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On this episode of data driven Frank and Andy interview Lauren

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Mafayo author of Designing Data Governance from the Ground

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Up Data governance has become more pressing of late,

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what with all the advancements in generative AI systems.

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Tune in for a fascinating look at data governances, civic

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technology, and more.

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You. Hello, and welcome to Data Driven

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Podcast. We cover the emergent fields of data science,

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AI, and machine learning. Today,

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

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sinus infection, but it's all

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

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

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

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

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I knew something was up because usually you communicate

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

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

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little bit for something that people will know more about

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

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

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

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

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

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case. It looks like that will be resolved this

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

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

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

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

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Lavinia. It is Lauren.

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

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author. Her book just came out talking about designing data

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governance, which is a topic that just more and more

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

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

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

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say, and doubly so if you're in the

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

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

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

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

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likewise. So your background is

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amazing. You studied overseas at Cambridge,

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I think. At LSE

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

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

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sure. And you also have spent

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some time doing arts and design, so

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I found that fascinating too. I actually

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

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closely with data scientists and engineers to

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design things like pipelines, cloud architecture,

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environments, different service models for

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

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

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who is tasked with helping the client define who their key

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

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interviews with people who fit those demographics to figure out what

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

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

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maps. And then ultimately I do work with people like

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you, data architects, engineers, scientists

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

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we uncovered in the user research. Fascinating.

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

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is a fascinating space that once upon a time

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

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

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firm that builds solutions for federal government

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agencies because as we all know, the federal government is

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the most progressive when it comes to tech and so they

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

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

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have that human centered approach that many of their tools

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

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try to improve them and improve the user experience.

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And user experience in that context is really about

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getting the right services to the American public, which I

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

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things like improving unemployment benefits and how

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

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

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roles, defining the service offerings

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that a Chief Data officer is going to provide its

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

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are really fascinating. And I think COVID was the

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final confirmation that all of these systems are long

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overdue for major upgrades which we are seeing

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

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user design or good user experience as part of the RFP

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

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probably not a bullet point on the list, not at

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worse. So for those not familiar with Civic

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Tech, how would you define it? I would define

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Civic Tech as technology which exists to serve

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

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public further by saying it's citizens of any

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country or area where

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the tech exists. And so for instance, Civic Tech

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encompasses the tech in a town

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

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serve residents of NATIC. So this could be anything from

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tech that allows people to pay their bills online

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to applying for benefits. And then likewise I

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

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federal agencies to improve the

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

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

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

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

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

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the Department of Agriculture there are many different

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subdivisions that serve different user groups. And

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

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groups are and figure out how we can tailor a user

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

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broadly define civic tech as any technology which

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serves the public. And the public can then be further

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defined into groups based on things like geography, but

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also things like role, the day to day experience,

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

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

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

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

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

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

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define data governance? I love the fact that you

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

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

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

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

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purely compliance and that it is a blocker

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to innovation and to tinkering. Other people think

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

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

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

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we will deliver it in the next contract after

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production. And that refrain is still unfortunately

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common. So I define data governance as the strategy you

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have to encompass the people, processes and

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

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

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know, is another buzzword that often means both everything

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

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reality is that most organizations have more data that

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they both ingest and produce than ever before.

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It is too big for one person

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

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holistic data governance strategy that is really

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a business strategy before a technical

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

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doing in development and production environments. It should be

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integrated into those environments. But at the same time,

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

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

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

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cultural transformation that your organization needs

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

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

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

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definition, right. My definition, that my operating

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

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

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

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well, not really much, because

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data governance is largely around,

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

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nothing is not technology. Right.

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And you need a vehicle to make it happen in

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the technology space. But the people in process part,

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

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

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

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

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cultural transformation that you can design and embed

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into your business strategy. You really cannot

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

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

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results of that and rather the results of not

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doing that now we are in a pseudo

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

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acting like there's a recession with both layoffs of

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employees, but also in their buying behaviors

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and in not buying as many cloud tools and

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

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that whatever technology you're investing in is

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producing tangible outputs for your organization. And so

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we're seeing the consequence of trying to divorce data

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

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

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

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

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

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

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

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as opposed to a byproduct of some other process.

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And maybe that's it now it's

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

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have processes not guards around it, but gatekeepers or gates

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

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

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I agree with that. I think data as an actual

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tangible asset is a relatively new concept, certainly

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

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is the pure volume of data that exists in the world

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today, there is more data produced and

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ingested than ever before, and that number is

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

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

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fact that now employees are doing work for their

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organizations on private devices, which means that you can be

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having organizational data that exists in several locations,

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which is a very tangible reality. And then I

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think that lends itself to the broader conversation

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

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

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

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do to be successful at data governance is

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defining clear data domains and subdomains within

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your organization. These are the key areas that your

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

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categorizing them more clearly, rolling them up to

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

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

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reframe big data in this way as an

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

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value to that, rather than the top down data

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as a service model that begins and ends with it

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and begins and ends with people who really lack the

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context to make those decisions about data and

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

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important. Lauren and what would you say

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to an enterprise or just maybe a small

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to medium sized company that says, yeah, we

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

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

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things and they can't bridge that

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gap? That's a great question because I

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think that is realistically. Where the biggest blockers

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occur, people are messy, they're

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intangible, they all have different motivations, even if

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

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

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in organizations who do not want change, they

245
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want things to say the same, they have a vested interest in it, even

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

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the long run. You will have people who are invested

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in not changing the status quo, especially as it pertains

249
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to data. I think a lot of that comes down to the fact that data

250
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governance has not been practiced to the degree that it should

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

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

253
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take to create data governance standards from scratch, but then to

254
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retroactively apply those standards to the data they have, it gets

255
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very overwhelming very quickly. And so what I would say to someone who is on

256
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the fence about implementing data governance is

257
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to start small. To start by

258
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looking at the key data domains in your organization.

259
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So these are the areas like sales Data, marketing data,

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customer success data, where your organization is

261
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producing and or ingesting data about

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

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small by not only defining those key data domains and

264
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respective subdomains. For instance, you could have a data domain on

265
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sales data and then two subdomains could be inbound and outbound

266
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leads and those are two subdomains you can collect data on. But

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then you also want to apply that data to a particular

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project that is contained and that has been

269
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already greenlit by the sea level leadership

270
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as having high value to the organization. I think

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that does two things. It helps you contain

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your efforts so that you are not reinventing the wheel

273
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across all areas of the organization, and it also

274
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ensures that you are working on something that senior

275
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leadership really cares about that is also essential. I talk in the

276
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book about finding the right sponsor for your data

277
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governance efforts, and that really is crucial because like any big

278
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transformation, it has to be a top down effort. If you're the Chief

279
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Data officer and your C suite, your chief

280
00:17:13,378 --> 00:17:16,790
executive officer is not on board with data governance,

281
00:17:18,090 --> 00:17:21,766
you can make some progress. Because, again, if you're a senior data leader,

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your entire job is to strategically manage data as an

283
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asset. And so you can make some progress. But without that high

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level buy in and without connecting your efforts back to the

285
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business, you're really going to stall. So I would say start

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small. Look for a strategic project where data governance

287
00:17:39,658 --> 00:17:43,314
can add value, and then do everything you possibly can to

288
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connect your governance efforts back to that business goal.

