June 21, 2024 • 24 mins
Can high-quality data be the key to unlocking the full potential of generative AI? Join us for an enlightening discussion with Nicola Askham, the Data Governance Coach, as she takes us on a journey from her early days at a large British bank to becoming a leading figure in data governance. Nicola sheds light on the current landscape of data governance, the unique challenges data teams face today, and the indispensable role it plays in the success of advanced technologies like generative AI. Through her expert lens, we examine how integrating AI governance and adhering to data privacy and security standards are not just important but essential for leveraging AI effectively.
In the second half of our conversation, Nicola shares actionable strategies to implement data governance in your organization. Discover how to identify real data problems and engage senior stakeholders by demonstrating data gaps. Learn about the power of collaborative workshops in creating conceptual data models and fostering a sense of ownership among business users. Nicola also guides us through the evolution from technical role-based access control to a holistic enterprise-wide data governance approach. Plus, hear her take on the exciting potential of generative AI to enhance data quality processes, making the dream of accessible and effective data governance a reality.
Follow Nicola at:
- Data Governance Coach website
- LinkedIn: https://www.linkedin.com/in/nicolaaskham/
- The Data Governance Podcast
What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:15):
Hello everybody, Thank you for tuning in to
today's episode of what's New inData.
I'm really excited about ourguest today.
We have Nicola Askam, who isthe data governance coach.
Nicola, how are you doing today?
Speaker 2 (00:27):
I'm very good, thank you.
Thank you for inviting me to beon your podcast.
Speaker 1 (00:31):
Yeah, absolutely.
You have so many great insightson data governance and making
it accessible for folks whofollow along with your work.
Tell the listeners a bit aboutyourself and what you worked on
in the past and what you'repassionate about.
So I listeners a bit aboutyourself and what you worked on
in the past and what you'repassionate about.
Speaker 2 (00:47):
So I'm very passionate about data governance
so it's hence the brand of thedata governance coach.
So what I do is mainly thesedays.
I'm much more into the coachingand trainings People, giving
them the capability to do datagovernance themselves.
I do a little bit of consulting, but in the past I have been a
(01:11):
consultant doing the interimdays governance lead or days
governance manager.
I actually made it up as I wentalong when I worked for a very
large british bank many yearsago.
So I've been doing datagovernance 21 years now and to
begin with I didn't even knowwhat it was called.
I was just talking to peopleabout very large British bank
many years ago.
So I've been doing datagovernance 21 years now and to
begin with I didn't even knowwhat it was called.
I was just talking to peopleabout roles and responsibilities
around data and trying to findout who cared about the quality
(01:33):
of the data, because, apart fromme and a small number of other
people, I was really strugglingto find out who cared.
So it was really hard in theearly days and I think that's
what I became quite passionateabout is that I felt somebody
should care about the data.
I don't think I explained itvery well in the early days, but
I think my passion was veryclear and that led people to
(01:59):
talk to me and eventually startdoing what I wanted them to do.
And then I discovered thatthere was a name for what I did,
and it was called datagovernance.
Speaker 1 (02:07):
Excellent and from your experience, you're
certainly an expert and it'sgreat to hear that.
There was a time earlier inyour career where you felt like
it was harder to explain justbecause of where we were, but
now you've found a lot of greatways to simplify it.
I want to get your take on thecurrent state of data governance
(02:30):
with all the other you knowparts of the data ecosystem
right now I think it's a.
Speaker 2 (02:36):
It's a really great time to be doing data governance
.
I mean, as far as I'm concerned, the last 21 years have been a
really great time to be doing it, but I think in the first half
or even more of that, it's beenreally hard and it's been only
really possible to do it formost organizations that were
regulated so financial services,pharmaceutical but what I found
(02:56):
, that the current state is areally exciting time because
everybody's very excited inthings like Gen AI and everybody
wants Gen AI, ai, and I thinkthey're slowly, or perhaps even
quicker than than we originallyhope, coming to the realization
that your data has to be goodenough for you to get the
results you want from gen ai.
(03:17):
And that starts with datagovernance.
We have to understand what datait is that we're going to feed
our models and we have to knowthat it's good enough quality,
and so I I think it's somethingI've been talking about probably
for the last 18 months sincegen ai really exploded, but I'm
really seeing that it's beingpicked up everywhere, and I went
to a massive gartner dataanalytics conference in london a
(03:38):
few weeks ago and that was kindof a ai and gen ai was kind of
mentioned in nearly everypresentation, but they were also
making this big thing that yourdata has to be AI ready, and so
I just spent the whole timegrinning because I was just
thinking this is really good.
Some really serious people arenow saying this, so I think
(04:00):
we're in very exciting times.
So I think we're in veryexciting times and, talking to
some of my clients, I thinkthey've been really trying hard
to get data governanceinitiatives going for a long
time, but suddenly there's aninterest in it because people
didn't want to do datagovernance for data governance
sake and if I can get that.
But there's a real tie tosomething that is fast paced,
(04:27):
fast moving and people want someoff so they don't get left
behind and it's really made themsuddenly interested in their
data.
Speaker 1 (04:32):
Yeah, you had a really great point about for ai
starts with really good data.
Else you're going to get issueswith hallucinations and
customer experiences where youknow the AI might think you're
someone else or you have adifferent problem than what the
customer originally came to youfor.
And the other part of this isyou know data privacy, data
(04:52):
security, like who has access towhat data.
You know who can copy that data.
