August 17, 2025 • 23 mins
James Bolle, Rethink Productivity's Head of Insight Development, reveals how businesses can transform raw productivity data into meaningful insights that drive tangible improvements. His career-spanning mission has focused on helping organisations make better decisions through data analysis, quality processes, and contextualisation.
- Rethink's rigorous three-layer quality control ensures trustworthy data analysis
- Standardised templates and visualisations make analysis more efficient and consistent
- Productivity data provides valuable benchmarking across industries
- Partnerships bring new methodologies, including video analysis
- AI offers significant potential but requires focus on specific, high-value applications
- Real examples show how small data insights created major productivity improvements
- Three key tips: know what you need to know, identify data gaps, structure data properly
- Even small businesses have valuable data that needs proper organisation
If you've enjoyed this episode, visit the Rethink Productivity website for more podcasts and resources to help your organisation improve efficiency and productivity.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:03):
Welcome to Rethink Revealed a podcast series from
Rethink Productivity that willdelve into the minds of our
productivity specialists to askthe deepest of productivity
questions.
And I'm your host, jamesBradbury-Willis, head of
Business Development at Rethink.
I'm a marketing and salesprofessional and I'm keen to get
the inside story from thepeople powering productivity.
And I'm keen to get the insidestory from the people powering
(00:25):
productivity.
I know how excited you all arethat I'm back for another
podcast or the fan mail iscausing issues at home but I
won't let that stop me.
Today we're joined by Bolle.
James Bolle, as well as having afabulous first name, he is also
our Head of Insight Developmenthere at Rethink.
He's the guy who takes raw dataand turns it into something
meaningful, spotting trends,uncovering opportunities and
(00:47):
helping our clients make smartdecisions.
With a background in consumerinsights, client services and
now leading our innovation andinsight, james is also behind
many of the ways we capture andanalyze data, including
partnering with the Universityof Portsmouth and exploring how
video and AI can unlock evenmore value Outside the world of
(01:07):
productivity.
James plays in a rock band andis currently training a puppy
which, depending on the day,might be the tougher challenge.
In this episode, we'll explorethe arts of data analysis and
how businesses can transformnumbers into action.
We'll dive into the qualitycontrol, the tools we use,
including AI, and what thefuture of Insight might look
(01:27):
like.
If you've ever wondered how wemake sense of the thousands of
data points we collect and turnthem into something that
actually drives change, thisepisode is for you, so let's get
into it.
Hey James, how are you doing?
Speaker 2 (01:39):
Yeah, great, thank you, james.
Very well today.
I've just had my oven fixed soI can cook dinner again.
I'm very happy you could feedthe family, which is always a
bonus.
Speaker 1 (01:49):
So, james, let's get cracking, based on a bit of a
little introduction, so we canget to know you a little bit
better.
Can you tell us a little bitabout your background and what
led you to the epicentre of dataand productivity that is
ReThink?
Speaker 2 (02:03):
Well, you summed up my background pretty well, james
.
I think my entire career hasbeen a quest to help people make
better decisions based on data,and the thing that really has
inspired me throughout that isthe idea that I'm helping people
understand their situation abit better and find their own
way.
So that started in marketresearch.
Long story short, I actuallychose to get into market
(02:23):
research because I did astatistics degree and I knew I
wanted a job where I could usemy skills to help people make
interesting decisions.
That led me into customerexperience, where I worked for
10 years again with data,helping to understand why people
behave the way they do.
And that led me into employeeexperience, where I was again
running surveys to help peopleunderstand their culture and how
(02:47):
people felt about working intheir businesses.
And it was when I was working incustomer experience that I met
Sue and Simon Hedo from RethinkProductivity.
Sue was a client of mine whenshe worked at Boots and then
became a colleague, and when arole became available at Rethink
kind of in 2023, it was achance that I jumped at because
(03:10):
I'd done some freelance workwith Sue and Simon previously.
Lots of interesting data in aninteresting area, and we really
can turn on lights for ourclients so they can find their
way.
It's that motivating factor ofhelping people understand what's
going on is a big win for me.
So yeah, jumped at the chancethen, and the thread that's gone
through everything is how doyou use data to make those
(03:33):
decisions and help peopleunderstand what to do?
So that's what led me here.
Speaker 1 (03:37):
And I suppose, from that respect, what is the most
rewarding part of your role?
Speaker 2 (03:41):
Well, it's when we really do help people find their
way.
So a few examples recently wewere working with a business
where their managers were doinglots and lots of admin and lots
of emails.
So, using AI, large languagemodels to analyze the notes that
our productivity consultantshad taken on the admin work and
(04:02):
the emails that were being sentto help understand what's
creating that traffic, to helpthem maybe reduce the admin load
on their managers the chartthat shows the issue for an
organization.
So, thinking of another clientwe worked with recently, showing
that some of their locationswere almost twice as efficient
as others, and really getting tothe heart of the variability in
that business and helping themunderstand what the next step to
(04:25):
take was or you know, whenyou're in a meeting with a
client, the question thatprompts a new way of thinking
about their business those arethe things that really reward me
, where working with Rethinkgives people a new perspective
and helps them be better, andthat really aligns, I think,
with with with rethink's purposeof surfacing great insights so
(04:46):
people can make positive changeso, james, moving on to our
quickfire questions, nice andnice and rapid answers good luck
.
Good luck with that.