289
00:17:47,750 --> 00:17:51,282
So it sounds like someone should write a book about doing

290
00:17:51,336 --> 00:17:55,074
data governance from scratch or something like that. That

291
00:17:55,112 --> 00:17:58,930
would be a nice idea. It would have helped me on some of my early

292
00:17:59,000 --> 00:18:02,742
projects, which is why I wrote the book that's well, I. Was

293
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going to lead into that. And you mentioned the book in your answer, and

294
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Lauren has written a book for those who are listening, and it's

295
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called Designing Data Governance from the Ground

296
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Up. And I just picked

297
00:18:17,398 --> 00:18:20,794
up the ebook. We were looking at your

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

299
00:18:24,532 --> 00:18:28,254
minutes before the show, and I said, that sounds

300
00:18:28,292 --> 00:18:31,774
like something I need to dig into. So I picked it up, I'll read

301
00:18:31,812 --> 00:18:35,618
it. I've got a little bit of vacation coming up here starting at

302
00:18:35,624 --> 00:18:39,154
the end of the month, so maybe I'll get to it then. I'm looking

303
00:18:39,192 --> 00:18:42,882
forward. Hopefully you'll read it on the plane there or

304
00:18:42,936 --> 00:18:46,602
back. Because I always joke that if someone's reading my book on a beach somewhere,

305
00:18:46,686 --> 00:18:50,354
something's gone wrong, because this is not exactly a light hearted beach

306
00:18:50,402 --> 00:18:53,798
read. And I always joke with people

307
00:18:53,964 --> 00:18:57,702
because when I encounter resistance to the concept of data governance, I

308
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joke with them, well, you might not want to read my book, but you're going

309
00:19:00,604 --> 00:19:03,974
to have to read the book at some point. So hopefully it will be helpful

310
00:19:04,022 --> 00:19:07,722
when you do. I look forward to it. And as we were talking

311
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a little in the virtual green room about this,

312
00:19:11,700 --> 00:19:15,038
and I said, I'm basically a data

313
00:19:15,124 --> 00:19:18,510
engineer. I came into data

314
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from software and I made the leap about

315
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probably 20 to 25 years ago when

316
00:19:26,328 --> 00:19:29,666
a lot of I would call it process

317
00:19:29,768 --> 00:19:33,534
control, because before I did software, I was in manufacturing.

318
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So it had a lot of the same types of

319
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thinking around engineering and process control.

320
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And even back then, some of the buzzwords that sound

321
00:19:44,860 --> 00:19:48,380
new in software are new ish we were doing in

322
00:19:48,830 --> 00:19:52,634
the 90s in manufacturing stuff like Kanban and Six

323
00:19:52,672 --> 00:19:55,782
Sigma and those sorts of metrics collection.

324
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And I was very fortunate to be trained by

325
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someone who was trained by W. Edwards Deming

326
00:20:02,442 --> 00:20:06,062
himself on that information. So very

327
00:20:06,116 --> 00:20:09,694
fresh, probably some insights that I'll never

328
00:20:09,732 --> 00:20:13,534
share, but just interesting to

329
00:20:13,652 --> 00:20:17,234
get. Definitely a true believer and someone who came at it with an open

330
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mind and really understood it, but

331
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these sorts of things that have grown out of that, and I see this as

332
00:20:24,648 --> 00:20:28,294
growing out of the data governance is one of the things that grew out of

333
00:20:28,412 --> 00:20:32,102
a combination of compliance and quality. Would you agree

334
00:20:32,156 --> 00:20:35,766
with that or would you correct me? No, I do agree with

335
00:20:35,788 --> 00:20:39,562
that. I think that actually hits the nail on the head. We

336
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have let data grow

337
00:20:42,912 --> 00:20:46,602
unchecked, broadly speaking, and

338
00:20:46,656 --> 00:20:50,374
that is because we just didn't know, as an industry

339
00:20:50,422 --> 00:20:54,094
and society how to manage it. You're exactly right that there are people who have

340
00:20:54,132 --> 00:20:57,966
been data architects, engineers, scientists for decades, and

341
00:20:57,988 --> 00:21:01,758
they've been doing this work for a very long time outside of

342
00:21:01,844 --> 00:21:05,554
the public view. But what's different about the work today is

343
00:21:05,592 --> 00:21:09,202
the volume of data that is produced by consumer products

344
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and the amount of sensitive data that is effectively

345
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floating out in the world today through various

346
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cloud systems and various products that are used. And

347
00:21:20,492 --> 00:21:24,086
to that end, we're now in the earliest stages of

348
00:21:24,108 --> 00:21:27,874
figuring out how to manage that from legislative standpoints, both

349
00:21:27,932 --> 00:21:31,706
in the US. And abroad. GDPR legislation in

350
00:21:31,728 --> 00:21:35,526
Europe comes to mind. That's fairly recent legislation that gives EU

351
00:21:35,558 --> 00:21:39,290
citizens a lot more personal rights over their personal

352
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data and what organizations can do in terms of profiting from

353
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that data. We do not have the equivalent of federal legislation

354
00:21:46,682 --> 00:21:50,414
here in the US. But I do see that changing over the next

355
00:21:50,452 --> 00:21:54,082
five to ten years. And I think what you also said about

356
00:21:54,136 --> 00:21:57,826
quality really rings true. That's a huge issue because

357
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we as an industry really lack consistent,

358
00:22:01,486 --> 00:22:04,978
clear standards which define what data quality

359
00:22:05,064 --> 00:22:08,710
is and how we should be measuring it. And that's a big difference.

360
00:22:08,780 --> 00:22:12,450
If you look at fields like medicine law areas

361
00:22:12,530 --> 00:22:16,054
that have very high impact on the

362
00:22:16,092 --> 00:22:19,514
public, they have pretty clear governing bodies and

363
00:22:19,552 --> 00:22:23,114
standards for how doctors and lawyers should do their

364
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work. We have things like IEEE, we have

365
00:22:26,944 --> 00:22:30,634
the association for Computing Machinery, we certainly have membership

366
00:22:30,682 --> 00:22:34,510
organizations where people can get together and discuss these things

367
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and debate these issues. But we really lack a

368
00:22:38,276 --> 00:22:41,454
clear framework for data quality and

369
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compliance, which I think is very long overdue. So

370
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I do see that as being the double pronged issue today. And I'm

371
00:22:49,118 --> 00:22:52,546
also curious what your take is, as someone who's been doing this work for

372
00:22:52,568 --> 00:22:56,242
decades. How have you seen data governance evolve

373
00:22:56,306 --> 00:22:59,800
from the 90s through to the present day?

374
00:23:00,410 --> 00:23:02,840
Well, it's interesting

375
00:23:05,530 --> 00:23:09,306
as I've made the transition from being an employee to

376
00:23:09,328 --> 00:23:12,970
being a consultant, which happened around 2005,

377
00:23:13,040 --> 00:23:16,842
2006, I definitely saw some difference there.

378
00:23:16,896 --> 00:23:20,558
But as an employee at one place, and actually I was a

379
00:23:20,564 --> 00:23:23,790
contractor there too, attempt

380
00:23:25,410 --> 00:23:29,070
they worked with medical devices. And so there

381
00:23:29,140 --> 00:23:32,874
I saw a strict compliance, but it almost fed down

382
00:23:32,932 --> 00:23:36,626
from the culture. You mentioned culture earlier as being very important.

383
00:23:36,728 --> 00:23:40,546
I totally agree. But it was almost an

384
00:23:40,568 --> 00:23:44,334
accidental culture shift that came from the medical

385
00:23:44,382 --> 00:23:47,480
device part, the medical part of the medical device field

386
00:23:47,930 --> 00:23:51,654
into all aspects of software and

387
00:23:51,692 --> 00:23:55,254
data. And it was really interesting to see how

388
00:23:55,452 --> 00:23:58,300
that sort of thinking led to

389
00:23:58,910 --> 00:24:02,522
almost a practice of data governance. And we weren't even

390
00:24:02,576 --> 00:24:06,266
calling it calling it data governance back then, right? We were

391
00:24:06,288 --> 00:24:10,026
just considering it software and data. That was

392
00:24:10,048 --> 00:24:13,566
all. I fell under that umbrella. And having that experience

393
00:24:13,668 --> 00:24:17,418
there was very eye opening and going from there to more of a startup

394
00:24:17,594 --> 00:24:21,386
culture, which not picking on startups. There's

395
00:24:21,418 --> 00:24:24,658
a priority difference, though, between that and somebody

396
00:24:24,824 --> 00:24:28,654
in kind of a more stayed and stable

397
00:24:28,782 --> 00:24:32,114
environment. And I'm not picking again, I'm not calling

398
00:24:32,232 --> 00:24:35,926
startups unstable. There's a lot of

399
00:24:35,948 --> 00:24:39,366
benefits to startups and a lot of

400
00:24:39,548 --> 00:24:43,314
innovative cultures, and some of that wasn't

401
00:24:43,362 --> 00:24:47,078
present in the more medical device environment. Some of the benefits

