Project Data Analytics - How do we leverage experience of project delivery? webinar, Question responses, 20 March 2018

Benefits Management
Specific Interest Group (SIG)
Project Data Analytics - How do we leverage
experience of project delivery? webinar
Questions and Answers
Tuesday 20 March 2018
Martin Paver
established London Project Data and Analytics Meetup
Webinar speaker

Please note that the answers given here are the opinions of the people concerned,
who are experienced practitioners and they do not necessarily reflect the guidance
in the APM BoK or other text books.
1. What are your thoughts on whether this is cultural change or change in processes to
getting to the end result you are trying to achieve?
It’s definitely a cultural challenge. The first step is to 'ignite the professional imagination' on the art of
the possible and build out from there. Processes can follow to formalise whatever successes have
been achieved. I'd recommend an agile based methodology with pilots. But work towards a bold and
transformative vision.

2. How do we stop organisations from forcing their rally drivers to go too fast / with the wrong
approach despite their professional opinion and advice?
There is a point when speed becomes reckless. If project managers are proven to be reckless then
there should be consequences. As an engineer, if I cut corners and my product costs my client a lot
of money then there are implications. We need to change the culture (and probably pay people more
as a consequence).
In time, machine learning should be able to provide early warnings. This should drive the agenda for
the project board and also help to shape coaching & assurance interventions.

3. Connected data - what is that picture telling us - what insight can you gain?
The connected data picture illustrates that there are connections in the data that we can explore
further or query. For example, understanding the connectivity between risks, how they evolve and
snowball, how the risks relate to lessons and impact. In essence it’s the ability to extract insights from
the connections in the data.

4. This feels time consuming and complex - i.e. resource heavy. Is that right? and how do you
justify the time collating this information and investing in the tools to mine that?
I don't believe it is. Organisations already collect schedule, risk, lesson, issue, cost, data, etc. It’s a
matter of extracting this against a series of use cases to solve specific problems. Some of the tools
are free, its matter of how they are integrated and supported in the 'stack'. In terms of justification, it’s
a conventional cost benefit analysis. I wouldn't recommend approaching this by promising a big bang
vision of a beautiful future, but to set that vision within the context of implementation sprints, with
gates to measure progress and build momentum.

5. Can you explain more about what is machine learning?
There is a lot of material on the internet. I would recommend Andrew Ng's videos such as
https://www.youtube.com/watch?v=UzxYlbK2c7E . The Udacity videos are a bit easier to understand
see https://www.youtube.com/watch?v=ICKBWIkfeJ8

6. Were the original budgets realistic based on reference based estimating or were the
budgets politically influenced?
There is a large body of work on this. I would recommend Bent Flyvbjerg's work. It depends on the
project. Some projects suffer from 'strategic misrepresentation', http://bit.ly/2IKj0xV i.e. the business
case is oversold. But this isn't the only reason for the cost misalignment. Bias is a major factor. It also
depends whether there is something to reference against - if it is a once in a lifetime, complex and
innovative project then the point of reference will require a greater level of abstraction.

7. If someone is looking to start a career in this sector, what kind of roles can the person apply
for?
It’s a very emergent industry. The P3M market isn't quite ready for this yet, but I expect this to change
by the end of this year. I'd recommend finding roles that enable you to delve into the detail, whilst
doing some of the Coursera or Udacity nano degrees.

8. You have already touched on the fact that PMs are, more often than not, pushed for time
and are, therefore, reactive. This, in my experience leads to pushing on to the next
stage/sprint without properly (if at all) recording lessons learnt. What's your recommendation
for approaching this problem? And how can we best promote the recording of meaningful
lessons?
The lessons are rarely useful to them so they try and fill in the forms and get on with their job. The
turning point will be when they understand the intrinsic value in the data and what it can do for them
and their organisation. When the Board understand the potential then I would envisage that it will
become a key part of any organisation's data to day work.

9. Does the data take account of the increased import costs for materials resulting from
weakened pound?
There are lessons associated with currency and commodity risks. The lessons could be to contract in
GBP or hedge forward.

10. Is data analytics compatible with BIM?
Yes. BIM is subset of project data. But it also extends beyond project 'delivery data' into virtual
models and a range of other attributes. They are complementary and there is an overlap.

11. Do you think that one of the problems that we face is that we don't have qualified people in
project management positions? I know for my industry, many individuals with Engineering
skills/experience are advanced into project management positions...
Partly. It depends on the sector. I've worked with some highly qualified and experienced PMs. I've
also worked with some who have fallen into it. A data centric approach will really help those with less
experience because it should provide checklists, prompts and insights. But we have to be careful
because intellectual ownership must reside with the individual. It would be a dangerous future if
projects failed because the 'computer told me to do it'. Less experienced PMs should also seek wise
counsel from others when they approach a difficult part of the rally track.

12. Is there a standard form of software, or a standard body where the data can be
collaboratively shared?
Not yet. Government currently have a reluctance to open up data. But we are falling behind other
countries (see itdashboard.gov). In my opinion if the UK wants to be a leader in this then we need
data. Government has most of it and have an obligation to share it. If you believe this then campaign
to open it up and we'll get there.
I'm working with the Major Projects Association to try and pool data, but it is very early days. There
are a lot of issues to consider to ensure that it is used in a mature way, isn't compromised and is
appropriately anonymised.

13. Whilst part of the problem is a personal lack of application of knowledge, how much is
also to do with organisational culture and commitment?
The culture is the main barrier. Please see my earlier answer. But culture can't be changed overnight.

14. With reference to what we are seeing in the news today, and bearing in mind GDPR, how
easy will it really be to 'pool data'?
GDPR is about personal data. This challenge relates to project data. I've already seen in some of the
data sets that names are redacted, but we don't need names or personal data for 99% of the use
cases. This may differ inside of an organisation if there is a decision to use personal delivery
performance to inform machine learning, e.g.' Tony always skips over front end loading and this
results in problems in design; we need to provide more guidance to Tony and ensure that we provide
appropriate coaching and oversight‘.

Thank you for your
engagement with this
webinar and
this QandA session.

These questions were raised
during a presentation delivered at
an APM webinar
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Project Data Analytics - How do we leverage experience of project delivery? webinar, Question responses, 20 March 2018

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Project Data Analytics - How do we leverage experience of project delivery? webinar, Question responses, 20 March 2018