I'm never, never one to use one, one word when ten will do,
okay well we'll see how we geton.
Speaker 1 (05:06):
We got ai guitar solo , or coffee, which one saves
your day more often um, well, Idon't drink caffeine, so it
can't be coffee.
Speaker 2 (05:16):
Um, guitar solos.
Honestly, I'm a bassist and Ispend my life trying to stop
guitars, guitarists solo, so wecan get on with the actual meat
of the song.
So I'm going to have to say AI.
I think in this case probablysomething that I do use day to
day.
Often it's a solution lookingfor a problem, but I think there
(05:37):
are lots of really neat waysthat you can use AI and, yeah,
I'm going to say AI, thank you.
Speaker 1 (05:42):
What's your guilty pleasure in excel?
Color coding cells, buildingformulas or hiding columns?
Speaker 2 (05:49):
uh, it's as simple as vlookups.
I love them good vlookup.
Speaker 1 (05:56):
Is quality actually um?
Which is harder to train aimodels or puppies?
Speaker 2 (06:03):
um, well, I mean, unfortunately it turns out you
can't just tell a puppysomething once and then it
understands it.
You have to repeat it over andover.
So probably puppies.
But the truth is, um, I don'tknow, uh, about training ai
models, that there are so manycosts associated with ai on top
(06:24):
of training the models.
I feel like actually trainingAI is the least of your problems
on AI.
So I'm going to say puppies.
Speaker 1 (06:30):
I agree with you on that.
Training is very difficult.
So, moving on to the generaldiscussion then, james so you
lead Rethink's insightdevelopment, can you walk us
through what that actuallyinvolves from a day-to-day and
from a cross-project perspective?
Speaker 2 (06:48):
Yeah, of course.
I mean.
The real heart of what RethinkProductivity do is based around
collecting data.
Wherever that data is, whereverpeople are doing processes, we
can go and collect the data, andwe have a team of superstar
productivity consultants whocollect that data using our
(07:09):
retime apps.
And that's the heart of thebusiness always has been and
always will be.
But then you need to figure outwhat are you going to do with
that data.
And something that Rethink isreally strong on is the level of
quality control, kind of thenumber of checks that go in on
top of that data.
This isn't people just kind ofbashing a clock and then we send
(07:31):
that raw data to our clients.
It will be checked by theproductivity consultants
themselves, it will be checkedby the project manager and then
somebody from the analysis teamwill check it a third time, so
we can be absolutely sure thatwhen we've got data to analyze,
it is of the highest quality.
Part of my my role isparticipating in in that, but
(07:53):
also because I'm head of insightdevelopment and not just head
of insight, it's also thinkingabout how modern technology such
as ai could complete some ofthe the narrow and routine
checks instead of instead ofpeople having to do it.
So once the data has beenchecked, it needs to be
organized and analyzed, andthat's the heart of the role
(08:13):
really.
So, day to day, I will be doinganalysis on client data with
Jamie and Sue.
I'll be working on theprocesses that we use to do that
client analysis.
So think about how to simplifythe analysis routines and maybe
productize it so that it getseasier and easier.
(08:33):
And then I'll be doing clientwork as well.
So, throughout my career, whileI've worked with data, one of
my skills is facilitation ofmeetings.
So when clients need workshopswhether it's process mapping,
whether it's customer experiencemapping, whether it's just
working through the data andcoming up with action plans I
also facilitate that.
So you know, the majority of mytime is analysing data and
(08:56):
thinking about our processes,with a smattering of client work
on top of it.
So, yeah, that's what I do dayto day and it's you know, on a
project, I'll be involved at thebeginning in terms of helping
understand how we're going toorganise the data once it's
collected and making sure theproject's set up right, and then
right at the very end in termsof cleaning, organising and
(09:17):
analysing that data.
Speaker 1 (09:18):
Yeah, great.
So I mean, I think you'vealready answered this question
is like once, obviously, onceyou've captured the data, what
are the?
You've already answered thisquestion is like once, obviously
, once you've captured the data,what you've mentioned the key
steps you take to transform itinto something meaningful and
actual for clients.
But is there a way you and yourteam, is there any ways that
you and your team have developedways of analyzing data?
Can you share how things likevideo analysis and partnerships
(09:39):
with university of portsmouthare helping shape the future of
what we do?
Yeah, absolutely.
Speaker 2 (09:45):
I mean, you know we talked about organising and
charting the data when I beganwith Rethink.
That was done ad hoc, perproject.
We now have a set of templatesthat we use and, you know, fixed
visualisations and set ways ofgetting the data so that we can
fulfil those visualisations,which has made us more
(10:08):
productive.
But we're also thinking about,you know, how do we build and
again using modern technologyhow do we build databases using
the right programming languages,python scripts built on SQL
databases and BI tools toautomate all that kind of stuff.
And you know we are I'mstanding on the shoulder of
(10:29):
giants here because we've beendoing this for 13 years and Sue
and Simon know the type ofanalysis you need to get to the
heart of the productivity issuesthat the businesses are facing.
So it's taking that knowledgeand that contextualization that
they can provide and trying tosimplify and productize that so
it's easier for people to get to.
I mean, there may be a worldsome years hence where people
(10:55):
don't need a presentation fromRethink because we've built a
system that can take all of ourretail knowledge, all of our
productivity knowledge, andautomate it alongside the data.
