Data Governance 2.0: Transforming Control into Collaboration with AI

[music]

Hi everybody. Welcome back to the Light

Zone Data Show. My name is George Fan

and I'm your host. And today we're going

to explore something that uh where we're

going to call data governance 2.0.

It's kind of the shift from governance

as a control function to governance as a

collaborative AI powered enabler. And to

get us through this evolution, I'm

joined by somebody who lives and

breathes these topics every single day.

Najim Ashua is the founder and CEO of

Dria and a longtime leader in data

governance, metadata management, and

data culture overall. Najim, I'm

thrilled to have you here.

>> Hi Joshu. Yeah, it's a pleasure and

honor to to be on Lights on Data the

show. So uh yeah I'm very excited how

about our conversation and the topic.

Yeah

>> likewise. So so you know I love to start

with the idea of data governance 2.0 and

what that means in practice and from

there we can explore maybe how AI

metadata and culture overall kind of

work together to support this new model.

So when you hear the phrase data

governance 2.0 what does that mean to

you? How is it different from the

traditional way that organizations

approach data governance? It's still

something that we need to kind of define

with more certainty what it stands for.

But I would state already that I think

data governance 2.0 is already trying to

uh miticate the ambiguity

of what we know so far as data

governance. Because data governance at

the moment there are tons of definitions

in understanding what data governance

stands for and how it should be uh uh

understood and lived within

organizations, what is part of data

governance and what is not part of data

governance. That at least um based on my

understanding was always something

causing conflicts. Yeah. amongst the

data professionals the data governance

manager that we're debating is is that

not part of data governance or not to

give you an example data literacy

programs so is data literacy part of a

data governance manager or not so I

think data governance 2.0 O is already

making sure that we all have a have a

have the same understanding

what does data governance really bring

to the table and what does it deal with

and I think that's already a big benefit

>> you know it's it's funny because uh data

governance is supposed to provide

clarity overall but even within the

definition there's so so many uh

different ones when you do a quick

Google search on what it is you get so

many different examples So even that

it's kind of conflicting with with its

hope to start with.

>> Yeah. Exactly. Yeah. And and I think

what data governance 2.0 is now

highlighting more is the entire topic of

stewardship. Yeah. Um and what

stewardship should all cover in a sense

so that it becomes something more

beneficial to the governance um or to

the organization overall. So I would say

when we look a little bit into the

history of data governance, it was very

much focusing on on the creation, the

maintenance of master data management,

transactional data, reference data. Um

and it was very much about having the

rules for for for the data life cycle.

Yeah. So now the the difference is now

that I think data creators if we want to

call it that way and data consumers in

data governance consum 2.0 O are getting

much much closer to each other and how

they define data, how the data should be

available to them and how data in

different scenarios will be applied and

has to in that sense also provide a

certain level of data quality.

So I think data governance now is much

more about um connecting rather than

clearly stating this is what you do and

this is what you not do. It's more about

making sure that end to end operating

models that are not only about data but

about tech and people and change and the

usage of of AI solution and so on that

this is holistically addressed. So it's

about bringing the right people to the

right conversations

and allowing them to define then the

necessities of how the data should be as

I said from a metadata perspective

should be defined how it should be

available throughout the life cycle.

Yeah, even to the extent of that we say

a life cycle of analytical purposes or

AI purposes how it is then throughout

the stages to to be then defined and the

quality is ensured and how stewardship

yeah and I don't talk only here about

the role of a data steward I talk about

the entire concept of stewardship how it

should be then addressed and how it

should be fluently yeah u handed over

the responsibilities

>> Mhm. Mhm. So it's looking at that whole

um data life cycle from data creation or

acquisition, maintenance, dissemination,

usage and the usage can be anything

anywhere from um showcasing that data in

a report to make it available for uh

into a data product or for AI, data

analytics, what have you and then tying

it all together within metadata and data

quality and observability, anything that

is needed to make sure that data gets

converted into that asset that we we

keep talking about.

>> Yes, I think so. And I think that u data

governance nowadays as I said it's

something that is more kind of kind of

present but intangibly kind of little

bit present. So it's a little bit

like when I have to imagine it like in

the past or the way when when I started

it was like u it's like the auditor came

to the room

>> and then it was like okay we have to

check if everything is according to

policies and the SOPs SOPs are addressed

and um you know that everything is

compliant and so on and nowadays the way

I see it is first of all is that we have

data domains yeah And uh these data

domain representatives whether it's a

domain owner or a data steward from

there that they together with other

domains and maybe a central team try to

answer business related questions such

as uh

uh when we think about how can we

improve our customers service or how can

we reduce maybe um certain procedures in

the supply chain.