402
00:24:47,164 --> 00:24:50,938
of that kind of drive and ambition and go, go

403
00:24:51,024 --> 00:24:54,714
and get things done. But it's very easy to overlook. And I saw

404
00:24:54,752 --> 00:24:58,378
it, I saw important aspects of

405
00:24:58,544 --> 00:25:01,822
what we now call data governance and really just good

406
00:25:01,876 --> 00:25:05,550
engineering practices. Some of that was overlooked, some of it was

407
00:25:05,620 --> 00:25:09,230
deprioritized for what I consider

408
00:25:09,300 --> 00:25:12,714
to be mostly legitimate business concerns in a startup

409
00:25:12,762 --> 00:25:16,530
world. I would agree with that. I think when you

410
00:25:16,600 --> 00:25:20,290
consider startups and the landscape they're in, they

411
00:25:20,360 --> 00:25:23,922
have to innovate and be different or else they will not

412
00:25:23,976 --> 00:25:27,686
survive in the marketplace. And so their priority really is to

413
00:25:27,708 --> 00:25:31,382
move fast and figure it out later. I gave a talk

414
00:25:31,436 --> 00:25:34,454
at Data Architecture Online last week and the

415
00:25:34,492 --> 00:25:37,990
keynote moderator made a joke about how

416
00:25:38,060 --> 00:25:41,574
developers are often like, don't bother me with requirements on

417
00:25:41,612 --> 00:25:45,094
coding, meaning they're tinkering and they'll figure it out

418
00:25:45,132 --> 00:25:48,566
later. And we've really taken that approach with data

419
00:25:48,668 --> 00:25:52,442
and that it's a really tricky balance

420
00:25:52,506 --> 00:25:56,234
to balance those standards and the creation of those standards

421
00:25:56,282 --> 00:25:59,998
with the need to innovate and stay

422
00:26:00,084 --> 00:26:03,774
in business. And that's really what startups are focused

423
00:26:03,822 --> 00:26:07,026
on. And then on the flip side, you have these

424
00:26:07,128 --> 00:26:10,670
large, highly regulated, highly bureaucratic industries

425
00:26:10,750 --> 00:26:13,330
like government, healthcare, medicine,

426
00:26:14,230 --> 00:26:17,622
law, which are highly regulated, and they have

427
00:26:17,676 --> 00:26:21,318
to exist to be stable and to provide

428
00:26:21,404 --> 00:26:25,026
services in a way that their users can rely

429
00:26:25,058 --> 00:26:28,618
on. And so innovating, not only is it not the

430
00:26:28,624 --> 00:26:31,500
priority in those environments very often, it's also

431
00:26:32,670 --> 00:26:36,442
an inherent risk because people in those environments are not

432
00:26:36,496 --> 00:26:40,282
really rewarded for doing something in a new way,

433
00:26:40,416 --> 00:26:44,160
but they will be very highly penalized if something goes wrong.

434
00:26:44,690 --> 00:26:48,442
I think you talked and touched on motivation earlier,

435
00:26:48,506 --> 00:26:52,186
and you really have to examine the motivations of whomever

436
00:26:52,218 --> 00:26:55,842
you're working with and consider the context. The book that I wrote is

437
00:26:55,896 --> 00:26:59,746
a 100 page six step guide to designing your

438
00:26:59,768 --> 00:27:03,250
first data governance program from scratch. And it is short

439
00:27:03,320 --> 00:27:06,986
enough because there is a lot of nuance when it comes to data governance.

440
00:27:07,118 --> 00:27:10,710
When you implement a data governance program for 100,000

441
00:27:10,780 --> 00:27:14,614
person multinational firm, that is going to look very different than doing

442
00:27:14,652 --> 00:27:18,454
it for a 25 person startup. But the

443
00:27:18,572 --> 00:27:22,106
key aspects of governance are the same,

444
00:27:22,208 --> 00:27:25,994
I argue, across those nuances. And so that's why the book

445
00:27:26,032 --> 00:27:29,162
is short in the first instance, because it's meant to be the first

446
00:27:29,296 --> 00:27:32,910
prelude to whatever gets more specific about

447
00:27:32,980 --> 00:27:36,462
how to do data governance in your own environment. And that context per

448
00:27:36,516 --> 00:27:40,318
environment is really crucial. No, I mean, that's a

449
00:27:40,324 --> 00:27:44,066
good point. Data governance, it's come up more and more in my

450
00:27:44,088 --> 00:27:47,646
day job as well, because it becomes and it's

451
00:27:47,678 --> 00:27:51,474
also interesting. And as the world's imagination is

452
00:27:51,512 --> 00:27:54,210
captured by generative AI,

453
00:27:55,190 --> 00:27:58,322
I think it's important to realize the generative

454
00:27:58,386 --> 00:28:02,230
AI. Well, first off, there's a lot of legal

455
00:28:02,650 --> 00:28:06,358
questions that remain unresolved, right? Like, if I tell it

456
00:28:06,364 --> 00:28:09,530
to produce a novel in the style of a particular author,

457
00:28:12,190 --> 00:28:15,594
andy's laughing because we've been doing some experiments with

458
00:28:15,632 --> 00:28:19,114
that. I was muted, but I was laughing. You were

459
00:28:19,152 --> 00:28:22,958
laughing. Yeah, more on that later. But no, I mean,

460
00:28:23,124 --> 00:28:26,526
what does that mean? If you produce an image in the style of a particular

461
00:28:26,628 --> 00:28:30,030
artist, obviously, that is

462
00:28:30,100 --> 00:28:33,710
but I think the legislative hammer is coming down on that.

463
00:28:33,780 --> 00:28:37,582
And my opinion is it's probably best to start with governance

464
00:28:37,646 --> 00:28:41,362
today to save you what a stitch in time will save nine

465
00:28:41,416 --> 00:28:44,180
legal bills later. Like something like that.

466
00:28:45,430 --> 00:28:48,520
Do you think that generative AI is really going to

467
00:28:50,250 --> 00:28:53,894
make the data governance cool, for lack of a better

468
00:28:53,932 --> 00:28:57,734
term? That's a really interesting question. I think it is absolutely going to make

469
00:28:57,772 --> 00:29:01,546
data governance essential. And I was speaking to somebody on

470
00:29:01,568 --> 00:29:05,210
a separate podcast this month about this very issue

471
00:29:05,360 --> 00:29:08,506
because you mentioned writing a book in the style of a particular

472
00:29:08,608 --> 00:29:11,802
author giving generative AI the prompt

473
00:29:11,866 --> 00:29:15,546
to write a novella in the style

474
00:29:15,578 --> 00:29:19,006
of cormac McCarthy, for example. In that case, you

475
00:29:19,028 --> 00:29:21,920
are maybe not

476
00:29:22,530 --> 00:29:26,334
copying or plagiarizing cormac McCarthy's work directly,

477
00:29:26,462 --> 00:29:30,142
or maybe you are. It really depends on whether the generative

478
00:29:30,206 --> 00:29:33,314
AI can actually understand what you mean, and it can understand

479
00:29:33,512 --> 00:29:37,030
cormac McCarthy's style of writing enough to

480
00:29:37,100 --> 00:29:40,310
produce a novella in his

481
00:29:40,460 --> 00:29:44,166
likeness, if you will. Likeness is a very interesting

482
00:29:44,268 --> 00:29:47,794
concept, I think, these days. And you're right, it is incredibly

483
00:29:47,842 --> 00:29:51,354
murky from the legal standpoint. And I was speaking on a

484
00:29:51,392 --> 00:29:55,210
podcast recently about this in the sense of

485
00:29:55,360 --> 00:29:58,810
where when we look at the legal landscape of generative AI, where

486
00:29:58,960 --> 00:30:02,398
is there going to be progress? And rather

487
00:30:02,484 --> 00:30:06,030
than making progress on the consumer data

488
00:30:06,100 --> 00:30:09,534
privacy and consumer rights aspect of the issue,

489
00:30:09,652 --> 00:30:13,166
I actually think that we're going to see more progress

490
00:30:13,278 --> 00:30:16,642
made and more cases brought to court on the grounds of

