name's Rob.
2:08
Lord, last four years I've been director of major programmes at
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BT International. So I lead all of our most
2:18
complex technology change digital transformation
2:21
programmes into our our, our multi multinational kind of
2:25
customers that really translates into accountability around
2:30
governance, delivery, insurance, management of risk.
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And I guess, you know, my interest in data has really been
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kind of fine-tuned and amplified by the the needs I have for it
2:42
to discharge my role successfully.
2:46
Can you give us an idea of the types of programmes maybe if you
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can't give company owns, but it's the sorts of things so we
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can get more of a handle on what it is you're you're working on.
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They're very kind of impactful technology change programmes.
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They often relate to kind of mission critical functions and
3:03
services. They can include critical
3:08
national infrastructure, but air traffic control, for example, or
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leading multinational global banks, Oregon national financial
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institutions, global logistic kind of providers and also
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foreign governments. So we'll provide secure
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telecommunication services for foreign governments as well as
3:29
UK government. Right.
3:30
So I mean the pressure's on, right?
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You can't mess around with these kind of critical infrastructure
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programmes, can you? Exactly.
3:38
And I mean, coming back to your, your points around kind of, you
3:40
know, data, I mean decisions without evidence in that type of
3:44
environment has a tendency to fail pretty quickly and it
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becomes quite public and quite messy pretty quickly.
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And without kind of naming and shaming between the three of us,
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we could probably come up with a quite a list of of of things
4:00
that kind of have met that condition.
4:03
Bit of a left field question, but what what do you enjoy about
4:06
your job? What is it about it that you
4:09
that that kind of really go gets you going?
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I really relish the the leadership aspect of it, but I
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also really appreciate the fact it is definitely not boring and
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it is definitely not kind of repetitive.
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Because whilst there are many kind of I guess common factors
4:25
in how one manages complex major programme, indeed runs a
4:30
portfolio programme programmes, how one manages risks and
4:34
issues. Actually, there's always kind of
4:36
variation in the kind of in the context, in the nuance in the in
4:40
the customer perspective that brings kind of daily challenges
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and all those things in terms of how I conquer that really
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motivates me. Gareth, tell us a bit about what
4:49
you do and a little about a little bit too about the APM
4:53
Data literacy Skills framework, which is quite a mouthful to
4:57
say. Yeah, it is, yes.
4:59
Well, so I've been living and breathing data for, for several
5:04
years now. But I, I guess it's something
5:07
that I've been interested in my whole career.
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It just took me a little while to realise that that's what I
5:12
was interested in. Have you always been in project
5:15
management? No, no, I've, I've, I guess I've
5:20
been adjacent to project management throughout my career
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and then have steadily moved closer and closer to it and
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taken on a more of a sort of hands on an operational role as
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as time's gone on. So I think that the data is all
5:39
around us all of the time. And the way that I see this,
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almost anything and everything that people get excited about,
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data's involved in it somewhere. It's just that nobody names it
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or recognises that it is data. And because of that, we miss so
5:57
many opportunities to build bridges and make connections
6:00
between different things. And for me, what data does is it
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gives just an insight into the this incredible complexity that
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surrounds us in everything that we do.
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And being able to sort of see that and tap into that and
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understand it a little bit just makes everything so much more
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vibrant to me and so much more interesting.
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Well, that so cleverly segues into my next question, which is
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what is data literacy and why does it matter?
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So you're already highlighting that I guess every project is a
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data project now, and is that how we should think?
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It's just part and parcel of everything that that you're
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working on. So why what is what does data
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literacy mean? And why, if you're listening to
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this, should it be something you should be interested in and
6:54
wanting to improve? So for me, it's, it's your
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ability to speak confidently about data and that then becomes
7:05
a universal language that enables you to have all sorts of
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very kind of detailed cultured conversations as well as being
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able to zoom all the way out to, to see the big picture.
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And being able to tap into that and have that kind of, yeah,
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have that literacy breeds a confidence and a curiosity.
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And as I say, it makes everything in more interesting
7:35
and richer and it helps people in in all sorts of different
7:40
roles to, to achieve their goals and do their jobs and reach
7:45
objectives much quicker and easier.
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So I think absolutely every project is a data project.
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It's just that most of us haven't realised that realised
7:55
that yet. What I want to, whilst I've got
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you, Gareth, is the, you've kind of led the APM data literacy
8:05
Skills framework, which is trying to give advice or it's,
8:11
it's kind of like a practical toolkit for project managers,
8:14
project leaders about the kind of skills they need when it
8:20
comes to data. And I can only think that this
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is going to grow in importance. This is not going to go away.
8:25
This is going to be a kind of core competency for project
8:29
professionals going forward. So perhaps for those who have
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thought data is for someone else, that that data won't be
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part of everyone's role when it comes to kind of delivering
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projects. Could you just very briefly
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describe what those kind of, I think there were five, weren't
8:47
there kind of? Five skills.
