Why Data Governance & Data Quality Are Important

You don't always have to be the smartest person in the room, but if you are one of the most willing to learn, that will get you very far. I went from talking about balance sheets to conversations about AI ethics and enterprise governance, and it felt like joining a new gym, it's very intimidating, but then eventually you start to enjoy the pain. But I learned that people don't want a perfect leader. They prefer a authentic leader, someone who's very comfortable in saying, I don't know yet, but we'll figure it out together.

But what helped me stay was my curiosity and also surrounding myself with people smarter than me. And sometimes be willing to ask the dumb questions that everyone else is secretly thinking, but they don't want to ask out loud. Your expertise, what you're really good at will get you in the room and, but your ability to build trust, influence, and uplift others is what will keep you in the room. Welcome back to Data Analytics Chat, the podcast where we discuss the world of data, ai and the career shaping it.

My guest today is Carol Kim, who is the executive director at IBM. In today's episode, we'll explore her journey, the challenges and the insights she has learned during her career today. And the data topic for this week will be why data governments and data quality are important. Carol, welcome to the podcast.

Thank you Ben. Thank you so much for having me. I am genuinely excited to talk about the two things I care deeply about. One is career 'cause it shaped who I am.

And data because it explains everything in the world. Yep. I'm looking forward to this. So do you wanna dive in?

Do you wanna walk us through your career journey and how you've ended up in your current role? Absolutely. And I'll start by saying that my career, it was not a straight line. I started in finance CPA by trade, so I did.

A lot of roles in treasury pricing, global finance, and planning across different parts of the world. And while doing financed, I realized a pattern which was quality of our data, and that really helped make a meaningful business decision. So that curiosity eventually led me to IBM's chief data office. And that's where I fell in love with the art and science of transforming raw and messy data into insights that people can trust.

And that was very satisfying for me to turn chaos into clarity. So fast forward to today. I'm the director of technology data and AI at IBM's Global Real Estate Organization. And what that means is I get to play with data design and technology to not only.

only, Reshape where we work, but how we work and also why we work. We don't just manage real estate, we create intelligent human-centered workplaces. So think of smart buildings that can predict maintenance, monitor energy usage or personalized employee experience. And for me, it's the most tangible version of where data and AI works together in the real world.

And like I know you've obviously born in Korea and you've traveled how was that for you? Oh, traveling. I grew up moving every three to five years, so traveling to me is a delight. I enjoy it very much.

Learning new cultures, meeting new people, eating new types of food for work as well. I traveled and worked in many different countries throughout the globe. I think it really helped shape who I am today. Yeah, no, definitely.

I think it's, obviously you, when you go to different cultures, you just learn, don't you? It's just, you adapt. Absolutely. So how, how did you find moving to different cultures?

'cause obviously you've gone from one side of the world to the other, that's completely different. It's interesting you asked that when, so in IBM Korea. I was told that I was too outspoken when I worked in IBM Australia. I was good.

I had no complaints when I came to the IBM us, they told me I was too quiet. You have to adapt to the norm of that culture and the expectations on how you wanna portray yourself. And that took time to adapt. But I quickly learned that.

It's a little bit of adjustment, a little bit of figuring out what people look for in a leadership quality or leadership trait and make sure that your strengths stand out. And what do you think made you be able to adapt? Because obviously is a skill in its own. Yes.

I would say continuous reinvention. Stay curious, stay adaptable. Stay humble. Another one was courage.

You don't always have to be the smartest person in the room, but if you are one of the most willing to learn, that will get you very far. I. And was that, how did you, obviously that willingness to learn, was that from family or was it something inside yourself? How did you.

Because everyone's got different personality and different goals, haven't they? And like you said, you don't need to be the smartest one in the room. You need to stick, I think, play to your strengths. I think for me it was because I had to move around every three to five years.

