On this episode of data driven Frank and Andy interview Lauren

Mafayo author of Designing Data Governance from the Ground

Up Data governance has become more pressing of late,

what with all the advancements in generative AI systems.

Tune in for a fascinating look at data governances, civic

technology, and more.

You. Hello, and welcome to Data Driven

Podcast. We cover the emergent fields of data science,

AI, and machine learning. Today,

I'm here with Andy. My voice is a little crackly because of a

sinus infection, but it's all

good. I've gotten on the meds and I am definitely feeling like

I'm on the mend. How are you doing, Andy? I'm well,

Frank. And I just heard how you were doing. Actually, I knew a little bit

about it because you texted me when you were in the throes of it, and

I knew something was up because usually you communicate

more. I was like, Frank's down for the weekend. And

I know you've been having very busy weekends the past

little bit for something that people will know more about

later, right? Much later, probably. But it's all

good. It is all good so far. It's ended well. So for

folks that we're going to release this episode, we're recording this on

July 17, we're going to release this probably on July

18. And you'll hear me refer to a legal

case. It looks like that will be resolved this

week, hopefully in one form or the other, and it's gone our way. That's all

I can say right now. But it is good news. Speaking of

good news, we have with us an excellent guest who's

based in the DC area. So not that far from Chateau

Lavinia. It is Lauren.

Sorry, she will correct me, but she's a published

author. Her book just came out talking about designing data

governance, which is a topic that just more and more

keeps coming up. And I think that if you're a data engineer and you think

I don't have to worry about that hold up. Maybe you should need to worry

about that. Even data scientists? Especially data scientists, I would

say, and doubly so if you're in the

generative AI space. I think we'll see what we get into that.

And she has a very interesting background, so I'll let her explain

it. Welcome to the show, Lauren. Thank you guys, for having me. I'm really

excited to be here and to chat with you all. Yeah, likewise,

likewise. So your background is

amazing. You studied overseas at Cambridge,

I think. At LSE

and the London School of. Economics, which is like, wow,

I half expected you to have a British accent, honestly, because I wasn't

sure. And you also have spent

some time doing arts and design, so

I found that fascinating too. I actually

am a service designer in my day job, and so I work very

closely with data scientists and engineers to

design things like pipelines, cloud architecture,

environments, different service models for

chief Data Officers. And so I always say as a service

designer that I'm the user advocate on a project. I'm the person

who is tasked with helping the client define who their key

user groups are. And once I do that, I conduct user

interviews with people who fit those demographics to figure out what

they like or dislike about a product or service. I capture

the results of those interviews and design assets like personas and journey

maps. And then ultimately I do work with people like

you, data architects, engineers, scientists

to build a product that will hopefully solve the pain points that

we uncovered in the user research. Fascinating.

And you were in the Civic Tech space if memory serves as well, which

is a fascinating space that once upon a time

I was on the Microsoft Civic Tech team. Yes, I am. So

I work for an organization called Steampunk and we're a human centered design

firm that builds solutions for federal government

agencies because as we all know, the federal government is

the most progressive when it comes to tech and so they

barely need us at all. But the reality actually is that they

need us quite a bit and that we very often come in and

have that human centered approach that many of their tools

were just not built with. And so then we come in and often

try to improve them and improve the user experience.

And user experience in that context is really about

getting the right services to the American public, which I

think is what makes the work so interesting. It's not commercial products, it's

things like improving unemployment benefits and how

easily it is for people to, how easy it is for people to access them,

improving the ease with which you can send folks overseas in official

roles, defining the service offerings

that a Chief Data officer is going to provide its

colleagues. And so the problems that you solve in Civic Tech I think

are really fascinating. And I think COVID was the

final confirmation that all of these systems are long

overdue for major upgrades which we are seeing

the influx of now. Yeah, you don't have kind of good

user design or good user experience as part of the RFP

that went out for building these large federal systems. That made was

probably not a bullet point on the list, not at

worse. So for those not familiar with Civic

Tech, how would you define it? I would define

Civic Tech as technology which exists to serve

the public. And the public is very broad. I would define the

public further by saying it's citizens of any

country or area where

the tech exists. And so for instance, Civic Tech

encompasses the tech in a town

that my hometown, for instance, NATIC, Massachusetts might use to

serve residents of NATIC. So this could be anything from

tech that allows people to pay their bills online

to applying for benefits. And then likewise I

work as a designer in the federal space. And so I work with US

federal agencies to improve the

way that they deliver services to the American public. And the

public in this case, is any American who needs to use

those services. But then we get more granular about who those

particular user groups are. So, for instance, I have worked on

many projects in the past with the Department of Agriculture, and within

the Department of Agriculture there are many different

subdivisions that serve different user groups. And

so then I will work with my client to define what those user

groups are and figure out how we can tailor a user

experience and a product to meet those unique needs. But I would

broadly define civic tech as any technology which

serves the public. And the public can then be further

defined into groups based on things like geography, but

also things like role, the day to day experience,

things like that. That's a good definition because it

used to be very nebulous in terms of what it meant and the implications

thereof. But I like your definition. It's probably the most cogent

I've heard to date of the field. Thank you.