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Five skills and what that why it is that you need to think about
8:54
these things and try and figure out how far along the kind of
8:58
scale you are. It comes when it comes to each
9:01
of those skills. Yes, yeah, of course.
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So the one of the reasons that I wanted to be involved in in this
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project from the APM was that I've been, so I've been doing a
9:15
lot of work in this space with project delivery organisations
9:20
across, across the project delivery sector, not just within
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our own company. There's various other things
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that I've been involved in across the, yeah, across the
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sort of the industry. And I say that we are still at a
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state of unconscious incompetence when it comes to
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data. What do you mean by unconscious
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incompetence? In so much as there's so much to
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know about it and lots of people either as you, as you said,
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think it's somebody else's responsibility to, to deal with
9:55
this or to think about this or it's seen as the, the, the role
10:02
of, of technologists or the IT department or it's, you know,
10:08
it's somebody else's responsibility.
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And, and I think it's, it's very easy to yeah, to, to not think
10:18
about it because it is it is complex and it is a, is a
10:21
learning a new language. It's difficult to do.
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And I think whilst lots of organisations have invested very
10:30
heavily in tools and visualisations and all sorts of
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other things, there is this unnamed issue that most people
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don't use many of those tools. They still rely on their gut and
10:45
they still rely on other. They won't rely on the company
10:50
spreadsheet. They'll rely or the, yeah, the
10:52
company's platform they'll have had.
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They'll have their own spreadsheet.
10:55
That's their version of of truth for for the things that they
11:00
need to focus on And and that well, yeah, that starts then get
11:06
into the the five skills that that we've introduced through
11:10
this framework. So the first of those is
11:13
managing project information. And I, I was challenged on why a
11:19
project leader needs to know about this.
11:21
Surely that's somebody else's job.
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But, but the fact is that if you as a project leader don't know
11:26
where your information is, you don't have a handle on the, the
11:31
methodology or the approach that you and your team are taking to,
11:36
to truth, to trust to the evidence that you're going to be
11:40
using for, for your project. Then it it, it makes life harder
11:47
for everybody in your team to be able to do their jobs because
11:50
everybody has their own version of things.
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They have to check, they have to search, they spend a lot of time
11:56
trying to discover what the right, yeah, the the right
11:58
answer is to things. And, and it is a it's a whilst
12:04
it's a fundamental thing for many projects.
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We don't necessarily broadcast that as leaders to say actually
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information management is really important.
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It's, it's certainly not the most exciting topic, but
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actually once you've got that right, that gives you a really
12:23
strong platform to, to unlock lots and lots of other
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capabilities. And that applies all the way
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through to artificial intelligence.
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Because if you're not putting in trustworthy data, then the
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outcomes that you're getting out from those tools become a lot
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less trustworthy, a lot harder to interpret.
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So that's the fair skill. Skill number 2 is then the sort
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of foundations of data. And really this is about the,
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the language of data and being able to, to distinguish between
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the, you know, the different averages, mode, median, mean,
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understanding the, the names of different charts that you might
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be looking at, understanding that in different situations,
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there are going to be different biases that will be introduced.
13:12
Understanding a whole raft of other concepts that don't take
13:17
long, and many of which are, are, are very simple and, and we
13:21
use all the time. It's just that we, we don't, we
13:24
aren't explicit about it. And what that skill 2 does is it
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starts to make some of those things more explicit so that
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everyone can relate to each other.
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And it, it means it makes life a lot easier, makes it a lot
13:35
easier to communicate between leaders and, and technologists
13:39
or technical experts. So the the third skill is then
13:44
about interpreting and influencing with data.
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So actually being able to spot trends, being able to, to
13:52
understand that, yeah, you may not be looking at all of the
13:58
data, starting to question where that data is coming from,
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starting to, to spot that some data, if data is missing or if
14:07
data is incorrect, then it might be throwing out your results or
14:11
introducing anomalies. And, and it's that curiosity of
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asking why and, and then being able to, to, to know what to do
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about it. The, the 4th skill is then the
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one that I guess lots of people jump on and, and it's all about
14:29
storytelling and visualising with data.
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And, and again, from a project leader's perspective, it's not
14:35
necessarily going to be the case that you are going to be the,
14:39
the, the expert that's able to produce incredibly detailed
14:44
visualisations using some specialist tools.
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But as a leader, you need to, need to be able to articulate
14:54
the, the story or talk about the storyboard and, and ultimately
14:59
ask direct somebody to, to be able to create that for you.
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And if you've not got that understanding of some of the,
15:06
of, of how those things get made-up, then it makes it much
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more difficult for you to be able to do that.
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And then the final skill, which is I think the one that most
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naturally sits with project leaders is then making decisions
15:22
with data. How do you make those decisions?
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How do you and, and for, for so many of us, that's then making
15:28
decisions under under uncertainty.