You know how is that data goingto propagate to other consumers
?
Right?
There's all these openquestions and pitfalls there and
I want to understand from youyou know where are most data
teams struggling with datagovernance right now?
Speaker 2 (05:13):
I think we've probably got two things.
I'm seeing quite a lot at themoment.
So one, picking up on whatyou're saying, is the how does
data governance provide almostlike that framework for
everything else to sit on whenit comes to AI because you say
there's so many facets of it andI'm leaning very much from my
own conversations with people isthat AI governance should
(05:36):
probably be a subset of datagovernance.
So I think data governancealready has a good network.
It already knows it has to bealigned with data privacy and
data security.
These are teams that all workwell together and I think if we
could put ai governance as asubset of data governance, I
think that would really help.
I think some organizations I'vespoken to have done that.
Quite naturally, others arekind of saying we know back off
(05:58):
data governance people, we justwant to get on, we're playing
with the fun technology kind ofthing.
But I think the other the, theone that has been around since
I've been doing data governanceand still is is the people, and
I think this is something thatalways surprises people that
data governance, particularly inthe early stages, is more about
the people than it is about thedata, because the data doesn't
(06:20):
deliberately make itself wrong.
That's the people and theuser's not understanding why
they have to check that it's theright data before they use it,
or why it's important to enterit in a certain format.
So and I think that that thepeople side of it is often
overlooked, and I think peopleare still.
That's still a core problemthat people struggle with, as
(06:41):
well as the the newer ones thatthey're facing I'm in a hundred
percent agreement with you.
Speaker 1 (06:46):
I I always say it's the people, not the pipelines,
because it really does take somelevel of intuition and
understanding of the businessthat you know you're operating
in and being able to executethere.
Uh, else, you know you're justmoving data around from.
You know one thing to anotherand you know you can generate a
(07:06):
bunch of reports, but are thosereports valuable?
That doesn't happen unless thepeople making the report really
understand kind of the vision ofthe company and its goals, you
know.
So let's say you start workingwith a team and they're
struggling with data governance.
You know what's yourfoundational advice that you
(07:27):
give them just to start thingsoff.
Speaker 2 (07:29):
So I think over the years, I've kind of worked out
that, as far as I'm concerned,there are six principles for
being successful when it comesto data government, and I think
the first one that I definitelymissed in the early days so I
would never criticize somebodyelse for missing it was the
understanding why you're doingdata governance in the first
(07:52):
place.
So look at the opportunitiesthat it brings to your
organization.
What business value does itdeliver?
And, of course, some of that'sgoing to be regulatory
requirements ticking that box.
But there's so much more around.
You know profit, cost reduction, efficiencies, customer service
, supporting innovative thingslike gen a I but that's not the
(08:15):
only thing that we do with ourdata.
So I think people just want todive in and be where I was 21
years ago and go.
We should make all our dataperfect and live happily ever
after, and that's not going tosell it to anybody.
We've got to understand what'sin it for your organization, so
I like to call that theopportunity.
I then think so many people justdon't have the capability when
(08:37):
they start doing data governance, and it's the same as exactly
what happened to me.
I'd worked for the bank formany years.
I started doing data governance, genuinely making it up as I
went along.
These days people have a bitmore resources available for
them to look at.
But people then say to me well,how do you know what this is?
(08:57):
We know you.
You've been in the bank foryears.
How do you know what this datagovernance thing is?
And I've seen my clients havethat time and time again.
Personally, I think it workedbetter if somebody who
understands the organizationleads the data governance
initiative.
But that means they don't knowdata governance.
So I always think that makingsure you you really understand
what you're doing, get sometraining that kind of thing is
really important.
(09:18):
The next one I always callcustom build.
There are some standard datagovernance frameworks on the
internet.
I can't pretend they don'texist, but I always say to my
clients they weren't designedfor your organization.
To be fair, they weren'tdesigned for any specific
organization.
They're just theory and youmight look at them for
inspiration.
(09:39):
If you've never done anythinglike this before, that's
probably a good idea.
But I've seen a lot of peoplemake mistakes by copying and
pasting a standard framework,then wondering why it doesn't
work for their organization.
I've worked in a number ofsectors where I've worked with
multiple companies in the samesector and I've never had
exactly the same data governanceframework.
Yes, there's always somecomponents that are similar, but
(10:02):
how we actually do it has gotto be bespoke to your
organization for it to work.
So I think that is key.
Next, I would say simplicity.
I really know that so manypeople like to overcomplicate
data governance and I think ifwe want to get around this
people challenge what we alreadyspoke about we absolutely got
to make what we're doing simple.
(10:23):
The one thing that I alwayslike to say when I'm trying to
get people to understand that isthink about our coffees and how
we do coffees and I see linkswith the custom build as well is
that these days, we can ordercoffee that is specific to us.
So at the moment, I can'ttolerate caffeine and I can't
(10:45):
tolerate dairy, which doesn'tmake very much for great coffee
drinking.
But this morning I went out andI had a decaffeinated oat milk
latte and that was perfect forme, didn't make me ill, made me
feel like I was having a coffeething this morning, so that's
really, really great.
But it's when the people thenstart saying and they, they want
101 other things, and you seepeople in the queue in front of
(11:06):
you coming up this hugelycomplicated order.
What we want is somethingthat's right for our
organization, but it's alsosimple so that people can get
their head around it.
I mean, I didn't think mycurrent coffee or quite a
mouthful but we need to makesure that this is simple, that
business are not off put by it.