Now this explains so how did you get into data governance, right? Because

this is something well, let's start before we do that. How would you

define data governance? I love the fact that you

start the conversation by asking me to define it, because I think like

many terms in tech, it is often left undefined. And that's

why there's not only a lot of confusion about it, but also a lot of

resistance to it. I think people have in their heads that governance is

purely compliance and that it is a blocker

to innovation and to tinkering. Other people think

that it is something that you can quote unquote, ship after

deployment. And I have had C suite leaders say as much. They've

said things like, we'll do data governance later, or

we will deliver it in the next contract after

production. And that refrain is still unfortunately

common. So I define data governance as the strategy you

have to encompass the people, processes and

tools that help you manage your data at scale. And I often

say manage your big data at scale. Big data, as we

know, is another buzzword that often means both everything

and nothing. But I use big data in this context because the

reality is that most organizations have more data that

they both ingest and produce than ever before.

It is too big for one person

or one team to manage on their own. And that's why you do need this

holistic data governance strategy that is really

a business strategy before a technical

strategy. Your data governance should never be divorced from what you're

doing in development and production environments. It should be

integrated into those environments. But at the same time,

I think people make a mistake when they think of data governance not just

as pure compliance, but also purely as a technical problem to

solve. Because the more complicated reality is that it's a

cultural transformation that your organization needs

to be invested in from the top down. And that's really how you

gain success from data governance. Now, that's a good way to put it.

And that's why I wanted to define it, because it doesn't have a very firm

definition, right. My definition, that my operating

definition is pretty close to yours. I'll say it's really because

in my day job at Red Hat is like they ask, well,

what does your product do for data governance? And I kind of laugh and say,

well, not really much, because

data governance is largely around,

yes, it's people, processes and technology. But 80% of that is

nothing is not technology. Right.

And you need a vehicle to make it happen in

the technology space. But the people in process part,

those are going to be the hard ones. Absolutely. And that's why

it is so tricky. I think it's also why

relatively few organizations have made a lot of headway. And that's also

why I think it's really important to frame data governance as a

cultural transformation that you can design and embed

into your business strategy. You really cannot

separate the two. I think a lot of people have been saying that

for quite some time now, but we're really seeing the

results of that and rather the results of not

doing that now we are in a pseudo

recession, if not an actual recession. Tech organizations have certainly been

acting like there's a recession with both layoffs of

employees, but also in their buying behaviors

and in not buying as many cloud tools and

pieces of software that they used to. And so it's more important than ever

that whatever technology you're investing in is

producing tangible outputs for your organization. And so

we're seeing the consequence of trying to divorce data

governance from your business strategy. It's just no longer

an option to separate the two. No, I totally agree.

And Andy looks like he has a question, but I want to get this out

there. I think part of it is that a lot of organizations, and I mean

legacy organizations probably, I would say federal, it would definitely fall on this,

is that it's only been in the recent years,

maybe decade, that we've thought of data as an asset

as opposed to a byproduct of some other process.

And maybe that's it now it's

something of value. And as with anything of value, you probably should

have processes not guards around it, but gatekeepers or gates

around it just to make sure it's not wasted, it's not

contaminated, that sort of thing. That's where my head is at.

I agree with that. I think data as an actual

tangible asset is a relatively new concept, certainly

within the last decade. And I think what's also new about it

is the pure volume of data that exists in the world

today, there is more data produced and

ingested than ever before, and that number is

certainly not going to go down. When you think about all of the Internet connected

