June 23, 2025 • 36 mins
Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVMÂ
🎙FULL SHOW NOTES
https://www.microsoftinnovationpodcast.com/699
What if the key to unlocking AI’s full potential isn’t more algorithms—but better data? In this episode, Harley Webster, founder of Amagi and Power BI lead, shares how his journey from geologist to data strategist shaped his obsession with clean, actionable data. From managing petabytes of oilfield data in Qatar to helping modern businesses unify fragmented systems, Harley reveals why data quality is the real foundation of AI success—and how Microsoft Fabric is changing the game. If you're navigating the intersection of business, AI, and data engineering, this conversation is a must-listen.
🔑KEY TAKEAWAYS
Data Quality is Non-Negotiable for AI: Harley emphasizes that without clean, governed, and well-modeled data, AI outputs are unreliable—no matter how advanced the model.
Microsoft Fabric is a Game-Changer: Fabric’s unified platform simplifies data engineering, enabling real-time pipelines, medallion architecture (bronze, silver, gold layers), and seamless integration with Power BI.
Start with Business Outcomes, Not Data Inventory: Harley’s approach is to define the top KPIs first, then trace back to the data sources—ensuring focus and ROI from day one.
Common Language is Critical in M&A: When merging data from different systems or companies, aligning terminology and definitions is essential to avoid misinterpretation and ensure accurate reporting.
ROI from Data Projects is Tangible: From automating manual reporting to protecting market share, Harley outlines how to quantify the business value of data initiatives—even in complex environments.
🧰 RESOURCES MENTIONEDÂ
👉 Microsoft Fabric – Unified data platform for engineering, analytics, and visualization: https://fabric.microsoft.com
👉 Power BI – Business intelligence and data visualization tool: https://powerbi.microsoft.com
👉 Dataflow Gen2 – Web-based data transformation tool in Microsoft FabricÂ
👉 `Natasha’s Foundation – UK charity supporting food allergy awareness: https://www.narf.org.uk
👉 VWS Software – Waste management software provider in the UK: https://www.vwssoftware.comÂ
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Thanks for listening 🚀 - Mark Smith
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Mark Smith (00:01):
Welcome to the Power Platform Show.
Thanks for joining me today.
I hope today's guest inspiresand educates you on the
possibilities of the MicrosoftPower Platform.
Now let's get on with the show.
Today's guest is from London inthe United Kingdom.
(00:26):
He's a founder and Power BIlead at Amagi.
Is Amagi correct?
Did I pronounce it right?
Harley Webster (00:33):
I pronounced it Amagi and we can maybe talk
about what it means later onthere we go.
Mark Smith (00:37):
We'll unpack that in a second.
He is an experienced dataprofessional, driving business
value through data visualization, analytics and process
implementation across multipleindustries and global markets
for the past 12 years.
You can find links to his bioand social media in the show
notes for this episode.
Welcome to the show, Harley.
Harley Webster (00:56):
Thank you very much, mark.
It's fantastic to be here and,as I was just telling you
earlier, this is my firstpodcast, so I'm very excited to
do this.
Mark Smith (01:06):
It's so cool to have you on, and your area of
expertise is kind of one of myfavorite topics that has
developed in the last 12 to 24months, which is data and the
importance of data if you wantAI to work correctly, and so
it's cool to have you come on.
Tell me about the name of yourcompany.
Harley Webster (01:27):
Yeah, so Amarji is, as I said, how I pronounce
it.
It's actually a word ofSumerian heritage, so Sumerian
being the first humancivilization on the earth which
is in modern-day Iraq.
Nice, yep, yep.
If people do look through mywork history, you can see I've
spent 10 years in the MiddleEast.
(01:48):
I love the region.
I married an Iraqi woman Wow,very good.
So we have this link to thebusiness name through that
family association.
It means freedom.
Mark Smith (02:00):
Is that right?
It means freedom.
Harley Webster (02:02):
It means freedom , and it can also mean freedom
from taxation, which is a bit ofa UK tax system based joke as
well.
Mark Smith (02:09):
It's interesting that you say that because I've
literally walked in from theother room speaking with my wife
about.
I did a piece of work on myselfmany years ago which was like
what do I want in life?
Like what is life all about forme?
And you know, the natural thingis you start with things like
money and happiness and thingslike that, and I just kept
(02:32):
refining and refining, andrefining and I distilled it down
to one word, which was freedom.
So my defining word for thelast 20 odd years has been
freedom.
Because when you look atfreedom, that means freedom of
choice, freedom of what you do,freedom of health care, freedom
of if you've got true freedom.
(02:54):
It's, um, it's incrediblypowerful thing.
So I love that name very coolso do I?
Harley Webster (03:02):
yep, I love it and, um, I think for me what it
means for me.
Um, I've worked in many bigorganizations where there is
bureaucracy.
As we all know it happens inbig organizations there's lots
of layers of middle managers.
I'm someone who just loves toget things done quickly.
I am quite impatient.
I love just to go and fixsomething, make it done, make it
complete, deliver something.
You know I'm one of.
I absolutely love scrum becauseit enables that uh kind of I
love just to go and fixsomething, make it done, make it
(03:23):
complete, deliver something.
You know I absolutely loveScrum because it enables that
kind of methodology in our work.
So for me, amarji means freedomin terms of this is my business
.
I can go and just do stuffquickly and get it done.
Mark Smith (03:38):
That's superb.
I love it.
Tell me about food, family andfun.
What do they mean to you?
Harley Webster (03:43):
Yeah, they're all very, very important things
to me, but we've got to startwith family first.
Um, I've already mentioned mywife of 10 years.
I couldn't have done anythingwithout her in terms of her
pushing me to to start thebusiness, uh, pushing me to go
out of my comfort zone.
You know, things like appearingon podcasts or making linkedin
posts or things like this, whichI'm so grateful for.
(04:05):
I have three children Keep meextremely busy, so you can
imagine what that's like havingthree kids in a business.
Mark Smith (04:13):
Yep.
Harley Webster (04:14):
My kids.
I've got one in the teenageyears, I've got one and two
girls close together in ageseven and five and yeah, it's
fantastic.
And food I am an absolutefoodie, like I.
I think you are as well, mark,because otherwise you wouldn't
ask all your guests the samequestion as I've been picking up
on um.
But for me, I'm an absolutefoodie.
(04:35):
I've been lucky enough to liveand visit many, many places.
Like I said, I had a big uhstint in the middle.
I've also worked in Copenhagen,in Denmark, aberdeen, scotland
all the glorious places oil andgas takes you.
But I always, always, alwaysthink, okay, where am I going
today and what I'm going to eatthere?
(04:56):
That is the thing that I alwaysthink of first, however, one of
my daughters has got severalvery severe food allergies, so
she gets anaphylactic shock froma variety of foods.
Um, so it's.
I'm always the chef in thehouse as well, so it has.
You know, when we cook a familymeal, I'm being very creative
(05:18):
now to ensure we're creatingfood that we love still, but
ensuring that we've got a safemeal for my daughter.
So I'm still cooking curriesand chili con carne and fajitas.
Mark Smith (05:30):
How old's your daughter?
She's six.
Interesting because my boy hadit, as in food allergies, that
we always had to have an EpiPenwith him.
I only once had to use it whenhe was about would have been
about nine.
He licked the cream, the milkoff a coffee cup and that was
(05:52):
enough to send him.
So he had dairy allergies, hehad nut allergies, fish
allergies and he's now 19, andhe has grown out of all of them.
19 and he has grown out of allof them.
But he didn't grow out of a lotof them till he was like 13, 14
years old before he did and heno longer has to have an epi pen
(06:14):
.