We're obviously some way fromthat right now, but that's the
world we're thinking about is,once it's organized, it's not
just about visualizing it, it'sabout interpreting it and
contextualizing it, and we'vegot 13 years of data that helps
(11:16):
us to do that, and that's wherepartner organizations have been
really useful.
So we've run a knowledgetransfer partnership with the
University of Portsmouth, whoyou mentioned, and our colleague
Rishan was our associate onthat project.
He's now a full-time employeeat Rethink and his project was
(11:37):
looking at how can we use somemodern techniques in order to
yield value from that historicdata.
So you could argue maybe astudy from 13 years ago isn't
relevant, given moderntechnology and given what's
happened since the pandemic.
But if you are a businessthinking about how efficient you
are or your productivity, it isuseful to know what the Rethink
(11:59):
database shows you from thelast five years in terms of how
other similar businesses aremore or less productive and what
techniques and tools they usethat make them so, because it's
not just knowing that you spend10% of your time with your
customers.
That's useful.
It's knowing how that compareswith your competitors, how they
free up more time to work withcustomers and how you can do it
as well.
(12:19):
That kind of tells the realstory, so the focus is always on
that.
Working with externalorganizations gives us new ideas
from academia, new ideas fromother industries that we haven't
thought about.
And we're actually about toembark on our second KTP with
the University of Portsmouthwhere we're looking at can we
(12:40):
use computer vision to analysevideos of productivity processes
?
So we're not looking to replaceour amazing productivity
consultants, but how can weenhance what they do?
Or how can we use video to dothings that they can't do, like
watch a till for the whole timethe store is open, count the
customers and how long they'rewaiting, and stuff like that.
(13:01):
We're looking at how we can dothat, and we're a relatively
small business.
We don't have that expertise inour organization, and
partnering with people like theUniversity of Portsmouth enables
us to take huge leaps forwardin those types of projects that
we wouldn't be able to make onour own.
Speaker 1 (13:20):
And I suppose with AI advancing so rapidly, what
opportunities and challenges doyou see when it comes to using
AI and productivity insights?
Speaker 2 (13:30):
Oh, yeah, I mean AI will revolutionize what we do,
but it hasn't done that yet.
Like, we work in quite aspecific niche in terms of the
types of data we collect and thetypes of interpretation we want
to make on it, and so you know,there are, there are ways that
you can train large languagemodels to with retrieval,
(13:51):
augmented generation, to to tobuild chatbots to interrogate
our data, but it's going to takea bit of work to get there
because some of the stuff out ofthe box I don't think it's
going to do do the job for usbecause it's too too generic for
what we're trying to solve.
Having said that, there arelots of narrow and routine tasks
within our business that wecould automate using AI, and
(14:13):
that's always on the table.
And, yeah, always scanning themarket for tools that we can buy
and import that are going tomake us more efficient.
But I think the journey to fullAI integration is going to be a
long one for us and you knowthere's still huge amounts of
value outside of AI.
(14:35):
For example, you know, can wefind links with our clients
between how their customers feelabout their experiences, how
their employees feel aboutworking there, and productivity?
I don't think an organisationhas effectively brought those
three things together yet, andit could be huge, like if you
think about investing inemployee experience, if one of
(14:56):
the outcomes of employeeexperience is that you save 10
seconds every time you stack ashelf in your supermarket.
It doesn't feel very sexy orexciting, but that could be
millions of pounds yourorganisation is saving every
year and people just aren'tlooking at these types of things
.
So you know, we're not justthinking about AI, we're also
thinking about how we can useexisting data in different
(15:16):
insightful ways Do you have,without obviously naming names?
Speaker 1 (15:20):
do you have any examples that you can think of
at the top of your head, thatyou have projects where you know
it seems like a small littlechange or a small something that
we've recorded has made such abig impact?
And by going through the data,all of a sudden there's this
picture emerges of a quick winor low flight, low hanging fruit
(15:43):
that can be plucked.
Can you think of anything?
Speaker 2 (15:45):
Well, I'll give you I mean this kind of off the top
of my head.
I'll give you a couple ofexamples.
One is not about quick wins,but it's about the link between
customer experience andproductivity.
Like, we were working with awell-known quick service
restaurant and we discoveredthat the average pace, the
average effectiveness with whichpeople worked in their stores
(16:05):
was correlated with their NPSscore, their net promoter score,
so the rating of how likelytheir customers were in those
stores to recommend going there.
And we've put that down.
Our hypothesis for explainingthat is because better managers
get their teams working moreefficiently and make their
customers happier, rather thanthere being a direct link
(16:26):
between you work moreefficiently, your customers will
be happier.
But that type of insight thenenables different thinking in
that organization in terms of,okay, well, where do we focus
our time and our training?
And if you're freeing up, ifyou're spending less time on a
process and freeing up somebudget for something, actually
investing in manager training isnot necessarily how you would
(16:48):
think to spend that money, butit could have a huge impact on
your productivity rather than ona new gadget or gizmo, I mean.
I think in terms of quick winsand small changes.
There are quick wins in everypresentation that we do, every
presentation that we do.
Thinking about a sushirestaurant we worked with
(17:10):
recently, it was taking them Ican't remember the exact numbers
, but it was taking them longerto make their sushi rolls than
other sushi restaurants we'veworked with in the past, and
they didn't use any sushi makingmachinery in their restaurants.