It has to be something they canget their heads around.
Um then, the last two arereally simple principles.
(11:30):
I like to say you have to launchyour data governance framework.
It is so.
It never ceases to amaze me.
I have calls with people whosay can you help?
I've been at this organizationsix months a year.
I've designed this perfect datagovernance framework and I've
even reviewed some and theydon't look bad, but they've not
actually done anything with it.
They've emailed it out and saidthere you go, john, that's the
(11:51):
data governance framework.
Go and do it and you go.
What's this?
So if we really have toimplement it, we need to do some
concrete action to actually doit, not just email out a link to
here's our new data governanceteams channel or whatever.
We've really got to make theeffort and actually make this
happen.
And then the other thing that Ialways like to remind people of
is you have to constantly evolveyour data governance framework.
(12:12):
I mean don't mean every week orevery month, but your
organization is changing, soyour data governance framework
needs to evolve and change.
So the AI governance is areally good example of that.
So a data governance frameworkI designed last year year before
would not have had any capacityor mention of AI governance,
(12:33):
but the ones that I'm working onwith my clients now are, and I
hope the clients that I helpeddesign think last year are now
going ah, we need to work out ifwe're taking on AI governance
and evolve our framework.
So don't ever forget it's notonce and done.
You have to keep evolving andchanging it.
So, yeah, a bit of a rattlethrough of six principles that I
like to share with people.
Speaker 1 (12:54):
Yes, it's great the way you frame these six
principles.
It's very actionable for teamsthat need to implement data
governance.
And I wanted to drill into thefirst one, which is, you know,
looking at the opportunity,because I'm a big proponent of,
you know, finding opportunitiesin a business, rather than, you
(13:14):
know, just thinking aboutproblems and backlog and debt.
So when teams are on thatjourney of looking for data
governance opportunities toimprove their current workflows,
how do they go about that?
Speaker 2 (13:31):
Well, I think there's probably two ways I'd recommend
.
So it depends on you know thelevel.
So one is let's go reallysenior and look at your
corporate strategy.
What is your organizationtrying to achieve in the next
three to five years?
And then either you might know,because you work with data in
your organization, or do someresearch, talk to people, find
(13:52):
out is your data currently goodenough to meet all those
objectives in that strategy?
And that's a way to really findthose opportunities to talk
about with senior stakeholders,because they're going to be
responsible for delivering partof those strategic objectives
and goals.
And if you can then say, ah, Ican help you do that, because,
did you know?
Your data's not good enough todo that at the moment, that's
(14:13):
really really, really, reallypowerful.
And then the other way to do itis is to solve some real
problems, some real pains.
So this is perhaps more of abottom-up approach and I think
you need to do both.
And for that we actually haveto talk to the people that are
working with the data, using it,trying to to make it work, and
that can be everybody, from,like the data engineers trying
(14:35):
to get data pipeline to justgeneral consumers of the data,
the business intelligence teamstrying to, to you know, provide
insights on this data.
Go and ask them.
What problems do they have, youknow?
Can they not source the data?
Is it not good enough quality?
Is it duplicated when they getit?
You know, and and I think, whenyou find, though, that's a
(14:55):
really good use case to startsolving problems, because I
definitely did it, and I see fartoo many other people repeating
my historic mistakes of sayinglet's just make everything
perfect, we'll live happily everafter, and that's not.
We've got to be solving realproblems and delivering real
value for our organization,otherwise, nobody will want to
do this.
Speaker 1 (15:17):
Absolutely.
Now, let's say, thoseopportunities have been
identified.
What are some practical ways tostart implementing data
governance within anorganization?
Speaker 2 (15:42):
senior stakeholders to perhaps be accountable for
some data set.
So one of my favorite things todo is to create conceptual data
models.
Now, I'm not a very good datamodel at all.
I can do very simple, very highlevel conceptual data model.
I don't always even call themthat, for our business users
Don't necessarily need to trainthem on how to do data modeling,
but I like to get some you knowthem on how to do data modeling
(16:04):
.
But I like to get a seniorstakeholder from a function
that's finance and ask them tocome along and bring some of
their subject matter expertsabout data along to the workshop
and I get them to brainstormwhat data they use and produce
in that area.
So we start building Isometimes call it a map rather
than the conceptual data model,because it makes them feel more
comfortable.
We start getting anunderstanding of what data they
use and produce and, from mypoint of view, I get them to
(16:26):
start thinking about data asseparate from the systems in
which it resides on.
Business people love thisconflation of data with the
systems it's on and I like tostart saying to them you're not
allowed to write any systemsnames on this diagram.
This is all the data.
I want to know what data youuse, what data do you produce.
(16:48):
And it's during those kind ofworkshops that really aren't
very long that you start peoplestart thinking about data as
perhaps an entity in its ownright that they'd not thought
about before.
They start selling, telling yousome of these problems, and you
can also start saying to peopleso you know, who do you think
should own this data?
Now, if I just met you for thefirst time today and said, oh,
john, I think you own such andsuch data, you might go, whoa,
(17:11):
who are you?
Why are you telling me I owndata?
But if you've just spent 90minutes going through a workshop
and I say, is there any of thisdata on this form, on this
diagram we've drawn that youwouldn't want anybody else in
this organization makingdecisions on, the chances are
you'll go oh yeah, that, that,that, that and that definitely
mine.
You've just agreed to be a dataowner, but I haven't done all
(17:32):
the.
