A Career in Data Management with Peggy Tsai – Episode 62

Watch this episode on YouTube: https://youtu.be/6JOJovX9y00

This week we welcome Peggy Tsai, Chief Data Officer for BigID. Peggy brings a unique perspective from building her career in data management, as opposed to starting deeply on the business-side or the technology-side and finding her way to data management at some point in the journey. We have an interesting conversation about the dynamics of driving data change in organizations. Enjoy!

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About Our Guest:

Peggy Tsai is the Chief Data Officer at BigID where she is responsible
for building the data strategy and enablement of data governance
capabilities for customers. BigID enables organizations to know
their enterprise data and take action for privacy, protection, and
perspective. With BigID, customers can proactively discover,
manage, and protect their regulated and sensitive data across
their data landscape.

Peggy has over 18 years of practitioner experience in data
management, stewardship and governance in the financial services
industry. Prior to joining BigID, she was Vice President of Data &
Analytics at Morgan Stanley where she helped run the data
governance program across the Wealth Management division. She
held various positions at Morgan Stanley where she supported the
data science teams on analytical data governance and led a project
team to document data lineage and business definitions across
enterprise systems in order to comply with Basel regulations. Peggy
was also Data Innovation Lead in the Enterprise Data Management
group at AIG. She was responsible for implementing enterprise data
management practices to support Anti-Money Laundering, Solvency II
and GDPR in the Latin American region and Commercial line of
business. Peggy also worked at S&P Global Ratings where she held
various positions in enterprise data group and technology in order to
drive the value of data between the business and IT.

Peggy has a Masters in Information Systems from New York University
and a Bachelors of Arts in Economics from Cornell University. She is
currently an adjunct faculty at Carnegie Mellon University’s Heinz
College CDAO certification program. In her spare time, she is a co-host
of a data and technology podcast called The Data Transformers. She is
also a founding member of the Women Leaders in Data & AI as well as
an advisor to several tech start-ups.

LinkedIn: https://www.linkedin.com/in/peggy-tsai-data/

BigID: https://www.bigid.com

Episode Transcript

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anthony_algmin: Welcome the Da Leadership Lessons podcast. I’m your host, Anthony J.

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anthony_algmin: Algmin. Data everywhere in our businesses and it takes leadership to make

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anthony_algmin: the most of it. We bring you the people stories and lessons to help you

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anthony_algmin: become a data leader. We’ve partnered with DATAVERSITY to provide listeners

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anthony_algmin: with twenty percent off your first training center purchase with Promo code

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anthony_algmin: “AlgminDL” go to DataLeadershipTraining.com to learn more today. On

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anthony_algmin: “AlgminDL” go to DataLeadershipTraining.com to learn more today. On

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anthony_algmin: episode sixty two, we welcome Peggy Tsai. Peggy is the chief data officer at

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anthony_algmin: Big ID, the Data intelligence platform that enables organizations to know

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anthony_algmin: their enterprise data and take action for privacy protection and

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anthony_algmin: perspective. Peggy is also an Adjunct Professor at Carnegie Mellon and a host

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anthony_algmin: of the Data Transformer’s podcast. Peggy, welcome to the show!

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peggy_tsai: Thanks, Anthony. I’m happy to be here today.

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anthony_algmin: So like we do with all our first time guests why don’t just take a moment

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anthony_algmin: and tell the audience a bit more about your career before Big ID and how

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anthony_algmin: it led you to doing what you do. Now

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peggy_tsai: Sure, so my career before big ID was mainly working in the Financial services

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peggy_tsai: industry. I worked in different data management organizations, Um, at S&P

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peggy_tsai: Global, AIG and Morgan Stanley, and my responsibilities were mainly around Um.

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peggy_tsai: operationalizing data management and data governance programs. I worked on

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peggy_tsai: several initiatives where I helped to secure the funding to develop a data

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peggy_tsai: governance program and to execute on a strategy in Ro. Map. A lot of my projects

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peggy_tsai: involved Um compliance with regulatory requirements such as Um,

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peggy_tsai: uh, G, Dpr, and with within financial services, Um C, car, and Um. Bcbs Two

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peggy_tsai: thirty nine, and most recently at Morgan Stanley. I really helped to bring

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peggy_tsai: together the business data stewards and bringing building a business glasry and

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peggy_tsai: helping build out their data quality dashboard. So all these efforts were really

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peggy_tsai: the different functions around Um, data management and data capabilities, and

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peggy_tsai: really helping to solve and support Um business problems, and one of the reasons

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peggy_tsai: why I a joint big I D. Was as a data practitioner, I just felt the struggles

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peggy_tsai: with the the technology tools that we were faced with, or actually limited by,

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peggy_tsai: and a lot of the data managment tasks are quite manual. surprisingly, or

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peggy_tsai: actually, maybe not surprisingly. and I felt that there could be better

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peggy_tsai: solutions out there, especially in the cusp of machine learning and a I, to

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peggy_tsai: really help automate a lot of the activities I felt were still done by

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peggy_tsai: spreadsheets and done by eyeballling exercises. So I mean, I really saw an

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peggy_tsai: opportunity to bring that and really join big. I. D,

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anthony_algmin: I have so many questions about big Idea and about being a chief D officer

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anthony_algmin: and all but first, I just I want to understand, cause, I think this. This

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anthony_algmin: question elicits a lot of interesting responses.

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anthony_algmin: Why data?

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anthony_algmin: Why why did you go into this data spacee in the in the first place?

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peggy_tsai: that’s a great question. I think you probably get many different answers. um,

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peggy_tsai: uh, depending on who you ask, I think a lot of people either uh, approach data

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peggy_tsai: from either a technology background. I think I’m one of the few that actually

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peggy_tsai: grew up in data management, and I think this is a rare breed of people. Um.

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peggy_tsai: because, from day one I almost fell into the role of data management, Data

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peggy_tsai: governance, and it just happened that the first Um role that I interviewed for

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peggy_tsai: at S and P. Global was a center of excellence group that kind of grew into a

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peggy_tsai: data center of excellence, and from there that’s where I really learned and

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peggy_tsai: honed in my skills around all parts of you know, data operations, data quality,

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peggy_tsai: stewardship, Data managementta, governance, Um, and I really loved it because I

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peggy_tsai: really felt it was the perfect bridge between the business side of the

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peggy_tsai: organization and the technology side, And I felt that data was really the bridge

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peggy_tsai: that connected all the applications and all the products that we were selling as

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peggy_tsai: well as I want. I really loved the technology side. I mean, granted, I know I’m

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peggy_tsai: not a programmer and I never will be a successful programmer, but I really love

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peggy_tsai: working with technology teams because they execute on the solution, so sort of

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peggy_tsai: um. expanding on. Um. You know, actually, one of my first career aspirations was

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peggy_tsai: to be a business analyst and I felt that you know, helping to explain the

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peggy_tsai: concepts of technology to the business and really helping technologists

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peggy_tsai: understand why they were building things I thought was so important. So I always

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peggy_tsai: felt that data filled that gap space. And that’s kind of really. why. Um, I

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peggy_tsai: enjoy being in data management. I, I continue to be in the space.

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anthony_algmin: You know, I think you’re right. I think that the data management

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anthony_algmin: practitioner most of the time comes from one of those different areascause

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anthony_algmin: You think about like we sitting in data management. We sit between. You

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anthony_algmin: know, Obviously the technology side of the house, and it’s a business and

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anthony_algmin: process side of things, but you’ve got like change management, like program

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anthony_algmin: and project management stuff’ve got. We’ve got all these areas in an

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anthony_algmin: organization that have relevany with data, and most of the time data

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anthony_algmin: management people come from one of them, but to grow up as a data management

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anthony_algmin: person and have done it for many years, it’s got to give you a depth of

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anthony_algmin: perspective where you’ve seen it from that vantage point in in every

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anthony_algmin: possible way, and I’m sure you would say well, I haven’t seen many things

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anthony_algmin: yet, but it you you have always seen it from a perspective of thinking about

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anthony_algmin: the connectivity between those things versus someone like myself. Who? I

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anthony_algmin: share a background, and I and I grew up in the technology, Um side of the

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anthony_algmin: financial industry, So it share the financial industry, But I was on the

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anthony_algmin: technology. So I was a programmer, database architect developer, and that

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anthony_algmin: type of person And and I’ll never forget I was you know, right out of

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anthony_algmin: college and I was doing some technology work and I had to create an e t. ▁l.