They discounted it because intrials, it turned out, the sushi
making machinery wasn't anyfaster than their best sushi
(17:30):
chefs.
But guess what?
Not all of their sushi chefswork at the same pace as their
best ones and actually, if theycould invest a small amount of
money in a sushi cutting machinefor all of their restaurants,
it would save them tons of time.
That adds up to huge amounts ofmoney over the course of a year
and pays for itself in a fewmonths.
So that's a really, really goodexample where going in, getting
(17:54):
robust data on how longprocesses take and actually
thinking about the variabilityyou're seeing and why it takes
so long, can yield quick winsreally, really quickly.
Obviously, quick wins yieldedquickly, but it's not rocket
science.
Speaker 1 (18:14):
It's something you can do straight away.
I always find it interestingwhen, when you see latest
technologies coming out or uh,quite often, uh solutions
finding problems particularly ifyou go to big shows just using
the data that we can capture andobviously then you analyze it
james is then actually buildingthat business case from the data
, which and the not just thedata, but from the, the analysis
(18:35):
work that we do on it then allof a sudden can make a huge
impact on a business.
So I always find that reallyinteresting.
What you do with the team isfascinating oh, yeah, it's.
Speaker 2 (18:46):
And, like the you know, I would say the same thing
to our clients in terms of youknow, are you looking to get
more from data?
Like they're probably.
They're probably looking at thelatest AI tools and thinking,
oh, that's a bit sexy, could bequite exciting.
But, like, what you need tothink about is where do your
people spend money most time andhow can you make that quicker?
(19:09):
If you're going to think abouthow to get more from your data,
think about what the businessneeds to know and define the
questions and the data needscorrectly.
You might not need AI or newtools to do that at all.
Actually, it might be that youjust need to think about the
data you've got slightlydifferently because you've not
framed the questions properly.
So, yeah, that's always kind ofmy advice for people looking to
(19:34):
get more from their data, and wesay it all the time, like in
this sushi example yeah, there'sa piece of technology that
could help cut the sushi quicker, but this isn't a
groundbreaking technology.
This is something that's beenaround for a while.
Just, you know, people didn'thave the data or, if they did
have the data, hadn't analyzedit in in the right way to make
the right decision.
(19:54):
So, yeah, let's.
Speaker 1 (19:55):
Let's focus on what you've got so without giving too
many tips away, james, becausewe're now moving into the top
three tips.
So, starting at three andcounting down to one, what three
pieces of advice would you giveto ensure the data is turned
into meaningful insights?
Speaker 2 (20:15):
can I support the question, james?
Because I don't think I.
I think I need to start withnumber one, because I went
number two.
Speaker 1 (20:22):
Number three might not make sense, so I mean we,
just you, totally revolutionizethe way we do this podcast.
Go for it it, James.
Start at one and go to three.
Speaker 2 (20:30):
You know I'm not one to criticize a format, but I
just it's all good.
Speaker 1 (20:34):
It's all good.
I'm flexible enough to changethe numbers around.
Speaker 2 (20:39):
Like I don't want to sound like Donald Rumsfeld, but
you need to know what you knowand know what you don't know.
That's the number one tip.
I mean it kind of links to whatI was just saying before, Like
if you don't know the types ofinformation you need, the types
of questions you need to answerfor your business, then you can
do all kinds of analysis on yourdata but you're not actually
making any difference to theorganization.
(21:00):
It's all a bit pointless.
It's a bit like a five-minuteguitar solo in a pop song.
It might be nice to play and afew people might get it, but
it's not really adding any value.
So, yeah, figure out what youknow, what you don't know, what
you need to know, and thenfigure out what data gaps you've
got would be tips one and two.
And tip three is, once you knowthose things, really focus on
(21:24):
how your data is organized andhow it's structured for analysis
.
It's not sexy, it's notexciting, but you but if your
data is all over the place indifferent databases, not
properly integrated, it'sdifficult to find, then it's
valueless because you're notever going to be able to analyse
it effectively.
So, yeah, counting back down,structure and organise your data
(21:48):
in light of the gaps that youhave and what you need to know.
Those are my top three tipsNice.
Speaker 1 (21:55):
I think that last one particularly is people probably
don't realize that they'vegathered lots of useful data
already.
It's just how it's beenorganized and where it is just
makes it impossible to thenanalyze it.
Speaker 2 (22:06):
Yeah.
Speaker 1 (22:07):
And even in.
Speaker 2 (22:08):
You know, in every business, if you're listening to
this and you you work forStarbucks, like, clearly you've
got loads of data and it will beall over the place.
But if you work in a, if yourun your own coffee shop on on
the on the high street in yourtown, you've got one branch,
you've still got loads of dataand it's still probably all over
the place and you can't makethe most of it unless you've
(22:28):
thought about what you need toknow and how over the place, and
you can't make the most of itunless you've thought about what
you need to know and how.
How can you structure it toanswer those questions?
So that would, yeah, that's myadvice for everyone no thanks,
james.
Speaker 1 (22:37):
That's some, some useful and really interesting
points you've come across thereand that that wraps up rethink
revealed.
Can you think how emotional areyou feeling right now?
Speaker 2 (22:46):
I'm very emotional.
I mean, I'm a bit worried thatI've done my normal, which is to
give five minute answersinstead of one minute answers,
but I've enjoyed it very much.