I had to have that innate curiosity in order to learn new things, new people, new culture, and for me. I don't like getting bored, so every couple of years I would raise my hand and move to a new role, ask for new opportunities, and I think it's a personality trait. Okay, good. And that was actually gonna be one, one of my questions is, how, obviously fair well done to IBM they've kept you, is it like over 20 years at one company and that's obviously, that's rare today.

So is it just 'cause you've moved about, is it, that's in different positions? Is that what's kept you at one company? I think it's when I think about, moments that really helped me shift the direction of my career because I went from finance into tech. One is stepping outside of your comfort zone.

So for me, leaping outside of my comfort zone of finance to the chief data office was a big leap. I went from talking about balance sheets to conversations about AI ethics and enterprise governance, and it felt like joining a new gym, it's very intimidating, but then eventually you start to enjoy the pain. And that's where I really had the front row seat to learn about global data strategy. And then, transformation, stepping into real estate was a totally new field for me as well.

And most people don't immediately connect buildings to data. And when I tell them what I do, they imagine, like I'm out there choosing carpet samples. But real estate is one of the richest data ecosystems we have. Think of sensors, internet of things, devices, IOT devices, occupancies, sustainability metrics, buildings today practically talk.

And when I saw how data could change how people feel in a space. I found this to be very interesting, which I enjoy very much. Brilliant. So then looking back, has there been standout moments that really shift the direction of your career?

One of the standout moments was when I realized that storytelling. Is a big factor in a career. When I was in finance, I realized I could present the same two versions of exact same financial analysis to a leadership team with two different narratives, and you get two different reactions. Even though it's the same data, one could create fear and one could create an opportunity.

So that's when I realized. Data doesn't speak for itself. People do. So storytelling is a very big thing that really, that I learned that really helped reshape and shift the direction of my career.

And like I said, moving outta my comfort zone moving into technology, and that took courage. And I'm glad I did. And because I love what I do. And then how did you get those storytelling skills?

Was it like, did you like mentoring, training? Obviously the way data's going now, even like data scientist roles, data engineering, they're becoming more business focused. So you need to be able to communicate with the business as opposed to previously where you are more in a tech team. Absolutely.

I consider myself the bridge between technology and the business side, so I help the business side understand what technology can and cannot do for them. And then I translate the business need back to the technology side so that our data scientists and engineers and developers and architects can translate that business need into what is required to build something that will be meaningful to the business. And sorry, how do you like learn known skills? 'cause obviously that's, I think that's so important in today's age.

How do you learn those skills? Know your audience. I always try to create a win-win situation that benefits both sides. I like to explain everything simply so that anyone could understand it, and I like to use a lot of examples.

Okay, good. Good. And then, what was, what's been the tough challenge that you've run into during your career? Let's see.

I could there's plenty of challenges. I could think of two off the top of my head. One was transitioning from being a individual contributor to being a leader. cause suddenly your success is measured by not what you deliver, but what your team delivers.

So for me it was like becoming a parent where. Everyone is an adult and they can quit. And it took time. But I learned that people don't want a perfect leader.

They prefer a authentic leader, someone who's very comfortable in saying, I don't know yet, but we'll figure it out together. So that was one the challenges that I had to figure out. And the other one is I think courage. Stepping Outta side of your comfort zone.

For me, moving out of finance, stepping into the world of data and AI and technology, it was very intimidating at first. I questioned, do I belong here? But what helped me stay was my curiosity and also surrounding myself with people smarter than me. And sometimes be willing to ask the dumb questions that everyone else is secretly thinking, but they don't want to ask out loud.

Yeah, definitely. And obviously even when I mean I tried to study computer science when I was at uni first year university bus, college dropout, and obviously even then there was rare, barely any women on the course. So how was that? Obviously you was massive junk from finance into tech.

When it's heavily male driven, especially back then, it's obviously slightly getting better nowadays. How did you find that? 'cause obviously that's a big step. It was fascinating.

I fell in love immediately. I just loved creating, combining data into insights and going to explain to other people what I found. And to me, that's what. Motivated me every day.