491
00:30:16,696 --> 00:30:20,146
copyright infringement. If you look at things like

492
00:30:20,248 --> 00:30:23,202
using a music in a movie or

493
00:30:23,336 --> 00:30:27,142
using images that a corporation owns in a book,

494
00:30:27,196 --> 00:30:31,014
I just went through this with my own book. I wanted to use

495
00:30:31,212 --> 00:30:34,946
commercial software to make a few diagrams

496
00:30:35,138 --> 00:30:38,626
and use templates to do it. And my editor

497
00:30:38,658 --> 00:30:42,346
said, are those templates that are pre built into the software? I

498
00:30:42,368 --> 00:30:45,958
said, yes. And he said, you either have to get permission

499
00:30:46,054 --> 00:30:49,434
legally from their legal department to use those in the book, or you have to

500
00:30:49,472 --> 00:30:53,274
create some from scratch and make them yourself. So I chose

501
00:30:53,322 --> 00:30:57,054
the latter because it was the path of least resistance. And I think

502
00:30:57,172 --> 00:31:01,006
when we consider generative AI and what that means for

503
00:31:01,108 --> 00:31:04,402
data, we in the United States are going to see more

504
00:31:04,456 --> 00:31:07,822
progress on the grounds of copyright

505
00:31:07,886 --> 00:31:11,086
infringement than we are on data privacy and consumer

506
00:31:11,118 --> 00:31:14,626
rights in the short term. Now, having said that, I think humans are

507
00:31:14,648 --> 00:31:17,910
inherently reactive. And I do foresee

508
00:31:18,410 --> 00:31:22,086
in the future, within the next five years, certainly there's going to

509
00:31:22,108 --> 00:31:25,794
be a data breach to such a degree

510
00:31:25,922 --> 00:31:29,274
that there is going to be enough groundswell for

511
00:31:29,392 --> 00:31:32,966
organizations to really get serious about protecting

512
00:31:33,078 --> 00:31:36,140
consumer rights and as it pertains to data.

513
00:31:37,070 --> 00:31:40,826
The other model you can look at is

514
00:31:40,848 --> 00:31:44,574
what's happened in cybersecurity three to five years ago. There were very

515
00:31:44,612 --> 00:31:47,934
few conversations happening about being proactive when it comes to

516
00:31:47,972 --> 00:31:51,694
cybersecurity. And in recent years, we've seen a

517
00:31:51,732 --> 00:31:54,866
large increase in breaches, not just within

518
00:31:55,048 --> 00:31:58,258
software companies, not just within organizations, but even

519
00:31:58,344 --> 00:32:01,854
breaches of oil and gas pipelines,

520
00:32:01,982 --> 00:32:05,550
things like that. And so just like with data governance

521
00:32:05,630 --> 00:32:09,286
no longer being a nice to have, it never was to begin with, but now

522
00:32:09,308 --> 00:32:12,946
it really is something that you need. Likewise, we're

523
00:32:12,978 --> 00:32:16,514
seeing tech teams really prioritize cyber,

524
00:32:16,642 --> 00:32:20,426
not just in their pipelines, not just on the technical side, but

525
00:32:20,448 --> 00:32:24,234
also creating a more cyber literate workforce. And. I think there's actually

526
00:32:24,272 --> 00:32:27,482
a lot that data practitioners can learn from their

527
00:32:27,536 --> 00:32:31,326
counterparts in Sizzos to drive the needle on that

528
00:32:31,348 --> 00:32:35,150
front. No, that's a good point. I think connecting those dots

529
00:32:35,490 --> 00:32:37,120
are important because

530
00:32:40,770 --> 00:32:44,370
when the C suite realizes that this isn't a game anymore,

531
00:32:45,190 --> 00:32:48,846
when the SCADA drivers got hacked,

532
00:32:48,878 --> 00:32:52,690
or when the Colonial pipeline incident happened,

533
00:32:52,840 --> 00:32:56,494
I think that realized in obviously a number of ransomware

534
00:32:56,542 --> 00:32:59,798
attacks. I think security became very serious, like, oh, wait a

535
00:32:59,804 --> 00:33:03,574
minute, this could affect us and it's not

536
00:33:03,612 --> 00:33:07,314
optional anymore, or nice to have. Right. And I think data governance

537
00:33:07,362 --> 00:33:11,066
is going to follow that same thing. I think

538
00:33:11,088 --> 00:33:14,746
that's an interesting take that you have, is that up till now, the only

539
00:33:14,768 --> 00:33:18,454
driver in this space has effectively been privacy legislation,

540
00:33:18,502 --> 00:33:21,886
right. GDPR probably being the poster child for

541
00:33:21,908 --> 00:33:25,520
that. But I can easily see

542
00:33:26,530 --> 00:33:30,154
fear of being involved in some massive

543
00:33:30,202 --> 00:33:34,046
copyright lawsuit would probably like, I know there's some

544
00:33:34,068 --> 00:33:37,726
controversy about how GPT was trained, right? Like he was trained on Twitter

545
00:33:37,758 --> 00:33:41,426
data and then Elon Musk said, wait a minute, did you get anyone's approval for

546
00:33:41,448 --> 00:33:45,266
that? On that

547
00:33:45,288 --> 00:33:48,982
note, I would also encourage people because every now and then I have

548
00:33:49,036 --> 00:33:52,786
the strong urge when I am transcribing,

549
00:33:52,898 --> 00:33:56,646
for instance, user interviews, to use a tool like chat GPT. It would be

550
00:33:56,668 --> 00:34:00,458
incredible if I could feed that video content into

551
00:34:00,544 --> 00:34:04,310
a system to spit out an accurate transcript.

552
00:34:04,390 --> 00:34:08,106
And that is absolutely not an option for the

553
00:34:08,128 --> 00:34:11,850
role that I'm in, for the industry I'm in. I cannot give that proprietary

554
00:34:11,930 --> 00:34:15,726
information to anyone outside of my organization. And if

555
00:34:15,748 --> 00:34:19,550
I did, the consequences would be things that I don't even

556
00:34:19,620 --> 00:34:23,230
really want to think about because I am beholden

557
00:34:23,310 --> 00:34:27,138
to keeping that information private. And what

558
00:34:27,224 --> 00:34:30,734
that calls to mind is the Samsung incident.

559
00:34:30,862 --> 00:34:34,462
Pretty early on in Chat GPT where folks fed

560
00:34:34,526 --> 00:34:37,938
proprietary Samsung data to chat GPT.

561
00:34:38,114 --> 00:34:41,942
OpenAI owns that now. Again,

562
00:34:42,076 --> 00:34:45,906
we as a society, we as an industry don't

563
00:34:46,098 --> 00:34:49,802
have the full context or real

564
00:34:49,856 --> 00:34:53,674
comprehension of what that actually means, what ownership really means.

565
00:34:53,792 --> 00:34:57,094
But on a very practical level, it does mean that highly

566
00:34:57,142 --> 00:35:00,878
sensitive commercial data is now with the hands

567
00:35:00,964 --> 00:35:04,734
of this very large nonprofit to be used

568
00:35:04,852 --> 00:35:08,414
in very different contexts in very different ways.

569
00:35:08,532 --> 00:35:12,362
And the consequences of that are really going to be felt

570
00:35:12,426 --> 00:35:15,380
and continue to be felt, I think, over the next several years.

571
00:35:16,150 --> 00:35:19,682
That's interesting. I was just going to say it's almost

572
00:35:19,736 --> 00:35:23,566
like the I'm not sure how accurate

573
00:35:23,598 --> 00:35:27,046
it is, but knowing the source I heard it from, it's probably

574
00:35:27,148 --> 00:35:30,200
likely that a

575
00:35:30,570 --> 00:35:33,986
game manufacturer received

576
00:35:34,098 --> 00:35:37,670
proprietary information from a defense contractor

577
00:35:38,110 --> 00:35:41,580
in the US. I don't want to get too specific.

