Hey, what's up, Toby? It's another week for the
Processed Ed podcast. Glad to be back. Glad to
see you. I know. We made it again. Hallelujah.
Today, the topic is Parkinson's Law. Do you know
what Parkinson's Law is? I know Parkinson's and
I know law, and I have a hunch it may not have
anything to do with either. No, it has nothing
to do with either, although I did, yeah, my uncle
did have Parkinson's, so that was really sad.
Yeah, that all right topic but no Parkins law.
All right, Parkinson's law is Limit time limit
the time it takes to complete a task to what
users expect it to take This is the back to the
give a if you need something done give it to
a busy person that is correct That is 100 % correct
Wow, that's actually Work fills the amount of
time that is given. Right? So you've been an
adjunct professor forever. Right. If you give
your students two weeks to do something, how
long does it take? Right. Usually about two and
a half. At least for about 20 % of the students.
Oh my God. Irony, Chris, it could be a two hour
task, right? That was me too in my undergraduate,
you know. Oh, I mean, it's all of us, right?
It is. It is. If I have three things to do on
a Saturday, I'll get two done. If I have 15 things
to done to do, I'll have 15 done by noon. It's
you're absolutely right. Yeah. Well, and it's
it's crazy, right? Because, like, first of all,
we were terrible at time managing time as humans.
Like we like. You figure out you get generative
AI to figure out how to predict time Well, and
you will be a trillionaire. You'll be the first
trillionaire on earth. I Think there's truth
to that time is so it's such an elusive construct
and we we inflict that concept on each other
and Then none of us managing well, so well maybe
and maybe I'm a little wrong here right because
we were supposed to have autonomous cars and
1980, I think, or something like that. Or the
2000s, because that was going to be them. And
we were going to fly to Mars. And apparently,
there's different constraints. But those are
key. We keep selling those items. Anyway, we
got a little bit distracted here. So Parkinson's
law and process. It's one of the reasons why
we have processed debt is Parkinson's law, because
we don't simply allocate time to getting better
at our craft. Yeah. Thinking back over your career,
were there certain conditions where that was
cultivated or has that always been a challenge
for you? Can you think of roles that you held
where you guys specifically blocked time to think
about process improvement? No. I mean, like,
I wish the answer was yes. Yeah. Right. Like,
what's funny, here's what's typically happened
in my career. I just got really dogged on something
that no one really cared about that was very
important. Yeah. And I just ground on that thing
for a year, for two years. It literally didn't
matter because I knew it was important and I
knew the outcome. When it landed would be incredibly
valuable. And so I would basically borrow and
steal people's time. And then I would literally
go and get things done. And then at the end,
they'd be like, oh my gosh, this is amazing.
Where has this been? You're like, no one saw
it, right? My time horizon was really long because
it was really about outcome. And yet, you know,
on the process side, that's really to deliver
to internal customers or external customers.
That's not necessarily around the process of
doing business, which again is like, you know,
last week we talked about rituals and the importance
of just doing things over and over again. Like
we don't think about the impact that we can have
on those repeatable processes if we improve the
repeatable ones. Because oftentimes we don't
think about the process of doing the work, we
just think of what's expected today, right? Which
is a fancy PowerPoint or a new spreadsheet, right?
What you're speaking to is, if we get 20 requests
a month, how do we think about how the request
shows up, how we prioritize or accept the request,
perform the work and deliver it? We're so often
just trying to catch the next ball and make sure
we don't drop it. Like you, I've done a considerable
amount of concerted work around process improvement
in teams, facilitating process change, helping
drive adoption. There's really only been one
or two roles in my career where I was held accountable
for a very structured recurring process. The
one that immediately comes to mind as you bring
this up was we would do monthly sales reports.
And at the time, our tools weren't sophisticated
enough and the tools we had made ugly stuff.