You know I haven't scared youoff with anything yet, I've just
made you want to do it.
So that's one of my favoriteways to start, because it really
starts people engaging and theystart talking to you about the
issues they have with the dataduring that and you can start to
build that relationship andstart to work out what you're
going to prioritize, because wecan't do data governance over
everything in a big bangapproach.
(17:52):
We're always going to have todo phases of what's going on
with data in our organization ata very high level is a really
great way to work out where arewe going to focus for our first
few phases.
Speaker 1 (18:08):
Yeah, that's excellent.
Such a great way to get started.
The data governance roadmapreally can start with teams
thinking that they're doing datagovernance but they're really
just doing role-based accesscontrol.
Like every database and datawarehouses has role-based access
control.
Or you can define like who getaccess?
Who get that access to one, towhat tables?
(18:28):
But that's really like atechnical way of looking at it
and very kind of narrow in itsscope.
So, going from that tosomething that's enterprise and
company-wide and truly datagovernance, it sounds like it's
a journey and, like you said,there's two ways you can go
Bottoms up, which is looking atit from the technical
(18:50):
implementer standpoint, and thentop down, which is more
enterprise and strategic to theexecutives.
Speaker 2 (18:56):
Absolutely.
You probably have to do a bitof both to be successful.
We need to just hope we meet inthe middle.
Speaker 1 (19:04):
Yeah, exactly.
So what are some of the thingsin the data governance industry
that are exciting you right now?
Speaker 2 (19:12):
I think it's a kind of double-sided coin of
excitement and scary, but it isthe Gen AI.
Coin of excitement and scary,but it is the gen ai because I
think that it really has thepotential to speed up some of
the perhaps laborious thingsthat you know, perhaps where
mistakes get made becausethey're boring and people don't
(19:33):
want to do them.
So I think it could speed upthings.
So like no check, no code, dataquality checks, so the business
users can say this is how Iknow my data is good enough to
use and provide a business rule,whereas without that kind of
technology we have a team ofanalysts writing sql and trying
to work out that business ruleinto into code and then we try
(19:56):
it and then we give the reportor the results to the business
to go well, that's not anexception, and then we have to
go through this iterativeprocess.
So I think we've got thepotential the AI to really help
with tasks like that and I thinkthat's really good.
But I think the flip side ofthat is that it's equally scary
that people think AI can do allthe data governance for them,
(20:18):
and you and I know we need thathuman context, that human
understanding some vendor aconference a few months ago,
very proudly told me that wedon't need any business
interaction, that their tooljust replaces data quality tools
and tells you whether the datais good enough.
And I said, well, how does itknow?
And it said because it knowswhether it's changed.
(20:40):
And I went but what if it waswrong in the first place?
And I think you're now going totell me that it's wrong because
it wasn't right in the firstplace and I've now changed it.
And I think in the end, fromdiscussing it with this chap, I
think we both agreed thatperhaps his tool was doing data
observability rather than dataquality.
So I think there's this greatpotential to take some of the
(21:04):
mundane, repetitive tasks toreally add some value by
automating them.
But I think we've got to reallymake sure we don't let our
business users get totallycarried away and think that the
machines can do everything forus and get it right.
We need that human context.
For what really is this data?
What really does make it goodenough and bad enough?
So we need that human context.
For what really is this data?
What really does make it goodenough and bad enough?
So we need that contextabsolutely.
Speaker 1 (21:26):
I don't doubt ai's capabilities to tell us about
data and metadata, but I 100,I'm skeptical that it's going to
actually tell you the realthings you need to know about
that data to make it useful foranalytics and insights
internally.
Because, yeah, there's always astory as to why, you know, data
(21:47):
is represented in a certain wayin some table and you know
there's some notes and someasterisk like hey well, this,
this column you can't exactly, Iknow it says you know lead
score, but it's actuallydeprecated and we don't use that
anymore.
Like there's always some.
You know, yeah, corner casesthat real organizational data
teams have to think about andyou need that, that, like you
(22:11):
said, that that human touch,that human context, to really
know how to action and organizethat data I've seen so many
problems that have been reportedas data quality issues over the
year that were not data qualityissues.
Speaker 2 (22:26):
The wrong data had been chosen because the name
looked like it would be theright thing but, exactly like
your example, users were usingit slightly differently.
Or, you know, we don't use thatcode anymore, so we just put
some useful information in thisfield instead and they were
getting really bizarre results.
And it was because the data wasnot what it ostensibly was
(22:47):
labeled at absolutely,absolutely.
Speaker 1 (22:51):
It's always going to require a lot of human
intervention.
I think there's a lot ofopportunities for ai to make
humans more productive and domore with with less, which is
great.
You know, I the people who havebeen using ai for a long time
now will even double down onthat view and say, yes, you need
(23:15):
a human in the loop, because aiby itself can, you know, make
the wrong decision from data andyou ultimately don't want to be
liable for something you knowmake the wrong decision from
data and you ultimately don'twant to be liable for something
that you know AI is just doingon autopilot for you.
Speaker 2 (23:30):
Oh, definitely not.
That's when you start gettingeven more of the AI horror
stories that we've come acrossalready.
Speaker 1 (23:36):
Yeah, absolutely Nicola.
Where can people follow alongwith your work?
Yeah, absolutely Nicola.
Where can people follow alongwith your work?
Speaker 2 (23:42):
So the best place is probably my website, which is
just nicolaaskamcom.
Follow me on LinkedIn as well.