Being a female I didn't see any hindrance from, my company was very supportive. My manager was very supportive. I believe I brought a very different perspective because I came from the finance side. So I was able to ROI in a space where it was very hard to.

Calculate the return on investment for doing data and analytics and creating models that have future value down the road, not immediate value. So to me I love the challenge and there's, I'm so happy to see more females enter the the space and I think it's a great experience for anyone. I think everyone should learn how data works. Yeah, and I think it's fascinating now.

Okay. As businesses are becoming more and more data driven, the people coming from that, the business domain, you can add so much value. 'cause you obviously know that whether it's procurement, marketing, finance, you, your specialists in that area. And if you can apply that with data, that's where you're gonna add so much value to the business.

Absolutely. So then as you've moved into leadership, what skills, mindsets have made the biggest difference for you? I think we touched on most of them already, Ben, but I would say clarity, accountability storytelling for sure. And continuously reinventing yourself.

For clarity, when, for me, I feel. Teams really thrive when the expectations are clear. What success is, how decisions are made, who owns what, because ambiguity is really where the stress breeds. And then accountability in the sense that you can hold people to a high bar and still be deeply human, meaning you don't have to be a micromanager and.

Storytelling is very important because if you can't articulate the why your message, it will never land and it won't move people and continuous reinvention. I try to embody that all the time. Stay curious, stay adaptable, stay humble, and learn new things. Brilliant.

Then, what advice would you give someone who wants to grow into a top leadership role? I would say two things. First, get really good at something and then second, get really good at people, right? Your expertise, what you're really good at will get you in the room and, but your ability to build trust, influence, and uplift others is what will keep you in the room.

And to begin at really good with people, you shouldn't rush it, right? Leadership is earned through consistency, not ambition. If you chase the title, you'll end up burning out, but if you chase impact I think you'll grow. So I would say get really good at something and then get really good at people.

Yep. I see. It's, that's the big challenge, isn't it? When people move into leadership, it's the them, the people.

Soft skills is, I see. The massive challenge. Yes. Okay.

Cool, cool. Let's dive into the data topic. And so we're gonna look at why is data governance and data quality important? Obviously, it's a massive and growing topic, in my opinion nowadays.

So we often hear the phrase garbage in, garbage out. So how does poor data quality actually impact decision making in real world business scenarios? Yes. That phrase that everyone says, garbage in, garbage out.

It's cute until it costs you millions, right? Poor data quality isn't just messy spreadsheets. It's. You could make bad decisions because the numbers didn't update properly.

You could miss compliance deadlines because two systems refuse to talk to each other. You could lose hours trying to clean your data instead of analyzing it. Or the worst part is when you actually lose people's trust because the data quality isn't there. So in my mind it's the hidden tax on every business, and most leaders don't even know they're paying it.

What do you think? It's just a sort of neglected in business context 'cause there's a lot more emphasis now nowadays on. Data quality isn't there as before it is mean businesses were more focused on, I guess on the building models, hiring data scientists, et cetera. So do you think it's just a more of a learning situation that we're in, or is there an alternative you could think of?

I think AI is a big boom right now, and in order to get AI to work, your data has to be there. You have to have good quality data, which I think people are seeing more and more because you can't do AI with bad data. So I think it's becoming more highlighted and it's, it's I'm trying to think. Yeah, data-driven decision making is such a key thing that everyone's striving to do within their departments and functional units, and without good data, you're never gonna get there.

So I think it the emphasis on good quality data that people can trust has become a big important topic that everyone is now looking to clean up their data and apply governance to it. and it's obviously, it's not an easy task either. It is obviously. Especially for more mature businesses, obviously they've got so much data and there's the complexities there.

So I think it is, yeah like you touched on, like to get AI working, you need to be, and you wanna get the high percentages, you need to be getting your data so good. Yeah. So in many companies claim to be, oh, sorry, to have a data-driven culture, but how can you be data-driven if your data isn't governed or trusted? E Exactly.