He's been phased onto thevarious things over time and
tested with the doctors andstuff.
But I know what that's like, asin you gotta read every food
package label to what it has init because you don't know what's
going to set it.
You know set it off.
Harley Webster (06:28):
So, particularly when eating out, it was a thing
that was looked at very closelyand from a young age you got
good at reading food labelsthemselves yeah, well, you heard
about the natasha's foundationuh, the girl who unfortunately
consumed sesame seeds inheathrow airport that was not on
the label of a sandwich andunfortunately passed away.
There is a charity in her namein the UK which conducts a lot
(06:54):
of research into food allergies,and it's one of the charities
that we support.
Mark Smith (06:58):
It's crazy how the effect something like that small
as a sesame seed, the impact itcan have on on different
people's bodies.
Um, what was your career?
Uh, journey into the area thatyou work in now.
How did you, you know, findyourself in this position?
Harley Webster (07:19):
yeah, I think most people probably don't
choose it, it just happens andthat was absolutely the same for
me.
But anyone you know younglistening who thinks I really
want to get into into data youknow, don't listen to what I'm
saying because I just fell intoit.
Uh, so I'm a geologist bybackground, so I studied geology
(07:39):
at university of portsmouth andum managed to get myself in the
oil and gas industry as a datamanager.
So data manager in oil and gasterms means you are responsible
for quality checking the datathat comes from drilling oil
wells or seismic surveys.
So a huge amount of money isspent drilling these oil holes
(08:05):
and you collect a lot terabytesof data as you drill.
It gets actually stored on SDcards and hard drives on the
device itself.
So you're measuring all theproperties about the rocks, so
the radioactive properties,porosity, permeability, all of
these things and that data comesin, gets packaged up, delivered
(08:26):
by the service companies, thebig companies Baker Hughes,
halliburton, schlumberger andsomebody's got to check all of
that data, make sure it's right,make sure all the metadata is
correct, load it into the rightdatabases or applications for
the analysts to then use.
So I had many years as a datamanager in various businesses in
(08:48):
oil and gas, and that'sprobably given me my kind of
little bit old school approachto data quality and data
governance as a foundation foreverything, because without
those two things you're notgoing to have any good analytics
and AI.
So from being a data manager, Iwas actually part of the
biggest oil and gas transitionin history.
(09:09):
So what do I mean by that?
In Qatar, I was working forMaersk Oil Qatar and they
actually lost the rights to oneof the oil fields, the Al
Shaheen oil field in Qatar, andthey lost it to Total or Total
won the bid, should we say.
So you've found yourself in aposition where you've got a
(09:32):
300,000 barrel of oil a day,field with thousands of
employees and petabytes of data,which has got Musk's
intellectual property, intertw,intertwined throughout it, and
you have to hand that over toanother company.
Wow, absolutely mammoth project, and I'm just so proud to be a
(09:53):
part of it.
So I actually just got a phonecall one day from um it was
actually from the head of pmofrom musk and he said I searched
the directory for data andyou're the only person that came
up that was online.
So I picked up my desk phone.
You're the only person thatcame up that was online, so I
picked up my desk phone.
You know the old Cisco phoneswe used to have on our desk.
And he said can you come andhave a chat?
So I had a chat and he said weneed somebody to design a
(10:16):
mechanism for tracking andsending our data across to the
data room for this transition.
Can you do it?
And I've always been a yes manin my career.
I've always just done thingslike move to Qatar when I was 21
or just said yes to stuff andyeah, I said yeah, okay, yep,
(10:36):
absolutely cool, let's do it.
And I was suddenly found myselfin a PMO team at the top of
Musk Oil, making these datatracking templates in SharePoint
and Spotfire.
Mark Smith (10:48):
Wow, wow.
I expected something much moreelaborate.
Oh, no way.
That's incredible.
Harley Webster (10:55):
Whatever you think about any industry in the
world, you know that they allrun on Excel and that is our
endeavor to.
That's one of our endeavors tochange.
But yeah, so we were trackingall of this data we were sending
.
We would get requests.
You know we'd get hundreds ofrequests for information every
day.
The request would come in.
We'd have to send it to theright department, collect the
(11:16):
data, strip the IP, send itacross and make sure all of that
was tracked.
So I started building thesedashboards and I started saying,
right, this is how much, howmany requests for information
we've had.
This is how many we'vesatisfied.
This is the SLA we've put on it.
We're not overdue on anything.
And I would make these reportsand we'd be suited up going to
the top floor of theseskyscrapers in Doha presenting
(11:38):
to Total and Qatar Petroleumsaying we're on track.
And it was all because we hadthis whole data visualization
system working just superefficiently.
Wow.
So yeah, absolutely amazing.
It's a year-long project thatwas.
And then I did transfer over toTotal, where I ended up being
(11:58):
the head of performancemanagement for the subsurface
division.
Mark Smith (12:02):
So let me get this right.
You went from the company thatdidn't win the Ford contract and
you then moved to the companythat won the Ford contract.
Harley Webster (12:11):
Yeah, as part of the deal, they had to offer
everyone jobs.
Wow yeah, fantastic project.
Mark Smith (12:18):
So a couple of things spring to mind.
You talked about you had tostrip off the IP.
What did that mean practically,give us an example of a piece
of IP that would need to beremoved from a data set to
transfer over.
Harley Webster (12:34):
Absolutely so.
When you have some raw datafrom an oil field, the
intellectual property would bethe analysis of that data, the
end result.
Mark Smith (12:45):
Yes, yes.
Harley Webster (12:47):
So what you can have in oil and gas is you have
log data or seismic data.
So log data is a y-axis linechart.
Essentially.
It might be incomplete, itmight need some correction, it
might need some data processingand it needs analysis.
So anything that Maerskemployees had done to that data
(13:07):
was intellectual property, so wejust gave them the raw data now
from musk's perspective.
Mark Smith (13:13):
I assume that the interpretation was of no value
to them after that fact, becausethey no longer had the contract
for that particular well as anexample, it could be, or if we
had similar oil fields withsimilar properties in other
parts of the world.
Harley Webster (13:25):
You would also want to know and have that
experience yeah because, as youknow, the earth crust has moved
over time, so you get oilreservoirs which are exactly the
same thousands of miles apart.
Mark Smith (13:37):
Wow, this is so, so interesting.
And so how did that transitionfor you to what tools do you use
today?
If I said what are your databuilding blocks and why I said
before data has become such animportant area, is because in
the area of AI, the results havehallucinations, in the
(14:01):
generative AI, for example and Igo, hmm, and I'm talking about
if this data is coming from yourorganization, and so from a
microsoft perspective, thatmight be the graph that they are
running like co-pilot runsagainst the graph and that's
might be all the organizationknowledge.
Every human being, in whateveryou do each day, whether you're
(14:23):
doing a PowerPoint presentation,updating an Excel spreadsheet,
working on a Word documentpeople introduce error.
Right, you didn't round outthat calculation correctly.
You didn't remove certainsentences from a RFX document,
whatever it is, and then youcreate copies of this and then
you say, hey, ai is trained onall my organization data, with
(14:48):
probably millions of microerrors built in all the way
through.
And then you go, oh, I got itwrong and you're like, hang on a
second, you trained it on allthis error prone data that you
have inside your organization.
That's why I believe that thetrue ai story is a data story,
because until you get your dataclean, filtered right and
(15:12):
probably narrowed down, ratherthan trained on the ocean, it
should be just trained onwhatever the task you're trying
to achieve, the data that'sspecific to that, you're going
to have a problem.