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anthony_algmin: So I had to move some data from place to place right, and even back then

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anthony_algmin: there was a part of me where I’m like, Okay, wait a second, So I’m supposed

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anthony_algmin: to move this data from system A to system B. How am I supposed to do that? I

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anthony_algmin: had a piece of a source to target mapping, but I didn’t actually have any

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anthony_algmin: real understanding of what I was doing and there were a whole bunch of

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anthony_algmin: holes, which as a twenty two year old person, I had plenty of confidence to

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anthony_algmin: go and solve for myself and just do the best I could, and I may or may not

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anthony_algmin: have codified some really incorrect assumptions A as part of that process,

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anthony_algmin: and the sad part was is that that’s kind of the norm a lot of the time is.

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anthony_algmin: That you’re going to turn over some critical piece of data movement to a kid

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anthony_algmin: who doesn’t know any better who knows what he can do, but not what you

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anthony_algmin: should do, or or you know, will just jump into it and try to solve the

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anthony_algmin: problems because there’re a go getter and not realize the the problems that

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anthony_algmin: they’re going to cause That may not even be seen for five years to come, And

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anthony_algmin: and so I learned from that experience,

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anthony_algmin: but if I had had in that organization and data management at the time twenty

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anthony_algmin: something years ago. Didn’t really exist in that organization. If I had had

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anthony_algmin: that, I could have avoided some mistakes that probably caused some pretty

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anthony_algmin: big headaches for somebody at some point on the the road. And and so

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anthony_algmin: have you been on

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anthony_algmin: the other side of that? Have you had the person who is like what I just

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anthony_algmin: described caused some headaches.

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peggy_tsai: not, so I understand what you mean and I think in many organizations, Um, at the

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peggy_tsai: beginning I had to learn what a technologist did. so I’ve had to look at E. t ▁

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peggy_tsai: code. Um. I had to learn sequel on my own. Um. I had to ▁ do source of target

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peggy_tsai: mappings. I had Um. I worked very closely with data architects and did a lot of

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peggy_tsai: logical mappings in Irwin, So I had to an E career in order to grain

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peggy_tsai: credibility, and really explain to technolog like yourself the value of data

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peggy_tsai: management. I had to learn really what Y, you had to do,

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peggy_tsai: but the extra layer on top of it was a lot of the ▁, logical conceptual Mo,

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peggy_tsai: modeling and trying to bring together. you know standardizations around. You

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peggy_tsai: know definitions and usage and consistency around Ha, the data sourceing, and I

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peggy_tsai: really inserted myself in data architecture conversations, and really, um,

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peggy_tsai: pushing the date architects to to find the master data for our customer data

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peggy_tsai: reference, data product data, and not um,

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peggy_tsai: allow them to build multiple hubs or multiple data warehouses, where we were

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peggy_tsai: pretty much in those days making copies and making Um downstream duplica copies,

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peggy_tsai: and they wasn’t unreconciled with a master, so from a data manag perspective, I

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peggy_tsai: always thought myself as seeing the whole end to end holistic picture of not

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peggy_tsai: just. How technology was handling the data but making sure that there were the

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peggy_tsai: least amount of business impact

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peggy_tsai: or even understanding the business impact first of

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peggy_tsai: all was questionw. The question too, was you know things like data lineage, and

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peggy_tsai: ensuring data quality was consistent throughout Um, the full end n life cycle,

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peggy_tsai: and properly deprecating the data as as needed per the data policies. So Um, a

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peggy_tsai: lot of my work was I felt was evangelizing and and building this state of

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peggy_tsai: culture

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peggy_tsai: with technologist, and also with the business teams themselves just so that they

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peggy_tsai: could understand it, You as a technologist, Um, a lot of my Pe technology peers

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peggy_tsai: didn’t understand what I was doing. Imagine the amount of effort I had to

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peggy_tsai: convince business people what a data management person did. So it? it was really

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peggy_tsai: tough. I think on both sides for state management folks like myself.

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anthony_algmin: Yeah, I, I. I smile because I. I. I think of that where on the business side

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anthony_algmin: people would quickly lump any data management person in with all those

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anthony_algmin: technology people doing magic

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anthony_algmin: somewhere else and that the technology people would also do the same thing

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anthony_algmin: with the a data management person. Oh, your business process person. You

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anthony_algmin: have no idea what we’re doing over here and so you’re kind of caught in the

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anthony_algmin: middle, and at the same rate, you’re trying to affect change and trying to

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anthony_algmin: foster collaboration and communications amongst all these different uh

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anthony_algmin: groups that are that are all trying to ideally be pulling in the same

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anthony_algmin: direction and you’re trying to connect them

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anthony_algmin: with some empathy for each other. Well, you’re stuck in this place where

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anthony_algmin: nobody has empathy for you. Like like So it’s It’s a difficult challenge and

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anthony_algmin: that’s where it’s like I alway. I, I often say with data management, I’m

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anthony_algmin: like this is a very tough job, but when done well, it can be extremely

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anthony_algmin: rewarding because you can see the outputs of your efforts through how a

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anthony_algmin: business could be successful. and and especially, um, once you uh, get into

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anthony_algmin: a place like, like, you’re now a chief data officer, which has a tremendous

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anthony_algmin: number of responsiilities, But you’re in an industry with what we are doing

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anthony_algmin: with big I. d, where you don’t do this. Just internally, you guys exist to

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anthony_algmin: provide

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anthony_algmin: capabilities to organizations all over the place. So can you talk a little

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anthony_algmin: bit about first? What was that transition like from going from big financial

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anthony_algmin: institution, types of of places, too more of a a software company That that

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anthony_algmin: has those kinds of folks as customers.

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peggy_tsai: Yeah, so um, let me just say that it was. It was a big. Uh, risk for me to take.

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peggy_tsai: I’m certainly the comfort level of working in financial services and continue to

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peggy_tsai: be in Um. A data management role in any industry, I think would be a safer

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peggy_tsai: option, but Um, call it a a midlife crisis, or you know, just of you know, S

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peggy_tsai: just a big risk I was willing to take at that point of my career two years ago

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peggy_tsai: was Um. one. I wanted to Um, impact a bigger change in the data management

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peggy_tsai: industry, and I thought that by reinventing or help on being part of the product

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peggy_tsai: design of a new product tool, Um would be even, would be exciting and have a

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peggy_tsai: bigger impact, Um. And then, secondly, I was also positioning myself as a data

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peggy_tsai: thought leader, Um, as in with within the industry as well, and I wanted those

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peggy_tsai: opportunities. Um to do so, whether it be writing or speaking at conferences or

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peggy_tsai: things like that, So I was really looking for a platform to to allow me to give

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peggy_tsai: to give me those opportunities. Um. but I would say at the end of the day, Um,

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peggy_tsai: it wasn’t much of a shift for me because I’ve always been Um. you know, a very

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peggy_tsai: hands on type of Um. Leader and worker, Um, So the the actual shift wasn’t as

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peggy_tsai: you know, surprising as some people have anticipated. but um, you know it is.

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peggy_tsai: Uh, I am learning about what a start up life looks like. what it’s like to work

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peggy_tsai: for a vendor. Now that I’m on the other side, Um, you know it it. I. I. I. I’ to

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peggy_tsai: know what it’s like to be on other side, so it’s very interesting. Um, having to

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peggy_tsai: to learn both perspectives.

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anthony_algmin: As the way you do one. what you do fundamentally changed with that shift in

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anthony_algmin: perspectives of chief data officer

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anthony_algmin: at

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anthony_algmin: an organization on the south side. a product side organization

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anthony_algmin: differ substantially from what a chief data officer would be in a financial

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anthony_algmin: institution.

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peggy_tsai: Uh, so that’s a difficult answer? be Um, question as only because I don’t think

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peggy_tsai: there’s a standard definition for a chief data officer in a Aveor organization.

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peggy_tsai: Um. I think it’s very easy to describe what a chief data officer does. Um for an

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peggy_tsai: industry. Um, and I say that because I’m also, as you said in the beginning, Um.