578
00:35:44,110 --> 00:35:47,946
It sounds like something is hitting the fan and it's not

579
00:35:47,968 --> 00:35:51,738
parmesan cheese. Well, it

580
00:35:51,744 --> 00:35:55,514
was an argument. The bit that I will share is it was an argument

581
00:35:55,562 --> 00:35:59,054
about someone had made a guess about what the

582
00:35:59,092 --> 00:36:02,894
interior of some piece of equipment looked like and someone said, no,

583
00:36:02,932 --> 00:36:06,290
it looks like this. And they actually

584
00:36:06,360 --> 00:36:09,854
supplied documents to prove that. And that wasn't

585
00:36:09,902 --> 00:36:13,698
good. Wow. Yeah, that was pretty wild. It was like

586
00:36:13,704 --> 00:36:17,038
all on discord server too. Exactly. Which was

587
00:36:17,144 --> 00:36:18,790
notoriously secure.

588
00:36:21,690 --> 00:36:25,458
So many wrong things about that, yet that happened. It's

589
00:36:25,474 --> 00:36:28,742
off the charts. But I mean, it's a good example of good

590
00:36:28,796 --> 00:36:32,646
intentions going horribly wrong. And you think that's

591
00:36:32,678 --> 00:36:36,054
a thing in data governance as well, like a risk?

592
00:36:36,182 --> 00:36:39,594
Absolutely. And when I talk about bias in AI, which is

593
00:36:39,632 --> 00:36:43,354
one, I don't believe, again, that data governance is separate

594
00:36:43,402 --> 00:36:46,590
from bias mitigation in the training

595
00:36:46,660 --> 00:36:50,126
process. I think data governance is a form of

596
00:36:50,308 --> 00:36:53,406
risk reduction and bias

597
00:36:53,598 --> 00:36:57,234
troubleshooting. And I do think that the

598
00:36:57,352 --> 00:37:01,140
overarching issue here is that we

599
00:37:01,590 --> 00:37:04,740
really need to think of this as an integrated problem

600
00:37:05,270 --> 00:37:08,598
that is one with the business. But I also think

601
00:37:08,684 --> 00:37:12,054
that people it's a misnomer to

602
00:37:12,092 --> 00:37:15,654
say, of course hackers have nefarious intent in many

603
00:37:15,692 --> 00:37:19,542
cases. Of course, there are always going to be people that want to manipulate

604
00:37:19,606 --> 00:37:23,334
data, that want to use it to cause harm.

605
00:37:23,382 --> 00:37:27,034
There's no doubt about that. But the vast majority of times when we

606
00:37:27,072 --> 00:37:30,654
see the biased outputs of algorithms or we

607
00:37:30,692 --> 00:37:34,366
see data governance gone wrong, no one was trying to

608
00:37:34,388 --> 00:37:38,074
harm someone. There was no negative

609
00:37:38,122 --> 00:37:41,966
intent. There are many complicated technical reasons why an

610
00:37:41,988 --> 00:37:45,762
algorithm can produce biased outputs towards one user group over

611
00:37:45,816 --> 00:37:49,534
another. And this is kind of where when people say, assume positive

612
00:37:49,582 --> 00:37:53,266
intent, I think that only goes so far because I

613
00:37:53,288 --> 00:37:56,814
don't believe that most developers or data scientists are

614
00:37:56,872 --> 00:38:00,694
trying to or executives are trying to harm people by

615
00:38:00,732 --> 00:38:04,534
a long shot. They're really doing the best that they can. But if the end

616
00:38:04,572 --> 00:38:07,670
result is still that people's

617
00:38:08,250 --> 00:38:11,482
rights are being abused, that

618
00:38:11,616 --> 00:38:15,274
resumes are getting screened out automatically instead of being

619
00:38:15,312 --> 00:38:16,970
given the proper consideration,

620
00:38:19,150 --> 00:38:22,350
if those negative results are still occurring, the intent,

621
00:38:22,850 --> 00:38:26,462
how much does it matter? But I do think that's an important

622
00:38:26,516 --> 00:38:30,014
distinction. Rather than painting the

623
00:38:30,052 --> 00:38:33,120
industry overall as a group of

624
00:38:33,570 --> 00:38:37,358
bad people with ill intent, I just don't think that's accurate, and I think there's

625
00:38:37,374 --> 00:38:40,962
a lot more nuance to it. It's also important, I think, to show

626
00:38:41,016 --> 00:38:44,578
that while these challenges are part of the job,

627
00:38:44,664 --> 00:38:48,454
they're inherent in the work of doing data today.

628
00:38:48,492 --> 00:38:52,082
Whether you're an engineer, a scientist, a governance

629
00:38:52,146 --> 00:38:55,398
person, this is part of the job. And so to that

630
00:38:55,484 --> 00:38:58,582
degree, it's somewhat inevitable, but it's not

631
00:38:58,636 --> 00:39:02,362
unsolvable. There are tactics that you can use to

632
00:39:02,416 --> 00:39:06,186
improve your work in this space, and so I don't want it

633
00:39:06,208 --> 00:39:09,658
to be a doom and gloom scenario. There are things that we can do

634
00:39:09,744 --> 00:39:13,482
as practitioners to avoid a lot of the consequences

635
00:39:13,546 --> 00:39:17,374
we're talking about, and there

636
00:39:17,412 --> 00:39:20,910
are a lot of blueprints out there for how to do this. Like I mentioned,

637
00:39:20,980 --> 00:39:24,194
cybersecurity is doing a lot to

638
00:39:24,312 --> 00:39:27,806
educate workforces on how to spot phishing attacks.

639
00:39:27,918 --> 00:39:31,410
Things like that if you look at it, governance

640
00:39:31,750 --> 00:39:35,374
from a stewardship perspective and a governance council

641
00:39:35,422 --> 00:39:39,154
perspective, if you've ever certified on a nonprofit board, nonprofits

642
00:39:39,202 --> 00:39:43,046
are actually surprisingly advanced when it comes to

643
00:39:43,068 --> 00:39:46,470
things like data governance. When I was writing the book, I found

644
00:39:46,540 --> 00:39:50,294
many universities washington University in St. Louis

645
00:39:50,342 --> 00:39:53,914
comes to mind that have full websites devoted to their

646
00:39:53,952 --> 00:39:57,254
data governance charter, who serves on the governance

647
00:39:57,302 --> 00:40:01,034
council, what they manage on it. And I'm sure those

648
00:40:01,072 --> 00:40:04,654
people would tell you that their governance council is far from perfect,

649
00:40:04,772 --> 00:40:08,218
but they're doing the work, they're holding themselves accountable,

650
00:40:08,314 --> 00:40:11,678
and they've set up the structure to succeed. So

651
00:40:11,764 --> 00:40:15,218
nonprofits and the cyberspace are both two

652
00:40:15,304 --> 00:40:18,834
really strong models to look towards when we're thinking about

653
00:40:18,952 --> 00:40:21,540
what the future of data governance looks like.

654
00:40:23,350 --> 00:40:26,834
No, that's a good way to look at it. It's an evolving

655
00:40:26,882 --> 00:40:30,354
field, and it's

656
00:40:30,402 --> 00:40:33,960
interesting how it's finally coming up, and it's becoming more and more

657
00:40:34,810 --> 00:40:38,518
prevalent, at least in the conversations I have. And

658
00:40:38,524 --> 00:40:42,298
that's encouraging to hear, because like I said, when I was pitching the book and

659
00:40:42,304 --> 00:40:45,994
then writing it, I felt confident that this

660
00:40:46,032 --> 00:40:49,690
information was necessary, that people in the field

661
00:40:49,840 --> 00:40:53,278
could use it. But at the same time, I was seeing

662
00:40:53,444 --> 00:40:57,294
relatively little being written about data governance. I was seeing a lot of

663
00:40:57,332 --> 00:41:00,702
articles on different things you could do with data from the data

664
00:41:00,756 --> 00:41:04,434
science side or engineering side, but I wasn't seeing a lot about

665
00:41:04,472 --> 00:41:07,714
governance, and there was that nagging part of me that

666
00:41:07,752 --> 00:41:11,474
worried. I feel confident about this book and

667
00:41:11,512 --> 00:41:15,234
its subject, and I do worry that it's going to

668
00:41:15,272 --> 00:41:18,966
land with a bit of a little thump and

669
00:41:18,988 --> 00:41:22,726
then go nowhere. But I've actually really seen the conversation in

670
00:41:22,748 --> 00:41:26,534
our industry shift this year. I think it's no accident that that

671
00:41:26,572 --> 00:41:30,214
happened when Chat GBT became mainstream, when Generative AI

672
00:41:30,262 --> 00:41:33,802
officially became mainstream. And that really was

673
00:41:33,936 --> 00:41:37,626
my thought all along, was that we were going to reach a

674
00:41:37,648 --> 00:41:41,466
tipping point where data governance was necessary. And so I would even

675
00:41:41,488 --> 00:41:44,654
go so far as to say when the book was in beta last fall, I

676
00:41:44,692 --> 00:41:48,110
still had some of those concerns about whether it was going to be

677
00:41:48,260 --> 00:41:51,918
relevant enough or perceived to be relevant enough, and

678
00:41:52,004 --> 00:41:54,690
I don't have that doubt anymore.