And so, if we look at a modernyou know the type of projects
that we're going into these daysaround AI and around the
(15:32):
Microsoft stack of buildingproducts, whether that be fabric
, whether that be purview,whether that be the power
platform, dynamics, etc.
Dynamics, et cetera we oftenare finding that we have to back
up and go back to the data andgo hang on a second.
Is it the right data?
Is it clean data?
(15:53):
Is it enriched data?
If you look at the tool setsthat you use now in the projects
that you're involved in, whatare they?
What are your tools of trade?
Harley Webster (16:04):
Yeah, so I think the revolution of data
engineering has happened withfabric.
It's, it's absolutely fantasticwhat we've got our fingertips,
uh, and it's the main tools Iuse and I promote in my
day-to-day projects.
So if we're talking about aiand we're talking about getting
that foundation right the dataquality, the data governance,
(16:27):
the data provenance you knowwhere does it come from so
important.
We have to look at what we'vegot our fingertips now.
And what do I?
What do we use?
So imagi we specialize in dataengineering, data visualization
and also the business consultingside of it, which is how to get
value from what we've done.
Microsoft Fabric has thisbeautiful way of just combining
(16:52):
all of the old kind of differentnamed tools that Microsoft had
under one blanket and the datapipelines.
To pull data from differentdata sources, clean it on the
fly, store it in the data lakehouse is absolutely brilliant.
There's also Dataflow Gen 2,which looks a lot like Power
(17:14):
Query in your browser.
We can use that to get datafrom other data sources combine
into the lake house.
And then we talk aboutmedallion architecture inside
data lake houses, where we haveour bronze, silver and gold.
I think everyone has adifferent definition of what
they would put in each bucket.
(17:34):
But you know, you can justreally quickly say bronze is raw
data, silver might be you'vecleansed the data and gold might
be you've built a view or datamodel that you can just use
straight away without having todo anything to the data.
So the tools I use on a dailybasis are all around Microsoft
Fabric and actually the easy bitis the data visualization,
(17:58):
because I think anyone can pickup Excel and make a nice table
and open Power BI and go therewe go.
I made a graph, there we go.
I made a table, an open PowerBI and go there we go.
I made a graph, there we go, Imade a table.
But the value comes in whenyou're working in organizations
who have got data in completelydifferent systems, different
geographies.
You might get mergers andacquisitions.
There's a recent client that weworked on who they've acquired
(18:21):
three very similar businesses,but they all use different tech
as their day-to-day tool.
So can we build a dashboardthat just needs to find out one
KPI across those threebusinesses?
Well, they all use differenttools and they call data
different things in thosedifferent tools.
So we need to combine this data, we need to clean it, we need
to agree to the governancestructure, we need to identify
(18:42):
data owners.
We need to ensure the qualityis as expected.
So when you do present that KPI, you don't get one of the MDs
saying Power BI is wrong.
Yeah.
Mark Smith (18:52):
Yeah, I was involved in a project of doing a whole
transition of software for arail company and they just
recently bought three or fourother rail companies in
different geographies, or fourother rail companies in
different geographies, and Icouldn't believe that, even
though these organizations arein the same industry, they had
(19:14):
different terms and phrases andthings for the same thing, and
if one person in that roletalked to somebody in another
company and they used thesewords, for whatever reason, they
found it very difficult to gono, that's what we call, or
that's what we call blah, blah,blah.
And so one of the first thingswas was building a common data
model, right in a commonlanguage, that all acquired
(19:39):
companies moved to and had anagreement on, so that they could
both communicate effectivelyabout the same thing and what
their data was telling them.
How do you do that for thecustomers that you work for?
What's that journey look likefor them, that you take them on?
Harley Webster (19:56):
I love calling it Babelfish.
Absolutely love it.
It's an absolute Babelfish.
This is where the businessconsulting side comes in, mark,
because you have to get peopleto agree to something and that's
always very difficult,especially now post-covid over
teams.
So I do recommend going to holdsessions in person where you
(20:18):
have to come to a common groundon these types of events, um.
So we have to go to thestakeholders who can make a
decision.
So it might be you might haveto say you know an opportunity
where you find there's two mdsin the same place.
I'm going to grab you guys for30 minutes.
We need to agree on ourbusinesses um regions so we can
(20:40):
good example yeah, so we cancreate kpis based on our regions
.
You're you're calling, you'recalling this the regions.
You're calling this theNorthwest and you're calling
this the North and you'recalling this the West.
So can we agree that it's theNorthwest?
Yeah yeah, these sessions haveto happen.
So you're all speaking the samelanguage, you're all singing
from the same hymn sheet.
Mark Smith (21:02):
Yeah, yeah.
In the work you've done in thelast couple of years, what are
the biggest pain points you'reseeing that organizations have
around their data?
Harley Webster (21:12):
I think, apart from the, there is the low
hanging fruit of.
People are quite good in Exceland Power BI at a base level,
I'm finding.
So they're quite happy makingmanual reports in excel.
They're quite happy maybetaking those data from various
sources, downloading it as csvs,combining it all together in
(21:34):
one excel and then putting powerbi on it.
I find that that definitelywhere I'm working on what I'm
seeing in in the region I'm inat the moment, it's quite people
are pretty good at that.
I think everyone is aware thatautomation and data engineering
and data warehouses and lakehouses exist, but they don't
(21:56):
really know what it is and whatit does and how much it costs.
So today I was talking to aclient who said look, we've got
this oracle data warehouse,we've got microsoft fabric,
we've got power bi, but we'redownloading our data into excel
and it takes us ages to um dothis every week.
How can we automate this?
So that is what the challengesI'm seeing now.
(22:19):
It's matured, I think, a lot inthe last two to three years.
And then you ask people okay,cool, yeah, we can do that.
What's your data quality like?
Oh, it's great, yeah, it's fine, yeah, it's really good.
We're really careful when wetype.
And then you ask people okay,cool, yeah, we can do that.
What's your data quality?
Like, oh it's great, yeah, it'sfine, yeah, it's really good,
we're really careful when wetype stuff in.
So have you got any data qualitydashboards showing you what
(22:39):
your data quality is like?
One example would be I workedwith a business who do waste
collection and they have tostart their shift, pick all
their bins and end their shift,and you want to get some kpis on
your durations on each day, oneach round, and you've got
durations that go on for daysand days and days because no
one's ever clicked end shift.
(23:00):
It's things like that.
So we create, you know,exception based reports, power
BI, which are really valuable aswell.
Where, for data quality, we'relooking at the data you want to
report on and then we're puttingin these statements like only
show me data that is wrong inthis instance.
So you're straight away findingthe needle in the haystack,
(23:24):
rather than having to go throughtons and tons of data to find
something to fix it and then youhave to implement that into the
business.
It's all well and good having adashboard telling you you've
got this problem here.
You've got this problem here.
Your data needs correcting here.
Someone's got to look at thatand then actually take an action
.
So the implementation isextremely, extremely important
(23:45):
implementation is extremely,extremely important.
Mark Smith (23:46):
That's yeah, you're so right.
How do you balance technicaland business acumen right?
Because data to a lot of peopleand anything technical to a lot
of business-minded people iskind of it's a foreign language
and my entire career has beenaround.
(24:07):
If I was to distill it down isthat I can take tech concepts
and turn them into businessoutcomes and I can take business
needs and turn them into techoutcomes.
That's my, my skill.
How do you?
How do you balance that assomeone, um, that is highly
technical, but know thatsometimes you need to explain
(24:28):
things in very non-technicalterms and sometimes and you need
to maybe do that two ways.
You know, when working withteams of technologists, how do
you balance it?