devices that exist, when you think about the explosion of remote work and the

fact that now employees are doing work for their

organizations on private devices, which means that you can be

having organizational data that exists in several locations,

which is a very tangible reality. And then I

think that lends itself to the broader conversation

that I see happening in data circles now about managing data more

as a product and less as a service, which is an approach

that I largely support because a big part of what you need to

do to be successful at data governance is

defining clear data domains and subdomains within

your organization. These are the key areas that your

organization collects data on, and then it gives you a way of

categorizing them more clearly, rolling them up to

specific owners. These would be equivalent to your product managers if we're

using the product analogy. So there's a lot being done to

reframe big data in this way as an

asset that you manage like a product. And I think there's a lot of

value to that, rather than the top down data

as a service model that begins and ends with it

and begins and ends with people who really lack the

context to make those decisions about data and

its quality across domains, I. Think that's really

important. Lauren and what would you say

to an enterprise or just maybe a small

to medium sized company that says, yeah, we

understand all of that and they kind of give mental assent to

it, but they think about their culture and the way they've always done

things and they can't bridge that

gap? That's a great question because I

think that is realistically. Where the biggest blockers

occur, people are messy, they're

intangible, they all have different motivations, even if

they work for the same organization, they not only have different roles,

but they have different end goals. Very often you have people

in organizations who do not want change, they

want things to say the same, they have a vested interest in it, even

if that is arguably not what is best for the organization in

the long run. You will have people who are invested

in not changing the status quo, especially as it pertains

to data. I think a lot of that comes down to the fact that data

governance has not been practiced to the degree that it should

have. And so when people look at how much data they

have in an organization and then they think about not only the work it would

take to create data governance standards from scratch, but then to

retroactively apply those standards to the data they have, it gets

very overwhelming very quickly. And so what I would say to someone who is on

the fence about implementing data governance is

to start small. To start by

looking at the key data domains in your organization.

So these are the areas like sales Data, marketing data,

customer success data, where your organization is

producing and or ingesting data about

from a high level. I would also tell them to start

small by not only defining those key data domains and

respective subdomains. For instance, you could have a data domain on

sales data and then two subdomains could be inbound and outbound

leads and those are two subdomains you can collect data on. But

then you also want to apply that data to a particular

project that is contained and that has been

already greenlit by the sea level leadership

as having high value to the organization. I think

that does two things. It helps you contain

your efforts so that you are not reinventing the wheel

across all areas of the organization, and it also

ensures that you are working on something that senior

leadership really cares about that is also essential. I talk in the

book about finding the right sponsor for your data

governance efforts, and that really is crucial because like any big

transformation, it has to be a top down effort. If you're the Chief

Data officer and your C suite, your chief

executive officer is not on board with data governance,

you can make some progress. Because, again, if you're a senior data leader,

your entire job is to strategically manage data as an

asset. And so you can make some progress. But without that high

level buy in and without connecting your efforts back to the

business, you're really going to stall. So I would say start

small. Look for a strategic project where data governance

can add value, and then do everything you possibly can to

connect your governance efforts back to that business goal.

So it sounds like someone should write a book about doing

data governance from scratch or something like that. That

would be a nice idea. It would have helped me on some of my early

projects, which is why I wrote the book that's well, I. Was

going to lead into that. And you mentioned the book in your answer, and

Lauren has written a book for those who are listening, and it's

called Designing Data Governance from the Ground

Up. And I just picked

up the ebook. We were looking at your

bio before the show. Frank and I connect about five or six

minutes before the show, and I said, that sounds

like something I need to dig into. So I picked it up, I'll read

it. I've got a little bit of vacation coming up here starting at

the end of the month, so maybe I'll get to it then. I'm looking

forward. Hopefully you'll read it on the plane there or

back. Because I always joke that if someone's reading my book on a beach somewhere,

something's gone wrong, because this is not exactly a light hearted beach

read. And I always joke with people

because when I encounter resistance to the concept of data governance, I

joke with them, well, you might not want to read my book, but you're going

to have to read the book at some point. So hopefully it will be helpful

when you do. I look forward to it. And as we were talking

a little in the virtual green room about this,

and I said, I'm basically a data

engineer. I came into data

from software and I made the leap about

probably 20 to 25 years ago when

a lot of I would call it process

control, because before I did software, I was in manufacturing.

So it had a lot of the same types of

thinking around engineering and process control.

And even back then, some of the buzzwords that sound

new in software are new ish we were doing in

the 90s in manufacturing stuff like Kanban and Six

Sigma and those sorts of metrics collection.

And I was very fortunate to be trained by

someone who was trained by W. Edwards Deming

himself on that information. So very

fresh, probably some insights that I'll never

share, but just interesting to

get. Definitely a true believer and someone who came at it with an open

mind and really understood it, but

these sorts of things that have grown out of that, and I see this as

growing out of the data governance is one of the things that grew out of

a combination of compliance and quality. Would you agree

with that or would you correct me? No, I do agree with

that. I think that actually hits the nail on the head. We

have let data grow

unchecked, broadly speaking, and

that is because we just didn't know, as an industry

and society how to manage it. You're exactly right that there are people who have

been data architects, engineers, scientists for decades, and

they've been doing this work for a very long time outside of

the public view. But what's different about the work today is

the volume of data that is produced by consumer products

and the amount of sensitive data that is effectively

floating out in the world today through various

cloud systems and various products that are used. And

to that end, we're now in the earliest stages of

figuring out how to manage that from legislative standpoints, both

in the US. And abroad. GDPR legislation in

Europe comes to mind. That's fairly recent legislation that gives EU

citizens a lot more personal rights over their personal

data and what organizations can do in terms of profiting from

that data. We do not have the equivalent of federal legislation

here in the US. But I do see that changing over the next

five to ten years. And I think what you also said about

quality really rings true. That's a huge issue because

we as an industry really lack consistent,

clear standards which define what data quality

is and how we should be measuring it. And that's a big difference.