Speaker 1 (22:54):
No, it's all good man , it's nice to speak to you and,
yeah, we'll catch up with youvery soon, take care.
Thank you, james, all the best,bye-bye.
Well, that's it for RethinkRevealed.
I hope you found, like me, youlearn something new.
You can find great podcastsfrom rethink productivity on our
website, which I'll link in theshow description along with the
(23:14):
music we use today.
I'll hopefully catch you againsoon for the next episode of
rethink revealed.
Until then, bye, bye.
Welcome to Rethink Revealed a podcast series from
Rethink Productivity that willdelve into the minds of our
productivity specialists to askthe deepest of productivity
questions.
And I'm your host, jamesBradbury-Willis, head of
Business Development at Rethink.
I'm a marketing and salesprofessional and I'm keen to get
the inside story from thepeople powering productivity.
And I'm keen to get the insidestory from the people powering
(00:25):
productivity.
I know how excited you all arethat I'm back for another
podcast or the fan mail iscausing issues at home but I
won't let that stop me.
Today we're joined by Bolle.
James Bolle, as well as having afabulous first name, he is also
our Head of Insight Developmenthere at Rethink.
He's the guy who takes raw dataand turns it into something
meaningful, spotting trends,uncovering opportunities and
(00:47):
helping our clients make smartdecisions.
With a background in consumerinsights, client services and
now leading our innovation andinsight, james is also behind
many of the ways we capture andanalyze data, including
partnering with the Universityof Portsmouth and exploring how
video and AI can unlock evenmore value Outside the world of
(01:07):
productivity.
James plays in a rock band andis currently training a puppy
which, depending on the day,might be the tougher challenge.
In this episode, we'll explorethe arts of data analysis and
how businesses can transformnumbers into action.
We'll dive into the qualitycontrol, the tools we use,
including AI, and what thefuture of Insight might look
(01:27):
like.
If you've ever wondered how wemake sense of the thousands of
data points we collect and turnthem into something that
actually drives change, thisepisode is for you, so let's get
into it.
Hey James, how are you doing?
Speaker 2 (01:39):
Yeah, great, thank you, james.
Very well today.
I've just had my oven fixed soI can cook dinner again.
I'm very happy you could feedthe family, which is always a
bonus.
Speaker 1 (01:49):
So, james, let's get cracking, based on a bit of a
little introduction, so we canget to know you a little bit
better.
Can you tell us a little bitabout your background and what
led you to the epicentre of dataand productivity that is
ReThink?
Speaker 2 (02:03):
Well, you summed up my background pretty well, james
.
I think my entire career hasbeen a quest to help people make
better decisions based on data,and the thing that really has
inspired me throughout that isthe idea that I'm helping people
understand their situation abit better and find their own
way.
So that started in marketresearch.
Long story short, I actuallychose to get into market
(02:23):
research because I did astatistics degree and I knew I
wanted a job where I could usemy skills to help people make
interesting decisions.
That led me into customerexperience, where I worked for
10 years again with data,helping to understand why people
behave the way they do.
And that led me into employeeexperience, where I was again
running surveys to help peopleunderstand their culture and how
(02:47):
people felt about working intheir businesses.
And it was when I was working incustomer experience that I met
Sue and Simon Hedo from RethinkProductivity.
Sue was a client of mine whenshe worked at Boots and then
became a colleague, and when arole became available at Rethink
kind of in 2023, it was achance that I jumped at because
(03:10):
I'd done some freelance workwith Sue and Simon previously.
Lots of interesting data in aninteresting area, and we really
can turn on lights for ourclients so they can find their
way.
It's that motivating factor ofhelping people understand what's
going on is a big win for me.
So yeah, jumped at the chancethen, and the thread that's gone
through everything is how doyou use data to make those
(03:33):
decisions and help peopleunderstand what to do?
So that's what led me here.
Speaker 1 (03:37):
And I suppose, from that respect, what is the most
rewarding part of your role?
Speaker 2 (03:41):
Well, it's when we really do help people find their
way.
So a few examples recently wewere working with a business
where their managers were doinglots and lots of admin and lots
of emails.
So, using AI, large languagemodels to analyze the notes that
our productivity consultantshad taken on the admin work and
(04:02):
the emails that were being sentto help understand what's
creating that traffic, to helpthem maybe reduce the admin load
on their managers the chartthat shows the issue for an
organization.
So, thinking of another clientwe worked with recently, showing
that some of their locationswere almost twice as efficient
as others, and really getting tothe heart of the variability in
that business and helping themunderstand what the next step to
(04:25):
take was or you know, whenyou're in a meeting with a
client, the question thatprompts a new way of thinking
about their business those arethe things that really reward me
, where working with Rethinkgives people a new perspective
and helps them be better, andthat really aligns, I think,
with with with rethink's purposeof surfacing great insights so
(04:46):
people can make positive changeso, james, moving on to our
quickfire questions, nice andnice and rapid answers good luck
.
Good luck with that.
I'm never, never one to use one, one word when ten will do,
okay well we'll see how we geton.
Speaker 1 (05:06):
We got ai guitar solo , or coffee, which one saves
your day more often um, well, Idon't drink caffeine, so it
can't be coffee.
Speaker 2 (05:16):
Um, guitar solos.
Honestly, I'm a bassist and Ispend my life trying to stop
guitars, guitarists solo, so wecan get on with the actual meat
of the song.