Harley Webster (24:40):
Yeah, absolutely .
It's a really, really goodquestion.
I think you have to if you'reanything like me, me, mark, and
I'm sure lots of peoplelistening.
If someone told us to go andanalyze a hundred things, we'd
go and analyze a hundred thingsbecause we just love doing it
and it's all useless becauseit's not actually providing any
value to the business.
You have to have your highlevel milestone and keep going
(25:04):
back to that milestone.
What are we trying to do here?
What is the roi?
How is what we are going to dohave an impact on the return on
investment?
So you know, you hire a projectand you pay x amount.
I guarantee that you'll get anroi on that, because I'll keep
making sure we keep going backto that sentence every meeting,
(25:27):
every morning, every single time.
This is what we're focused on.
You can do a lot of cool thingsand get lost by doing.
You know, oh, you might see anopportunity to do some machine
learning on this thing, but itdoesn't actually have any impact
on the ROI.
So you're right and I thinkthat is to be honest.
I think that is kind ofsomething that I'm proud to say
(25:48):
is one of my strengths, that Ican always give it back to the
business to say, look, this ishow you're going to get value
out of what we're doing.
But yeah, you're right,struggling to explain things
like the importance of DataLakehouse to stakeholders has
been interesting.
Holders has been interesting.
(26:09):
What has really helped me andthat what might really help some
people listening, is if you goback 10 years, let's say what
are we looking at to get a datawarehouse in an organization in
terms of investment and in termsof support staff on site?
Because you're going to have anon-site server.
You're going to have people onsite that have to maintain that.
You're going to be looking at,you know, if you're having data
(26:29):
integration layers.
You're looking at Denodo oryou're looking at Oracle.
You're looking at reallyexpensive high commitment
on-prem solutions.
So you say to them we need adata warehouse.
In those days that's going tocost $100,000 a year, let's say
for a medium-sized business.
Now we're talking about I canget you a Microsoft Fabric for
(26:49):
you know, I think we need thismany SKUs.
I think we're going to needthis much compute power.
It's going to cost us $1,000 amonth on the SaaS Wow, that
usually makes people listen.
Mark Smith (27:04):
Yeah, you know what surprised me and what you said
there was is that one, thatfabric cost point very
interesting, because I've heardpeople go fabric seems expensive
, um, and I'm like, uh, do youknow what is actually running in
the background, like how muchheavy lifting is done for you?
(27:27):
And you just did a greatcomparison of what is done for
you by fabric and therefore theprice point is crazy good.
The other thing is you said ROIfrom a data person and I'm like
how do you quantify return oninvestment for an organization?
Because I'm like that's likehow many grains of sand are on
(27:52):
the seashore?
How do you quantify aninvestment, and you know, even
at $100,000, I thought that'd be, you know, for a large
organization in themulti-millions, you know their
data strategy cost.
And how do you then, you know,quantify to a CXO-type role the
(28:14):
justification for this outlaywhich is going to might be a
multi-year project before theyactually start seeing some of
the returns.
How have those conversationsgone down for you?
Harley Webster (28:27):
Yeah, fantastic.
The obvious one is theefficiency gains.
That's the obvious one thateveryone goes for.
Automation get data qualitymeans you'll make the right
decisions faster.
You'll have faster access todata.
You won't need to create thesedaily reports.
You won't need to create theseweekly reports.
(28:47):
You'll have better KPIs thatlook forward, that predict, so
there's an efficiency gain there.
We've done some maths before.
I think it was around with oneof our clients looking at a
before and after shot.
So this was their dailyreporting load was around 30
(29:09):
emails, so 30 excel sheets theywere making sending out emails.
It's a relatively small clientwhich has grown um and we looked
at how long it took them toprepare those reports and then
what we saved in terms ofautomation.
Uh, it was something like 40,40 faster, yeah.
(29:29):
So there's that one.
And then when you're looking atmuch bigger organizations who
need more than just anefficiency game, you can start
looking at other areas of um roi, and one really interesting one
is market share gain, orprotecting your business from
(29:51):
other businesses taking yourmarket share Interesting.
So if you are not a leaderinside your business areas of
data and your competitors are,they are going to be better
decision makers, faster decisionmakers and they're going to
take some of your market away.
So if I asked you let's say,mark, you are CEO of a big
(30:14):
company, and I'm going to askedyou, let's say, mark, you are
CEO of a big company and I'mgoing to tell you is there?
If your data is absolutely bangon, and we are using AI and
we've done the whole shebang,we've got everything implemented
do you think there is a 1%chance that will protect 1% of
(30:34):
your market?
Mark Smith (30:36):
That's an interesting question.
Harley Webster (30:40):
Let's go down.
Do you think there's a 0.5%chance it will protect 0.5% of
your market and if I say yes,then you can say cool, that's 50
million.
Mark Smith (30:50):
Yeah, yeah, crazy, right, crazy, very right, crazy,
very, very interesting, veryinteresting space to be in.
My final question for you iswhat happens in the scenario you
go into an organization.
They say here's our data andyou do or don't believe them.
(31:10):
And what I'm talking aboutthere is that how do you really
find out all the data in anorganization, the piece of
software that is not storingthat data in a format in a
location that's accessible?
It's you know.
You haven't bought it into alake yet.
How do you kind of do a, a stocktake of the data of an
(31:34):
organization?
Do you have like a methodology,a process that you go through
to make sure that you have thedata inventory down?
There's not something hiddenaway that's kind of going to be
the missing link into a massiveproject because, oh, we didn't
consider that was important, wedidn't, oh, didn't.
We know you needed that.
You know and people will tellyou, you know.
(31:57):
The other common one I comeacross is our data's clean.
You're like, right, you know.
Do you have frameworks that youuse or patterns that you use to
ascertain?
One have we got all the datathe organization has before we
start getting the outputs thatwe need?
And then two how do youascertain the robustness or the
(32:24):
data integrity that you're askedto work with?
Harley Webster (32:27):
Yeah, I mean your first point.
I don't have a magic bulletthere at all.
And if we go back to thebeginning of our conversation, I
said I'm someone that likes todo things quickly and get
results, so I look at theoutcomes first.
Tell me the top 20 KPIs in yourbusiness, and where does that
data come from and where is itstored?
Okay, cool, let's get those 20done.
(32:50):
There we go Results.
Fantastic, We've got them done.
Mark Smith (32:54):
I love it.
So you work from the end andmine backwards, absolutely.
Harley Webster (32:58):
And for your next question about the
integrity and data quality andour data is clean.
I mean coming from aconsultancy.
If you go into a business andyou tell them, you know, I think
we can do this in two weeks andon day one you realize that
data is an absolute mess, thenyou've shot yourself in the foot
.
So we do like to do a littlebit of a recce before we do a
(33:19):
proposal.
Yeah, and I did mention dataquality dashboards and that is
something that we will dostraight away and we'll show the
client.
Mark Smith (33:28):
Yeah, so have you got your own kind of tool set
that you would run in andquickly, that you would get a
sense check of what you'redealing with?
Harley Webster (33:39):
Yeah, absolutely .
It depends what industry.
We do some repeat work in wasteindustry in the UK we partnered
with a brilliant company calledVWS Software who make the main
tool and database for a lot ofthe waste management companies
in the in the uk and I believethey've just expanded to
australia.
Um need to get to your islandsnext um, we're a very small
(34:03):
market yeah, um, so they'rerepeat um.
We have repeat clients andrepeat industries so we can go
in with a template straight away.
We've already made the views,we've already made the
dashboards.
We can go in and go boom, yourday is 60 clean.
So we're going to need a weekto clean your data before you
get actual results.