You have the day together with the other

subject matter experts when it comes to

process, when it comes to the

applications and so on. sit together and

try to see how they can first of all

make sure that everyone is using the

same semantics when they talk about the

data objects and then that there's a

clear understanding of how the data

needs to be so that the process or the

functional uh the functional capability

is performing better.

>> Yeah.

>> Yeah. And that's an

>> so good

>> and I think that is where the data

governance now comes in by not only

providing

data repositories that give us a better

understanding of this is how we define

for example uh headcount and this is how

we define

numbers of employees. It's also about

who is the right person to talk to when

you're dealing with these topics. Yeah.

So it's the connector of data, people

and responsibilities so that everything

and and you said it is becoming more

enabled to operate more efficiently.

>> Right. And uh yeah and I like what what

you said and how it evolved from um

providing that compliance into more

having an enabler function because

compliance happens anyways if if you

shoot for enablement, right? I I see it

as a subset and you shouldn't just stop

at compliance and what I've talked to

companies in the past it's very similar

to what you're what you're mentioning

when they've even shifted that mentality

that it can be more that's when really

magic happens because data governance

can provide it its benefit so much more

than just being compliant just being

controlling

>> yeah definitely it is it should be much

more and I think um for me it's like

when when we look into into tours. Yeah.

Uh let's let's take American football.

Yeah. Where we have like these these uh

these playbooks. Yeah. With all these uh

uh positions and movements and so on. So

whether it's a defense, whether it's the

offense, whether whe regardless if you

are at the front or you you are scoring

wise behind it, it it gives you a

playbook, a scenario how to respond to

these different uh circumstances

and for different roles and

responsibilities. And that's how I see

data governance. Data governance is the

okay, I don't really know how to

continue. So I I go to my data

governance experts or um uh I look into

the playbooks and then it tells me ah

you have issues with data profiling or

you don't really know how to um identify

the detail data quality defects and so

on. So how do you do it to who to who do

you have to speak to and so on. So it's

it's the it's a answer provider, a

solution provider that solves the

issues. Yeah. And naturally it becomes

the a different it it becomes as I said

the enabler rather than the oh we now

have to involve data governance because

we cannot go into the next phase of the

project unless we don't have the

approval you know.

>> Yeah. And uh I I see this as a natural

evolution because data has definitely

moved from just reporting to uh really

operating the business in real time. And

with AI, with automation, you know,

product analytics, um

data is something that drives decisions

instantly. So you need to have something

a bit more nimble and not have data

governance like you said seen as uh as

that roadblock that you need to go

through to ask permission for things and

move the project forward.

>> Yes. And I think when we address now a

little bit also the topic of uh what has

so much changed is the fact that now

when we talk about the interaction with

data, we now have not only the data

domain representatives or the life

sector representatives, we have also AI

agents that are dealing with the data.

So that means we have here also

collaboration and interaction with uh

artificial intelligence that is creating

maintaining using data and and it also

has to kind of obviously uh fulfill here

certain expectations

>> and I think that is something that uh is

is of very importance because I think at

the moment we already have a situation

where it is quite difficult to anchor

strongly the work responsibilities of

data whether we call it data ownership

or accountability and so on. But when we

talk now about the agents that are also

taking over maybe accountability and

ownership that's a different ball game.

Yeah.

>> Yeah. and and but it all starts with a

common understanding of what data

governance should really bring to the

table. And for me it's much more

something that is principle based

allowing procedures to be more uh

efficient across people and system and I

am a fan of as much data governance

whatever we know which aspect we address

that uh that is necessary. Yeah. So I

wouldn't go always fullblow really uh

because the level of maturity also in

governance is very different from

function to function to entity to entity

from division to division. So we

naturally have not a

common maturity level of data

governance. So that means we always have

then to make compromises in the

expectations and in the interaction when

it comes to different data governance

aspects.

>> Mhm. And what would this then look into

practice? How um would that data

governance program look like from let's

say the the more traditional version

that we're we're familiar with? I think

I mean I think um for me data governance

in general is something that will be

federated. It has to be federated.

>> Yes. Having that said I think there's

always a level of degree of governance

that needs to be centralized and it

always comes down to how healthy

your data governance is performing. So

level of understanding and level of

experience determine how much you can

decentralize of your data governance.

How much a domain can take over uh your

data platform can take over or how much

it can be um governance principles,

rules and so on can be uh be part of the

code. Yes. So computational governance.