And so we were basically downloading data and
manipulating it in Excel and then painting crap
into PowerPoint, but super time intensive. But
because we had The reporting cycle, the goal
was to get fresh information into the hands of
decision makers as quickly as we could, which
I think that goal is still very much the same
around, right? But at the time, this predates
any of our cloud -based stuff. And so much of
the fantasy of data warehouses, we were doing
it in Excel. We'd take a flat file, we'd dump
in the flat file, and then it was pre -mapped
to populate all of the appropriate reports. So,
if there was a delay, it was in capturing those
reports that were structured in Excel and grabbing
the screenshots and pasting them into a new PowerPoint
file that we could then distribute. What's interesting,
though, is that over the course of about four
months, we cut our time in half and cut our time
in half again. And as an Excel jockey, you appreciate,
oh, what will Visual Basic do for me? And I think
that today's automation tools, the challenge
is that we haven't done the process first. Instead,
we lead with tooling instead of with process
and desired outcome. And so it's really hard
to know that what we're delivering is useful
if all we're doing is accelerating how fast we
push stuff out. And so that first one is almost
by necessity a little bit of a slow slog. Because
we have to explore, we have to understand what's
available, and then we have to make conscious
choices about what's included. So, Parkinson's
Law is really interesting because unless there's
urgency in that, the design of a process can
take months. When in truth, it's like, how many
requests did we get last month? How many results
did we deliver? And how do we do those? There's
outliers, let's identify them, but that cycle
time? You know, if you're producing, if you're
doing that hard look at process once a month
for three or four months in a row, it can get
tightened so fast. Well, what's interesting is
like that you're talking about like precision.
Yeah. Right. And I think one of the maybe myths
around process is that they have to always be
precise. Right. And in knowledge work, they just
don't. Right. And what I mean by that is like,
you might have a metric of I want to make sure
that from a quantitative and qualitative, I want
to be able to quantify that there's X amount
of people that I have that I'm actively engaging.
The qualitative, that can be kind of nebulous.
And so it doesn't necessarily have to be super
precise, but it has to be repeatable. And so
again, it's like, you know, the whole Parkinson's
love, like, look, can you make it better? You
100 % can, right? And you always should. But
that's an infinite problem, right? Which is like,
look, on the process side of the house, like,
it should be consistently getting better. And
it should be tightening from a, like you said,
from an output standpoint. and from a quality
standpoint, but we're funny as humans because
we just want it to be shiny. And so we change
it all the time. And then you don't have process.
You basically have new shiny things that may
or may not align to the decision maker. Because
what you don't want is you don't want a board
member to be just amused and amazed with the
shiny object. when you don't understand that
that shiny object is 0 .1 % of your total revenue.
Which is a legitimate risk, right? We get excited
about things that excite us. So new information
or a new toy, a new tool, it quickly captures
our attention. I do think that, I think it's
easy to neglect process. especially recurring
processes, because they aren't shiny and sexy.
It's like the drum beat, you know? A couple exceptions
in drumming aficionados, most people might know
the names of the lead singer, the lead guitarist,
not always the drummer or the bassist. Those
are those very measured, recurring things that
everybody else falls in step with. whether we
call it ritual or recurring process, there's
a lot to be said for putting it on the clock.
I do think having a net setting expectations
around a healthy duration, this is where benchmarking
I think helps us get out of the Parkinson's law
as teams within organizations. When we go out
and find out what is the typical, in the accounting
and finance world, they'll talk about the month
and close. Business is done for the month of
October. So how many days until we can see the
results, until all the financials are settled?
And over the years, teams have said we can't
possibly do it in less than 12 days. And other
teams will say heads are going to roll if it
takes more than two. How fascinating that we
could have such a variety of expectations when
they're doing the same thing. They're closing
the books for the month. and yet it has everything
to do with what's deemed tolerable. What's interesting,
and we both have been around fintech and finance
so much throughout our career, it's very interesting
because that ritual of the monthly close and
the standard reporting, which is the balance
sheet, the income statement, and the cash flow
statement, You can drive a lot of business just
on the monthly close and those three documents,
right? But you go outside of finance and you
go into operations and you might have inventory
metrics and you have sales, but like, it gets
so much more nebulous, right? And so inconsistent,
even though most businesses... are really doing
the same thing, but there just lacks inconsistency.
And some of that is because you're trying to
push those three reports, the ballot sheet, the
income statement. That's right. Well, it's interesting.
One of the most sophisticated reporting teams
I worked on, and I continue to reference them.