Awesome, um, other industriesit's obviously more difficult
(34:27):
because you need to learn thebusiness.
You need to learn what datayou're looking at, what limits
to set in the data, um spikes,to look for this type of thing.
But with the user interface youget with Fabric now I wouldn't
need to do anything like that inPower BI.
I would do it using SQL orPython, just in Fabric, yeah.
Mark Smith (34:51):
Harley, this has been so interesting talking to
you.
I feel like you'd be a goodperson to sit down and have a
drink with to the fat over warstories.
Thank you so much for coming onthe show.
Harley Webster (35:03):
It's been a pleasure, mark.
Yeah, absolutely fantastic,really enjoyed it, thank you.
Mark Smith (35:08):
Hey, thanks for listening.
I'm your host businessapplication MVP Mark Smith,
otherwise known as the NZ365 guy.
If there's a guest you'd liketo see on the show, please
message me on LinkedIn.
If you want to be a supporterof the show, please check out
buymeacoffeecom.
Forward slash NZ365 guy.
Stay safe out there and shootfor the stars.
Welcome to the Power Platform Show.
Thanks for joining me today.
I hope today's guest inspiresand educates you on the
possibilities of the MicrosoftPower Platform.
Now let's get on with the show.
Today's guest is from London inthe United Kingdom.
(00:26):
He's a founder and Power BIlead at Amagi.
Is Amagi correct?
Did I pronounce it right?
Harley Webster (00:33):
I pronounced it Amagi and we can maybe talk
about what it means later onthere we go.
Mark Smith (00:37):
We'll unpack that in a second.
He is an experienced dataprofessional, driving business
value through data visualization, analytics and process
implementation across multipleindustries and global markets
for the past 12 years.
You can find links to his bioand social media in the show
notes for this episode.
Welcome to the show, Harley.
Harley Webster (00:56):
Thank you very much, mark.
It's fantastic to be here and,as I was just telling you
earlier, this is my firstpodcast, so I'm very excited to
do this.
Mark Smith (01:06):
It's so cool to have you on, and your area of
expertise is kind of one of myfavorite topics that has
developed in the last 12 to 24months, which is data and the
importance of data if you wantAI to work correctly, and so
it's cool to have you come on.
Tell me about the name of yourcompany.
Harley Webster (01:27):
Yeah, so Amarji is, as I said, how I pronounce
it.
It's actually a word ofSumerian heritage, so Sumerian
being the first humancivilization on the earth which
is in modern-day Iraq.
Nice, yep, yep.
If people do look through mywork history, you can see I've
spent 10 years in the MiddleEast.
(01:48):
I love the region.
I married an Iraqi woman Wow,very good.
So we have this link to thebusiness name through that
family association.
It means freedom.
Mark Smith (02:00):
Is that right?
It means freedom.
Harley Webster (02:02):
It means freedom , and it can also mean freedom
from taxation, which is a bit ofa UK tax system based joke as
well.
Mark Smith (02:09):
It's interesting that you say that because I've
literally walked in from theother room speaking with my wife
about.
I did a piece of work on myselfmany years ago which was like
what do I want in life?
Like what is life all about forme?
And you know, the natural thingis you start with things like
money and happiness and thingslike that, and I just kept
(02:32):
refining and refining, andrefining and I distilled it down
to one word, which was freedom.
So my defining word for thelast 20 odd years has been
freedom.
Because when you look atfreedom, that means freedom of
choice, freedom of what you do,freedom of health care, freedom
of if you've got true freedom.
(02:54):
It's, um, it's incrediblypowerful thing.
So I love that name very coolso do I?
Harley Webster (03:02):
yep, I love it and, um, I think for me what it
means for me.
Um, I've worked in many bigorganizations where there is
bureaucracy.
As we all know it happens inbig organizations there's lots
of layers of middle managers.
I'm someone who just loves toget things done quickly.
I am quite impatient.
I love just to go and fixsomething, make it done, make it
complete, deliver something.
You know I'm one of.
I absolutely love scrum becauseit enables that uh kind of I
love just to go and fixsomething, make it done, make it
(03:23):
complete, deliver something.
You know I absolutely loveScrum because it enables that
kind of methodology in our work.
So for me, amarji means freedomin terms of this is my business
.
I can go and just do stuffquickly and get it done.
Mark Smith (03:38):
That's superb.
I love it.
Tell me about food, family andfun.
What do they mean to you?
Harley Webster (03:43):
Yeah, they're all very, very important things
to me, but we've got to startwith family first.
Um, I've already mentioned mywife of 10 years.
I couldn't have done anythingwithout her in terms of her
pushing me to to start thebusiness, uh, pushing me to go
out of my comfort zone.
You know, things like appearingon podcasts or making linkedin
posts or things like this, whichI'm so grateful for.
(04:05):
I have three children Keep meextremely busy, so you can
imagine what that's like havingthree kids in a business.
Mark Smith (04:13):
Yep.
Harley Webster (04:14):
My kids.
I've got one in the teenageyears, I've got one and two
girls close together in ageseven and five and yeah, it's
fantastic.
And food I am an absolutefoodie, like I.
I think you are as well, mark,because otherwise you wouldn't
ask all your guests the samequestion as I've been picking up
on um.
But for me, I'm an absolutefoodie.
(04:35):
I've been lucky enough to liveand visit many, many places.
Like I said, I had a big uhstint in the middle.
I've also worked in Copenhagen,in Denmark, aberdeen, scotland
all the glorious places oil andgas takes you.
But I always, always, alwaysthink, okay, where am I going
today and what I'm going to eatthere?
(04:56):
That is the thing that I alwaysthink of first, however, one of
my daughters has got severalvery severe food allergies, so
she gets anaphylactic shock froma variety of foods.
Um, so it's.
I'm always the chef in thehouse as well, so it has.
You know, when we cook a familymeal, I'm being very creative
(05:18):
now to ensure we're creatingfood that we love still, but
ensuring that we've got a safemeal for my daughter.
So I'm still cooking curriesand chili con carne and fajitas.
Mark Smith (05:30):
How old's your daughter?
She's six.
Interesting because my boy hadit, as in food allergies, that
we always had to have an EpiPenwith him.
I only once had to use it whenhe was about would have been
about nine.
He licked the cream, the milkoff a coffee cup and that was
(05:52):
enough to send him.
So he had dairy allergies, hehad nut allergies, fish
allergies and he's now 19, andhe has grown out of all of them.
19 and he has grown out of allof them.
But he didn't grow out of a lotof them till he was like 13, 14
years old before he did and heno longer has to have an epi pen
(06:14):
.
He's been phased onto thevarious things over time and
tested with the doctors andstuff.
But I know what that's like, asin you gotta read every food
package label to what it has init because you don't know what's
going to set it.
You know set it off.
Harley Webster (06:28):
So, particularly when eating out, it was a thing
that was looked at very closelyand from a young age you got
good at reading food labelsthemselves yeah, well, you heard
about the natasha's foundationuh, the girl who unfortunately
consumed sesame seeds inheathrow airport that was not on
the label of a sandwich andunfortunately passed away.
There is a charity in her namein the UK which conducts a lot
(06:54):
of research into food allergies,and it's one of the charities
that we support.
Mark Smith (06:58):
It's crazy how the effect something like that small
as a sesame seed, the impact itcan have on on different
people's bodies.
Um, what was your career?
Uh, journey into the area thatyou work in now.
How did you, you know, findyourself in this position?
Harley Webster (07:19):
yeah, I think most people probably don't
choose it, it just happens andthat was absolutely the same for
me.