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And, and often people then do rely on their guts to to make
15:35
decisions. And, and, and many leaders will
15:39
say, well, yeah, I don't have the information at my
15:42
fingertips, so I have to rely on my gut for these things.
15:45
It's not, it's not available to me.
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And in many situations, that's fine.
15:51
That's borne out of lots of different experience.
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But then those experiences, as project delivery gets ever more
16:00
complex, our own experiences can only ever provide a sliver of of
16:06
the answer in a sliver of the understanding.
16:09
And without tapping into these other skills and these other
16:12
tools, then you are limiting your your ability to continue to
16:18
be able to make good decisions. So thanks, Gareth, that was a
16:23
really, really concise way of summing these skills up.
16:29
Rob, what is there anything you'd like to add about how you
16:33
perceive data literacy and why it matters and also how what's
16:40
it like within your own organisation?
16:42
I want to ask both of you bring it back to the profession and
16:45
how far on the what the profession needs, you know, to
16:48
do to get up to speed on this. But yeah, Rob, I'd like to hear
16:52
your thoughts on that. Yeah.
16:54
So, so, so just in answer to your first question, I guess
16:57
it's probably quite a lot of crossover with my thoughts and,
17:01
and Gareth's thoughts. But I guess to me, kind of data
17:03
and literacy is about, it's about the ability to ask the
17:06
right questions of the data, which is something that Gareth's
17:10
answers kind of explored. It's also understanding its
17:14
limits. So again, kind of Gareth talked
17:17
about incomplete data sets and I guess having 100% information is
17:22
almost utopia if you like that you'll never quite reach.
17:26
And it's absolutely about using it to make better decisions.
17:33
It's quite easy for some folks to fall into the trap of being
17:37
obsessed around perhaps data inputs and the process
17:41
management around that. But much more important is the
17:44
ability to answer the So what question.
17:47
So what's the, what's the output or impact that's going to have a
17:52
result, a positive result for you, for your customer, for your
17:55
own organisation or for your supply chain, whichever
17:58
stakeholders you're serving. So that's that's why it's so
18:02
kind of important to me and I think it, it really is an aid to
18:06
improve predictability assurance, especially when
18:11
you're dealing with large portfolios of of work and to
18:14
build trust through objectivity. What What do you both think
18:21
about where the profession is along when it comes to data
18:24
literacy? Like, is there a lot of room for
18:26
improvement? What really needs to shift
18:29
across the profession to to make this happen?
18:33
I would say, I mean, I'm going to, I'm going to kind of base my
18:37
answer closer to home, so to speak in terms of what I see
18:40
across my own kind of organisation.
18:42
I think it's fair to say it's a pretty uneven kind of landscape.
18:48
So what is absolutely true is we've definitely made big bounce
18:54
forwards in terms of common tooling and column common
18:58
systems and common dashboards when it comes to data analysis
19:03
and management information. But there's definitely, there's
19:09
definitely a kind of gap perhaps in in how that's kind of
19:13
interpreted and how we kind of pivot from analysis to action.
19:18
I mean, some people are kind of masters in that, in that domain,
19:21
but I think there's a lot of variation in, in skills and
19:24
performance in, in, in that regard.
19:26
And we definitely need to be a more challenging organisation in
19:32
terms of the exploitation of that data management information
19:35
don't kind of fall into track just consuming it kind of almost
19:39
passively and make sure in programme management we're we're
19:43
challenging kind of assumptions. We're really clear on
19:45
definitions and we're really trying to drive quality from it.
19:50
Thank you. That's probably going to be a
19:52
common situation across many organisations that people work
19:55
for who are listening to this. Gareth, what would you like to
19:58
say on that? Well, so I mean that everything
20:01
that Rob said I think resonates across our organisation and
20:06
many, many other organisations that and, and for a lot of
20:10
people, the first step is making sure that there is common
20:12
tooling and that and that the inputs are starting to be
20:17
standardised to a degree. And that's hard.
20:22
That's, that's not an easy task in and of itself.
20:25
But as, as Rob said, the So what can often get lost either either
20:32
in terms of people aren't quite sure what they're asking for or
20:36
what the insight is that they're looking for off the back of it.
20:39
And we we spend so much time kind of doing the plumbing,
20:44
putting together the pipes and the wires, Everyone forgets
20:47
about the the water or the, you know, the fluid that's coming
20:51
out the other end. What and what are we trying to
20:54
do with it? How are we going to use it?
20:56
And yeah, so we end up kind of spending lots of time trying to
21:02
perfect things that that actually don't need to be
21:05
perfected. It can be good enough.
21:07
And I think often, yeah, the perfection side of things gets
21:11
in the way of of just having good enough data or a good
21:15
enough set of visuals to be able to answer the immediate question
21:19
and then make the decision and move on to the next thing.
21:24
So and and I think a lot of this is then amplified by AAI at the
21:30
moment. There's there's lots of noise
21:34
about it and there's so many positive things, but my fear is
21:39
that without some of the foundations, we're at risk of
21:42
trying to run before we can walk and that then that just sets us
21:48
up for a four if we're not careful.