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peggy_tsai: I’m an adjunct faculty member at Carnegie Mellon, where I’m helping to to

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peggy_tsai: support the Chief Data officers certification. So Um, you know, those are the

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peggy_tsai: curriculum that we created and I help support. Um is for a very standard, Um,

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peggy_tsai: Chief Data or Chie Data Analytics officer, Um. I would say for a a Cio in a

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peggy_tsai: vendor organization, I’ve seen Um, C, E Os that Um in a vendor act more like a

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peggy_tsai: technical practitioner, Um a technical expertise, Um a subject matter expert. So

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peggy_tsai: I would say for myself, My role

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peggy_tsai: is about Um. Business development so really working, and helping to understand

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peggy_tsai: how our customers are implementing data governance and helping them to

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peggy_tsai: understand where they are on their road map in Su support, and giving them ideas

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peggy_tsai: and assessing how they can be, Um, more quickly approving in in their world map,

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peggy_tsai: Um, I also do things like marketing activities, which I again, One of the

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peggy_tsai: reasons why I joined Big I. D is to continue my my platform. Speak at

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peggy_tsai: conferences, Um, participate in in writing white papers or blocks as I see fit,

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peggy_tsai: Um, and also a product development. I mean, that’s again the core reason why

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peggy_tsai: join big. I. D was to help influence, develop, help prioritize the features that

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peggy_tsai: I believe is most needed in the workplace and and then socializing it with my

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peggy_tsai: peers, and with uh, you know, colleagues in the in the industry that are now Um.

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peggy_tsai: Still solving that problem right, so that’s been kind of my my remit as a a

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peggy_tsai: chief date officer so far, and Um. it’s It’s a very new role for me. So Um.

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peggy_tsai: there’s a lot of exciting things I want to do for Um. next year, twenty twenty

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peggy_tsai: two and really building out Um. internal data governance practice and really

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peggy_tsai: play the traditional role of a chief data officer Where internally we have

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peggy_tsai: governance practices, standardization and usage and measurement, Um and I, I,

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peggy_tsai: you know, there’s a lot of opportunities. Uh. I could see Um data governments

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peggy_tsai: being implemented.

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anthony_algmin: Yeah, I think it. it’s It’s almost a cliche of you know, vendor

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anthony_algmin: organizations that don’t do well themselves what it is that they’re selling

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anthony_algmin: in the marketplace. and and I understand why, Because the needs of that

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anthony_algmin: organization are different in what they’re focused on is providing the

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anthony_algmin: customer side service. But it really, when it comes to data, there is not a

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anthony_algmin: good excuse for Um. you know being

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anthony_algmin: less than that, Um

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anthony_algmin: in in your own practice, and so I think that’s definitely

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anthony_algmin: a a good area of emphasis. But I also like the the notion of you know a

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anthony_algmin: chief date officer and a vendor organization as having a greater degree of

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anthony_algmin: outreach and connection to the end customers. Then what you would typically

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anthony_algmin: see in a Um, kind of introspective C O role in a financial institution or

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anthony_algmin: like that, we’re going to be very focused on your operational considerations

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anthony_algmin: or or compliance and governance. Things. You see a lot of legal emphasis

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anthony_algmin: especially in the financial world, Uh, for C e Os. Whereas I like the energy

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anthony_algmin: that a c e o has to have in an organization like big, I, D. Because you, you

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anthony_algmin: need to reach and influence both product design and the way you’re engaging

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anthony_algmin: with your clients, And you want to kind of show and lead by example in what

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anthony_algmin: you do internally from an operational perspective as well, so the facets of

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anthony_algmin: a Cio role I think could be more exciting on the vendor’s side, though,

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anthony_algmin: I think to your earlier point is probably going to be less well defined and

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anthony_algmin: less consistent from organization to organization ’cause at the end of the

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anthony_algmin: day I love saying things that get people all riled up, and I like. To think

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anthony_algmin: about like data governance, or even my personal favorite is data leadership,

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anthony_algmin: or you can talk about positions or whatever, But it’s like if you’re not

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anthony_algmin: accomplishing a meaningful goal for your business, doing this thing that you

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anthony_algmin: think is important. stop doing that thing. Stop doing date a gonance. If

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anthony_algmin: it’s not helping anything, if it’s just causing problems. find a better way.

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anthony_algmin: Don’t get hung up on what you’re supposed to do. Recognize that what your

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anthony_algmin: organization needs may not be a chief data officer right now, may just

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anthony_algmin: simply be a few data stewards. That can cut through some of the confusion

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anthony_algmin: and get things moving again. Like Just focus on what matters and will drive

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anthony_algmin: business success. And it’ll get you to a point where eventually a chief data

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anthony_algmin: officer at an organization, there is always a circumstance where if there’s

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anthony_algmin: an organization with a chief Dta officer somewhere, sometim that chief data

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anthony_algmin: officer role was created. It was created when it didn’t exist previously. At

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anthony_algmin: some point in that journey, I think there’s plenty of organizations out

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anthony_algmin: there who probably really need a chief data officer, and if they had one

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anthony_algmin: would have no idea what to do with.

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anthony_algmin: And and so how do you reach? I guess through my kind of me endering

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anthony_algmin: brainstorming, which I tend to do. how do you reach a point in an

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anthony_algmin: organization where it’s like Yes, Now it’s the time. let’s get somebody into

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anthony_algmin: this chief data officer. roll

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anthony_algmin: and

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anthony_algmin: figure out what that should be, and and you’ll make it their own. Um,

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anthony_algmin: did, and I didn’t ask you this before we were on the call and I regret not

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anthony_algmin: doing it. but hey W. we’re alive. It’s it’s fine. Were you the first chief

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anthony_algmin: date officer? A big I. D. Is this a created position for you coming up?

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anthony_algmin: Yeah, and so and you’d had another

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peggy_tsai: Yes, Yes, I’m the first one.

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anthony_algmin: role epic? I. D. Previous to that, can you talk about you

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anthony_algmin: did right? or was I incorrect?

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peggy_tsai: no, No. My previous title was Um. vice president of data

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peggy_tsai: solution. So it’s It’s almost like the the precursor, Um to being a chief data

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peggy_tsai: officer, Um, and I mean I, I think what you’re saying, Anthony, Um,

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peggy_tsai: I think organizations don’t necessarily need a person designated with the title

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peggy_tsai: chief Data officer. I think that, but there does need to be someone who is

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peggy_tsai: responsible for the data strategy and executing on that strategy and being Um.

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peggy_tsai: Having that power mean power, meaning the funding, the ability to hire and build

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peggy_tsai: a team right. So Um, and I really think it depends on you know, the really, the

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peggy_tsai: maturity and the direction of that organization, Um, and also the fact that I

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peggy_tsai: agree with you. The, the whole definition of data governance and what it means

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peggy_tsai: varies from organization organization. Ive, I’ve talked to companies that don

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peggy_tsai: don’t even need want to use a word data, Stewart, You know, they have data

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peggy_tsai: enablers and they have very specific goals and milestones that they want them to

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peggy_tsai: achieve. And that’s great. It’s perfect and I think that will get them to that

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peggy_tsai: next level And it’s a matter of moving levels to a greater maturity, Um.

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peggy_tsai: But there are also organizations that you know, have a chief data officer. Um.

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peggy_tsai: but they you know, but with with Um, you know complexities as organizations,

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peggy_tsai: different lines of business, competing priorities. whether it’s what technology

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peggy_tsai: or regulations or you know, Even trying to just move the needle and making an

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peggy_tsai: impact on the business, it’s very hard for those enterprise chief data officers

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peggy_tsai: to to really make an impact. Um.

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peggy_tsai: So, I think Uh,

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peggy_tsai: chief state officer is not always a glamorous role, but I think it’s a very

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peggy_tsai: important role where you know you do get to um influence and certainly get to

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peggy_tsai: Um, make a difference, and again important to rally the troops. I mean, I, I do

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peggy_tsai: that constantly Everyda in my in my day to day work is Um. You know, working

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peggy_tsai: working well with people. I think that’s almost the natural trait of a the deer,

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peggy_tsai: a data leader to try to um influence and get people to change, and and move

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peggy_tsai: towards his vision that the chief daated officer has um put upon

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anthony_algmin: Yeah, and I certainly I would agree with you about you Don’t want to get

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anthony_algmin: hung up on the Chief data officer designation and I think that

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anthony_algmin: it’s important too to for anybody who’s like. I think a lot of the audience

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anthony_algmin: would say Hey, I’d love to be a chiefd officer. That sounds great. I also

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anthony_algmin: worry about organizations or individuals that get themselves into these

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anthony_algmin: situations where the expectations for a chief data officer are often

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anthony_algmin: extremely high, and there are plenty of organizations who have not given

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anthony_algmin: that role the corresponding level of empowerment to be able to

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anthony_algmin: achieve those Hi expectations, whether it’s in terms of a team underneath

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anthony_algmin: them, a resources from a financial perspective, or whatever whatever the

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anthony_algmin: goals are, there has to be a path to success,

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anthony_algmin: and

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anthony_algmin: it scares me sometimes. With this rush to creating a position to solve that

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anthony_algmin: problem, it’s like buying the product to solve all our problems again. You

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anthony_algmin: know what’s the shiny object today that we arere going to go put a bunch of

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anthony_algmin: money into that. We hope we will solve the hard problem so that we don’t

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anthony_algmin: actually have to do that ourselves. And that chief date officer is just

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anthony_algmin: another way of buying the thing to try to avoid the hard problems, which,

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anthony_algmin: with data you know there’s there’s amplifiers, and they are going to amplify

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anthony_algmin: whatever you have, and and solving the data challenges that your

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anthony_algmin: organization faces will inevitably be difficult. and just throwing

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anthony_algmin: amplifiers at noise creates louder noise. It doesn’t create music.