But anyone you know younglistening who thinks I really
want to get into into data youknow, don't listen to what I'm
saying because I just fell intoit.
Uh, so I'm a geologist bybackground, so I studied geology
(07:39):
at university of portsmouth andum managed to get myself in the
oil and gas industry as a datamanager.
So data manager in oil and gasterms means you are responsible
for quality checking the datathat comes from drilling oil
wells or seismic surveys.
So a huge amount of money isspent drilling these oil holes
(08:05):
and you collect a lot terabytesof data as you drill.
It gets actually stored on SDcards and hard drives on the
device itself.
So you're measuring all theproperties about the rocks, so
the radioactive properties,porosity, permeability, all of
these things and that data comesin, gets packaged up, delivered
(08:26):
by the service companies, thebig companies Baker Hughes,
halliburton, schlumberger andsomebody's got to check all of
that data, make sure it's right,make sure all the metadata is
correct, load it into the rightdatabases or applications for
the analysts to then use.
So I had many years as a datamanager in various businesses in
(08:48):
oil and gas, and that'sprobably given me my kind of
little bit old school approachto data quality and data
governance as a foundation foreverything, because without
those two things you're notgoing to have any good analytics
and AI.
So from being a data manager, Iwas actually part of the
biggest oil and gas transitionin history.
(09:09):
So what do I mean by that?
In Qatar, I was working forMaersk Oil Qatar and they
actually lost the rights to oneof the oil fields, the Al
Shaheen oil field in Qatar, andthey lost it to Total or Total
won the bid, should we say.
So you've found yourself in aposition where you've got a
(09:32):
300,000 barrel of oil a day,field with thousands of
employees and petabytes of data,which has got Musk's
intellectual property, intertw,intertwined throughout it, and
you have to hand that over toanother company.
Wow, absolutely mammoth project, and I'm just so proud to be a
(09:53):
part of it.
So I actually just got a phonecall one day from um it was
actually from the head of pmofrom musk and he said I searched
the directory for data andyou're the only person that came
up that was online.
So I picked up my desk phone.
You're the only person thatcame up that was online, so I
picked up my desk phone.
You know the old Cisco phoneswe used to have on our desk.
And he said can you come andhave a chat?
So I had a chat and he said weneed somebody to design a
(10:16):
mechanism for tracking andsending our data across to the
data room for this transition.
Can you do it?
And I've always been a yes manin my career.
I've always just done thingslike move to Qatar when I was 21
or just said yes to stuff andyeah, I said yeah, okay, yep,
(10:36):
absolutely cool, let's do it.
And I was suddenly found myselfin a PMO team at the top of
Musk Oil, making these datatracking templates in SharePoint
and Spotfire.
Mark Smith (10:48):
Wow, wow.
I expected something much moreelaborate.
Oh, no way.
That's incredible.
Harley Webster (10:55):
Whatever you think about any industry in the
world, you know that they allrun on Excel and that is our
endeavor to.
That's one of our endeavors tochange.
But yeah, so we were trackingall of this data we were sending
.
We would get requests.
You know we'd get hundreds ofrequests for information every
day.
The request would come in.
We'd have to send it to theright department, collect the
(11:16):
data, strip the IP, send itacross and make sure all of that
was tracked.
So I started building thesedashboards and I started saying,
right, this is how much, howmany requests for information
we've had.
This is how many we'vesatisfied.
This is the SLA we've put on it.
We're not overdue on anything.
And I would make these reportsand we'd be suited up going to
the top floor of theseskyscrapers in Doha presenting
(11:38):
to Total and Qatar Petroleumsaying we're on track.
And it was all because we hadthis whole data visualization
system working just superefficiently.
Wow.
So yeah, absolutely amazing.
It's a year-long project thatwas.
And then I did transfer over toTotal, where I ended up being
(11:58):
the head of performancemanagement for the subsurface
division.
Mark Smith (12:02):
So let me get this right.
You went from the company thatdidn't win the Ford contract and
you then moved to the companythat won the Ford contract.
Harley Webster (12:11):
Yeah, as part of the deal, they had to offer
everyone jobs.
Wow yeah, fantastic project.
Mark Smith (12:18):
So a couple of things spring to mind.
You talked about you had tostrip off the IP.
What did that mean practically,give us an example of a piece
of IP that would need to beremoved from a data set to
transfer over.
Harley Webster (12:34):
Absolutely so.
When you have some raw datafrom an oil field, the
intellectual property would bethe analysis of that data, the
end result.
Mark Smith (12:45):
Yes, yes.
Harley Webster (12:47):
So what you can have in oil and gas is you have
log data or seismic data.
So log data is a y-axis linechart.
Essentially.
It might be incomplete, itmight need some correction, it
might need some data processingand it needs analysis.
So anything that Maerskemployees had done to that data
(13:07):
was intellectual property, so wejust gave them the raw data now
from musk's perspective.
Mark Smith (13:13):
I assume that the interpretation was of no value
to them after that fact, becausethey no longer had the contract
for that particular well as anexample, it could be, or if we
had similar oil fields withsimilar properties in other
parts of the world.
Harley Webster (13:25):
You would also want to know and have that
experience yeah because, as youknow, the earth crust has moved
over time, so you get oilreservoirs which are exactly the
same thousands of miles apart.
Mark Smith (13:37):
Wow, this is so, so interesting.
And so how did that transitionfor you to what tools do you use
today?
If I said what are your databuilding blocks and why I said
before data has become such animportant area, is because in
the area of AI, the results havehallucinations, in the
(14:01):
generative AI, for example and Igo, hmm, and I'm talking about
if this data is coming from yourorganization, and so from a
microsoft perspective, thatmight be the graph that they are
running like co-pilot runsagainst the graph and that's
might be all the organizationknowledge.
Every human being, in whateveryou do each day, whether you're
(14:23):
doing a PowerPoint presentation,updating an Excel spreadsheet,
working on a Word documentpeople introduce error.
Right, you didn't round outthat calculation correctly.
You didn't remove certainsentences from a RFX document,
whatever it is, and then youcreate copies of this and then
you say, hey, ai is trained onall my organization data, with
(14:48):
probably millions of microerrors built in all the way
through.
And then you go, oh, I got itwrong and you're like, hang on a
second, you trained it on allthis error prone data that you
have inside your organization.
That's why I believe that thetrue ai story is a data story,
because until you get your dataclean, filtered right and
(15:12):
probably narrowed down, ratherthan trained on the ocean, it
should be just trained onwhatever the task you're trying
to achieve, the data that'sspecific to that, you're going
to have a problem.
And so, if we look at a modernyou know the type of projects
that we're going into these daysaround AI and around the
(15:32):
Microsoft stack of buildingproducts, whether that be fabric
, whether that be purview,whether that be the power
platform, dynamics, etc.
Dynamics, et cetera we oftenare finding that we have to back
up and go back to the data andgo hang on a second.
Is it the right data?
Is it clean data?
(15:53):
Is it enriched data?
If you look at the tool setsthat you use now in the projects
that you're involved in, whatare they?
What are your tools of trade?
Harley Webster (16:04):
Yeah, so I think the revolution of data
engineering has happened withfabric.
It's, it's absolutely fantasticwhat we've got our fingertips,
uh, and it's the main tools Iuse and I promote in my
day-to-day projects.
So if we're talking about aiand we're talking about getting
that foundation right the dataquality, the data governance,
(16:27):
the data provenance you knowwhere does it come from so
important.
We have to look at what we'vegot our fingertips now.
And what do I?
What do we use?
So imagi we specialize in dataengineering, data visualization
and also the business consultingside of it, which is how to get
value from what we've done.