21:50
Yeah, Yeah. You're not the first person to
21:52
raise that issue. I think many organisations are
21:54
worried about this. But it's really interesting what
21:58
you're both saying about there kind of being pockets of
22:00
probably higher expertise and understanding and, and other
22:04
areas across an organisation where it's not that much
22:09
interest or doing the bare minimum or misunderstanding of
22:11
what it could do or like under appreciation of how it can help
22:15
you in your work. What how so how should one go
22:20
about improving data literacy? I guess within, I guess within 3
22:26
parameters, there's an organisation who works with on
22:29
projects as a project team and then as an individual.
22:32
So the, the framework is brilliant across all of those,
22:36
particularly for as an individual to understand.
22:38
But honestly, like where do you, how do you go about improving
22:42
this? Someone might be listening who
22:44
is project manager, project leader realises the importance
22:47
of this, perhaps not receiving that much support from the
22:50
organisation. So how do you go about at those
22:52
3 levels, team, organisation, individual to kind of really
22:56
getting to grips with this and and learning about how to do it
23:01
right? So weren't, yeah, as you say,
23:05
that's that's exactly how we designed the the framework as a
23:12
as a conversation starter as much as anything else at at
23:15
those 3 levels. So it's, it's easy enough to
23:19
pick it up and, and just run through and the, the, the
23:24
categories there are sort of five levels of of skill and they
23:29
tie back to the, the competencies from awareness
23:33
through to expert in in aligned to APM competence frameworks.
23:39
So there's, it's easy enough for somebody to pick it up and just
23:45
take a look and, and give themselves 5-10 minutes to
23:49
process and work out where they see themselves across each of
23:52
the five levels. And, and from that
23:55
self-assessment, work out which of those we have where they may
24:00
have some blind spots. And there there's so many.
24:05
I mean, it's so easy now to start Googling once you've done
24:07
that, once you've got that. So hopefully that at the an
24:10
individual level, what the framework does is it gives you
24:12
that that. So it's like a checklist, isn't
24:16
it? Like, what do I know?
24:18
What don't I know? What do I need to find out more
24:20
about? Yeah, exactly.
24:23
And then? And then what, what you can do
24:28
with it at the team level is use it at the beginning of a team
24:31
meeting as a just taking five questions, one on each of them.
24:36
Get get each of each of your team members to score where they
24:40
see themselves against each of the five metrics.
24:43
It's simple enough and, and there's a there's, it's easy
24:46
enough to almost cherry pick the things that are then relevant to
24:49
your situation, your project to be able to to put those into
24:54
right. OK, well, for, for the level of
24:57
competence, actually, that's what this means for us.
25:00
And so make it quite specific to your particular project.
25:03
And then is everyone reached that level of competence or
25:05
actually are still people moving towards it and proficient but
25:10
not yet practised or yeah, there's so there's, there's
25:12
various different ways then that a team can do that.
25:16
And and then from that very quick 5-10 minute exercise as a
25:22
team, being able to to identify where there may well be some
25:26
blind spots, where there may be some untapped, untapped
25:30
expertise may well be that you have a somebody that's
25:35
incredibly passionate about visualising data that that
25:39
hasn't necessarily been yet or is is being underutilised it.
25:45
It seems like an obvious thing to me that if you have a team
25:49
where you decide that you say you have someone who's expert
25:52
has more expertise or understanding one thing that you
25:54
can quickly have a session, you know, like even a lunchtime
25:59
session where people come together and say, I can show you
26:02
this. I, you know, it's that sharing
26:04
that it doesn't have to be a formal thing.
26:06
It's like sharing the knowledge that people have already.
26:09
Is that a good idea? I'm just thinking of how people
26:11
could do something quite quickly.
26:14
Yeah, absolutely. It's, it's and a lot of it
26:17
because this is a, it's a soft skill.
26:19
It's not a technical skill. And it's, it's not as hard as
26:24
people think. It's because there are so many
26:27
practical examples when you start thinking about it.
26:31
And I think the challenge is that often data is seen as this
26:34
sort of ephemeral conceptual thing.
26:37
But it, it's, it's document management, our, our monthly
26:44
reporting, our, yeah, feedback mechanisms.
26:49
It's, it's, it's embedded within everything that we're doing.
26:53
And then so you can have a 10 minute conversation or a, yeah,
26:56
a lunchtime session to have somebody just show what they've
27:00
been doing or what they've what they've learnt and, and speak
27:03
about it. That's really great.
27:06
And then so at the, I think so we've covered kind of individual
27:10
project. What about an organisation?
27:11
Because then surely I'm jumping in here.
27:14
But if you have one team that's doing interesting things, it's
27:17
about sharing the knowledge across the whole organisation.
27:20
Exactly. Yeah, yeah.