So I'm going to have to say AI.
I think in this case probablysomething that I do use day to
day.
Often it's a solution lookingfor a problem, but I think there
(05:37):
are lots of really neat waysthat you can use AI and, yeah,
I'm going to say AI, thank you.
Speaker 1 (05:42):
What's your guilty pleasure in excel?
Color coding cells, buildingformulas or hiding columns?
Speaker 2 (05:49):
uh, it's as simple as vlookups.
I love them good vlookup.
Speaker 1 (05:56):
Is quality actually um?
Which is harder to train aimodels or puppies?
Speaker 2 (06:03):
um, well, I mean, unfortunately it turns out you
can't just tell a puppysomething once and then it
understands it.
You have to repeat it over andover.
So probably puppies.
But the truth is, um, I don'tknow, uh, about training ai
models, that there are so manycosts associated with ai on top
(06:24):
of training the models.
I feel like actually trainingAI is the least of your problems
on AI.
So I'm going to say puppies.
Speaker 1 (06:30):
I agree with you on that.
Training is very difficult.
So, moving on to the generaldiscussion then, james so you
lead Rethink's insightdevelopment, can you walk us
through what that actuallyinvolves from a day-to-day and
from a cross-project perspective?
Speaker 2 (06:48):
Yeah, of course.
I mean.
The real heart of what RethinkProductivity do is based around
collecting data.
Wherever that data is, whereverpeople are doing processes, we
can go and collect the data, andwe have a team of superstar
productivity consultants whocollect that data using our
(07:09):
retime apps.
And that's the heart of thebusiness always has been and
always will be.
But then you need to figure outwhat are you going to do with
that data.
And something that Rethink isreally strong on is the level of
quality control, kind of thenumber of checks that go in on
top of that data.
This isn't people just kind ofbashing a clock and then we send
(07:31):
that raw data to our clients.
It will be checked by theproductivity consultants
themselves, it will be checkedby the project manager and then
somebody from the analysis teamwill check it a third time, so
we can be absolutely sure thatwhen we've got data to analyze,
it is of the highest quality.
Part of my my role isparticipating in in that, but
(07:53):
also because I'm head of insightdevelopment and not just head
of insight, it's also thinkingabout how modern technology such
as ai could complete some ofthe the narrow and routine
checks instead of instead ofpeople having to do it.
So once the data has beenchecked, it needs to be
organized and analyzed, andthat's the heart of the role
(08:13):
really.
So, day to day, I will be doinganalysis on client data with
Jamie and Sue.
I'll be working on theprocesses that we use to do that
client analysis.
So think about how to simplifythe analysis routines and maybe
productize it so that it getseasier and easier.
(08:33):
And then I'll be doing clientwork as well.
So, throughout my career, whileI've worked with data, one of
my skills is facilitation ofmeetings.
So when clients need workshopswhether it's process mapping,
whether it's customer experiencemapping, whether it's just
working through the data andcoming up with action plans I
also facilitate that.
So you know, the majority of mytime is analysing data and
(08:56):
thinking about our processes,with a smattering of client work
on top of it.
So, yeah, that's what I do dayto day and it's you know, on a
project, I'll be involved at thebeginning in terms of helping
understand how we're going toorganise the data once it's
collected and making sure theproject's set up right, and then
right at the very end in termsof cleaning, organising and
(09:17):
analysing that data.
Speaker 1 (09:18):
Yeah, great.
So I mean, I think you'vealready answered this question
is like once, obviously, onceyou've captured the data, what
are the?
You've already answered thisquestion is like once, obviously
, once you've captured the data,what you've mentioned the key
steps you take to transform itinto something meaningful and
actual for clients.
But is there a way you and yourteam, is there any ways that
you and your team have developedways of analyzing data?
Can you share how things likevideo analysis and partnerships
(09:39):
with university of portsmouthare helping shape the future of
what we do?
Yeah, absolutely.
Speaker 2 (09:45):
I mean, you know we talked about organising and
charting the data when I beganwith Rethink.
That was done ad hoc, perproject.
We now have a set of templatesthat we use and, you know, fixed
visualisations and set ways ofgetting the data so that we can
fulfil those visualisations,which has made us more
(10:08):
productive.
But we're also thinking about,you know, how do we build and
again using modern technologyhow do we build databases using
the right programming languages,python scripts built on SQL
databases and BI tools toautomate all that kind of stuff.
And you know we are I'mstanding on the shoulder of
(10:29):
giants here because we've beendoing this for 13 years and Sue
and Simon know the type ofanalysis you need to get to the
heart of the productivity issuesthat the businesses are facing.
So it's taking that knowledgeand that contextualization that
they can provide and trying tosimplify and productize that so
it's easier for people to get to.
I mean, there may be a worldsome years hence where people
(10:55):
don't need a presentation fromRethink because we've built a
system that can take all of ourretail knowledge, all of our
productivity knowledge, andautomate it alongside the data.
We're obviously some way fromthat right now, but that's the
world we're thinking about is,once it's organized, it's not
just about visualizing it, it'sabout interpreting it and
contextualizing it, and we'vegot 13 years of data that helps
(11:16):
us to do that, and that's wherepartner organizations have been
really useful.
So we've run a knowledgetransfer partnership with the
University of Portsmouth, whoyou mentioned, and our colleague
Rishan was our associate onthat project.
