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
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