I also have a podcast myself,so if anybody wants to learn and
we only discuss data governanceon it, I'm afraid, but it's
called the Data GovernancePodcast If anybody does have an
interest in it and wants to findout more.
Speaker 1 (24:03):
Excellent.
Nicola Ascom, the DataGovernance Coach with the Data
Governance Podcast, thank you somuch for joining today's
episode of what's New in DataFor all the listeners.
Those resources that Nicolamentioned will be down in the
show notes and, nicola, thankyou so much for joining today
and thank you to the listenersfor tuning in.
Speaker 2 (24:23):
Thank you for having me as a guest.
Hello everybody, Thank you for tuning in to
today's episode of what's New inData.
I'm really excited about ourguest today.
We have Nicola Askam, who isthe data governance coach.
Nicola, how are you doing today?
Speaker 2 (00:27):
I'm very good, thank you.
Thank you for inviting me to beon your podcast.
Speaker 1 (00:31):
Yeah, absolutely.
You have so many great insightson data governance and making
it accessible for folks whofollow along with your work.
Tell the listeners a bit aboutyourself and what you worked on
in the past and what you'repassionate about.
So I listeners a bit aboutyourself and what you worked on
in the past and what you'repassionate about.
Speaker 2 (00:47):
So I'm very passionate about data governance
so it's hence the brand of thedata governance coach.
So what I do is mainly thesedays.
I'm much more into the coachingand trainings People, giving
them the capability to do datagovernance themselves.
I do a little bit of consulting, but in the past I have been a
(01:11):
consultant doing the interimdays governance lead or days
governance manager.
I actually made it up as I wentalong when I worked for a very
large british bank many yearsago.
So I've been doing datagovernance 21 years now and to
begin with I didn't even knowwhat it was called.
I was just talking to peopleabout very large British bank
many years ago.
So I've been doing datagovernance 21 years now and to
begin with I didn't even knowwhat it was called.
I was just talking to peopleabout roles and responsibilities
around data and trying to findout who cared about the quality
(01:33):
of the data, because, apart fromme and a small number of other
people, I was really strugglingto find out who cared.
So it was really hard in theearly days and I think that's
what I became quite passionateabout is that I felt somebody
should care about the data.
I don't think I explained itvery well in the early days, but
I think my passion was veryclear and that led people to
(01:59):
talk to me and eventually startdoing what I wanted them to do.
And then I discovered thatthere was a name for what I did,
and it was called datagovernance.
Speaker 1 (02:07):
Excellent and from your experience, you're
certainly an expert and it'sgreat to hear that.
There was a time earlier inyour career where you felt like
it was harder to explain justbecause of where we were, but
now you've found a lot of greatways to simplify it.
I want to get your take on thecurrent state of data governance
(02:30):
with all the other you knowparts of the data ecosystem
right now I think it's a.
Speaker 2 (02:36):
It's a really great time to be doing data governance
.
I mean, as far as I'm concerned, the last 21 years have been a
really great time to be doing it, but I think in the first half
or even more of that, it's beenreally hard and it's been only
really possible to do it formost organizations that were
regulated so financial services,pharmaceutical but what I found
(02:56):
, that the current state is areally exciting time because
everybody's very excited inthings like Gen AI and everybody
wants Gen AI, ai, and I thinkthey're slowly, or perhaps even
quicker than than we originallyhope, coming to the realization
that your data has to be goodenough for you to get the
results you want from gen ai.
(03:17):
And that starts with datagovernance.
We have to understand what datait is that we're going to feed
our models and we have to knowthat it's good enough quality,
and so I I think it's somethingI've been talking about probably
for the last 18 months sincegen ai really exploded, but I'm
really seeing that it's beingpicked up everywhere, and I went
to a massive gartner dataanalytics conference in london a
(03:38):
few weeks ago and that was kindof a ai and gen ai was kind of
mentioned in nearly everypresentation, but they were also
making this big thing that yourdata has to be AI ready, and so
I just spent the whole timegrinning because I was just
thinking this is really good.
Some really serious people arenow saying this, so I think
(04:00):
we're in very exciting times.
So I think we're in veryexciting times and, talking to
some of my clients, I thinkthey've been really trying hard
to get data governanceinitiatives going for a long
time, but suddenly there's aninterest in it because people
didn't want to do datagovernance for data governance
sake and if I can get that.
But there's a real tie tosomething that is fast paced,
(04:27):
fast moving and people want someoff so they don't get left
behind and it's really made themsuddenly interested in their
data.
Speaker 1 (04:32):
Yeah, you had a really great point about for ai
starts with really good data.
Else you're going to get issueswith hallucinations and
customer experiences where youknow the AI might think you're
someone else or you have adifferent problem than what the
customer originally came to youfor.
And the other part of this isyou know data privacy, data
(04:52):
security, like who has access towhat data.
You know who can copy that data.
You know how is that data goingto propagate to other consumers
?
Right?
There's all these openquestions and pitfalls there and
I want to understand from youyou know where are most data
teams struggling with datagovernance right now?
Speaker 2 (05:13):
I think we've probably got two things.
I'm seeing quite a lot at themoment.
So one, picking up on whatyou're saying, is the how does
data governance provide almostlike that framework for
everything else to sit on whenit comes to AI because you say
there's so many facets of it andI'm leaning very much from my
own conversations with people isthat AI governance should
(05:36):
probably be a subset of datagovernance.
So I think data governancealready has a good network.