It's been years and years ago. Within about 48
hours of billing cycle completing, and it was
a prolonged billing cycle. It was like a 72 hour
billing cycle. But within about 48 hours of finishing
it, we had pre -populated templates so that we
could start to have, we could see who our top
10 customers were by region and how they changed
month over month. We could see where we were
churning. on product and we could see where our
units times price variances were showing up.
It didn't happen overnight. And yet, I've yet
in my career to see comparable reporting. Part
of it is I've drifted further away from that
functional team, but rarely have I seen that
kind of intelligence produced, let alone produced
so rapidly after a month. And I think about,
it's kind of like driving. And so if all you
have is a windshield and no dashboard, You're
guessing at your speed and you're hoping you're
not out of gas. I think most of us had a car
with a gas gauge at some point. But with today's
dashboards and cars, we have a lot more information
from engine health to projected miles based on
the gas and the tank. Make better decisions when
we have more information at our fingertips. For
sure. Yeah, this idea is long. Well, it's interesting,
right? You brought up 72 hours, right? You also
talked about two -day book close and a 10 -day
book close, right? And the reality is, is you
know what happens? You've got this habit of a
10 -day close, a two -day close, and a 72 -hour
close, right? The reality is, is like someone
gave them a deadline. Those deadlines are important,
right? Now, that being said, like it probably
took years to get to the 72 hour close but like
someone was always pushing on that and I think
that's ultimately like kind of the main point
here, which is Parkinson's law says look Here
is what you know in sports parlance. Here's what
the standard is Yeah, and that standard needs
to be met and so it's always pushing towards
that whatever that standard is and I think a
lot of times businesses forget that there's people
doing things and actually doing a 70 % job oftentimes
is going to be better than that 95 high precision
that an executive expects because there's some
fudging of numbers in getting to the 70 to 90%.
Like the decision -making ability on 70 % is
going to be far outweigh the time and effort
that it takes to get to the 97. when that extra
precision is actually not even paying off. Yeah.
That's a really good point, Chris. I think we're
always making decisions with incomplete information,
right? And so, whereas Parkinson's law would
have us fill the time we have with whatever tasks
we have, the flip side is... And I don't know
what the law is, but it's how fast can we make
a decision? How much information is necessary
to make the decision? That's called Bayesian
statistics, my friend. That's what that's called.
You're the dumb guy on our podcast. You just
know the names of these things. I'll tell you,
I had a ticket. I had this upcoming trip and
I had a ticket that had, and we're in the middle
of a government shutdown. had a ticket that has
me leaving Portland to Denver to DC, and then
on to Spain. And in the middle of a shutdown
and many weeks in, I'm thinking, what are the
odds that I'll get to Spain anywhere near the
day I plan? By contrast, I went out and I started
looking around. I have a ticket now that takes
me directly from Portland to Amsterdam. So it
completely pulls me out of the briar patch, right?
And so then I have a, so I trade two stops in
a unmanaged environment. So I have no control
over that time, but I've also introduced all
these other actors that could change my timeline
in the original case. Now I have a single actor,
which isn't in the middle of a, of a governmental
shutdown. So these dynamics, right, the likelihood
and how much information It's really interesting
because I remember thinking, I'm super uncomfortable
with backing out of this ticket and yet I sure
slept better after I rescheduled. I feel like
that's true of so many elements in life. We're
committed to it. It's a plan we've had in motion.
We feel like it's settled law. It's like sunk
cost. Even though I was able to refund tickets,
yeah, there's a lot of these moving parts. What's
interesting is like, process debt, right? So
like, make sure that you allocate time to improve,
right? That's kind of the moral of the story.
And then, you know, maybe next time we'll talk
about patient statistics, which is essentially
every time you get new data, update your prediction.
And that's, you know, I was a senior product
manager for a very long time. And that frustrates
the business more than anything. But the reality
is, the more information you have, the better
your prediction should be. The problem is, is
oftentimes the more information you have, the
longer your timelines are. Because you run into
the unknown unknowns, and you start to knock
those down. And so maybe that's our next podcast
is on Bayesian statistics. I'd love to come back
to that in the future. Yeah, let's do it. And
get more. That sounds good. Cool, man. Free time,
buddy. It's always good. All right. Thanks again.
Do this. All right. See you. Bye.