So it always kind of depends, but the

target is to have as much as possible

decentralized

>> and then with a certain amount of uh

maybe KPIs and and uh reporting

structure allowing then the the central

team to coordinate and make sure that

there is improvement and the governance

principles are in a

um enabling as I said functions and

business to to continue. So that is

maybe one key aspect. Do you see then

the central team being more uh similar

to a center of excellence where they're

providing templates, best practices to

to then uh disseminate to uh these

individual teams within their own units

within their own areas of the business

to do their own mini data governance

programs as it pertains to their

ecosystem. I think I think yes to a

degree this will be uh this will be one

of the main aspects of their of their

responsibilities to provide as you said

the templates the good practices within

the organization that have proven that

for this organization this way of

governance or uh procedure works. The

other aspect is that they as I said at

the beginning they are also from my

perspective also an entity that I mean

we talk nowadays about data as a shared

asset as a shared good.

>> This is why the topic of ownership is

also so difficult because how do you

want to have ownership for something

that is yeah a shared asset a shared

good. So it causes a lot of um debates

and conversations. So um to what extent

is the accountability which I preferred

yeah maybe in one domain and then the

accountability then will be transitioned

or handed over to another I think that's

also a responsibility of the central

team to make this clear judgments if

there's no nothing coming out of the

domains then based on their

responsibility they should have the

decision right to make some calls in

these kind of scenarios. Thank you. Y um

and you mentioned AI a couple times and

AI agents. Uh I haven't seen it in

practice yet to be honest within the

data governance domain though I've seen

um you know service providers providing

tools for agents on data quality on data

classification on uh semantic tagging

lineage.

Have you have you seen a successful uh

agent within the data governance domain

being deployed and maybe some learnings

from there?

>> Yeah, I think I mean if I have the same

observation as you that there's a lot of

heat around it let's say that way that

uh this is the future and this is how it

will be. I have seen occasionally some

examples where it really worked out and

um the governance aspect here that I

would like to highlight was that in the

scenarios that I was dealing with it was

always very much compliance

or regulatoratory

strong industries and sectors

>> right

>> so here it was a lot about governance in

the sense of um making sure that the

agents are working in an ethical manner

So when we have to deal with uh personal

informations of the the insurance the

the

policy or of the patient data and so on.

So again here it was very much about

governance having an eye on is it

ethical is it morally acceptable to walk

that path. How do we make sure that uh

the agents are not using really the data

that is uh that is in a way anonymized

or sodomized? Yeah. Synthetic data. So

to what degree can we allow judgments

about how data products or data insights

are valid when we use

>> uh synthetic data. So here the

governance aspect was very much about

these ethical questions and in that

sense it was not only one domain but

many domains uh trying to solve these

these questions together.

>> No makes sense and especially with AI

implementations nowadays uh you need

kind of need to design for explanability

not just accuracy uh you need that

transparency and try and understand the

best

as possible what's within that black box

that we we often uh uh see on our end.

And for that we need that clear

ownership

um traceable lineage

you know the human in the loop review

for sensitive use cases and so much more

and of course the metadata as well and

the semantics that go with it and data

governance is a key for all of this.

>> Exactly.

The question is who has a better

understanding all the data output? Is it

a human? Is it is it a human or is it a

machine? Depending how the data has been

created and uh bit made available and so

on. It can be either and we have to make

sure that definitions, metrics, logics

and how it is interpreted a human and a

machine where it is necessary have the

same understanding.

>> Yeah. And maybe it's yeah the and also

the question will arise do we really

want in certain business uh functions

and and and in business processes that a

machine takes over these kind of

decisions or uh execution steps. So

while we explore certain use cases, we

have to touch on these kind of questions

and uh and and and ask us what do we

really want. Yeah. So there are some

maybe no-brainer situations where we

think like yes uh the machine can do

that but then there are ones which is

very grayish to answer that question.

>> Right. It's um yeah that that semantics

of it is kind of funny because oftent

times a company goes into an AI

deployment

uh without the company really investing

in metadata first and understanding

their metadata and then they're

expecting for the AI to magically figure

things out and I mean I think it can

certain parts of it

>> but then it understanding might be

different from your understanding when

you're looking at it and might not be

the correct one. What what would you say

are some maybe some practical steps that

organizations can take to make that

metadata visible and meaningful

uh and maybe daily work?

>> I think there are different different

ways of doing it. It always kind of

depends again on the circumstances.

Uh there's also the question of what

stakeholder do you have in front of you

>> and you prefer to do it very top down

which would be

>> you come through a semantic or you come

through maybe even knowledge management

and then the semantic layer the

conceptual layer and then you go you all

the way down to a physical um meta data

model. That would be maybe one way

especially if you to uh regulatory

requirements.