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peggy_tsai: And just to be honest, I actually never wanted to be a chief data officer. That

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peggy_tsai: was actually a title that I always wanted to avoid only because of you know,

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peggy_tsai: large organizations. there’s just a lot of politics involved and I’m just the

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peggy_tsai: type of person you know, just just wants to get things done. That’s work. And

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peggy_tsai: execute and not deal with all the other noise. I feel like, Um, that’s where

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peggy_tsai: I’ve always had a lot of frustrations. Um, when trying to to deliver was all

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peggy_tsai: this? It wasn’t the ability to execute. It was all the extra noise. That was.

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peggy_tsai: you know, stopping stopping us from getting things done on time. So, um,

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peggy_tsai: yeah, so if I and I think that’s the biggest challenge for Chief date officers

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peggy_tsai: in other industries is to show value, show value quickly and to really align

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peggy_tsai: themselves with the business initiatives, but the same time,

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peggy_tsai: um, you know,

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peggy_tsai: meet expectations that other people have put on you that necesarily not be on on

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peggy_tsai: their road map.

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anthony_algmin: Yeah, I, I can appreciate that notion of

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anthony_algmin: wanting to just make the data better and get you know what the organization

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anthony_algmin: needs without being part of that political environment of senior leadership

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anthony_algmin: Of trying to navigate all of that. I, I’ve often joked where, like a lot of

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anthony_algmin: us went into data because we like to do technology stuff, but we didn’t

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anthony_algmin: necessarily want to like bang out computer code all day. And so we wanted to

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anthony_algmin: do something that was a little bit more relevant to the business. But we

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anthony_algmin: like the the technology the ones in ▁zero’s component of it. and then we

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anthony_algmin: that most of data work is people based. It’s all about

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anthony_algmin: communications and working with others and collaborating and stuff. And and

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anthony_algmin: I think the C Eo becomes, You know, To your point, I think a big, important

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anthony_algmin: part of that is is rallying

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anthony_algmin: folks around a common vision. This is the leadership side of being a chief

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anthony_algmin: date officer Is that is not different than being a leader in any other

346
00:27:03,291 –> 00:27:09,291
anthony_algmin: function. Is is how can we orient people with divergent or disparate

347
00:27:09,625 –> 00:27:12,416
anthony_algmin: perspectives and goals about what they’re trying to do or different

348
00:27:12,500 –> 00:27:16,125
anthony_algmin: perspectives on what they think is most important. How do we align them to a

349
00:27:16,166 –> 00:27:21,000
anthony_algmin: common goal and and move forward in a you know way that is going to help our

350
00:27:21,208 –> 00:27:24,583
anthony_algmin: organizations thrive? I, I think at the end of the day we, You know, the

351
00:27:24,666 –> 00:27:28,750
anthony_algmin: chief data officer just has a different set of tools, but is a leader just

352
00:27:28,916 –> 00:27:31,541
anthony_algmin: like any of the other senior leaders in in an organization.

353
00:27:33,625 –> 00:27:37,166
peggy_tsai: well, actually, I think the the qualifications of a Chief date officer

354
00:27:38,500 –> 00:27:43,875
peggy_tsai: is is really quite high, because that person is expected to know about the

355
00:27:44,041 –> 00:27:48,500
peggy_tsai: business operations of the business, and as well as understand the latest

356
00:27:48,500 –> 00:27:49,500
peggy_tsai: technologies,

357
00:27:50,583 –> 00:27:54,750
peggy_tsai: and on top of that no data management. And I think also that the role of the

358
00:27:54,750 –> 00:28:00,416
peggy_tsai: chief date officer, has you know, has grown beyond basic data, ment. it’s about

359
00:28:00,666 –> 00:28:06,666
peggy_tsai: data engineering, Deta, analytics, data science, and the that has also fallen

360
00:28:06,916 –> 00:28:12,666
peggy_tsai: under the responsibilities of A of a chief data officer, and on top of that you

361
00:28:12,750 –> 00:28:17,958
peggy_tsai: know new security, cybers, security, risk and privacy. Um, you know, chief

362
00:28:18,416 –> 00:28:22,833
peggy_tsai: officers are now responsible for the operationalization of all these new

363
00:28:23,000 –> 00:28:24,125
peggy_tsai: regulations. Um,

364
00:28:25,375 –> 00:28:29,083
peggy_tsai: so I think it’s just become a very complex job,

365
00:28:30,125 –> 00:28:35,208
peggy_tsai: And Um, you know that’s why they need at. I will call them deputies. You know,

366
00:28:35,458 –> 00:28:41,000
peggy_tsai: deputy chief data officers that have to be Um, hyper focusoed on each of these

367
00:28:41,291 –> 00:28:46,250
peggy_tsai: sub areas for a chief data officer. But ▁ultimately end the day, a chief officer

368
00:28:46,500 –> 00:28:50,666
peggy_tsai: has to um, have that full purview and really understand everything that’s going

369
00:28:50,833 –> 00:28:56,750
peggy_tsai: on his a organization. Um, So that’s why I think, Um, the the skill set and also

370
00:28:57,083 –> 00:29:01,791
peggy_tsai: the responsibility of a cheap officer has really grown a lot in the last. I

371
00:29:01,875 –> 00:29:03,208
peggy_tsai: would say, five, five,

372
00:29:03,208 –> 00:29:04,208
peggy_tsai: seven years,

373
00:29:05,000 –> 00:29:08,833
anthony_algmin: would. absolutely, I would absolutely agree with that, and I, part of me

374
00:29:08,916 –> 00:29:11,958
anthony_algmin: wonders is like. Was this what we were going for with the chief information

375
00:29:12,333 –> 00:29:15,791
anthony_algmin: officer all along? like? Is this what we were trying to do, but just that

376
00:29:16,041 –> 00:29:20,333
anthony_algmin: kind of verged into a more technology focused role? I don’t. I don’t know if

377
00:29:20,333 –> 00:29:23,458
anthony_algmin: there is a consistent answer to that either, but it definitely feels like

378
00:29:23,958 –> 00:29:25,708
anthony_algmin: data has certainly become

379
00:29:27,208 –> 00:29:31,208
anthony_algmin: central to the business proposition for most organizations at this point, I

380
00:29:31,291 –> 00:29:32,583
anthony_algmin: think, and the

381
00:29:32,583 –> 00:29:34,416
anthony_algmin: chief D office are absolutely. Yeah,

382
00:29:34,416 –> 00:29:39,458
peggy_tsai: name one. Yeah, exactly. I asked the audience to name one where name a company

383
00:29:39,708 –> 00:29:44,750
peggy_tsai: where data is not the main focus and it’s not embedded in the business strategy.

384
00:29:45,375 –> 00:29:47,708
peggy_tsai: Um. for a success in that company so

385
00:29:47,708 –> 00:29:52,416
anthony_algmin: yeah, yeah, absolutely. And so it is an interesting evolution to see where

386
00:29:52,500 –> 00:29:55,875
anthony_algmin: the data every where the chief date officer is now, and where the data

387
00:29:56,125 –> 00:30:01,166
anthony_algmin: organizations you know, and and the capabilities are. Um. So it’s not just a

388
00:30:01,208 –> 00:30:06,500
anthony_algmin: team deep in I, T. anymore, creating a few reports that go out, you know via

389
00:30:07,083 –> 00:30:11,291
anthony_algmin: you know, print. you know, To depend how old, go print them out and put

390
00:30:11,458 –> 00:30:15,291
anthony_algmin: them. I remember when we were printing out reports and putting them on desks

391
00:30:15,541 –> 00:30:19,791
anthony_algmin: of other people in the organization that that evolve to P. d. Fs, and and

392
00:30:19,875 –> 00:30:24,583
anthony_algmin: other dashboards and such things, but it, um, it definitely has evolved

393
00:30:24,833 –> 00:30:28,833
anthony_algmin: quite substantially from there, where many you functions inside the

394
00:30:28,916 –> 00:30:32,416
anthony_algmin: organization are completely dependent on working with data. You know, data

395
00:30:32,583 –> 00:30:35,958
anthony_algmin: analysts is just part of every job. You know. I think we’re starting to see.