He's now a full-time employeeat Rethink and his project was
(11:37):
looking at how can we use somemodern techniques in order to
yield value from that historicdata.
So you could argue maybe astudy from 13 years ago isn't
relevant, given moderntechnology and given what's
happened since the pandemic.
But if you are a businessthinking about how efficient you
are or your productivity, it isuseful to know what the Rethink
(11:59):
database shows you from thelast five years in terms of how
other similar businesses aremore or less productive and what
techniques and tools they usethat make them so, because it's
not just knowing that you spend10% of your time with your
customers.
That's useful.
It's knowing how that compareswith your competitors, how they
free up more time to work withcustomers and how you can do it
as well.
(12:19):
That kind of tells the realstory, so the focus is always on
that.
Working with externalorganizations gives us new ideas
from academia, new ideas fromother industries that we haven't
thought about.
And we're actually about toembark on our second KTP with
the University of Portsmouthwhere we're looking at can we
(12:40):
use computer vision to analysevideos of productivity processes
?
So we're not looking to replaceour amazing productivity
consultants, but how can weenhance what they do?
Or how can we use video to dothings that they can't do, like
watch a till for the whole timethe store is open, count the
customers and how long they'rewaiting, and stuff like that.
(13:01):
We're looking at how we can dothat, and we're a relatively
small business.
We don't have that expertise inour organization, and
partnering with people like theUniversity of Portsmouth enables
us to take huge leaps forwardin those types of projects that
we wouldn't be able to make onour own.
Speaker 1 (13:20):
And I suppose with AI advancing so rapidly, what
opportunities and challenges doyou see when it comes to using
AI and productivity insights?
Speaker 2 (13:30):
Oh, yeah, I mean AI will revolutionize what we do,
but it hasn't done that yet.
Like, we work in quite aspecific niche in terms of the
types of data we collect and thetypes of interpretation we want
to make on it, and so you know,there are, there are ways that
you can train large languagemodels to with retrieval,
(13:51):
augmented generation, to to tobuild chatbots to interrogate
our data, but it's going to takea bit of work to get there
because some of the stuff out ofthe box I don't think it's
going to do do the job for usbecause it's too too generic for
what we're trying to solve.
Having said that, there arelots of narrow and routine tasks
within our business that wecould automate using AI, and
(14:13):
that's always on the table.
And, yeah, always scanning themarket for tools that we can buy
and import that are going tomake us more efficient.
But I think the journey to fullAI integration is going to be a
long one for us and you knowthere's still huge amounts of
value outside of AI.
(14:35):
For example, you know, can wefind links with our clients
between how their customers feelabout their experiences, how
their employees feel aboutworking there, and productivity?
I don't think an organisationhas effectively brought those
three things together yet, andit could be huge, like if you
think about investing inemployee experience, if one of
(14:56):
the outcomes of employeeexperience is that you save 10
seconds every time you stack ashelf in your supermarket.
It doesn't feel very sexy orexciting, but that could be
millions of pounds yourorganisation is saving every
year and people just aren'tlooking at these types of things
.
So you know, we're not justthinking about AI, we're also
thinking about how we can useexisting data in different
(15:16):
insightful ways Do you have,without obviously naming names?
Speaker 1 (15:20):
do you have any examples that you can think of
at the top of your head, thatyou have projects where you know
it seems like a small littlechange or a small something that
we've recorded has made such abig impact?
And by going through the data,all of a sudden there's this
picture emerges of a quick winor low flight, low hanging fruit
(15:43):
that can be plucked.
Can you think of anything?
Speaker 2 (15:45):
Well, I'll give you I mean this kind of off the top
of my head.
I'll give you a couple ofexamples.
One is not about quick wins,but it's about the link between
customer experience andproductivity.
Like, we were working with awell-known quick service
restaurant and we discoveredthat the average pace, the
average effectiveness with whichpeople worked in their stores
(16:05):
was correlated with their NPSscore, their net promoter score,
so the rating of how likelytheir customers were in those
stores to recommend going there.
And we've put that down.
Our hypothesis for explainingthat is because better managers
get their teams working moreefficiently and make their
customers happier, rather thanthere being a direct link
(16:26):
between you work moreefficiently, your customers will
be happier.
But that type of insight thenenables different thinking in
that organization in terms of,okay, well, where do we focus
our time and our training?
And if you're freeing up, ifyou're spending less time on a
process and freeing up somebudget for something, actually
investing in manager training isnot necessarily how you would
(16:48):
think to spend that money, butit could have a huge impact on
your productivity rather than ona new gadget or gizmo, I mean.
I think in terms of quick winsand small changes.
There are quick wins in everypresentation that we do, every
presentation that we do.
Thinking about a sushirestaurant we worked with
(17:10):
recently, it was taking them Ican't remember the exact numbers
, but it was taking them longerto make their sushi rolls than
other sushi restaurants we'veworked with in the past, and
they didn't use any sushi makingmachinery in their restaurants.
They discounted it because intrials, it turned out, the sushi
making machinery wasn't anyfaster than their best sushi
(17:30):
chefs.
But guess what?
Not all of their sushi chefswork at the same pace as their
best ones and actually, if theycould invest a small amount of
money in a sushi cutting machinefor all of their restaurants,
it would save them tons of time.
That adds up to huge amounts ofmoney over the course of a year
and pays for itself in a fewmonths.