If you look at fields like medicine law areas

that have very high impact on the

public, they have pretty clear governing bodies and

standards for how doctors and lawyers should do their

work. We have things like IEEE, we have

the association for Computing Machinery, we certainly have membership

organizations where people can get together and discuss these things

and debate these issues. But we really lack a

clear framework for data quality and

compliance, which I think is very long overdue. So

I do see that as being the double pronged issue today. And I'm

also curious what your take is, as someone who's been doing this work for

decades. How have you seen data governance evolve

from the 90s through to the present day?

Well, it's interesting

as I've made the transition from being an employee to

being a consultant, which happened around 2005,

2006, I definitely saw some difference there.

But as an employee at one place, and actually I was a

contractor there too, attempt

they worked with medical devices. And so there

I saw a strict compliance, but it almost fed down

from the culture. You mentioned culture earlier as being very important.

I totally agree. But it was almost an

accidental culture shift that came from the medical

device part, the medical part of the medical device field

into all aspects of software and

data. And it was really interesting to see how

that sort of thinking led to

almost a practice of data governance. And we weren't even

calling it calling it data governance back then, right? We were

just considering it software and data. That was

all. I fell under that umbrella. And having that experience

there was very eye opening and going from there to more of a startup

culture, which not picking on startups. There's

a priority difference, though, between that and somebody

in kind of a more stayed and stable

environment. And I'm not picking again, I'm not calling

startups unstable. There's a lot of

benefits to startups and a lot of

innovative cultures, and some of that wasn't

present in the more medical device environment. Some of the benefits

of that kind of drive and ambition and go, go

and get things done. But it's very easy to overlook. And I saw

it, I saw important aspects of

what we now call data governance and really just good

engineering practices. Some of that was overlooked, some of it was

deprioritized for what I consider

to be mostly legitimate business concerns in a startup

world. I would agree with that. I think when you

consider startups and the landscape they're in, they

have to innovate and be different or else they will not

survive in the marketplace. And so their priority really is to

move fast and figure it out later. I gave a talk

at Data Architecture Online last week and the

keynote moderator made a joke about how

developers are often like, don't bother me with requirements on

coding, meaning they're tinkering and they'll figure it out

later. And we've really taken that approach with data

and that it's a really tricky balance

to balance those standards and the creation of those standards

with the need to innovate and stay

in business. And that's really what startups are focused

on. And then on the flip side, you have these

large, highly regulated, highly bureaucratic industries

like government, healthcare, medicine,

law, which are highly regulated, and they have

to exist to be stable and to provide

services in a way that their users can rely

on. And so innovating, not only is it not the

priority in those environments very often, it's also

an inherent risk because people in those environments are not

really rewarded for doing something in a new way,

but they will be very highly penalized if something goes wrong.

I think you talked and touched on motivation earlier,

and you really have to examine the motivations of whomever

you're working with and consider the context. The book that I wrote is

a 100 page six step guide to designing your

first data governance program from scratch. And it is short

enough because there is a lot of nuance when it comes to data governance.

When you implement a data governance program for 100,000

person multinational firm, that is going to look very different than doing

it for a 25 person startup. But the

key aspects of governance are the same,

I argue, across those nuances. And so that's why the book

is short in the first instance, because it's meant to be the first

prelude to whatever gets more specific about

how to do data governance in your own environment. And that context per

environment is really crucial. No, I mean, that's a

good point. Data governance, it's come up more and more in my

day job as well, because it becomes and it's

also interesting. And as the world's imagination is

captured by generative AI,

I think it's important to realize the generative

AI. Well, first off, there's a lot of legal

questions that remain unresolved, right? Like, if I tell it

to produce a novel in the style of a particular author,

andy's laughing because we've been doing some experiments with

that. I was muted, but I was laughing. You were

laughing. Yeah, more on that later. But no, I mean,

what does that mean? If you produce an image in the style of a particular

artist, obviously, that is

but I think the legislative hammer is coming down on that.

And my opinion is it's probably best to start with governance

today to save you what a stitch in time will save nine

legal bills later. Like something like that.