>> Um um I mean being we have in Europe we

have now the CSRD uh which is about

corporate social responsibility

uh directives. This is the the full name

of it. And here we talk about a lot

about uh KPIs

um about gender pay gap emission rates

by different uh uh uh energy sources or

and so on and so on. So here you come

through the centic layer based on the

regulatory requirements

>> right. So you do um so what I would

recommend how we did it with our uh

partners and projects was we took the

regulatory requirements and then we talk

with the legal team the the

manufacturing team the HR teams and so

on and so on about how much are they

already using these kind of information

do they have already reports in place

and then we would uh liken the shadow a

little bit. So this is again maybe a

good example where a governance manager

would kind of lead the conversation

about how are your procedures, how do

you fulfill it currently these kind of

requirements and so on. Then someone who

is more responsible for data

architecture, data modeling is observing

this conversation and is writing down

all the data terms and objects. Yeah.

and is trying maybe to kind of draw a a

model yeah on a on a semantic layer. So

this is maybe a way of how to start it

and then bringing the logic into a a

view. Yeah. Where you can see maybe a

little bit of lineage and relationship

between um these data objects and the

semantics. The other way is obviously if

you're dealing with a very technical

team then I think there are now these

solutions which can give you uh quite

good overviews on your physical data

model. Yeah. And allowing you to start

from that side that you say okay this is

how our physical data model looks like

and now let's start to bridge it towards

a logical and conceptual layer. So it

depends obviously of the organization

>> that the experience of the people that

you have and you can engage your

conversations.

>> Yeah. Oh definitely. And uh you know

mentioning of the different people that

you have within the organization

culture. Culture is often maybe the

hardest part of of data governance.

>> How how would you say or what would be

some of the characteristics of that

culture for a successful data governance

2.0? U implementation

>> I think what is important is here to

when we talk about data governance 2.0

Oh, and how we should address the

cultural topic is we should start to

kind of isolate it the organizational

culture in general or when we talk about

data literacy or data fluency.

Um I'm actually not a fan to always give

it a very you know like specific term. I

would say data literacy should be part

of the company literacy.

>> Yeah. Mhm.

>> So facet of it the same as a data

strategy is a part of the company

strategy. Yeah. So by creating these

barriers in front of our eyes then this

is business literacy, this is data

literacy, this is process literacy and

so on we we hinder people to see that

this is actually everything belongs

together. So I think the questions that

here data governance

should ask especially on top of

management is what do we really want to

govern? Yeah. And uh is it data as

something that is reactively looked in

or is it um a business model, a product,

a service that we want to bring out into

the market and we want to yeah generate

revenue and then we maybe look then as a

data governance letter just strongly

into the data aspect of it and can make

others more aware of. Yeah, but I would

say the data governance 2.0 Oh starts

not with the question of uh where is

your data and uh how have you documented

it in so the question is always which

purpose does it support what's the

benefit of having this data set and data

uh reports and data products and so on

and so on what's the business what's the

business model behind it and I think

this is where data governance manager

also have to have more a stronger

understanding understanding of the

business model, the the operation model

overall and the strategy of the

organization because then they can

better most in place

and advocate for the necessity of and

enabling data governance because it all

starts with what do we really want to do

with the data? Yeah.

>> Mhm. For um companies, organizations

that are listening to our conversation

now and they're thinking, okay, how can

I improve my data strategy? How can I

improve my data governance program or

even start one? Um how can I engage with

uh with Dria? What what is the the first

steps that you usually go through?

>> I mean how you can engage with us is

very easy. You can go on our website.

You can contact us on LinkedIn or have a

session with me or my colleagues. And

the the first thing that we normally do

is we try to understand what is really

the issue in the role because as I said

a data quality problem is only a problem

if it's not supporting the business. So

in that sense we would like to

understand what is really the problem

where they do need support and then it's

very much about meeting them there where

they have the gaps. It can be skill-wise

it can be capability wise it can be

educational wise. So for us, execution

and literacy goes hand in hand. Maybe is

something where they need more support

on the literacy to be better in

execution. Maybe they have the concepts

and the literacy, but they don't know

how to bring it into the operating room.

So based on these conversations that

we're going to have, we then engage with

them. And um uh I always say like uh

it's a little bit of a tapus setup that

we do. So you don't have to go for the

whole picture. It can be very use case

specific capability uh wise and uh yeah

step by step taking it from there

>> driven. Yeah thanks so much. Thank you.

Thank you for sharing uh these

>> these best practices and uh yeah data

governance 2.0. I'm hoping more and more

we're going to hear about this and not

only that but we're going to see

companies really adopting all of these

and start implementing it into the right

direction holistically. Thanks so much

Najim.

>> Thank you George. Thank you for having

me and having the

>> Oh, it's been a pleasure. Thanks

everybody and until uh next time let's

keep putting the lights on data.