679
00:41:55,990 --> 00:41:59,518
So it's interesting. I see that there's an Audible version too. That's

680
00:41:59,534 --> 00:42:03,266
awesome. There is. And so they did turn it into an audiobook. So if

681
00:42:03,288 --> 00:42:06,854
people want to read it, they can either pick up an e

682
00:42:06,892 --> 00:42:10,566
copy, which is available on any ereader, they can also

683
00:42:10,668 --> 00:42:13,798
order a print copy, but it is also available on

684
00:42:13,884 --> 00:42:17,714
audiobooks. So if people want to utilize that I know

685
00:42:17,772 --> 00:42:21,546
that audiobooks are preferred for people on the go. I listen to

686
00:42:21,568 --> 00:42:25,354
them at the gym or on planes, and so I

687
00:42:25,392 --> 00:42:27,980
find that audiobooks can be a great

688
00:42:28,590 --> 00:42:32,234
alternative. If you don't have that time to sit and read every

689
00:42:32,272 --> 00:42:35,726
day, you probably at least are sitting down at some point during the

690
00:42:35,748 --> 00:42:39,466
day, whether on a commute, whether on a plane. And so hopefully the audiobook

691
00:42:39,498 --> 00:42:43,102
can help. No, absolutely. Because

692
00:42:43,156 --> 00:42:46,894
of circumstances related to what I mentioned early

693
00:42:46,932 --> 00:42:49,586
in the show about the good news, I was just spending a lot of time

694
00:42:49,608 --> 00:42:53,454
in the car between here and Pittsburgh. So I've gotten a lot of audio

695
00:42:53,502 --> 00:42:57,298
books done in there and I think this is an

696
00:42:57,304 --> 00:43:01,046
awesome conversation. This could probably go on for the 2 hours, but I want

697
00:43:01,068 --> 00:43:04,646
to switch to the pre canned questions. But while

698
00:43:04,828 --> 00:43:08,674
hopefully Lauren, you've had a chance to review those before. Oh, Andy

699
00:43:08,722 --> 00:43:12,074
just posted them, it looks like. Well, let me post them over

700
00:43:12,112 --> 00:43:15,546
here in our team's chat. Oh, I just did

701
00:43:15,568 --> 00:43:19,130
it. They're not brain teasers,

702
00:43:19,710 --> 00:43:23,150
but they're just fun little questions that we have, we ask of every guest.

703
00:43:24,210 --> 00:43:27,994
But I will point out that Audible is a sponsor

704
00:43:28,042 --> 00:43:30,254
of Data Driven, and if you go to

705
00:43:30,292 --> 00:43:33,482
thedatadedrivenbook.com, you could pick up a free book.

706
00:43:33,556 --> 00:43:36,894
And I'm looking forward to listening to your book. Lauren.

707
00:43:37,022 --> 00:43:39,300
Awesome. Thank you so much. That really means

708
00:43:40,150 --> 00:43:43,938
excellent. Yes. And if listeners want to

709
00:43:43,944 --> 00:43:47,266
buy the book, you can go to Pragueprog.com. That's

710
00:43:47,298 --> 00:43:50,966
Pragprog.com. The book is

711
00:43:50,988 --> 00:43:54,614
called Designing Data Governance from the Ground Up, and your listeners can

712
00:43:54,652 --> 00:43:57,894
use the code Datagov 23 all

713
00:43:57,932 --> 00:44:01,510
Caps to get 35% off the e copy.

714
00:44:01,590 --> 00:44:04,746
So if folks are interested and they need a little bit of a

715
00:44:04,768 --> 00:44:08,506
boost, that code should be good, and I

716
00:44:08,528 --> 00:44:11,946
would love to know what folks think. So I'm happy to be connected with on

717
00:44:11,968 --> 00:44:15,818
LinkedIn and if folks want to leave reviews of the book on sites

718
00:44:15,834 --> 00:44:19,386
like Amazon and Goodreads, that is also hugely helpful.

719
00:44:19,498 --> 00:44:22,974
Those reviews really do make a difference in books getting found and

720
00:44:23,012 --> 00:44:26,446
discovered on those platforms, so every review helps.

721
00:44:26,638 --> 00:44:30,354
Awesome. All right, our first question. How did you find

722
00:44:30,392 --> 00:44:34,100
your way into Data? Did you find Data or did Data find you?

723
00:44:34,550 --> 00:44:38,246
Data did find me. I'm a writer at heart,

724
00:44:38,348 --> 00:44:41,826
and I have a background in mixed methods

725
00:44:41,858 --> 00:44:45,574
research, journalism, and digital media and

726
00:44:45,612 --> 00:44:48,690
content management. I started using open source CMS

727
00:44:48,770 --> 00:44:52,506
systems to manage that content. So that's my

728
00:44:52,528 --> 00:44:56,314
first foray into open source tech and communities. But I

729
00:44:56,352 --> 00:44:59,546
didn't really get interested in Data until I was a research analyst at

730
00:44:59,568 --> 00:45:03,402
Gartner and I started learning about AI

731
00:45:03,466 --> 00:45:07,018
that way. That's where I started hearing about different types of AI,

732
00:45:07,114 --> 00:45:10,430
things like natural language processing versus robotic process

733
00:45:10,500 --> 00:45:14,274
automation and how you could use these different types of tech to

734
00:45:14,312 --> 00:45:17,794
solve very specific business problems. And I was

735
00:45:17,832 --> 00:45:21,346
surprised by how interesting I found

736
00:45:21,528 --> 00:45:25,378
that whole aspect of it and how interesting I found the fact that at

737
00:45:25,384 --> 00:45:29,158
the end of the day, AI is data, and the more

738
00:45:29,244 --> 00:45:32,582
you learn about data and the more you know about it, the more you can

739
00:45:32,636 --> 00:45:35,030
use those technologies effectively.

740
00:45:37,910 --> 00:45:41,634
Awesome. You want to take the next question, Andy?

741
00:45:41,682 --> 00:45:43,240
Yes, sure. Sorry.

742
00:45:46,170 --> 00:45:49,558
I was thinking of how that parallels Frank's story a little bit.

743
00:45:49,724 --> 00:45:53,306
I beat Frank up about this every chance I get because I

744
00:45:53,328 --> 00:45:56,986
begged him for, like, ten years to come over to

745
00:45:57,168 --> 00:46:00,170
data and specifically analytics and business

746
00:46:00,240 --> 00:46:03,414
intelligence because Frank is a gifted natural

747
00:46:03,462 --> 00:46:06,670
artist. He's one of those people that can draw.

748
00:46:06,820 --> 00:46:10,586
And I'm almost 60 years old. I still can't

749
00:46:10,618 --> 00:46:14,106
color in the lines. So I had to do something like data engineering

750
00:46:14,138 --> 00:46:17,706
that didn't require that artistic bend.

751
00:46:17,818 --> 00:46:21,666
But I was thinking of that, as you mentioned, that could I use this

752
00:46:21,688 --> 00:46:23,940
to beat Frank up and see, I did

753
00:46:25,270 --> 00:46:29,060
it's in love. Frank, you know that. Oh, I totally know. I totally know.