Do you think that generative AI is really going to

make the data governance cool, for lack of a better

term? That's a really interesting question. I think it is absolutely going to make

data governance essential. And I was speaking to somebody on

a separate podcast this month about this very issue

because you mentioned writing a book in the style of a particular

author giving generative AI the prompt

to write a novella in the style

of cormac McCarthy, for example. In that case, you

are maybe not

copying or plagiarizing cormac McCarthy's work directly,

or maybe you are. It really depends on whether the generative

AI can actually understand what you mean, and it can understand

cormac McCarthy's style of writing enough to

produce a novella in his

likeness, if you will. Likeness is a very interesting

concept, I think, these days. And you're right, it is incredibly

murky from the legal standpoint. And I was speaking on a

podcast recently about this in the sense of

where when we look at the legal landscape of generative AI, where

is there going to be progress? And rather

than making progress on the consumer data

privacy and consumer rights aspect of the issue,

I actually think that we're going to see more progress

made and more cases brought to court on the grounds of

copyright infringement. If you look at things like

using a music in a movie or

using images that a corporation owns in a book,

I just went through this with my own book. I wanted to use

commercial software to make a few diagrams

and use templates to do it. And my editor

said, are those templates that are pre built into the software? I

said, yes. And he said, you either have to get permission

legally from their legal department to use those in the book, or you have to

create some from scratch and make them yourself. So I chose

the latter because it was the path of least resistance. And I think

when we consider generative AI and what that means for

data, we in the United States are going to see more

progress on the grounds of copyright

infringement than we are on data privacy and consumer

rights in the short term. Now, having said that, I think humans are

inherently reactive. And I do foresee

in the future, within the next five years, certainly there's going to

be a data breach to such a degree

that there is going to be enough groundswell for

organizations to really get serious about protecting

consumer rights and as it pertains to data.

The other model you can look at is

what's happened in cybersecurity three to five years ago. There were very

few conversations happening about being proactive when it comes to

cybersecurity. And in recent years, we've seen a

large increase in breaches, not just within

software companies, not just within organizations, but even

breaches of oil and gas pipelines,

things like that. And so just like with data governance

no longer being a nice to have, it never was to begin with, but now

it really is something that you need. Likewise, we're

seeing tech teams really prioritize cyber,

not just in their pipelines, not just on the technical side, but

also creating a more cyber literate workforce. And. I think there's actually

a lot that data practitioners can learn from their

counterparts in Sizzos to drive the needle on that

front. No, that's a good point. I think connecting those dots

are important because

when the C suite realizes that this isn't a game anymore,

when the SCADA drivers got hacked,

or when the Colonial pipeline incident happened,

I think that realized in obviously a number of ransomware

attacks. I think security became very serious, like, oh, wait a

minute, this could affect us and it's not

optional anymore, or nice to have. Right. And I think data governance

is going to follow that same thing. I think

that's an interesting take that you have, is that up till now, the only

driver in this space has effectively been privacy legislation,

right. GDPR probably being the poster child for

that. But I can easily see

fear of being involved in some massive

copyright lawsuit would probably like, I know there's some

controversy about how GPT was trained, right? Like he was trained on Twitter

data and then Elon Musk said, wait a minute, did you get anyone's approval for

that? On that

note, I would also encourage people because every now and then I have

the strong urge when I am transcribing,

for instance, user interviews, to use a tool like chat GPT. It would be

incredible if I could feed that video content into

a system to spit out an accurate transcript.

And that is absolutely not an option for the

role that I'm in, for the industry I'm in. I cannot give that proprietary

information to anyone outside of my organization. And if

I did, the consequences would be things that I don't even

really want to think about because I am beholden

to keeping that information private. And what

that calls to mind is the Samsung incident.

Pretty early on in Chat GPT where folks fed

proprietary Samsung data to chat GPT.

OpenAI owns that now. Again,

we as a society, we as an industry don't

have the full context or real

comprehension of what that actually means, what ownership really means.

But on a very practical level, it does mean that highly

sensitive commercial data is now with the hands

of this very large nonprofit to be used

in very different contexts in very different ways.

And the consequences of that are really going to be felt

and continue to be felt, I think, over the next several years.

That's interesting. I was just going to say it's almost

like the I'm not sure how accurate

it is, but knowing the source I heard it from, it's probably

likely that a

game manufacturer received

proprietary information from a defense contractor

in the US. I don't want to get too specific.

It sounds like something is hitting the fan and it's not

parmesan cheese. Well, it

was an argument. The bit that I will share is it was an argument

about someone had made a guess about what the

interior of some piece of equipment looked like and someone said, no,

it looks like this. And they actually

supplied documents to prove that. And that wasn't

good. Wow. Yeah, that was pretty wild. It was like

all on discord server too. Exactly. Which was

notoriously secure.

So many wrong things about that, yet that happened. It's

off the charts. But I mean, it's a good example of good

intentions going horribly wrong. And you think that's

a thing in data governance as well, like a risk?