396
00:30:37,208 –> 00:30:41,875
anthony_algmin: I am at least hoping we’re going to start to see the being a data steward

397
00:30:42,416 –> 00:30:43,416
anthony_algmin: and actually having

398
00:30:44,916 –> 00:30:50,416
anthony_algmin: ownership or curation responsibilities for data starting to become part of

399
00:30:50,500 –> 00:30:54,583
anthony_algmin: every job. Just like being a data analyst has been. Have you do you see any

400
00:30:54,750 –> 00:31:00,166
anthony_algmin: of the signs of that on the horizon, or do you think that may not be where

401
00:31:00,166 –> 00:31:03,208
anthony_algmin: we’re heading?

402
00:31:03,208 –> 00:31:07,958
peggy_tsai: well, I’m certainly seeing a lot more job descriptions with the title Data

403
00:31:07,958 –> 00:31:08,958
peggy_tsai: steward

404
00:31:09,458 –> 00:31:16,125
peggy_tsai: in Embedded. I see that a lot with our customers. Um. but you know the lot of

405
00:31:16,208 –> 00:31:22,583
peggy_tsai: the issues that I faced back. Um, you know my A I G and Morgan Stanley days was

406
00:31:22,750 –> 00:31:23,875
peggy_tsai: the fact that Um,

407
00:31:25,166 –> 00:31:27,291
peggy_tsai: they wouldn’t create a roles

408
00:31:28,333 –> 00:31:32,583
peggy_tsai: where entirely you were a data steward, because they felt that being a steward

409
00:31:33,000 –> 00:31:38,125
peggy_tsai: was maybe twenty five percent of your job, which is fine. I mean, I, sometimes

410
00:31:38,416 –> 00:31:44,125
peggy_tsai: you know the responsibilities can e and flow, but not to have it in your goals

411
00:31:44,333 –> 00:31:49,791
peggy_tsai: and responsibilities means that you’re not actually getting credit for doing a

412
00:31:49,958 –> 00:31:54,583
peggy_tsai: stwich of role. And then what ends up happening is that you’re you know, your

413
00:31:54,916 –> 00:31:56,750
peggy_tsai: your managers will um

414
00:31:57,791 –> 00:32:02,250
peggy_tsai: expect you to do it, but not, You don’t get rewarded or compensated for actually

415
00:32:02,500 –> 00:32:07,625
peggy_tsai: doing your job. So be if it’s not written down clearly and articulate in your

416
00:32:07,875 –> 00:32:11,875
peggy_tsai: goals and responsibilities, you know, doesn’t always get done. and I think

417
00:32:11,958 –> 00:32:18,833
peggy_tsai: that’s where organizations need to um, do better in terms of Um, putting that

418
00:32:19,458 –> 00:32:24,500
peggy_tsai: Pri. putting that responsibility as a priority in Um. You know the job functions

419
00:32:25,000 –> 00:32:32,208
peggy_tsai: and you know Um. resourcing people better, and Um, thinking that you know, there

420
00:32:32,333 –> 00:32:37,083
peggy_tsai: has to be a better allocation to Dta management functions, especially if you’re

421
00:32:37,208 –> 00:32:38,500
peggy_tsai: not going to hire

422
00:32:39,708 –> 00:32:45,208
peggy_tsai: a person dedicated to that, so I mean, I, I think it. Hopefully, it’s improved

423
00:32:45,375 –> 00:32:49,291
peggy_tsai: in terms of the mindset of Um, data driven organizations

424
00:32:50,333 –> 00:32:55,458
peggy_tsai: to to have that Um. but I do see it better actually in a lot of the technology

425
00:32:56,125 –> 00:33:00,583
peggy_tsai: companies that are very focused on data science analytics, visualization

426
00:33:00,916 –> 00:33:07,708
peggy_tsai: reporting, Um. there are clearly diliy roles for for those Um functions. Um, You

427
00:33:07,791 –> 00:33:12,416
peggy_tsai: know what I’m most excited about is you know. Sometimes they clearly identify

428
00:33:12,583 –> 00:33:17,791
peggy_tsai: you know, data curation data quality, Um, data reconciliation as as part of that

429
00:33:17,875 –> 00:33:21,791
peggy_tsai: responsibility, and those fall under Um. Data management, so, even though they

430
00:33:21,791 –> 00:33:25,958
peggy_tsai: don’t have a specific function for data management, they are putting that

431
00:33:26,125 –> 00:33:32,416
peggy_tsai: responsibility, and in the at least inro, you know, update analyst, or Um, a

432
00:33:32,583 –> 00:33:37,958
peggy_tsai: data scientist, So regardless of what you call it I, you know that title and

433
00:33:38,041 –> 00:33:40,041
peggy_tsai: responsibility. it it needs to be somewhere.

434
00:33:40,250 –> 00:33:45,291
anthony_algmin: So and I would agree with everything you just said, And it gets me thinking

435
00:33:45,625 –> 00:33:51,458
anthony_algmin: that a lot of the folks that were going to be asking to take on these kinds

436
00:33:51,625 –> 00:33:55,458
anthony_algmin: of responsibilities in the future and tip toing towards. And I think there

437
00:33:55,625 –> 00:33:59,375
anthony_algmin: points around needing to have that as part of job descriptions formally, and

438
00:33:59,458 –> 00:34:02,416
anthony_algmin: having it written, I think that you’re absolutely right it’. It’s absolutely

439
00:34:02,583 –> 00:34:07,458
anthony_algmin: essential, but to do any of that we’re going to need to teach folks how to

440
00:34:07,625 –> 00:34:11,791
anthony_algmin: do these things, and to to start to have that awareness and and build that

441
00:34:11,875 –> 00:34:15,083
anthony_algmin: up. and so from your perspective, I know with with your the work that you do

442
00:34:15,166 –> 00:34:17,958
anthony_algmin: as the adjun Professor Carneie Mellon. Through the work that you’re doing

443
00:34:18,041 –> 00:34:21,875
anthony_algmin: with the C O program and to the speaking, and, and even the the podcast work

444
00:34:22,041 –> 00:34:24,750
anthony_algmin: that you’re doing in the thought leadership space, you know you’. you’re

445
00:34:24,916 –> 00:34:29,375
anthony_algmin: putting a significant amount of your time towards educating the world,

446
00:34:29,791 –> 00:34:32,125
anthony_algmin: educating people on these different topics.

447
00:34:33,291 –> 00:34:37,625
anthony_algmin: How do you do it? Like what? How do you think we should go and start to

448
00:34:37,708 –> 00:34:42,333
anthony_algmin: build these capabilities in our organizations? And where do you start

449
00:34:43,625 –> 00:34:47,875
anthony_algmin: in? in? in terms of I guess data literacy, or in terms of just starting to

450
00:34:47,958 –> 00:34:54,333
anthony_algmin: give people some tools to be data stewards as part of their existing roles,

451
00:34:57,916 –> 00:34:58,916
peggy_tsai: so I

452
00:34:59,500 –> 00:35:00,500
peggy_tsai: I think, Um,

453
00:35:00,500 –> 00:35:04,500
peggy_tsai: I think of of myself as a true data management practitioner and I think If you

454
00:35:04,666 –> 00:35:06,666
peggy_tsai: ask many folks like myself,

455
00:35:08,125 –> 00:35:13,541
peggy_tsai: Um, you know, they are just basic fundamental things that you know. We learn

456
00:35:14,041 –> 00:35:19,458
peggy_tsai: through you know, through through dema, through reading through data maturity

457
00:35:19,625 –> 00:35:23,875
peggy_tsai: assessments, and having gone through them, I would say most of what I’ve learned

458
00:35:23,958 –> 00:35:28,583
peggy_tsai: is has been on the job. Um, but I think there has been good

459
00:35:30,208 –> 00:35:35,958
peggy_tsai: curriculum, but it’s been spread out and I think that’s really the reason why I

460
00:35:36,250 –> 00:35:42,208
peggy_tsai: enjoy teaching and I enjoy doing my podcast because I like to reinforce the