Microsoft Fabric has thisbeautiful way of just combining
(16:52):
all of the old kind of differentnamed tools that Microsoft had
under one blanket and the datapipelines.
To pull data from differentdata sources, clean it on the
fly, store it in the data lakehouse is absolutely brilliant.
There's also Dataflow Gen 2,which looks a lot like Power
(17:14):
Query in your browser.
We can use that to get datafrom other data sources combine
into the lake house.
And then we talk aboutmedallion architecture inside
data lake houses, where we haveour bronze, silver and gold.
I think everyone has adifferent definition of what
they would put in each bucket.
(17:34):
But you know, you can justreally quickly say bronze is raw
data, silver might be you'vecleansed the data and gold might
be you've built a view or datamodel that you can just use
straight away without having todo anything to the data.
So the tools I use on a dailybasis are all around Microsoft
Fabric and actually the easy bitis the data visualization,
(17:58):
because I think anyone can pickup Excel and make a nice table
and open Power BI and go therewe go.
I made a graph, there we go.
I made a table, an open PowerBI and go there we go.
I made a graph, there we go, Imade a table.
But the value comes in whenyou're working in organizations
who have got data in completelydifferent systems, different
geographies.
You might get mergers andacquisitions.
There's a recent client that weworked on who they've acquired
(18:21):
three very similar businesses,but they all use different tech
as their day-to-day tool.
So can we build a dashboardthat just needs to find out one
KPI across those threebusinesses?
Well, they all use differenttools and they call data
different things in thosedifferent tools.
So we need to combine this data, we need to clean it, we need
to agree to the governancestructure, we need to identify
(18:42):
data owners.
We need to ensure the qualityis as expected.
So when you do present that KPI, you don't get one of the MDs
saying Power BI is wrong.
Yeah.
Mark Smith (18:52):
Yeah, I was involved in a project of doing a whole
transition of software for arail company and they just
recently bought three or fourother rail companies in
different geographies, or fourother rail companies in
different geographies, and Icouldn't believe that, even
though these organizations arein the same industry, they had
(19:14):
different terms and phrases andthings for the same thing, and
if one person in that roletalked to somebody in another
company and they used thesewords, for whatever reason, they
found it very difficult to gono, that's what we call, or
that's what we call blah, blah,blah.
And so one of the first thingswas was building a common data
model, right in a commonlanguage, that all acquired
(19:39):
companies moved to and had anagreement on, so that they could
both communicate effectivelyabout the same thing and what
their data was telling them.
How do you do that for thecustomers that you work for?
What's that journey look likefor them, that you take them on?
Harley Webster (19:56):
I love calling it Babelfish.
Absolutely love it.
It's an absolute Babelfish.
This is where the businessconsulting side comes in, mark,
because you have to get peopleto agree to something and that's
always very difficult,especially now post-covid over
teams.
So I do recommend going to holdsessions in person where you
(20:18):
have to come to a common groundon these types of events, um.
So we have to go to thestakeholders who can make a
decision.
So it might be you might haveto say you know an opportunity
where you find there's two mdsin the same place.
I'm going to grab you guys for30 minutes.
We need to agree on ourbusinesses um regions so we can
(20:40):
good example yeah, so we cancreate kpis based on our regions
.
You're you're calling, you'recalling this the regions.
You're calling this theNorthwest and you're calling
this the North and you'recalling this the West.
So can we agree that it's theNorthwest?
Yeah yeah, these sessions haveto happen.
So you're all speaking the samelanguage, you're all singing
from the same hymn sheet.
Mark Smith (21:02):
Yeah, yeah.
In the work you've done in thelast couple of years, what are
the biggest pain points you'reseeing that organizations have
around their data?
Harley Webster (21:12):
I think, apart from the, there is the low
hanging fruit of.
People are quite good in Exceland Power BI at a base level,
I'm finding.
So they're quite happy makingmanual reports in excel.
They're quite happy maybetaking those data from various
sources, downloading it as csvs,combining it all together in
(21:34):
one excel and then putting powerbi on it.
I find that that definitelywhere I'm working on what I'm
seeing in in the region I'm inat the moment, it's quite people
are pretty good at that.
I think everyone is aware thatautomation and data engineering
and data warehouses and lakehouses exist, but they don't
(21:56):
really know what it is and whatit does and how much it costs.
So today I was talking to aclient who said look, we've got
this oracle data warehouse,we've got microsoft fabric,
we've got power bi, but we'redownloading our data into excel
and it takes us ages to um dothis every week.
How can we automate this?
So that is what the challengesI'm seeing now.
(22:19):
It's matured, I think, a lot inthe last two to three years.
And then you ask people okay,cool, yeah, we can do that.
What's your data quality like?
Oh, it's great, yeah, it's fine, yeah, it's really good.
We're really careful when wetype.
And then you ask people okay,cool, yeah, we can do that.
What's your data quality?
Like, oh it's great, yeah, it'sfine, yeah, it's really good,
we're really careful when wetype stuff in.
So have you got any data qualitydashboards showing you what
(22:39):
your data quality is like?
One example would be I workedwith a business who do waste
collection and they have tostart their shift, pick all
their bins and end their shift,and you want to get some kpis on
your durations on each day, oneach round, and you've got
durations that go on for daysand days and days because no
one's ever clicked end shift.
(23:00):
It's things like that.
So we create, you know,exception based reports, power
BI, which are really valuable aswell.
Where, for data quality, we'relooking at the data you want to
report on and then we're puttingin these statements like only
show me data that is wrong inthis instance.
So you're straight away findingthe needle in the haystack,
(23:24):
rather than having to go throughtons and tons of data to find
something to fix it and then youhave to implement that into the
business.
It's all well and good having adashboard telling you you've
got this problem here.
You've got this problem here.
Your data needs correcting here.
Someone's got to look at thatand then actually take an action
.
So the implementation isextremely, extremely important
(23:45):
implementation is extremely,extremely important.
Mark Smith (23:46):
That's yeah, you're so right.
How do you balance technicaland business acumen right?
Because data to a lot of peopleand anything technical to a lot
of business-minded people iskind of it's a foreign language
and my entire career has beenaround.
(24:07):
If I was to distill it down isthat I can take tech concepts
and turn them into businessoutcomes and I can take business
needs and turn them into techoutcomes.
That's my, my skill.
How do you?
How do you balance that assomeone, um, that is highly
technical, but know thatsometimes you need to explain
(24:28):
things in very non-technicalterms and sometimes and you need
to maybe do that two ways.
You know, when working withteams of technologists, how do
you balance it?
Harley Webster (24:40):
Yeah, absolutely .
It's a really, really goodquestion.
I think you have to if you'reanything like me, me, mark, and
I'm sure lots of peoplelistening.
If someone told us to go andanalyze a hundred things, we'd
go and analyze a hundred thingsbecause we just love doing it
and it's all useless becauseit's not actually providing any
value to the business.
You have to have your highlevel milestone and keep going
(25:04):
back to that milestone.
What are we trying to do here?
What is the roi?
How is what we are going to dohave an impact on the return on
investment?
So you know, you hire a projectand you pay x amount.
I guarantee that you'll get anroi on that, because I'll keep
making sure we keep going backto that sentence every meeting,
(25:27):
every morning, every single time.
This is what we're focused on.
You can do a lot of cool thingsand get lost by doing.
You know, oh, you might see anopportunity to do some machine
learning on this thing, but itdoesn't actually have any impact
on the ROI.
So you're right and I thinkthat is to be honest.
I think that is kind ofsomething that I'm proud to say
(25:48):
is one of my strengths, that Ican always give it back to the
business to say, look, this ishow you're going to get value
out of what we're doing.