Absolutely. And when I talk about bias in AI, which is

one, I don't believe, again, that data governance is separate

from bias mitigation in the training

process. I think data governance is a form of

risk reduction and bias

troubleshooting. And I do think that the

overarching issue here is that we

really need to think of this as an integrated problem

that is one with the business. But I also think

that people it's a misnomer to

say, of course hackers have nefarious intent in many

cases. Of course, there are always going to be people that want to manipulate

data, that want to use it to cause harm.

There's no doubt about that. But the vast majority of times when we

see the biased outputs of algorithms or we

see data governance gone wrong, no one was trying to

harm someone. There was no negative

intent. There are many complicated technical reasons why an

algorithm can produce biased outputs towards one user group over

another. And this is kind of where when people say, assume positive

intent, I think that only goes so far because I

don't believe that most developers or data scientists are

trying to or executives are trying to harm people by

a long shot. They're really doing the best that they can. But if the end

result is still that people's

rights are being abused, that

resumes are getting screened out automatically instead of being

given the proper consideration,

if those negative results are still occurring, the intent,

how much does it matter? But I do think that's an important

distinction. Rather than painting the

industry overall as a group of

bad people with ill intent, I just don't think that's accurate, and I think there's

a lot more nuance to it. It's also important, I think, to show

that while these challenges are part of the job,

they're inherent in the work of doing data today.

Whether you're an engineer, a scientist, a governance

person, this is part of the job. And so to that

degree, it's somewhat inevitable, but it's not

unsolvable. There are tactics that you can use to

improve your work in this space, and so I don't want it

to be a doom and gloom scenario. There are things that we can do

as practitioners to avoid a lot of the consequences

we're talking about, and there

are a lot of blueprints out there for how to do this. Like I mentioned,

cybersecurity is doing a lot to

educate workforces on how to spot phishing attacks.

Things like that if you look at it, governance

from a stewardship perspective and a governance council

perspective, if you've ever certified on a nonprofit board, nonprofits

are actually surprisingly advanced when it comes to

things like data governance. When I was writing the book, I found

many universities washington University in St. Louis

comes to mind that have full websites devoted to their

data governance charter, who serves on the governance

council, what they manage on it. And I'm sure those

people would tell you that their governance council is far from perfect,

but they're doing the work, they're holding themselves accountable,

and they've set up the structure to succeed. So

nonprofits and the cyberspace are both two

really strong models to look towards when we're thinking about

what the future of data governance looks like.

No, that's a good way to look at it. It's an evolving

field, and it's

interesting how it's finally coming up, and it's becoming more and more

prevalent, at least in the conversations I have. And

that's encouraging to hear, because like I said, when I was pitching the book and

then writing it, I felt confident that this

information was necessary, that people in the field

could use it. But at the same time, I was seeing

relatively little being written about data governance. I was seeing a lot of

articles on different things you could do with data from the data

science side or engineering side, but I wasn't seeing a lot about

governance, and there was that nagging part of me that

worried. I feel confident about this book and

its subject, and I do worry that it's going to

land with a bit of a little thump and

then go nowhere. But I've actually really seen the conversation in

our industry shift this year. I think it's no accident that that

happened when Chat GBT became mainstream, when Generative AI

officially became mainstream. And that really was

my thought all along, was that we were going to reach a

tipping point where data governance was necessary. And so I would even

go so far as to say when the book was in beta last fall, I

still had some of those concerns about whether it was going to be

relevant enough or perceived to be relevant enough, and

I don't have that doubt anymore.

So it's interesting. I see that there's an Audible version too. That's

awesome. There is. And so they did turn it into an audiobook. So if

people want to read it, they can either pick up an e

copy, which is available on any ereader, they can also

order a print copy, but it is also available on

audiobooks. So if people want to utilize that I know

that audiobooks are preferred for people on the go. I listen to

them at the gym or on planes, and so I

find that audiobooks can be a great

alternative. If you don't have that time to sit and read every

day, you probably at least are sitting down at some point during the

day, whether on a commute, whether on a plane. And so hopefully the audiobook

can help. No, absolutely. Because

of circumstances related to what I mentioned early

in the show about the good news, I was just spending a lot of time

in the car between here and Pittsburgh. So I've gotten a lot of audio

books done in there and I think this is an

awesome conversation. This could probably go on for the 2 hours, but I want

to switch to the pre canned questions. But while

hopefully Lauren, you've had a chance to review those before. Oh, Andy

just posted them, it looks like. Well, let me post them over

here in our team's chat. Oh, I just did

it. They're not brain teasers,

but they're just fun little questions that we have, we ask of every guest.