461
00:35:42,333 –> 00:35:48,750
peggy_tsai: basic data principles that I think often get missed. because it’s not talked

462
00:35:48,916 –> 00:35:55,083
peggy_tsai: about. It’s seen as too basic to foundational, but be, and a lot of the Ne, up

463
00:35:55,083 –> 00:36:00,666
peggy_tsai: up and coming people that are joining the work force or trans ing into data from

464
00:36:01,208 –> 00:36:06,500
peggy_tsai: technology or from another Um. background. They don’t learn that F fundamentals,

465
00:36:07,166 –> 00:36:13,458
peggy_tsai: and I think that that’s where they don’t grasp the complexity. The heavy lil

466
00:36:13,791 –> 00:36:19,958
peggy_tsai: work that’s needed Um to make a successful data found to build a successful data

467
00:36:20,125 –> 00:36:24,333
peggy_tsai: foundation. And that’s scalable and levergable Um. And also you know, future

468
00:36:24,750 –> 00:36:29,875
peggy_tsai: proofing yourself for all privacy and security regulations are coming out there,

469
00:36:30,250 –> 00:36:34,500
peggy_tsai: and that’s my fear, right. That’s that’s what I love to talk about. Um is what

470
00:36:34,666 –> 00:36:40,125
peggy_tsai: you’re doing what you’re building, scalable and reusable and most often not

471
00:36:40,583 –> 00:36:44,583
peggy_tsai: people that don’t know these data measurement principles they’re doing. They’re

472
00:36:44,666 –> 00:36:50,208
peggy_tsai: building anhoc solutions, point bandate solutions, and they’re gonna quickly

473
00:36:50,416 –> 00:36:55,166
peggy_tsai: realize with different regulations or different business initiatives what

474
00:36:55,208 –> 00:37:01,000
peggy_tsai: they’ve done. They haven’t done the proper Um classification tagging labeeling

475
00:37:01,208 –> 00:37:06,666
peggy_tsai: definition standardization across the board, Um. They’re gonna have to um,

476
00:37:07,208 –> 00:37:12,041
peggy_tsai: realize that later on, Um, and kind of, and that’s why a lot of organizations

477
00:37:12,208 –> 00:37:16,583
peggy_tsai: every two to three years they rip everything apart and try to build it again,

478
00:37:16,916 –> 00:37:21,875
peggy_tsai: But you know they don’t have that basic good building blocks Um set up for.

479
00:37:22,125 –> 00:37:26,666
peggy_tsai: That’s a hard heavy lifting work. So a couple of years ago I used to talk about

480
00:37:26,833 –> 00:37:31,541
peggy_tsai: a I governance for ethics and modeling, and I think Um,

481
00:37:32,583 –> 00:37:36,333
peggy_tsai: it hasn’t really picked up it. I think it has it a little bit, but I think it’s

482
00:37:36,500 –> 00:37:42,125
peggy_tsai: gonna pick up even more. I think with a lot more talks about ethics and using

483
00:37:42,333 –> 00:37:48,833
peggy_tsai: data for good, I think that’s gonna. I’m gonna see that cycle up again. Um, but

484
00:37:49,000 –> 00:37:53,458
peggy_tsai: right now everyone is so concerned about privacy and they sh as they should be,

485
00:37:53,791 –> 00:37:54,791
peggy_tsai: and

486
00:37:55,625 –> 00:37:59,416
peggy_tsai: organizations that have done a good job with their data, Governmentver Pro

487
00:38:00,041 –> 00:38:02,583
peggy_tsai: are not worried about privacy compliance. It’s

488
00:38:03,875 –> 00:38:09,083
peggy_tsai: is the folks that haven’t done anything or haven’t invested there in their data

489
00:38:09,625 –> 00:38:11,291
peggy_tsai: programs, that are

490
00:38:11,958 –> 00:38:12,958
peggy_tsai: you know,

491
00:38:13,625 –> 00:38:20,416
peggy_tsai: really worried and not know and find themselves building point point solutions

492
00:38:20,666 –> 00:38:24,750
peggy_tsai: for each state regulation or each countrygulation, which is a shame.

493
00:38:24,750 –> 00:38:28,041
anthony_algmin: Yeah, those organizations that have approached data management or dated

494
00:38:28,166 –> 00:38:33,375
anthony_algmin: governance based on checking the boxes solely to satisfy regulatory or audit

495
00:38:33,958 –> 00:38:36,916
anthony_algmin: concerns, they are in a world of hurt right now because

496
00:38:37,166 –> 00:38:41,291
anthony_algmin: they are not going to have that muscle. Memory that ability as an

497
00:38:41,375 –> 00:38:44,750
anthony_algmin: organization to adapt to all of the change that’s coming to wear. Like if

498
00:38:44,833 –> 00:38:48,666
anthony_algmin: they just done what they should do in the first place, they’d be able to

499
00:38:48,750 –> 00:38:52,750
anthony_algmin: adapt to the specifics a lot a lot easier. So I know we’re starting to get

500
00:38:52,750 –> 00:38:55,458
anthony_algmin: short on time, but there was a couple of questions that. I. I wanted to make

501
00:38:55,541 –> 00:39:00,416
anthony_algmin: sure that we had time force, so one is to kind of turn it around to a lot of

502
00:39:00,500 –> 00:39:04,333
anthony_algmin: the folks in this audience are going to be somewhere in their um journey in

503
00:39:04,500 –> 00:39:10,416
anthony_algmin: data management and wide net to all of that, but may want to be on the path

504
00:39:10,666 –> 00:39:14,500
anthony_algmin: to a chief data officer role some day or to to a a more senior role than

505
00:39:14,750 –> 00:39:18,833
anthony_algmin: than what they have, and I think that a a key part of that is you and I both

506
00:39:19,000 –> 00:39:22,916
anthony_algmin: would would, I’m sure agree is being a thought. Leader, being a person out

507
00:39:23,000 –> 00:39:27,791
anthony_algmin: there speaking or doing things to add to that conversation. And so I’m

508
00:39:27,958 –> 00:39:32,416
anthony_algmin: curious. Like what advice do you have for folks out there that just don’t

509
00:39:32,583 –> 00:39:37,083
anthony_algmin: know how to do that or where to go to start to become a thought leader

510
00:39:37,375 –> 00:39:40,916
anthony_algmin: themselves outside of the walls of of their company? What what advice would

511
00:39:40,916 –> 00:39:45,166
anthony_algmin: you have for them?

512
00:39:45,166 –> 00:39:51,625
peggy_tsai: Um. so I would say that there’s plenty of great Linkton groups that are all

513
00:39:51,875 –> 00:39:53,375
peggy_tsai: about networking

514
00:39:55,083 –> 00:39:58,333
peggy_tsai: depending on where you live, on which city’,

515
00:39:59,458 –> 00:40:04,416
peggy_tsai: ups, or or data conferences. if you have the ability to attend. Um.

516
00:40:05,625 –> 00:40:10,041
peggy_tsai: I would certainly recommend that, Um, but I would say that what I started up

517
00:40:10,250 –> 00:40:14,666
peggy_tsai: doing is Um, because I was lucky enough to work for you know, financial services

518
00:40:14,916 –> 00:40:17,541
peggy_tsai: companies that had these data,

519
00:40:18,125 –> 00:40:24,250
peggy_tsai: Um solutions in place. Um. I worked with them, right I, I learned from them. I,

520
00:40:25,000 –> 00:40:29,875
peggy_tsai: it asked them to invite me to conferences where they were speaking. I could be

521
00:40:29,875 –> 00:40:33,958
peggy_tsai: a, if I can get a free ticket, Uh, ’cause they’re usually uh, a little bit

522
00:40:34,125 –> 00:40:39,958
peggy_tsai: pricey, but I would say that the data community is is really small, so whether

523
00:40:40,208 –> 00:40:45,000
peggy_tsai: it’s reaching out to me or to plenty of other data, thought leaders out there,

524
00:40:45,458 –> 00:40:46,458
peggy_tsai: Um, and asking

525
00:40:47,791 –> 00:40:51,791
peggy_tsai: what tips they have with in terms of meet ups and learning,

526
00:40:52,833 –> 00:40:58,333
peggy_tsai: Uh, I would recommend that, and certainly reading. There’s plenty of not just

527
00:40:58,583 –> 00:41:02,750
peggy_tsai: this podcast and plenty of other podcasts, Um to read and learn about what

528
00:41:02,916 –> 00:41:08,250
peggy_tsai: people are saying and doing. I, I think that gives you that education. Um, if

529
00:41:08,416 –> 00:41:12,750
peggy_tsai: filling any gaps in any education that you, you think you may have, and I think

530
00:41:12,916 –> 00:41:17,083
peggy_tsai: the third piece of advice is Um, forming your own point of view, I think that’s

531
00:41:17,125 –> 00:41:18,125
peggy_tsai: that’s really important.