It already knows it has to bealigned with data privacy and
data security.
These are teams that all workwell together and I think if we
could put ai governance as asubset of data governance, I
think that would really help.
I think some organizations I'vespoken to have done that.
Quite naturally, others arekind of saying we know back off
(05:58):
data governance people, we justwant to get on, we're playing
with the fun technology kind ofthing.
But I think the other the, theone that has been around since
I've been doing data governanceand still is is the people, and
I think this is something thatalways surprises people that
data governance, particularly inthe early stages, is more about
the people than it is about thedata, because the data doesn't
(06:20):
deliberately make itself wrong.
That's the people and theuser's not understanding why
they have to check that it's theright data before they use it,
or why it's important to enterit in a certain format.
So and I think that that thepeople side of it is often
overlooked, and I think peopleare still.
That's still a core problemthat people struggle with, as
(06:41):
well as the the newer ones thatthey're facing I'm in a hundred
percent agreement with you.
Speaker 1 (06:46):
I I always say it's the people, not the pipelines,
because it really does take somelevel of intuition and
understanding of the businessthat you know you're operating
in and being able to executethere.
Uh, else, you know you're justmoving data around from.
You know one thing to anotherand you know you can generate a
(07:06):
bunch of reports, but are thosereports valuable?
That doesn't happen unless thepeople making the report really
understand kind of the vision ofthe company and its goals, you
know.
So let's say you start workingwith a team and they're
struggling with data governance.
You know what's yourfoundational advice that you
(07:27):
give them just to start thingsoff.
Speaker 2 (07:29):
So I think over the years, I've kind of worked out
that, as far as I'm concerned,there are six principles for
being successful when it comesto data government, and I think
the first one that I definitelymissed in the early days so I
would never criticize somebodyelse for missing it was the
understanding why you're doingdata governance in the first
(07:52):
place.
So look at the opportunitiesthat it brings to your
organization.
What business value does itdeliver?
And, of course, some of that'sgoing to be regulatory
requirements ticking that box.
But there's so much more around.
You know profit, cost reduction, efficiencies, customer service
, supporting innovative thingslike gen a I but that's not the
(08:15):
only thing that we do with ourdata.
So I think people just want todive in and be where I was 21
years ago and go.
We should make all our dataperfect and live happily ever
after, and that's not going tosell it to anybody.
We've got to understand what'sin it for your organization, so
I like to call that theopportunity.
I then think so many people justdon't have the capability when
(08:37):
they start doing data governance, and it's the same as exactly
what happened to me.
I'd worked for the bank formany years.
I started doing data governance, genuinely making it up as I
went along.
These days people have a bitmore resources available for
them to look at.
But people then say to me well,how do you know what this is?
(08:57):
We know you.
You've been in the bank foryears.
How do you know what this datagovernance thing is?
And I've seen my clients havethat time and time again.
Personally, I think it workedbetter if somebody who
understands the organizationleads the data governance
initiative.
But that means they don't knowdata governance.
So I always think that makingsure you you really understand
what you're doing, get sometraining that kind of thing is
really important.
(09:18):
The next one I always callcustom build.
There are some standard datagovernance frameworks on the
internet.
I can't pretend they don'texist, but I always say to my
clients they weren't designedfor your organization.
To be fair, they weren'tdesigned for any specific
organization.
They're just theory and youmight look at them for
inspiration.
(09:39):
If you've never done anythinglike this before, that's
probably a good idea.
But I've seen a lot of peoplemake mistakes by copying and
pasting a standard framework,then wondering why it doesn't
work for their organization.
I've worked in a number ofsectors where I've worked with
multiple companies in the samesector and I've never had
exactly the same data governanceframework.
Yes, there's always somecomponents that are similar, but
(10:02):
how we actually do it has gotto be bespoke to your
organization for it to work.
So I think that is key.
Next, I would say simplicity.
I really know that so manypeople like to overcomplicate
data governance and I think ifwe want to get around this
people challenge what we alreadyspoke about we absolutely got
to make what we're doing simple.
(10:23):
The one thing that I alwayslike to say when I'm trying to
get people to understand that isthink about our coffees and how
we do coffees and I see linkswith the custom build as well is
that these days, we can ordercoffee that is specific to us.
So at the moment, I can'ttolerate caffeine and I can't
(10:45):
tolerate dairy, which doesn'tmake very much for great coffee
drinking.
But this morning I went out andI had a decaffeinated oat milk
latte and that was perfect forme, didn't make me ill, made me
feel like I was having a coffeething this morning, so that's
really, really great.
But it's when the people thenstart saying and they, they want
101 other things, and you seepeople in the queue in front of
(11:06):
you coming up this hugelycomplicated order.
What we want is somethingthat's right for our
organization, but it's alsosimple so that people can get
their head around it.
I mean, I didn't think mycurrent coffee or quite a
mouthful but we need to makesure that this is simple, that
business are not off put by it.
It has to be something they canget their heads around.
Um then, the last two arereally simple principles.
(11:30):
I like to say you have to launchyour data governance framework.
It is so.
It never ceases to amaze me.
I have calls with people whosay can you help?
I've been at this organizationsix months a year.
I've designed this perfect datagovernance framework and I've
even reviewed some and theydon't look bad, but they've not
actually done anything with it.
They've emailed it out and saidthere you go, john, that's the
(11:51):
data governance framework.
Go and do it and you go.
What's this?
So if we really have toimplement it, we need to do some
concrete action to actually doit, not just email out a link to
here's our new data governanceteams channel or whatever.