But I will point out that Audible is a sponsor

of Data Driven, and if you go to

thedatadedrivenbook.com, you could pick up a free book.

And I'm looking forward to listening to your book. Lauren.

Awesome. Thank you so much. That really means

excellent. Yes. And if listeners want to

buy the book, you can go to Pragueprog.com. That's

Pragprog.com. The book is

called Designing Data Governance from the Ground Up, and your listeners can

use the code Datagov 23 all

Caps to get 35% off the e copy.

So if folks are interested and they need a little bit of a

boost, that code should be good, and I

would love to know what folks think. So I'm happy to be connected with on

LinkedIn and if folks want to leave reviews of the book on sites

like Amazon and Goodreads, that is also hugely helpful.

Those reviews really do make a difference in books getting found and

discovered on those platforms, so every review helps.

Awesome. All right, our first question. How did you find

your way into Data? Did you find Data or did Data find you?

Data did find me. I'm a writer at heart,

and I have a background in mixed methods

research, journalism, and digital media and

content management. I started using open source CMS

systems to manage that content. So that's my

first foray into open source tech and communities. But I

didn't really get interested in Data until I was a research analyst at

Gartner and I started learning about AI

that way. That's where I started hearing about different types of AI,

things like natural language processing versus robotic process

automation and how you could use these different types of tech to

solve very specific business problems. And I was

surprised by how interesting I found

that whole aspect of it and how interesting I found the fact that at

the end of the day, AI is data, and the more

you learn about data and the more you know about it, the more you can

use those technologies effectively.

Awesome. You want to take the next question, Andy?

Yes, sure. Sorry.

I was thinking of how that parallels Frank's story a little bit.

I beat Frank up about this every chance I get because I

begged him for, like, ten years to come over to

data and specifically analytics and business

intelligence because Frank is a gifted natural

artist. He's one of those people that can draw.

And I'm almost 60 years old. I still can't

color in the lines. So I had to do something like data engineering

that didn't require that artistic bend.

But I was thinking of that, as you mentioned, that could I use this

to beat Frank up and see, I did

it's in love. Frank, you know that. Oh, I totally know. I totally know.

Yeah. It only took the collapse of Silverlight

and Windows Phone for me to see the light. I'm so sorry that

happened. That's okay. Our second question.

Lauren, what's your favorite part of your current gig?

My favorite part of my current gig is talking

to users of a particular product. And

when the light bulb goes off between what they're saying

is a pain point and a possible solution that we can build or

design, that gets really exciting to me. And

so you can get a little overwhelmed by all of the user interviews

that you do, especially in the beginning when you're taking in a lot of information.

But then as you zoom back and then start looking at the big

picture to see how you might solve some of those

challenges with technology, that's where I see the

real clear overlap between those user interviews and

what is designed and put out into the world through tech. And

that's really exciting to me. Got you.

Our next complete the sentences when I'm not working. Well, we have

three questions sorry, too much coffee. We

have three questions that are complete the sentence. Right. So the first one is, when

I'm not working, I enjoy blank. I enjoy

traveling. I love to travel as much as my time

and money allow. And one of the cool things about working in Tech is that

you get to attend a lot of conferences that are in really cool places. So

by virtue of being in Tech, I've gotten to see a lot of

new cities and even some countries in places.

For instance, I'm scheduled to go to North

Macedonia next month to help teach at a tech

camp in Orid, North Macedonia. And I would not

be going if not for my career in Tech. But I love

to explore new places, and doing that is one of the few things that actually

gets me to turn my brain off, and that's one of the things that I

value about it. So I do that as much as time and money

allow. I am with you. Yes. I like to not

look at a calendar. That's kind of my thing. Yeah.

And it's a luxury in this day and age, and when I get

to do it, that's really special Macedonia.

I've never been into that part of the world and I am jealous.

Yes, I'm looking forward to it. Other than Croatia,

I haven't been to the Balkans. I've seen very little of Central and

Eastern Europe as a region. And that's the thing about travel. As much

as you've seen, there's always more to see and you know that

you can't possibly scratch the surface of all of it. So I really

value every opportunity that I get to see something new.

Excellent. So our second complete the sentence is I think the

coolest thing in technology today is blank.

I think the coolest thing in technology today

is the opportunity to

get time back to plan more

effectively. And so that might sound like a catch

22, but I think when we look for opportunities to

automate really repetitive tasks that take people hours,

if not days to complete, it does give you a lot of

time back to be more strategic about how you complete

the essence of your work. And so one example of that is I teach a

course on interaction design at George Washington University and I had a student this past

semester ask me about the

impact that I think AI will have on the design profession. And I said,

well, you're already using AI and design today because it's embedded

into Canva and mural and all of the

software that you use to make these designs. And you're

already pretty adept at using AI, but what it can't do

is teach you to get really granular about the best

way to design that technology to

do a particular task that can solve a user need. And

so I think that that is what's really cool. I think

that is what is not easy to be easily automated.