532
00:41:18,583 –> 00:41:23,791
peggy_tsai: Um to to hear what other people are saying, thought leaders are saying. But I

533
00:41:23,875 –> 00:41:29,083
peggy_tsai: think what’s needed is new ideas, new perspectives. And if you have an ocur,

534
00:41:29,291 –> 00:41:32,833
peggy_tsai: articulate your point of view, and then you know, write a paper about it.

535
00:41:33,166 –> 00:41:38,833
peggy_tsai: present in a conference. I think that will get a lot of people excited and a lot

536
00:41:38,916 –> 00:41:43,875
peggy_tsai: of discussions going on, but you know there’s plenty of opportunities out there,

537
00:41:44,125 –> 00:41:46,500
peggy_tsai: whether especially virtually um,

538
00:41:47,625 –> 00:41:53,791
peggy_tsai: for people to to connect to to learn and to to really build their their skill

539
00:41:54,125 –> 00:41:59,708
peggy_tsai: set and knowledge. And I certainly recommend uh, doing that and take taking that

540
00:41:59,791 –> 00:42:05,208
peggy_tsai: extra effort to network and to talk to people. I know that’s certainly outside

541
00:42:05,375 –> 00:42:10,500
peggy_tsai: of everyone’s day to jobs, but taking that time and and Um, making that

542
00:42:10,583 –> 00:42:15,541
peggy_tsai: investment in your personal career growth is is really going to pay dividends

543
00:42:15,875 –> 00:42:20,416
peggy_tsai: especially. Um. You know, there’s you know you want to find someone who can be a

544
00:42:20,416 –> 00:42:25,375
peggy_tsai: future resource for you, whether it’s learning opportunities or Um. You know

545
00:42:25,625 –> 00:42:30,416
peggy_tsai: being opportunities to to speak, and that will slowly build your momentum of

546
00:42:31,083 –> 00:42:35,708
peggy_tsai: being more of a thought leader and having that recognition. So, depending on

547
00:42:35,791 –> 00:42:40,916
peggy_tsai: your goals, there’s many opportunities to do so and I, you know, just just get

548
00:42:41,083 –> 00:42:46,416
peggy_tsai: started and I, I think it’s so easy now with social media link, Twitterram,

549
00:42:47,625 –> 00:42:49,958
peggy_tsai: Um, and and on all different types of podcasts

550
00:42:49,958 –> 00:42:54,250
anthony_algmin: I, That’s fantastic advice. I think that’s that’s right on. I don’t and I

551
00:42:54,416 –> 00:42:57,458
anthony_algmin: rarely don’t have something I want to add to something like, but I think you

552
00:42:57,708 –> 00:43:01,208
anthony_algmin: covered. I think that’s exactly the right advice. learn everything you can

553
00:43:01,791 –> 00:43:05,541
anthony_algmin: and just start finding opportunities to engage in that conversation, one one

554
00:43:05,708 –> 00:43:09,000
anthony_algmin: through networking and finding opportunities to speak. Attend conferences.

555
00:43:09,166 –> 00:43:13,708
anthony_algmin: attend your smaller group sessions. Having just the the dialogue with people

556
00:43:13,875 –> 00:43:17,375
anthony_algmin: in the room like that. That’s a great place to start. So excellent advice.

557
00:43:17,625 –> 00:43:22,333
anthony_algmin: The last thing. I just feel like we didn’t get into enough detail on what

558
00:43:22,500 –> 00:43:26,750
anthony_algmin: big I actually does and what I understand that a little bit more. and just

559
00:43:27,000 –> 00:43:30,166
anthony_algmin: make sure that there’s an opportunity for people out in the audience that

560
00:43:30,583 –> 00:43:33,291
anthony_algmin: want to reach out to you or understand what big idea does. A little bit

561
00:43:33,458 –> 00:43:36,166
anthony_algmin: better. I’d like you to, if you have just a little bit more information

562
00:43:36,583 –> 00:43:40,125
anthony_algmin: about what your organization does and and how maybe people should reach out

563
00:43:40,166 –> 00:43:43,458
anthony_algmin: to the organization to reach out to you. Uh, for further conversations and

564
00:43:43,458 –> 00:43:47,083
anthony_algmin: then we’ wrap it up and and call it an episode. This is flown by again. I,

565
00:43:47,375 –> 00:43:50,916
anthony_algmin: the forty plus minutes. I’m like, Oh no, we. we’re a super log. so I’ll stop

566
00:43:51,083 –> 00:43:53,541
anthony_algmin: talking and and let you cover a little bit more a big idea.

567
00:43:56,583 –> 00:44:03,541
peggy_tsai: So first frame it. But with the business problem that I felt was huge in the

568
00:44:03,625 –> 00:44:08,416
peggy_tsai: data, met forward for data measurement practitioner, Um, I’ve always been told

569
00:44:08,833 –> 00:44:13,708
peggy_tsai: Peggy, don’t boil the ocean. We don’t have enough resources to govern our

570
00:44:13,875 –> 00:44:19,708
peggy_tsai: critical data elements. Um, we cannot understand everything that’s happening in

571
00:44:19,708 –> 00:44:24,583
peggy_tsai: the structured space. Forget about unstructured, right, uh, and we all know that

572
00:44:24,833 –> 00:44:30,041
peggy_tsai: a lot of the Um. great information is actually from unstructured And you know

573
00:44:30,333 –> 00:44:35,958
peggy_tsai: the amount of time it took to Um. you know, just curate and standardize data.

574
00:44:37,000 –> 00:44:39,375
peggy_tsai: It just took a lot of time and effort and

575
00:44:40,500 –> 00:44:45,958
peggy_tsai: again. One of the reasons why I joint big. I. D was two years ago they built

576
00:44:46,208 –> 00:44:54,916
peggy_tsai: this patented methodology to use machine learning in a I to discover data on the

577
00:44:55,000 –> 00:45:00,125
peggy_tsai: ground level, And this was for me revolutionary because I’ve always taken a top

578
00:45:00,250 –> 00:45:05,291
peggy_tsai: down approach to data governance. Right. what are your business units? What are

579
00:45:05,458 –> 00:45:11,166
peggy_tsai: my high level domains and then drill down to the, from the conceptual down to

580
00:45:11,291 –> 00:45:12,500
peggy_tsai: the physical layer.

581
00:45:13,541 –> 00:45:18,583
peggy_tsai: What if we could turn that model upside down and use machine learning to help

582
00:45:18,833 –> 00:45:24,500
peggy_tsai: augment what a data stewardt does? So what does a data stewwardt do Right? So

583
00:45:24,833 –> 00:45:30,583
peggy_tsai: it’s understanding and knowing the data that’s in your domain. What are all the?

584
00:45:31,708 –> 00:45:35,791
peggy_tsai: What are obvious? Uh, what are your logical concepts, But where does that data

585
00:45:35,958 –> 00:45:40,250
peggy_tsai: reside in your organization? Whether it’s your down to the table scheme, a

586
00:45:40,333 –> 00:45:44,333
peggy_tsai: column level, but also to your documents that contain all that information.