We've really got to make theeffort and actually make this
happen.
And then the other thing that Ialways like to remind people of
is you have to constantly evolveyour data governance framework.
(12:12):
I mean don't mean every week orevery month, but your
organization is changing, soyour data governance framework
needs to evolve and change.
So the AI governance is areally good example of that.
So a data governance frameworkI designed last year year before
would not have had any capacityor mention of AI governance,
(12:33):
but the ones that I'm working onwith my clients now are, and I
hope the clients that I helpeddesign think last year are now
going ah, we need to work out ifwe're taking on AI governance
and evolve our framework.
So don't ever forget it's notonce and done.
You have to keep evolving andchanging it.
So, yeah, a bit of a rattlethrough of six principles that I
like to share with people.
Speaker 1 (12:54):
Yes, it's great the way you frame these six
principles.
It's very actionable for teamsthat need to implement data
governance.
And I wanted to drill into thefirst one, which is, you know,
looking at the opportunity,because I'm a big proponent of,
you know, finding opportunitiesin a business, rather than, you
(13:14):
know, just thinking aboutproblems and backlog and debt.
So when teams are on thatjourney of looking for data
governance opportunities toimprove their current workflows,
how do they go about that?
Speaker 2 (13:31):
Well, I think there's probably two ways I'd recommend
.
So it depends on you know thelevel.
So one is let's go reallysenior and look at your
corporate strategy.
What is your organizationtrying to achieve in the next
three to five years?
And then either you might know,because you work with data in
your organization, or do someresearch, talk to people, find
(13:52):
out is your data currently goodenough to meet all those
objectives in that strategy?
And that's a way to really findthose opportunities to talk
about with senior stakeholders,because they're going to be
responsible for delivering partof those strategic objectives
and goals.
And if you can then say, ah, Ican help you do that, because,
did you know?
Your data's not good enough todo that at the moment, that's
(14:13):
really really, really, reallypowerful.
And then the other way to do itis is to solve some real
problems, some real pains.
So this is perhaps more of abottom-up approach and I think
you need to do both.
And for that we actually haveto talk to the people that are
working with the data, using it,trying to to make it work, and
that can be everybody, from,like the data engineers trying
(14:35):
to get data pipeline to justgeneral consumers of the data,
the business intelligence teamstrying to, to you know, provide
insights on this data.
Go and ask them.
What problems do they have, youknow?
Can they not source the data?
Is it not good enough quality?
Is it duplicated when they getit?
You know, and and I think, whenyou find, though, that's a
(14:55):
really good use case to startsolving problems, because I
definitely did it, and I see fartoo many other people repeating
my historic mistakes of sayinglet's just make everything
perfect, we'll live happily everafter, and that's not.
We've got to be solving realproblems and delivering real
value for our organization,otherwise, nobody will want to
do this.
Speaker 1 (15:17):
Absolutely.
Now, let's say, thoseopportunities have been
identified.
What are some practical ways tostart implementing data
governance within anorganization?
Speaker 2 (15:42):
senior stakeholders to perhaps be accountable for
some data set.
So one of my favorite things todo is to create conceptual data
models.
Now, I'm not a very good datamodel at all.
I can do very simple, very highlevel conceptual data model.
I don't always even call themthat, for our business users
Don't necessarily need to trainthem on how to do data modeling,
but I like to get some you knowthem on how to do data modeling
(16:04):
.
But I like to get a seniorstakeholder from a function
that's finance and ask them tocome along and bring some of
their subject matter expertsabout data along to the workshop
and I get them to brainstormwhat data they use and produce
in that area.
So we start building Isometimes call it a map rather
than the conceptual data model,because it makes them feel more
comfortable.
We start getting anunderstanding of what data they
use and produce and, from mypoint of view, I get them to
(16:26):
start thinking about data asseparate from the systems in
which it resides on.
Business people love thisconflation of data with the
systems it's on and I like tostart saying to them you're not
allowed to write any systemsnames on this diagram.
This is all the data.
I want to know what data youuse, what data do you produce.
(16:48):
And it's during those kind ofworkshops that really aren't
very long that you start peoplestart thinking about data as
perhaps an entity in its ownright that they'd not thought
about before.
They start selling, telling yousome of these problems, and you
can also start saying to peopleso you know, who do you think
should own this data?
Now, if I just met you for thefirst time today and said, oh,
john, I think you own such andsuch data, you might go, whoa,
(17:11):
who are you?
Why are you telling me I owndata?
But if you've just spent 90minutes going through a workshop
and I say, is there any of thisdata on this form, on this
diagram we've drawn that youwouldn't want anybody else in
this organization makingdecisions on, the chances are
you'll go oh yeah, that, that,that, that and that definitely
mine.
You've just agreed to be a dataowner, but I haven't done all
(17:32):
the.
You know I haven't scared youoff with anything yet, I've just
made you want to do it.
So that's one of my favoriteways to start, because it really
starts people engaging and theystart talking to you about the
issues they have with the dataduring that and you can start to
build that relationship andstart to work out what you're
going to prioritize, because wecan't do data governance over
everything in a big bangapproach.
(17:52):
We're always going to have todo phases of what's going on
with data in our organization ata very high level is a really
great way to work out where arewe going to focus for our first
few phases.
Speaker 1 (18:08):
Yeah, that's excellent.
Such a great way to get started.