27:22
And, and then, and then there's different ways that it can start
27:24
to apply between, yeah, inter team conversations and, and two
27:29
teams picking up and comparing where they are all the way up
27:33
to. It's, it's when we designed it,
27:37
we were thinking that actually you could build out, build out a
27:40
much more comprehensive sort of benchmarking exercise to, to
27:44
assess the, the whole organisation or large swathes of
27:48
an organisation or particular capabilities within an
27:54
organisation to, to benchmark where they are and then
27:58
introduce some specific measures to improve in, in certain areas
28:04
and and then re rebaseline or reassess.
28:08
Thanks, Gareth. Rob, how do you working within a
28:12
massive organisation like EED? How, how do you go about
28:16
assessing where people are on a kind of data literacy journey?
28:20
And any advice or things that you've seen work when it comes
28:24
to improving data literacy, understanding and skills and
28:27
competencies at individual team and and kind of leadership level
28:31
particularly interesting for you as a leader, like how one, how
28:35
one makes it known that this is something people need to care
28:39
about. I think there's so many sources
28:43
of information, learning and knowledge out there that are
28:47
available through open sources that there's no real excuse for
28:52
ignorance on anybody's kind of part.
28:54
I think even if you're, even if your company perhaps isn't as
28:57
supportive as they can be, you know, folks can use obviously
29:01
leverage the APM framework or LinkedIn training or other
29:05
mechanisms to, to, to kind of build the kind of foundation of
29:09
their knowledge. Then I think with that kind of
29:11
foundation, you know, established, I think there's a
29:15
lot to be said through kind of learning through lived
29:18
experience. So try and put that learning
29:20
into direct practise. I think some of that is almost
29:24
about forming habits in terms of how we use data management
29:31
discipline to drive good outcomes.
29:33
So a couple of practical examples might be to establish a
29:39
contract with yourself that you always triangulate multiple data
29:44
sources. You don't fall foul of being,
29:47
you know, biassed towards one data source.
29:50
You use multiple sources and take a 360 perspective around
29:54
the problem or the requirement that you're trying to kind of
29:57
solve. You might also make a contract
29:59
with yourself that you're always going to use data to inform
30:03
your, you know, decisions, whether that be a decision
30:07
internal to your team or a decision that's paramount to
30:10
your kind of customer. So I think you know, kind of
30:16
being a self learner, utilising those open sources, I think
30:21
building on those foundations through learning by lived
30:25
experience and then establishing some habits and principles that
30:30
you contract to work by. I think all of those things
30:33
would have a positive multiplying kind of effect to
30:37
then a bigger kind of organisational level.
30:40
I think it's really important that we see data info management
30:47
as being very much kind of, you know, core to our business, core
30:51
to how we operate. It's not something that sits on
30:54
the periphery. It's not an adjacency that's
30:57
only done by kind of specialist. It should be informing
31:01
everybody. Now there's an onus that flows
31:03
from that around the company establishing a strategy for for
31:09
how that should be done because as Gareth said earlier, data is
31:12
all around us. It kind of informs everything
31:15
we've done. And actually if we don't have a
31:17
strategy, we'll kind of lack structure and actually this
31:21
thing will kind of grow very organically and could actually
31:25
grow in quite a disorganised way that can't then be exploited to
31:29
best effect. So, and also if you've got a
31:33
strategy and you're clear on the North Star, what you're trying
31:36
to achieve and how data management contribute,
31:38
contribute towards that, it does make kind of investment
31:43
decisions and also investment dilemmas perhaps easier to
31:47
grapple with. And who's who does that sit
31:49
with? At which level of an
31:50
organisation should should this, who, who should be setting the
31:54
strategy? I mean, this is going right to
31:55
the top of the business. Yeah, right.
31:59
I guess it differs business by business in terms of how the
32:04
data and insight is represented, you know, at board level or
32:07
perhaps one level down or or or two level down.
32:11
And we, we're a technology company.
32:13
So we, we, we tend to establish very, very close relationships
32:17
with our CTIO function because quite often they're the
32:21
mechanism apparatus, the plumbing, as Gareth said
32:24
earlier, which enables this to be done at a kind of
32:28
organisational level, a corporate level.
32:33
So yeah, we're, we're, we're quite system orientated, CTIO
32:37
orientated, but it differs by company.
32:41
What can I ask both of you, what you think are the biggest
32:43
barriers to data literacy at the moment and how they might be
32:47
overcome? Maybe Rob, I think.
32:51
That the the the greatest barriers probably learning
32:56
knowledge, culture, mindset. I think all the component parts
33:01
of this topic, it's not rocket science in isolation.
33:07
It's not mega hard things to do, but actually it's how you make
33:12
it a organisational wide kind of endeavour and bring some
33:21
standardisation to that. Make it business as usual and
33:27
put the enablers in place to achieve that kind of outcome.