But yeah, you're right,struggling to explain things
like the importance of DataLakehouse to stakeholders has
been interesting.
Holders has been interesting.
(26:09):
What has really helped me andthat what might really help some
people listening, is if you goback 10 years, let's say what
are we looking at to get a datawarehouse in an organization in
terms of investment and in termsof support staff on site?
Because you're going to have anon-site server.
You're going to have people onsite that have to maintain that.
You're going to be looking at,you know, if you're having data
(26:29):
integration layers.
You're looking at Denodo oryou're looking at Oracle.
You're looking at reallyexpensive high commitment
on-prem solutions.
So you say to them we need adata warehouse.
In those days that's going tocost $100,000 a year, let's say
for a medium-sized business.
Now we're talking about I canget you a Microsoft Fabric for
(26:49):
you know, I think we need thismany SKUs.
I think we're going to needthis much compute power.
It's going to cost us $1,000 amonth on the SaaS Wow, that
usually makes people listen.
Mark Smith (27:04):
Yeah, you know what surprised me and what you said
there was is that one, thatfabric cost point very
interesting, because I've heardpeople go fabric seems expensive
, um, and I'm like, uh, do youknow what is actually running in
the background, like how muchheavy lifting is done for you?
(27:27):
And you just did a greatcomparison of what is done for
you by fabric and therefore theprice point is crazy good.
The other thing is you said ROIfrom a data person and I'm like
how do you quantify return oninvestment for an organization?
Because I'm like that's likehow many grains of sand are on
(27:52):
the seashore?
How do you quantify aninvestment, and you know, even
at $100,000, I thought that'd be, you know, for a large
organization in themulti-millions, you know their
data strategy cost.
And how do you then, you know,quantify to a CXO-type role the
(28:14):
justification for this outlaywhich is going to might be a
multi-year project before theyactually start seeing some of
the returns.
How have those conversationsgone down for you?
Harley Webster (28:27):
Yeah, fantastic.
The obvious one is theefficiency gains.
That's the obvious one thateveryone goes for.
Automation get data qualitymeans you'll make the right
decisions faster.
You'll have faster access todata.
You won't need to create thesedaily reports.
You won't need to create theseweekly reports.
(28:47):
You'll have better KPIs thatlook forward, that predict, so
there's an efficiency gain there.
We've done some maths before.
I think it was around with oneof our clients looking at a
before and after shot.
So this was their dailyreporting load was around 30
(29:09):
emails, so 30 excel sheets theywere making sending out emails.
It's a relatively small clientwhich has grown um and we looked
at how long it took them toprepare those reports and then
what we saved in terms ofautomation.
Uh, it was something like 40,40 faster, yeah.
(29:29):
So there's that one.
And then when you're looking atmuch bigger organizations who
need more than just anefficiency game, you can start
looking at other areas of um roi, and one really interesting one
is market share gain, orprotecting your business from
(29:51):
other businesses taking yourmarket share Interesting.
So if you are not a leaderinside your business areas of
data and your competitors are,they are going to be better
decision makers, faster decisionmakers and they're going to
take some of your market away.
So if I asked you let's say,mark, you are CEO of a big
(30:14):
company, and I'm going to askedyou, let's say, mark, you are
CEO of a big company and I'mgoing to tell you is there?
If your data is absolutely bangon, and we are using AI and
we've done the whole shebang,we've got everything implemented
do you think there is a 1%chance that will protect 1% of
(30:34):
your market?
Mark Smith (30:36):
That's an interesting question.
Harley Webster (30:40):
Let's go down.
Do you think there's a 0.5%chance it will protect 0.5% of
your market and if I say yes,then you can say cool, that's 50
million.
Mark Smith (30:50):
Yeah, yeah, crazy, right, crazy, very right, crazy,
very, very interesting, veryinteresting space to be in.
My final question for you iswhat happens in the scenario you
go into an organization.
They say here's our data andyou do or don't believe them.
(31:10):
And what I'm talking aboutthere is that how do you really
find out all the data in anorganization, the piece of
software that is not storingthat data in a format in a
location that's accessible?
It's you know.
You haven't bought it into alake yet.
How do you kind of do a, a stocktake of the data of an
(31:34):
organization?
Do you have like a methodology,a process that you go through
to make sure that you have thedata inventory down?
There's not something hiddenaway that's kind of going to be
the missing link into a massiveproject because, oh, we didn't
consider that was important, wedidn't, oh, didn't.
We know you needed that.
You know and people will tellyou, you know.
(31:57):
The other common one I comeacross is our data's clean.
You're like, right, you know.
Do you have frameworks that youuse or patterns that you use to
ascertain?
One have we got all the datathe organization has before we
start getting the outputs thatwe need?
And then two how do youascertain the robustness or the
(32:24):
data integrity that you're askedto work with?
Harley Webster (32:27):
Yeah, I mean your first point.
I don't have a magic bulletthere at all.
And if we go back to thebeginning of our conversation, I
said I'm someone that likes todo things quickly and get
results, so I look at theoutcomes first.
Tell me the top 20 KPIs in yourbusiness, and where does that
data come from and where is itstored?
Okay, cool, let's get those 20done.
(32:50):
There we go Results.
Fantastic, We've got them done.
Mark Smith (32:54):
I love it.
So you work from the end andmine backwards, absolutely.
Harley Webster (32:58):
And for your next question about the
integrity and data quality andour data is clean.
I mean coming from aconsultancy.
If you go into a business andyou tell them, you know, I think
we can do this in two weeks andon day one you realize that
data is an absolute mess, thenyou've shot yourself in the foot
.
So we do like to do a littlebit of a recce before we do a
(33:19):
proposal.
Yeah, and I did mention dataquality dashboards and that is
something that we will dostraight away and we'll show the
client.
Mark Smith (33:28):
Yeah, so have you got your own kind of tool set
that you would run in andquickly, that you would get a
sense check of what you'redealing with?
Harley Webster (33:39):
Yeah, absolutely .
It depends what industry.
We do some repeat work in wasteindustry in the UK we partnered
with a brilliant company calledVWS Software who make the main
tool and database for a lot ofthe waste management companies
in the in the uk and I believethey've just expanded to
australia.
Um need to get to your islandsnext um, we're a very small
(34:03):
market yeah, um, so they'rerepeat um.
We have repeat clients andrepeat industries so we can go
in with a template straight away.
We've already made the views,we've already made the
dashboards.
We can go in and go boom, yourday is 60 clean.
So we're going to need a weekto clean your data before you
get actual results.
Awesome, um, other industriesit's obviously more difficult
(34:27):
because you need to learn thebusiness.
You need to learn what datayou're looking at, what limits
to set in the data, um spikes,to look for this type of thing.
But with the user interface youget with Fabric now I wouldn't
need to do anything like that inPower BI.
I would do it using SQL orPython, just in Fabric, yeah.
Mark Smith (34:51):
Harley, this has been so interesting talking to
you.
I feel like you'd be a goodperson to sit down and have a
drink with to the fat over warstories.
Thank you so much for coming onthe show.
Harley Webster (35:03):
It's been a pleasure, mark.
Yeah, absolutely fantastic,really enjoyed it, thank you.
Mark Smith (35:08):
Hey, thanks for listening.
I'm your host businessapplication MVP Mark Smith,
otherwise known as the NZ365 guy.
If there's a guest you'd liketo see on the show, please
message me on LinkedIn.
If you want to be a supporterof the show, please check out
buymeacoffeecom.
Forward slash NZ365 guy.
Stay safe out there and shootfor the stars.
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