And I think that if we can use technology to do

the dull stuff, for instance, using natural language processing to comb

through hundreds of documents and get you the information you need within

minutes, that is on the surface kind of boring,

but it's also hugely valuable. It's better in many cases than

what humans can do and it gives you more time back.

Good answer.

Oh, you're on mute, Frank. Frank, I'm on mute. Sorry,

but I was coughing. The third and final complete the sentence is I look

forward to the day when I can use technology to blank

to drive. I would really love. I

grew up learning to drive in the suburbs of Boston and then I moved to

Washington DC. Which means that driving is not a fun

experience for me. And I do look forward to the

day when the technology for self driving cars is advanced

enough that I can use it to just get in the

car, have it drive for me. I

do not know what exactly that looks like beyond this idea that I just

shared because obviously self Driving Cars and Regulation

is a whole other podcast. But I do look forward to the day

when, like, planes being effectively flown on

autopilot today. I do look forward to the day when we can actually do that

with cars. I wholeheartedly agree on

that one. Driving in there's something about driving in and around

DC that is just an unpleasant experience. It is. And it's gotten

worse over the pandemic, for sure. I notice a lot more speeding,

a lot more people running red lights, a lot more people going through intersections.

And as someone who straddled the border of DC and Maryland

for seven years, maryland drivers are truly terrifying.

And so I hope that self driving

cars can alleviate a lot of that. As a Maryland resident,

I do not disagree. I was

just going to interject that here in Farmville, Virginia. It's tough, too. I

mean, just the other day there were like five cars at the light.

It's a rough one. The struggle is real,

by the way. I agree with self driving, even though it's all

rural around me. Share something different about

yourself, Lauren. But we remind all of our guests

that we want to keep our clean rate. Yes. So

something different about me is that I foster

dogs. So I have a dog myself. I have a

rescue dog who is my little work from home buddy.

But I also foster dogs every now and then. And so I fostered

a total I did the math recently. I've fostered a total of

ten within the past two years. And so every now and then

I have two pups at home, and I always encourage people to

foster whenever I can. We're in the summer right now.

Summer is a notoriously busy season at Shelter. So

if you have ever considered fostering a

dog, a cat, any other animal that just needs a home to

decompress in before they get adopted, I highly recommend that people

consider it. That's cool. My wife and I have done the

same, and we've only managed to keep two.

Yeah, well, so one of them I did end up adopting. I did

adopt one foster, but the others and

people say they're like, well, is it hard to give them up?

And it is to some extent, but I also think,

you know, when you're a stop on their journey versus

their final destination and it's hard to

explain it more than that, but it is a gut feeling. And

so I think you actually know, like I said,

I highly encourage people to do it. The way I also sell it to people

is you get all the fun of having a pet around without

the bills and long term responsibility. So

that's also good if you just want a little buddy for a while

but don't want a pet long term, that works out, too. It is a bit

like Uber for dogs in that sense, or whatever animal.

Yeah, no,

we had a whole litter of puppies once that were fostered with us, and it

was really cool to have that little baby puppy experience,

but. Yeah, it sounds like a lot of work, though.

It was. And then as they got adopted, I was like, okay,

yeah. I'm happy to see them go to their new homes where they're the center

of attention.

That's part of the justification for moving where we did now, where we have like,

four acres, was for the dogs, basically. I work hard

so my dog has a better life. Oh, totally.

I work to support my dog. At the end of the day,

we have a dog, but we're owned by five cats. Share it

on. That's also a good way to put it. Yeah. You're including

the dog. The dog is also owned by the cats, I'm guessing.

And our final question, where can people find more about you and what you're

up to? Yes. So I am active on LinkedIn, so

if people want to connect to me, I would welcome that. I'm on there under

my full name, and then they can also, like I

mentioned, go to Pragprov.com to find the book.

So that would be fantastic if your listeners want to find it and

download it and then let me know what they think. So those are the main

avenues. I am on Twitter as well, although less so

these days. And I am trying out new

platforms like Threads. I'm active on

Instagram already, and so I did decide to

try out Threads as well. That is TBD, but that's used

in more of a personal context. I don't talk to my friends

about data governance in my everyday life, but that's also partially why I like

talking to people like you about it. Cool. Well, thank you. And

with that, we'll Let Bailey, our AI

assistant, and the show. Thanks for joining us.

Thank you, guys. Thanks for listening to data driven

have you checked out Data Driven magazine yet? We are looking for

writers for the Autumn 2023 issue. Please check

out Data Driven magazine.com for more information. Thanks

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