The data governance roadmapreally can start with teams
thinking that they're doing datagovernance but they're really
just doing role-based accesscontrol.
Like every database and datawarehouses has role-based access
control.
Or you can define like who getaccess?
Who get that access to one, towhat tables?
(18:28):
But that's really like atechnical way of looking at it
and very kind of narrow in itsscope.
So, going from that tosomething that's enterprise and
company-wide and truly datagovernance, it sounds like it's
a journey and, like you said,there's two ways you can go
Bottoms up, which is looking atit from the technical
(18:50):
implementer standpoint, and thentop down, which is more
enterprise and strategic to theexecutives.
Speaker 2 (18:56):
Absolutely.
You probably have to do a bitof both to be successful.
We need to just hope we meet inthe middle.
Speaker 1 (19:04):
Yeah, exactly.
So what are some of the thingsin the data governance industry
that are exciting you right now?
Speaker 2 (19:12):
I think it's a kind of double-sided coin of
excitement and scary, but it isthe Gen AI.
Coin of excitement and scary,but it is the gen ai because I
think that it really has thepotential to speed up some of
the perhaps laborious thingsthat you know, perhaps where
mistakes get made becausethey're boring and people don't
(19:33):
want to do them.
So I think it could speed upthings.
So like no check, no code, dataquality checks, so the business
users can say this is how Iknow my data is good enough to
use and provide a business rule,whereas without that kind of
technology we have a team ofanalysts writing sql and trying
to work out that business ruleinto into code and then we try
(19:56):
it and then we give the reportor the results to the business
to go well, that's not anexception, and then we have to
go through this iterativeprocess.
So I think we've got thepotential the AI to really help
with tasks like that and I thinkthat's really good.
But I think the flip side ofthat is that it's equally scary
that people think AI can do allthe data governance for them,
(20:18):
and you and I know we need thathuman context, that human
understanding some vendor aconference a few months ago,
very proudly told me that wedon't need any business
interaction, that their tooljust replaces data quality tools
and tells you whether the datais good enough.
And I said, well, how does itknow?
And it said because it knowswhether it's changed.
(20:40):
And I went but what if it waswrong in the first place?
And I think you're now going totell me that it's wrong because
it wasn't right in the firstplace and I've now changed it.
And I think in the end, fromdiscussing it with this chap, I
think we both agreed thatperhaps his tool was doing data
observability rather than dataquality.
So I think there's this greatpotential to take some of the
(21:04):
mundane, repetitive tasks toreally add some value by
automating them.
But I think we've got to reallymake sure we don't let our
business users get totallycarried away and think that the
machines can do everything forus and get it right.
We need that human context.
For what really is this data?
What really does make it goodenough and bad enough?
So we need that human context.
For what really is this data?
What really does make it goodenough and bad enough?
So we need that contextabsolutely.
Speaker 1 (21:26):
I don't doubt ai's capabilities to tell us about
data and metadata, but I 100,I'm skeptical that it's going to
actually tell you the realthings you need to know about
that data to make it useful foranalytics and insights
internally.
Because, yeah, there's always astory as to why, you know, data
(21:47):
is represented in a certain wayin some table and you know
there's some notes and someasterisk like hey well, this,
this column you can't exactly, Iknow it says you know lead
score, but it's actuallydeprecated and we don't use that
anymore.
Like there's always some.
You know, yeah, corner casesthat real organizational data
teams have to think about andyou need that, that, like you
(22:11):
said, that that human touch,that human context, to really
know how to action and organizethat data I've seen so many
problems that have been reportedas data quality issues over the
year that were not data qualityissues.
Speaker 2 (22:26):
The wrong data had been chosen because the name
looked like it would be theright thing but, exactly like
your example, users were usingit slightly differently.
Or, you know, we don't use thatcode anymore, so we just put
some useful information in thisfield instead and they were
getting really bizarre results.
And it was because the data wasnot what it ostensibly was
(22:47):
labeled at absolutely,absolutely.
Speaker 1 (22:51):
It's always going to require a lot of human
intervention.
I think there's a lot ofopportunities for ai to make
humans more productive and domore with with less, which is
great.
You know, I the people who havebeen using ai for a long time
now will even double down onthat view and say, yes, you need
(23:15):
a human in the loop, because aiby itself can, you know, make
the wrong decision from data andyou ultimately don't want to be
liable for something you knowmake the wrong decision from
data and you ultimately don'twant to be liable for something
that you know AI is just doingon autopilot for you.
Speaker 2 (23:30):
Oh, definitely not.
That's when you start gettingeven more of the AI horror
stories that we've come acrossalready.
Speaker 1 (23:36):
Yeah, absolutely Nicola.
Where can people follow alongwith your work?
Yeah, absolutely Nicola.
Where can people follow alongwith your work?
Speaker 2 (23:42):
So the best place is probably my website, which is
just nicolaaskamcom.
Follow me on LinkedIn as well.
I also have a podcast myself,so if anybody wants to learn and
we only discuss data governanceon it, I'm afraid, but it's
called the Data GovernancePodcast If anybody does have an
interest in it and wants to findout more.
Speaker 1 (24:03):
Excellent.
Nicola Ascom, the DataGovernance Coach with the Data
Governance Podcast, thank you somuch for joining today's
episode of what's New in DataFor all the listeners.
Those resources that Nicolamentioned will be down in the
show notes and, nicola, thankyou so much for joining today
and thank you to the listenersfor tuning in.
Speaker 2 (24:23):
Thank you for having me as a guest.
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