You can't be data driven if your data is lying to you. Governance is your GPS. It tells you where your data came from, who owns it, how is it protected, is it biased or not? So I think being data-driven isn't about pretty dashboards.

It's really about the confidence around can you trust the data and the insights that it's telling you. What does data driven actually mean in practice for you? In practice. Being able to make decisions with confidence because the data the insight is coming from data that is governed and is of good quality and that it could be trusted.

Okay. know. I wanna do that one again. alright, just alright, let me, I'll do the question again.

So what does data driven actually mean in practice? I. Sorry, hold on. I wanna, I'm trying to think of a good answer here, Ben.

What do you think? Hold on. Data driven. I'm trying to think.

Should I give an example related to real estate? Okay. In my mind. Being, sorry.

In my mind, being data driven refers to making decisions, strategies, or actions based on the data analysis and interpretation, RA rather than intuition as assumptions or personal experience. So in practice it means collecting the relevant data from a reliable source that you can trust. It means analyzing the data using. Methods, tools to extract the insight and then be able being able to make the decision based on evidence that's supported by data trends and patterns, and one that you can continuously improve and build on as new data becomes available.

Okay good. Because that's ev everyone claims to be data driven. Dunno, at the moment mean, I think it's a, it is like it, everyone's on a different journey, in my opinion. Like data.

Like everyone's at a different stage. Yes. Yes. So where do you typically see data governance breakdown first?

Is it people, processes, or technology? I think it's always the people. I think technology will do exactly what you configure it to do. Processes can always be documented and edited, but people, they have opinions, they have calendars, they have different interpretations of a spreadsheet.

So I see it typically breakdown on the people side side first, So that's why at IBM, we created a data officer council. executives across each business unit who co-own. Our data governance standards so that everyone is speaking the same data language, which is half the battle then. So is it more, in your opinion, like leadership, getting people on board?

Is that the, where the big challenge is to execute? Yeah, if you start creating data standards within your own business department and it doesn't span out across the entire enterprise, you're siloed yet again. So we're trying to do this from the top, bottom across the entire enterprise so that everyone speaks the same language from the beginning. Yeah.

No, I like that. It's 'cause it's a new, obviously data's been around, but it's obviously new for many people within a business, isn't it? Everyone's gotta learn. There's the knowledge transfer.

It's a whole, it's a whole transformation piece. Absolutely. So what would be the hidden costs of ignoring data quality? Beyond compliance, fines or technical debt, I would say one of the biggest cost is lost opportunity because when people don't trust the data, they stop using it.

And when they stop using it, the innovation and what you've built till now, it just collapses. People don't use it and you lose that competitive advantage. So for me, clean data doesn't just reduce risk, it creates momentum where you continuously build on what all your deliverables in the past, and it creates opportunity. And then have you seen, have you been in like meetings where sort of executive decisions have been made on floor data then?

Say that one more time. Have you seen, have you been in sort of meeting where executive decisions have been made on floor data then? Absolutely yes. As long as they can trust the data and everyone agrees with it.

Yes, decisions are made based on the data. so it's important. So lot of work must go into this. Okay, cool.

And then if you were advising a company starting from scratch. What would you have as the non-negotiables for building an effective data governance? I would say three things. Number one, clear ownership.

Every data set needs one name next to it, not a team, not a committee, but one person, because if everyone owns it, no one really owns it. So I would say number one is clear data ownership. Second one would be data literacy. Everyone should understand what good data means.

It's not a technical skill, it's a business survival skill. So once you know what good data is you'll know when you see bad data. And then third I would go to automation and ai. Use AI to monitor your automation and ai, right?

Automate lineage quality checks, alerts so that governance isn't manual policy. Policying. Nobody wants to manually track data fields. So combine automation, transparency, accountability, and then governance becomes an enabler, not a bottleneck.

Brilliant. So thank you for your time today. Been great to have your insights and obviously your discussion around data governance and data quality, and also hearing about your career. It's obviously amazing to hear that you've moved from domain into data Thank you so much, Ben.

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