754
00:46:30,070 --> 00:46:33,746
Yeah. It only took the collapse of Silverlight

755
00:46:33,778 --> 00:46:37,414
and Windows Phone for me to see the light. I'm so sorry that

756
00:46:37,452 --> 00:46:41,250
happened. That's okay. Our second question.

757
00:46:41,420 --> 00:46:44,330
Lauren, what's your favorite part of your current gig?

758
00:46:44,910 --> 00:46:48,266
My favorite part of my current gig is talking

759
00:46:48,368 --> 00:46:51,580
to users of a particular product. And

760
00:46:52,910 --> 00:46:56,590
when the light bulb goes off between what they're saying

761
00:46:56,660 --> 00:47:00,334
is a pain point and a possible solution that we can build or

762
00:47:00,372 --> 00:47:03,646
design, that gets really exciting to me. And

763
00:47:03,668 --> 00:47:07,466
so you can get a little overwhelmed by all of the user interviews

764
00:47:07,498 --> 00:47:10,706
that you do, especially in the beginning when you're taking in a lot of information.

765
00:47:10,888 --> 00:47:14,386
But then as you zoom back and then start looking at the big

766
00:47:14,408 --> 00:47:18,050
picture to see how you might solve some of those

767
00:47:18,120 --> 00:47:21,894
challenges with technology, that's where I see the

768
00:47:21,932 --> 00:47:25,430
real clear overlap between those user interviews and

769
00:47:25,500 --> 00:47:29,174
what is designed and put out into the world through tech. And

770
00:47:29,212 --> 00:47:32,040
that's really exciting to me. Got you.

771
00:47:33,130 --> 00:47:36,730
Our next complete the sentences when I'm not working. Well, we have

772
00:47:36,800 --> 00:47:40,538
three questions sorry, too much coffee. We

773
00:47:40,544 --> 00:47:44,026
have three questions that are complete the sentence. Right. So the first one is, when

774
00:47:44,048 --> 00:47:47,518
I'm not working, I enjoy blank. I enjoy

775
00:47:47,604 --> 00:47:51,262
traveling. I love to travel as much as my time

776
00:47:51,316 --> 00:47:55,086
and money allow. And one of the cool things about working in Tech is that

777
00:47:55,108 --> 00:47:58,546
you get to attend a lot of conferences that are in really cool places. So

778
00:47:58,568 --> 00:48:02,162
by virtue of being in Tech, I've gotten to see a lot of

779
00:48:02,216 --> 00:48:05,490
new cities and even some countries in places.

780
00:48:05,910 --> 00:48:09,454
For instance, I'm scheduled to go to North

781
00:48:09,502 --> 00:48:13,222
Macedonia next month to help teach at a tech

782
00:48:13,276 --> 00:48:16,934
camp in Orid, North Macedonia. And I would not

783
00:48:16,972 --> 00:48:20,726
be going if not for my career in Tech. But I love

784
00:48:20,748 --> 00:48:24,022
to explore new places, and doing that is one of the few things that actually

785
00:48:24,076 --> 00:48:27,754
gets me to turn my brain off, and that's one of the things that I

786
00:48:27,792 --> 00:48:31,082
value about it. So I do that as much as time and money

787
00:48:31,136 --> 00:48:34,934
allow. I am with you. Yes. I like to not

788
00:48:34,992 --> 00:48:38,398
look at a calendar. That's kind of my thing. Yeah.

789
00:48:38,564 --> 00:48:42,414
And it's a luxury in this day and age, and when I get

790
00:48:42,452 --> 00:48:46,042
to do it, that's really special Macedonia.

791
00:48:46,106 --> 00:48:49,726
I've never been into that part of the world and I am jealous.

792
00:48:49,918 --> 00:48:53,678
Yes, I'm looking forward to it. Other than Croatia,

793
00:48:53,694 --> 00:48:57,234
I haven't been to the Balkans. I've seen very little of Central and

794
00:48:57,272 --> 00:49:01,058
Eastern Europe as a region. And that's the thing about travel. As much

795
00:49:01,064 --> 00:49:04,806
as you've seen, there's always more to see and you know that

796
00:49:04,828 --> 00:49:08,390
you can't possibly scratch the surface of all of it. So I really

797
00:49:08,460 --> 00:49:11,994
value every opportunity that I get to see something new.

798
00:49:12,192 --> 00:49:15,946
Excellent. So our second complete the sentence is I think the

799
00:49:15,968 --> 00:49:19,130
coolest thing in technology today is blank.

800
00:49:20,430 --> 00:49:24,142
I think the coolest thing in technology today

801
00:49:24,276 --> 00:49:27,520
is the opportunity to

802
00:49:27,970 --> 00:49:31,214
get time back to plan more

803
00:49:31,252 --> 00:49:34,506
effectively. And so that might sound like a catch

804
00:49:34,538 --> 00:49:38,162
22, but I think when we look for opportunities to

805
00:49:38,296 --> 00:49:42,130
automate really repetitive tasks that take people hours,

806
00:49:42,200 --> 00:49:45,874
if not days to complete, it does give you a lot of

807
00:49:45,912 --> 00:49:49,570
time back to be more strategic about how you complete

808
00:49:49,640 --> 00:49:53,478
the essence of your work. And so one example of that is I teach a

809
00:49:53,484 --> 00:49:57,334
course on interaction design at George Washington University and I had a student this past

810
00:49:57,372 --> 00:50:00,120
semester ask me about the

811
00:50:00,570 --> 00:50:04,410
impact that I think AI will have on the design profession. And I said,

812
00:50:04,480 --> 00:50:07,734
well, you're already using AI and design today because it's embedded

813
00:50:07,782 --> 00:50:11,434
into Canva and mural and all of the

814
00:50:11,472 --> 00:50:15,226
software that you use to make these designs. And you're

815
00:50:15,258 --> 00:50:19,006
already pretty adept at using AI, but what it can't do

816
00:50:19,108 --> 00:50:22,702
is teach you to get really granular about the best

817
00:50:22,756 --> 00:50:26,574
way to design that technology to

818
00:50:26,612 --> 00:50:30,434
do a particular task that can solve a user need. And

819
00:50:30,472 --> 00:50:34,194
so I think that that is what's really cool. I think

820
00:50:34,232 --> 00:50:37,790
that is what is not easy to be easily automated.

821
00:50:37,870 --> 00:50:41,494
And I think that if we can use technology to do

822
00:50:41,532 --> 00:50:45,074
the dull stuff, for instance, using natural language processing to comb

823
00:50:45,122 --> 00:50:48,742
through hundreds of documents and get you the information you need within

824
00:50:48,796 --> 00:50:52,566
minutes, that is on the surface kind of boring,

825
00:50:52,598 --> 00:50:56,346
but it's also hugely valuable. It's better in many cases than

826
00:50:56,368 --> 00:50:59,260
what humans can do and it gives you more time back.

827
00:51:00,590 --> 00:51:01,580
Good answer.

828
00:51:05,440 --> 00:51:09,184
Oh, you're on mute, Frank. Frank, I'm on mute. Sorry,

829
00:51:09,222 --> 00:51:12,976
but I was coughing. The third and final complete the sentence is I look

830
00:51:12,998 --> 00:51:15,920
forward to the day when I can use technology to blank

831
00:51:16,580 --> 00:51:20,240
to drive. I would really love. I

832
00:51:20,310 --> 00:51:24,036
grew up learning to drive in the suburbs of Boston and then I moved to

833
00:51:24,058 --> 00:51:27,572
Washington DC. Which means that driving is not a fun

834
00:51:27,626 --> 00:51:31,076
experience for me. And I do look forward to the

835
00:51:31,098 --> 00:51:34,804
day when the technology for self driving cars is advanced

836
00:51:34,852 --> 00:51:38,216
enough that I can use it to just get in the

837
00:51:38,238 --> 00:51:42,072
car, have it drive for me. I

838
00:51:42,126 --> 00:51:45,804
do not know what exactly that looks like beyond this idea that I just

839
00:51:45,842 --> 00:51:49,624
shared because obviously self Driving Cars and Regulation

840
00:51:49,752 --> 00:51:53,164
is a whole other podcast. But I do look forward to the day

841
00:51:53,282 --> 00:51:56,556
when, like, planes being effectively flown on

842
00:51:56,578 --> 00:52:00,336
autopilot today. I do look forward to the day when we can actually do that

843
00:52:00,358 --> 00:52:04,096
with cars. I wholeheartedly agree on

844
00:52:04,118 --> 00:52:07,664
that one. Driving in there's something about driving in and around

845
00:52:07,702 --> 00:52:11,404
DC that is just an unpleasant experience. It is. And it's gotten

846
00:52:11,452 --> 00:52:15,296
worse over the pandemic, for sure. I notice a lot more speeding,

847
00:52:15,328 --> 00:52:18,864
a lot more people running red lights, a lot more people going through intersections.