So that's a really, really goodexample where going in, getting
(17:54):
robust data on how longprocesses take and actually
thinking about the variabilityyou're seeing and why it takes
so long, can yield quick winsreally, really quickly.
Obviously, quick wins yieldedquickly, but it's not rocket
science.
Speaker 1 (18:14):
It's something you can do straight away.
I always find it interestingwhen, when you see latest
technologies coming out or uh,quite often, uh solutions
finding problems particularly ifyou go to big shows just using
the data that we can capture andobviously then you analyze it
james is then actually buildingthat business case from the data
, which and the not just thedata, but from the, the analysis
(18:35):
work that we do on it then allof a sudden can make a huge
impact on a business.
So I always find that reallyinteresting.
What you do with the team isfascinating oh, yeah, it's.
Speaker 2 (18:46):
And, like the you know, I would say the same thing
to our clients in terms of youknow, are you looking to get
more from data?
Like they're probably.
They're probably looking at thelatest AI tools and thinking,
oh, that's a bit sexy, could bequite exciting.
But, like, what you need tothink about is where do your
people spend money most time andhow can you make that quicker?
(19:09):
If you're going to think abouthow to get more from your data,
think about what the businessneeds to know and define the
questions and the data needscorrectly.
You might not need AI or newtools to do that at all.
Actually, it might be that youjust need to think about the
data you've got slightlydifferently because you've not
framed the questions properly.
So, yeah, that's always kind ofmy advice for people looking to
(19:34):
get more from their data, and wesay it all the time, like in
this sushi example yeah, there'sa piece of technology that
could help cut the sushi quicker, but this isn't a
groundbreaking technology.
This is something that's beenaround for a while.
Just, you know, people didn'thave the data or, if they did
have the data, hadn't analyzedit in in the right way to make
the right decision.
(19:54):
So, yeah, let's.
Speaker 1 (19:55):
Let's focus on what you've got so without giving too
many tips away, james, becausewe're now moving into the top
three tips.
So, starting at three andcounting down to one, what three
pieces of advice would you giveto ensure the data is turned
into meaningful insights?
Speaker 2 (20:15):
can I support the question, james?
Because I don't think I.
I think I need to start withnumber one, because I went
number two.
Speaker 1 (20:22):
Number three might not make sense, so I mean we,
just you, totally revolutionizethe way we do this podcast.
Go for it it, James.
Start at one and go to three.
Speaker 2 (20:30):
You know I'm not one to criticize a format, but I
just it's all good.
Speaker 1 (20:34):
It's all good.
I'm flexible enough to changethe numbers around.
Speaker 2 (20:39):
Like I don't want to sound like Donald Rumsfeld, but
you need to know what you knowand know what you don't know.
That's the number one tip.
I mean it kind of links to whatI was just saying before, Like
if you don't know the types ofinformation you need, the types
of questions you need to answerfor your business, then you can
do all kinds of analysis on yourdata but you're not actually
making any difference to theorganization.
(21:00):
It's all a bit pointless.
It's a bit like a five-minuteguitar solo in a pop song.
It might be nice to play and afew people might get it, but
it's not really adding any value.
So, yeah, figure out what youknow, what you don't know, what
you need to know, and thenfigure out what data gaps you've
got would be tips one and two.
And tip three is, once you knowthose things, really focus on
(21:24):
how your data is organized andhow it's structured for analysis
.
It's not sexy, it's notexciting, but you but if your
data is all over the place indifferent databases, not
properly integrated, it'sdifficult to find, then it's
valueless because you're notever going to be able to analyse
it effectively.
So, yeah, counting back down,structure and organise your data
(21:48):
in light of the gaps that youhave and what you need to know.
Those are my top three tipsNice.
Speaker 1 (21:55):
I think that last one particularly is people probably
don't realize that they'vegathered lots of useful data
already.
It's just how it's beenorganized and where it is just
makes it impossible to thenanalyze it.
Speaker 2 (22:06):
Yeah.
Speaker 1 (22:07):
And even in.
Speaker 2 (22:08):
You know, in every business, if you're listening to
this and you you work forStarbucks, like, clearly you've
got loads of data and it will beall over the place.
But if you work in a, if yourun your own coffee shop on on
the on the high street in yourtown, you've got one branch,
you've still got loads of dataand it's still probably all over
the place and you can't makethe most of it unless you've
(22:28):
thought about what you need toknow and how over the place, and
you can't make the most of itunless you've thought about what
you need to know and how.
How can you structure it toanswer those questions?
So that would, yeah, that's myadvice for everyone no thanks,
james.
Speaker 1 (22:37):
That's some, some useful and really interesting
points you've come across thereand that that wraps up rethink
revealed.
Can you think how emotional areyou feeling right now?
Speaker 2 (22:46):
I'm very emotional.
I mean, I'm a bit worried thatI've done my normal, which is to
give five minute answersinstead of one minute answers,
but I've enjoyed it very much.
Speaker 1 (22:54):
No, it's all good man , it's nice to speak to you and,
yeah, we'll catch up with youvery soon, take care.
Thank you, james, all the best,bye-bye.
Well, that's it for RethinkRevealed.
I hope you found, like me, youlearn something new.
You can find great podcastsfrom rethink productivity on our
website, which I'll link in theshow description along with the
(23:14):
music we use today.
I'll hopefully catch you againsoon for the next episode of
rethink revealed.
Until then, bye, bye.
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