33:31
Be it like in the same way, cybersecurity Once Upon a time
33:35
was this peripheral area of expertise that used to take
33:40
place in a dark room which nobody really understood
33:43
anything about. Whereas actually everybody now
33:45
understands that cybersecurity and information security is
33:48
everybody's responsibility. There's there's certain kind of
33:50
parallels with with data management I would suggest.
33:55
Gareth, what would you say are the biggest sort of barriers?
33:58
Actually, Joe, what I wanted just to point out the way that
34:00
you said that this is about, it's not a tech skill, it's a
34:04
soft skill. That is a massive shift in
34:09
perception because I imagine pizza, one of the barriers might
34:12
be it's just techie. It's not.
34:15
I don't want to go down that route.
34:17
It's. Not that's really important to
34:19
make that distinction. Very much so.
34:22
And and I think there so some of the things that Rob touched on
34:26
in terms of leadership and culture and habits.
34:29
Coming back to something Rob said earlier.
34:31
And the way that you almost need to look at this is that it is
34:37
it's literacy, it is a language to learn.
34:41
And anyone that's tried learning a second language will know that
34:43
the hardest thing to do is to actually speak it.
34:47
You can, you can be great at all of your yeah, that, that verbs
34:51
and, and of being able to read the language.
34:53
But until you can have a conversation about it.
34:56
And that's, that's when you get the richness.
34:58
And that's when your, your yeah, your fluency really starts to
35:05
take off. And that's, it's, it's that sort
35:09
of fear factor of, of I'm going to, I'm going to stumble over my
35:13
words. I'm going to say the wrong
35:14
thing. I'm what I'm going to say, I'm
35:17
going to use the wrong terminology and yeah, and, and
35:21
yeah, I'm going to fail. And so there's a real sense in
35:23
and around this that's that's hard to overcome, but just start
35:29
talking. And as Rob said, it's it's that
35:32
habit of yeah, being able to find and it may well be on a
35:37
daily report that you look at. And so today, tomorrow, rather
35:42
than just skimming over it because it's what you've always
35:46
kind of snow blind to it because you see it everyday.
35:49
Stop and just take it, take a breath and look at the details.
35:55
How old is it? What's the source of the data
35:58
that you're getting that's on the report?
36:00
Are there any biases that you can see from it?
36:03
What's the story that the reports telling you?
36:06
Is it the same story that was being told yesterday or yeah.
36:10
Is, is there something that you don't understand or you don't,
36:13
you're not quite clear on? And then ask the question,
36:17
what's the, what does this mean? Why, why does this and this not
36:21
equal each other today when they did yesterday?
36:23
It's, it's those kind of things that you then start to see these
36:27
examples everywhere. And, and it's that, it's, it's
36:31
that individual habit that organisations need to then kind
36:36
of, you know, three, leadership and culture and cultural change
36:40
to, to be able to sort of embody and harness in the right way.
36:44
Otherwise, again, as Rob says it, it grows organically and
36:48
which is great, but it doesn't necessarily then align to the
36:52
organisational objectives and the organisational priorities.
36:55
And then there's a sweet spot to hit in and amongst all of that.
36:59
Advertising with the way that people are experimenting with
37:01
AI. So lots of companies like
37:03
individuals going off and doing their own thing in certain
37:07
departments going off and it all gets a little bit chaotic and
37:10
messy and people doing stuff because they feel like they
37:13
ought to. But what, why are you doing
37:15
this? And let's let's share knowledge
37:17
and think of it in a kind of more strategic way.
37:22
So really the one of the most important questions I haven't
37:25
asked you yet, although we have touched on it a bit is why
37:28
bother? So what are the benefits that
37:30
are kind of So what to, to cracking data literacy?
37:34
And can you give me some examples, example or two where
37:41
you've used it, you've, you've had good data literacy on
37:44
particular projects or programme and it's brought better outcomes
37:47
or benefits. So Rob, if you'd like to go
37:50
first and then Gareth. So there's a, there's a number
37:54
of things that this kind of drives and enables.
37:58
I mean, one of the first things is, is kind of confident
38:01
executive decisions. I mean what I'm involved in is
38:04
always major programmes. So big, big technology change
38:07
programmes that have big impact to our customer organisation and
38:11
to their, their end users and their end customers.
38:14
So data definitely, definitely, definitely enables and supports
38:19
confident executive decisions. And often those confident
38:23
executive decisions actually are a fuel and propellant for a
38:29
programme. Because actually if you get into
38:31
decision kind of paralysis because you haven't got good
38:36
data, empirical inputs, that can be really, really dangerous for
38:40
programmes. Health and success, perhaps
38:44
building on that a little bit, good data management also assist
38:49
greatly around risk management. So it's how we use it to drive
38:55
early intervention in kind of failed delivery.