848
00:52:18,912 --> 00:52:22,452
And as someone who straddled the border of DC and Maryland

849
00:52:22,516 --> 00:52:26,292
for seven years, maryland drivers are truly terrifying.

850
00:52:26,356 --> 00:52:30,084
And so I hope that self driving

851
00:52:30,132 --> 00:52:33,860
cars can alleviate a lot of that. As a Maryland resident,

852
00:52:33,940 --> 00:52:37,564
I do not disagree. I was

853
00:52:37,602 --> 00:52:41,276
just going to interject that here in Farmville, Virginia. It's tough, too. I

854
00:52:41,298 --> 00:52:44,910
mean, just the other day there were like five cars at the light.

855
00:52:49,060 --> 00:52:52,210
It's a rough one. The struggle is real,

856
00:52:56,580 --> 00:53:00,004
by the way. I agree with self driving, even though it's all

857
00:53:00,042 --> 00:53:03,764
rural around me. Share something different about

858
00:53:03,802 --> 00:53:07,408
yourself, Lauren. But we remind all of our guests

859
00:53:07,504 --> 00:53:11,204
that we want to keep our clean rate. Yes. So

860
00:53:11,242 --> 00:53:14,404
something different about me is that I foster

861
00:53:14,532 --> 00:53:18,248
dogs. So I have a dog myself. I have a

862
00:53:18,254 --> 00:53:22,036
rescue dog who is my little work from home buddy.

863
00:53:22,068 --> 00:53:25,896
But I also foster dogs every now and then. And so I fostered

864
00:53:25,928 --> 00:53:29,756
a total I did the math recently. I've fostered a total of

865
00:53:29,778 --> 00:53:33,580
ten within the past two years. And so every now and then

866
00:53:33,650 --> 00:53:37,456
I have two pups at home, and I always encourage people to

867
00:53:37,478 --> 00:53:40,960
foster whenever I can. We're in the summer right now.

868
00:53:41,030 --> 00:53:44,768
Summer is a notoriously busy season at Shelter. So

869
00:53:44,854 --> 00:53:48,416
if you have ever considered fostering a

870
00:53:48,438 --> 00:53:52,196
dog, a cat, any other animal that just needs a home to

871
00:53:52,218 --> 00:53:56,068
decompress in before they get adopted, I highly recommend that people

872
00:53:56,154 --> 00:53:59,876
consider it. That's cool. My wife and I have done the

873
00:53:59,898 --> 00:54:02,810
same, and we've only managed to keep two.

874
00:54:03,420 --> 00:54:06,984
Yeah, well, so one of them I did end up adopting. I did

875
00:54:07,022 --> 00:54:10,696
adopt one foster, but the others and

876
00:54:10,718 --> 00:54:14,316
people say they're like, well, is it hard to give them up?

877
00:54:14,418 --> 00:54:17,310
And it is to some extent, but I also think,

878
00:54:18,160 --> 00:54:21,800
you know, when you're a stop on their journey versus

879
00:54:21,880 --> 00:54:25,180
their final destination and it's hard to

880
00:54:25,330 --> 00:54:28,956
explain it more than that, but it is a gut feeling. And

881
00:54:28,978 --> 00:54:32,656
so I think you actually know, like I said,

882
00:54:32,678 --> 00:54:35,936
I highly encourage people to do it. The way I also sell it to people

883
00:54:35,958 --> 00:54:39,668
is you get all the fun of having a pet around without

884
00:54:39,754 --> 00:54:43,430
the bills and long term responsibility. So

885
00:54:44,280 --> 00:54:47,924
that's also good if you just want a little buddy for a while

886
00:54:48,042 --> 00:54:51,736
but don't want a pet long term, that works out, too. It is a bit

887
00:54:51,758 --> 00:54:55,524
like Uber for dogs in that sense, or whatever animal.

888
00:54:55,652 --> 00:54:58,490
Yeah, no,

889
00:55:00,460 --> 00:55:04,296
we had a whole litter of puppies once that were fostered with us, and it

890
00:55:04,318 --> 00:55:07,912
was really cool to have that little baby puppy experience,

891
00:55:08,046 --> 00:55:11,884
but. Yeah, it sounds like a lot of work, though.

892
00:55:12,002 --> 00:55:15,230
It was. And then as they got adopted, I was like, okay,

893
00:55:15,600 --> 00:55:19,424
yeah. I'm happy to see them go to their new homes where they're the center

894
00:55:19,462 --> 00:55:20,320
of attention.

895
00:55:24,180 --> 00:55:27,984
That's part of the justification for moving where we did now, where we have like,

896
00:55:28,022 --> 00:55:31,764
four acres, was for the dogs, basically. I work hard

897
00:55:31,802 --> 00:55:34,500
so my dog has a better life. Oh, totally.

898
00:55:35,400 --> 00:55:39,030
I work to support my dog. At the end of the day,

899
00:55:40,200 --> 00:55:43,576
we have a dog, but we're owned by five cats. Share it

900
00:55:43,598 --> 00:55:47,428
on. That's also a good way to put it. Yeah. You're including

901
00:55:47,444 --> 00:55:50,520
the dog. The dog is also owned by the cats, I'm guessing.

902
00:55:52,700 --> 00:55:56,456
And our final question, where can people find more about you and what you're

903
00:55:56,488 --> 00:56:00,236
up to? Yes. So I am active on LinkedIn, so

904
00:56:00,258 --> 00:56:03,644
if people want to connect to me, I would welcome that. I'm on there under

905
00:56:03,682 --> 00:56:07,436
my full name, and then they can also, like I

906
00:56:07,458 --> 00:56:10,864
mentioned, go to Pragprov.com to find the book.

907
00:56:10,982 --> 00:56:14,784
So that would be fantastic if your listeners want to find it and

908
00:56:14,822 --> 00:56:18,564
download it and then let me know what they think. So those are the main

909
00:56:18,602 --> 00:56:22,452
avenues. I am on Twitter as well, although less so

910
00:56:22,506 --> 00:56:25,860
these days. And I am trying out new

911
00:56:26,010 --> 00:56:29,316
platforms like Threads. I'm active on

912
00:56:29,418 --> 00:56:32,804
Instagram already, and so I did decide to

913
00:56:33,002 --> 00:56:36,824
try out Threads as well. That is TBD, but that's used

914
00:56:36,862 --> 00:56:40,552
in more of a personal context. I don't talk to my friends

915
00:56:40,606 --> 00:56:44,284
about data governance in my everyday life, but that's also partially why I like

916
00:56:44,322 --> 00:56:48,044
talking to people like you about it. Cool. Well, thank you. And

917
00:56:48,082 --> 00:56:51,464
with that, we'll Let Bailey, our AI

918
00:56:51,512 --> 00:56:55,052
assistant, and the show. Thanks for joining us.

919
00:56:55,186 --> 00:56:58,776
Thank you, guys. Thanks for listening to data driven

920
00:56:58,888 --> 00:57:02,716
have you checked out Data Driven magazine yet? We are looking for

921
00:57:02,738 --> 00:57:06,524
writers for the Autumn 2023 issue. Please check

922
00:57:06,562 --> 00:57:10,372
out Data Driven magazine.com for more information. Thanks

923
00:57:10,426 --> 00:57:14,244
for listening, and be sure to rate and review us on whatever podcasting app

924
00:57:14,282 --> 00:57:15,330
you are listening to us on.