Andrew Andrews: Expanding the data conversation

Introduction to the World of Data Management

Loris:

Today, I have the pleasure of speaking with Andrew Andrews, Data Governance Manager at ANZ and Vice-President of DAMA Australia. For those unfamiliar, DAMA stands for the Data Management Association—a global non-profit dedicated to advancing information management practices and helping data professionals level up their skills. DAMA Australia has been active since 1981, organizing conferences, workshops, and webinars to promote best practices in data.

I was introduced to Andrew by James Price, Director of Experience Matters, who appears in Doug Laney’s book Infonomics. While reading the book, I reached out to James, which led me to discover the Leader’s Data Manifesto and dataleaders.org. Naturally, I volunteered to translate the manifesto into Italian, and that’s how Andrew and I first connected.

The world of data may be big, but it’s also tightly knit. Today, we’ll dive into some of the key challenges in data—everything from data management to data literacy. I have a feeling much of what we discuss will be evergreen, and I’m excited to get started.

Andrew:

Thanks, Loris! I’m thrilled to be here. It’s not every day that I get to talk with a like-minded individual who’s as passionate about the human side of data as I am. I’m really focused on making these concepts accessible to a broader audience, so it’s a privilege to have this conversation with you.

The Power of Sharing and Networking in Data

Loris:

I’ve been looking forward to this because there’s an aspect of data I’ve wanted to explore for a while. Recently, just being active on LinkedIn and sharing my ideas—whether right or wrong—has been enough to attract some great conversations. You’re a perfect example! We’re here now, in this virtual room, because of that act of sharing.

I’ve realized how powerful it can be to let go of fears and stories we tell ourselves. We all have strengths and weaknesses, or at least we think we know what they are. I’ve told myself plenty of stories in the past, but there’s real value in just putting yourself out there, meeting people, and exchanging ideas.

In the data space, I feel we need to do more of that. What’s your take? How do you see the gap?

Andrew:

The more we communicate and exchange ideas—whether through debates, discussions, or informal chats—the better. These conversations can cover anything from ethics and privacy to how we build better data models. The data spectrum is massive!

At one end, you’ve got big philosophical questions like privacy and the balance between individual rights and the greater good. At the other end, there are highly technical issues like optimizing databases and improving governance frameworks. It’s all connected, and the impact of data is enormous.

Think about it: every organization has assets—people, expertise, finances, physical resources. Data is one of those assets, but it’s often invisible. And yet, without data, nothing happens. Every interaction creates data, whether it’s subjective information or quantitative metrics.

It’s up to communicators—people like you and me—to share knowledge, encourage debate, and help people understand how data affects them and the broader community. We need to lift the quality of data and make it more useful. Well-managed data can truly improve people’s lives.

A Journey into Data Management

Loris:

Absolutely! Let’s dig into that idea of well-structured data. How did you first get into data management?

Andrew:

My journey into data management started earlier than most—way back in the mid-1970s. I was in grade five when my dad bought me a 15-in-one electronics kit from the local store. I became obsessed with wiring circuits, soldering, and building digital clocks. That passion for technology stuck with me.

In high school, the South Australian education system offered access to an IBM 360 mainframe. My math teacher invited me to join the computer club, and that’s where I learned how to write code using optical mark cards. We were coding in BASIC and APL, which stood for A Programming Language. APL used Greek symbols, and since I’m of Greek descent, it felt like a perfect fit!

I spent summers at the Angle Park Computing Center, hacking away on terminals and writing code. That’s where the tech bug really bit me.

Building a Career in Data

Loris:

That’s a technical start! It sounds like you were coding before coding was even cool.

Andrew:

Yeah, I guess you could say that! After high school, I studied math and science at Adelaide University, where I formally learned programming. By the early ’80s, I was writing code for the Department of Finance in Canberra. Later, I returned to South Australia to join the Australian Bureau of Statistics (ABS), where I started working with SAS, a statistical analysis software.

In the mid-’80s, I was building BI platforms in SAS, creating reports, graphs, and analytics tools. I even taught people how to analyze data, which was still a new concept at the time. That’s when I really caught the data bug.

When Data Management Became About More Than Code

Loris:

And when did it hit you that data management wasn’t just about writing code but about something bigger?

Andrew:

It clicked for me in the early ’90s when I was working at WorkCover in South Australia, a government organization focused on workers’ compensation and injury management. My team was responsible for building both an online transaction processing system and a reporting platform in SAS.

We analyzed patterns in injury claims and long-term prognosis, using statistical data to guide decisions. That’s when I realized how powerful well-managed data could be. It wasn’t just about compliance or creating reports—it was about improving real-life outcomes.

The Evolution of Data Management

Loris:

Fast-forward to today. With 30 years of experience, how do you feel the field of data management has changed?

Andrew:

It’s evolved massively. Back in the day, data professionals were seen as nerdy backroom specialists. We did necessary work, but the broader public didn’t see the value.

The pandemic changed that. Suddenly, data was front and center. Every day, people tuned in to see COVID case numbers, infection rates, and mortality statistics. The John Hopkins dashboard became a global reference point. Data wasn’t just for specialists anymore; it became part of everyone’s daily life.

The Challenge of Clean, Accessible Data

Loris:

It’s true. COVID forced the world to recognize the importance of data. But within organizations, it’s still a challenge. Even if people understand the value of clean, accessible data, life gets in the way—deadlines, pressure, and messy spreadsheets. What do you think we’re missing to change that?

Andrew:

It’s all about maturity—organizational and cultural maturity. Look, we value money. That’s why we have global standards for financial reporting. Every debit and credit must be accounted for with precision. Health data is catching up, but education data, for example, still lags behind.

Most organizations haven’t fully grasped the impact of poor-quality data—or the potential benefits of high-quality data. It’s hard for them to connect a data point to a real-world outcome. That’s why organizations often tolerate bad data practices, like faking dates to bypass validation rules.

Connecting the Dots on Data Quality

Loris:

And those little shortcuts create big problems later.

Andrew:

Exactly. Bad data gets passed down the chain, causing errors and outliers that someone eventually has to clean up. Organizations aren’t mature enough to recognize that higher-quality data inputs mean better outcomes. We need to help them connect those dots.

Loris:

I think part of the problem is that many organizations don’t even know what good data management feels like. They’ve never experienced a world where data is easily accessible and trustworthy.

I’ve been experimenting with this for my podcast. Six months ago, I only had a theoretical understanding of how clean, well-structured data could improve my workflow. Now, I feel the difference. I’ve cleaned up my backend systems, and it’s night and day.

Of course, it’s just me managing my own data, so it’s easier. But in a large organization with thousands of people, that’s a whole different challenge. How do you even begin to implement a data management program at that scale?

Andrew:

It’s tough. The biggest challenge is culture. Let’s say you join an organization that’s been around for 50 years. Chances are, they’ve got 10 or 15 different definitions of what a “customer” is. Each department uses its own definition based on its specific context.

As a Chief Data Officer (CDO), your first task is to get everyone to agree on a single definition. That’s not easy. Then you have to help people unlearn their old practices and adopt new ones. And finally, you’ve got to make sure those changes stick across the organization.

Transforming Organizational Culture

Loris:

And that’s the hardest part—changing behavior and habits.

Andrew:

Exactly. The real challenge isn’t setting the rules. It’s getting people to change how they work. 90% of my work is about change management: helping people move from practice A to practice B and building the skills they need to succeed.