39:02
And we are actually right now looking at how AI and predictive
39:09
analytics can kind of further take us on the journey of kind
39:13
of almost, you know, reactive to proactive to predictive and
39:18
almost at the planning stage informed by AI and predictive
39:23
analytics. Plan for success on day zero
39:27
because you've got all of the data benefit of all the failings
39:31
of projects and programmes that you've run before and the perils
39:35
kind of associated with, with that, which if we can harness
39:39
that will be extremely helpful in minimising any variation in
39:45
our major programme performance. And probably lastly, just how it
39:52
can help us almost in the visualisation and the
39:57
articulation of confidence levels.
40:00
I mean, I work in a world of IT and network, so kind of
40:04
precision and reliability. Is everything we can put a
40:09
number on pretty much everything as well.
40:12
So being kind of empirically kind of enabled and be able to
40:18
embed that in the confidence levels we can communicate for
40:21
our storytelling is all quite key to to stakeholder
40:24
management. I've been thinking just the
40:28
headlines at the moment about a conflict, I guess that's what we
40:33
call it, incursion into Iran and the global impact of that on
40:41
markets, on energy prices. And every month there's
40:46
something else that is disrupting the kind of the
40:50
business as usual for many organisations, many
40:53
international ones. Does data literacy give you a
40:59
sense of kind of greater understanding of or prediction
41:03
or stability? Is that something that is
41:06
relevant to helping manage the ever greater uncertainty and
41:10
volatility that certainly multinational organisations are
41:14
are trying to get grapple with at the moment?
41:17
As an international company that deals in IT and network services
41:22
into multinational companies and we've got good representation in
41:25
that region. The current situation is, is
41:27
definitely a concern. I mean, I think it's also kind
41:30
of Fair to say that we, you know, we are leveraging, you
41:35
know, our existing kind of data management methods where it
41:39
comes to service assurance. So in that sense how we operate
41:45
is is kind of BAU, but what we tend to do is the wrapper around
41:49
it is more our business continuity management, crisis
41:54
management kind of arrangements. And you know, we have daily
41:58
stand ups where we use quite specific data points to kind of
42:02
confirm and assure that both our primary services, secondary
42:06
services, resilient services are still intact and has no impact
42:11
to the customer and is not presenting any further risk or
42:15
threat to the to the customer. So this is something we're we're
42:19
putting in practise kind of daily through those command and
42:22
control arrangements. OK, thank you.
42:24
And and Gareth, what why should we bother with data literacy?
42:29
What benefits does it bring? I think so, as Rob touched on
42:34
many of these things already in terms of the ability to speed up
42:38
governance, reduce friction between people and across teams,
42:42
win people around with stories that are backed up by evidence
42:46
rather than yeah, yeah, leaving things to, to chance, bridging
42:52
the gap between technical specialists and senior
42:55
leadership, reducing the amount of rework.
42:58
These are all things that that data literacy contributes to.
43:02
But for me, the, the biggest benefit is that it turns your
43:08
data and your information into strategic assets.
43:12
And then the benefits and and the achievements that you can,
43:17
yeah, that you can make happen with it are literally only
43:21
limited by your imagination. I guess this comes all the way
43:25
back to your first question as to why this excites me so much
43:28
is that it is. And especially now with, with
43:32
the the advances and the exponential advances in the, the
43:36
quality of AI tooling, all reliant on good quality data,
43:42
but you're literally only limited by your imagination.
43:45
So risk mitigation, how we can get better at predicting and
43:49
preventing risks and identification of variants,
43:52
greater cost control, new service lines.
43:56
And yeah, so whatever it is that you're trying to achieve, data
44:00
literacy, data management and treating your data as a
44:05
strategic asset will help you get there faster than you are
44:08
doing guaranteed. Before you wrap up, because I'd
44:14
like to ask you, each of you, if there's one takeover, one piece
44:17
of advice you'd like to pass on what what that might be.
44:21
But there's a kind of lot of thought going around at the
44:25
moment. We haven't really talked too
44:27
much about AI, but it I guess it was thinking about the soft
44:34
skills that the skills you need as a project professional to
44:37
thrive in the next 5 to 10 years.
44:42
And AI is only going to increase in its importance in the way
44:47
that projects are managed to run, delivered.
44:52
If you are, if someone's listening to this, thinking what
44:56
the skills I need to hone, part of that is a critical skill will
45:01
be kind of fluency with data and as part of that AI.
45:07
Would you agree with that? I would absolutely I would
45:13
absolutely kind of agree with that.
45:15
And I guess there is some very tight relationship come
45:18
crossover between kind of data literacy and the the power and
45:23
opportunity around AII mean in the world of project and
45:28
programme management. I I don't see AI replacing the
45:34
human in terms of project and programme manager.
45:37
I see. I see kind of AI and data
45:40
literacy as being mechanisms by which we can make the the job of
45:45
those individuals easier. Certainly kind of automate some
45:48
of the repeatable tasks when it comes to like programme control.
45:51
So how we manage risks, how we manage issues, how we deal with
45:55
assumptions, how we enable good decision support, so on and and
46:00
so forth. And, and really kind of enable
46:03
the and project to programme management.