587
00:45:45,708 –> 00:45:49,791
peggy_tsai: I want to be able to use machine learning to find all that information

588
00:45:50,208 –> 00:45:54,833
peggy_tsai: from me automatically. So we call that process classification, and we have

589
00:45:55,625 –> 00:46:01,541
peggy_tsai: hundreds and hundreds of classifiers That can a atically look inside your

590
00:46:01,708 –> 00:46:07,166
peggy_tsai: documents, your tables, and discern the difference between an account number and

591
00:46:07,208 –> 00:46:10,916
peggy_tsai: a d of birth. And the reason why we do that is because we use a lot of

592
00:46:11,000 –> 00:46:17,458
peggy_tsai: contextualizations and validations and checks to verify that this Um. data birth

593
00:46:17,708 –> 00:46:22,208
peggy_tsai: is surrounded by other context information, like a a name, a person,

594
00:46:23,208 –> 00:46:27,708
peggy_tsai: Um, versus an account number which may be surrounded by account information. For

595
00:46:27,708 –> 00:46:32,916
peggy_tsai: for example, so it’s using the power of machine learning to do things faster

596
00:46:32,916 –> 00:46:33,916
peggy_tsai: scale,

597
00:46:34,416 –> 00:46:40,250
peggy_tsai: larger scale, Fa, a larger scale and be able to say yes, Yes, we can boil the

598
00:46:40,250 –> 00:46:44,666
peggy_tsai: oceion because we could connect to all your data sources. Data, Lakes, Ho Dubes,

599
00:46:45,208 –> 00:46:47,458
peggy_tsai: Sharepot Powerpoint Excel

600
00:46:48,458 –> 00:46:49,458
peggy_tsai: emails

601
00:46:50,208 –> 00:46:55,541
peggy_tsai: and tell you exactly where you have your sensitive critical information. And we,

602
00:46:55,791 –> 00:47:00,250
peggy_tsai: big I d started out as a privacy compliance company with ▁j d, p. R, and

603
00:47:00,666 –> 00:47:05,375
peggy_tsai: California Consumer Privacy Act because you needed to find all the related

604
00:47:05,708 –> 00:47:09,625
peggy_tsai: information that companies were were keeping on one person Like For you example,

605
00:47:10,041 –> 00:47:12,333
peggy_tsai: Anthony, what information does a

606
00:47:12,416 –> 00:47:16,583
peggy_tsai: retail company have on you Because you do you do business with them, And they

607
00:47:16,666 –> 00:47:19,958
peggy_tsai: may have that information in the sales database and marketing babies,

608
00:47:20,333 –> 00:47:23,791
peggy_tsai: operational database, and all the individual stores may have their own

609
00:47:23,958 –> 00:47:28,208
peggy_tsai: individual Bas databases. So imagine them having to collect all that information

610
00:47:28,583 –> 00:47:32,208
peggy_tsai: within a matter of, you know, twenty four to seventy two hours, as per the

611
00:47:32,250 –> 00:47:37,000
peggy_tsai: policy regulation, and then figure out whether not they need to purge, delete,

612
00:47:37,166 –> 00:47:41,458
peggy_tsai: or keep all that information. And you think that on a much larger scale, and

613
00:47:41,541 –> 00:47:43,083
peggy_tsai: then on a data management side,

614
00:47:44,125 –> 00:47:49,208
peggy_tsai: it’s not just private data, it’s it’s all critical data which can span many

615
00:47:49,458 –> 00:47:52,416
peggy_tsai: different domains. So how can we help

616
00:47:53,875 –> 00:47:59,541
peggy_tsai: stewards find all this information govern things more consistently with better

617
00:47:59,791 –> 00:48:05,625
peggy_tsai: data quality rules. As because we know where all the instantiions of the data is

618
00:48:05,875 –> 00:48:09,458
peggy_tsai: right, And how can we give machine learning insights like

619
00:48:10,583 –> 00:48:15,291
peggy_tsai: Syol, Similar things. We can group things similarly helping with dejuplication

620
00:48:15,791 –> 00:48:16,791
peggy_tsai: use cases,

621
00:48:17,708 –> 00:48:22,666
peggy_tsai: Um, and just getting that automated insights and telling you Hey, In your

622
00:48:22,833 –> 00:48:27,291
peggy_tsai: business Glossry, we see five things that look very S. similar and we can tell

623
00:48:27,375 –> 00:48:31,708
peggy_tsai: you how it’s map to twenty five different places Do you approve And it’s a

624
00:48:31,708 –> 00:48:36,750
peggy_tsai: matter of yes. I can. I. I can say yes. It’s versus a steward, having to work

625
00:48:36,916 –> 00:48:41,625
peggy_tsai: with the I T system owner and having to do all that mapping and inv validate

626
00:48:41,791 –> 00:48:47,000
peggy_tsai: that through multiple workflows, so it helps with compliance policy management,

627
00:48:48,041 –> 00:48:53,958
peggy_tsai: understanding and managing where data shouldn’t be, Um, where data is You know

628
00:48:54,250 –> 00:49:00,916
peggy_tsai: Um for for many data stwards, So thatash is one very small benefit

629
00:49:01,375 –> 00:49:06,500
peggy_tsai: for a data steward, and I can Um, talk about that for other data personas, and

630
00:49:06,625 –> 00:49:07,625
peggy_tsai: how it helps them,

631
00:49:08,666 –> 00:49:14,125
peggy_tsai: and how it helps a data’s persona, working with a privacy team, working with a

632
00:49:14,208 –> 00:49:19,625
peggy_tsai: security team working with a. Technology team, Many benefits to having more of

633
00:49:19,791 –> 00:49:22,416
peggy_tsai: an automated data discovery, catalogu

634
00:49:23,458 –> 00:49:29,458
peggy_tsai: and governance capabilities like stewardship, data quality, data lineage, These

635
00:49:29,541 –> 00:49:35,291
peggy_tsai: are all the things that I really pushed hard. because Um, there’s first of all,

636
00:49:35,541 –> 00:49:37,166
peggy_tsai: not just a need but a want

637
00:49:38,333 –> 00:49:43,375
peggy_tsai: to now have automated controls, automated monitoring, better audit trails. And

638
00:49:43,541 –> 00:49:51,958
peggy_tsai: how do we save time and improve the r o y for cos that are making these heavy

639
00:49:52,125 –> 00:49:56,041
peggy_tsai: investments, but not always seeing the value and return, so

640
00:49:57,166 –> 00:50:03,083
peggy_tsai: that in a nutshell hopefully cause who big idea is, and what my personal mission

641
00:50:03,083 –> 00:50:04,541
peggy_tsai: for for big ideas.

642
00:50:04,541 –> 00:50:06,666
anthony_algmin: that’s awesome And and I would say

643
00:50:06,916 –> 00:50:08,916
anthony_algmin: I, I have to think that the

644
00:50:10,333 –> 00:50:16,666
anthony_algmin: ground up a kind of amplification, the the leveraging of more sophisticated

645
00:50:17,541 –> 00:50:21,166
anthony_algmin: machine learning and analysis tools to help supplement our government’s

646
00:50:21,208 –> 00:50:24,750
anthony_algmin: efforts. It. its. It’s essential at this point with the with the growth that

647
00:50:24,833 –> 00:50:26,041
anthony_algmin: we’re trying to manage,

648
00:50:27,083 –> 00:50:30,166
anthony_algmin: top down will never get us the whole way any morere. and and any

649
00:50:30,250 –> 00:50:33,458
anthony_algmin: organization of scale and and almost really any organization you’re Cannna.

650
00:50:33,625 –> 00:50:38,583
anthony_algmin: need to figure things out in ways that we couldn’t do before. but now we

651
00:50:38,666 –> 00:50:41,791
anthony_algmin: have access to these technologies and applying them in the government space,

652
00:50:41,958 –> 00:50:45,208
anthony_algmin: I think is is a really exciting time like. it’s really cool to see that

653
00:50:45,375 –> 00:50:47,958
anthony_algmin: that’s what you guys are working on And that’s what you’re You’re doing

654
00:50:48,125 –> 00:50:52,750
anthony_algmin: there. I. It’s a a world of potential, Um, and that that many organizations

655
00:50:52,916 –> 00:50:57,166
anthony_algmin: really need so Peggy. we are super out of time now, Um, but they thank you

656
00:50:57,458 –> 00:51:00,833
anthony_algmin: so much for being on the show today and and talking with me it’s been an

657
00:51:00,833 –> 00:51:03,666
anthony_algmin: absolute pleasure.

658
00:51:04,500 –> 00:51:08,583
peggy_tsai: My pleasure is mine as well. Anthony and thank you to the audience and

659
00:51:08,583 –> 00:51:11,625
peggy_tsai: had such a great time on today’s podcast, so thank you so much.

660
00:51:11,625 –> 00:51:14,333
anthony_algmin: Thank you all for joining us today as always, you’ll find more

661
00:51:14,500 –> 00:51:17,208
anthony_algmin: information about our guests and links and the show notes. Go to

662
00:51:17,375 –> 00:51:18,625
anthony_algmin: DataLeadershipLessons.com to subscribe to the podcast or check out

663
00:51:18,625 –> 00:51:20,500
anthony_algmin: DataLeadershipLessons.com to subscribe to the podcast or check out

664
00:51:20,583 –> 00:51:23,958
anthony_algmin: past episodes and accelerate your journey with training at

665
00:51:24,083 –> 00:51:28,083
anthony_algmin: DataLeadershipTraining.com. stay safe during these unusual times and go make an impact!