46:05
It could be, could have always like pushed up the, the value
46:08
chain and give them the time and space to actually spend more
46:12
time managing upwards and outwards rather than downwards
46:16
and, and, and inwards. And some of the data will give
46:20
them the kind of the, the confidence and assurance that
46:24
their internal machine is working as expected and they
46:28
won't have to spend unnecessary time checking and duplicating
46:32
the work of others. So I see, I see all of these
46:35
things coming together as a bit of a positive ecosystem.
46:39
What do you mean by managing upwards?
46:40
Can you just expand on that? Yes, yes, I can.
46:44
I mean, I think again kind of reflect on on operations kind of
46:49
nearer to home, we can find that our projects and programme
46:55
managers spend too much time managing internal functions and
47:00
teams and assuring that their performance that's contributing
47:04
to the customer outcome you know is on track, is on plan.
47:08
It's the right point of quality, it's the right point of cost,
47:11
it's the right point of schedule, so on and so forth.
47:15
Through kind of data management techniques.
47:18
Some of that assurance can be done for them through system
47:22
means. I actually just trigger
47:24
interventions when strictly necessary rather than having a
47:27
big kind of time consumption of ongoing kind of checking and
47:32
assurance. And actually far more value can
47:35
be given by actually working outwards and upwards into the
47:39
client organisation, acting as their trusted advisor, helping
47:44
them with their decision making that's going to lead to the
47:47
joint success or failure of a programme, so on and so forth.
47:50
So that's that's what I meant by that Quit.
47:54
Thanks for a really interesting Gareth.
47:56
What? What would you like to say?
47:59
I'd say that there's not a magic bullet for this, that it's not
48:03
something that you can, you can go on a single training course
48:07
and all of a sudden you can, you can do all of these things.
48:10
You have to practise it, you have to engage with it.
48:13
And so I would encourage people to to take a look at the skills
48:17
framework that we've produced, get a sense of where you are and
48:20
where you might want to improve. And yeah, there are, there are
48:25
then so many online sort of Open Access resources for people to
48:30
to start Googling, start start making use of.
48:34
And AI is a fantastic tool to just yeah, if you, if you don't
48:38
want to have a conversation with, with another person, just
48:41
start talking to one of the yeah, your AI tool of choice.
48:44
And, and, and you will very quickly learn a lot more about
48:49
this subject. I guess the, the, in terms of,
48:54
as you mentioned, some of those kind of core capabilities for,
48:57
for project leaders going forwards.
49:00
And, and obviously, yeah, the, the, the piece around AI and AI
49:07
is not going to replace the need for data literacy.
49:10
What it's going to do is amplify all sorts of capabilities, but
49:16
also potentially the mistakes and data literacy is one part of
49:22
enabling you to be able to determine the difference between
49:25
the two and, and avoid some of those pitfalls.
49:28
Make sure that you are in a in a place to be able to understand
49:33
the options that are that are being presented to you and and
49:36
make the right decisions. Rob, is there one piece of
49:41
advice take away you'd like to pass on to listeners?
49:44
Yeah, it's probably, I guess it's probably more of a strap
49:47
line than one piece, but definitely be self starter, self
49:50
learner, be curious and put in practise would be it.
49:55
So about mindset, yeah, OK, like that.
49:58
Gareth, what would yours be? So I've spent the first half of
50:03
my career focusing on unravelling digital pipes and
50:07
wires and, and a lot around that kind of plumbing.
50:11
And I, I want my second second-half of my career to be
50:17
about making sure that the people at the end of those pipes
50:20
actually know what they're doing with the insight that's flowing
50:23
through them. And so my piece of advice is be
50:27
constantly asking So what? And and it's it's the coining
50:31
phrase that Rob used earlier. What is the action that this is
50:36
going to drive? What's the value that this is
50:39
going to drive? How can we make it real?
50:43
Rather than, yeah, focusing on capturing more and more data
50:49
with with little without that. So what without that purpose.
50:53
So I guess, yeah, it's that sense of purpose and and
50:59
forethought of what you're going into and why.
51:02
Yeah, Know why you need to dip data you're after and what
51:06
you're going to do and have that understanding, just being smart
51:09
about it. OK, that is really fantastic.
51:12
Thank you both. It just leaves me to say thanks
51:15
for for joining us on the OPM podcast.
51:17
Really appreciated. Appreciate you sparing the time
51:21
and what an interesting conversation.
51:23
So thanks very much. Thank you.
51:26
Thank you very much, Emma. Thanks again to Rob and Gareth
51:28
for joining us and to you for listening to the APM Podcast.
51:32
If you're interested in finding out more about data literacy,
51:35
please go to theresourcessectionoftheapmwebsite@apm.org.uk
51:40
and look out for the Spring 2026 issue projects where we go into
51:44
the subjects in more depth. Anyway, don't forget to look out
51:47
for more episodes or to rate and reviews.
51:49
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52:05
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