Data is useful when it's structured and easy to use in context. But why is it? Learn with me as I speak with Vice President of DAMA Australia Andrew Andrews.
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Loris: [00:00:00] So today I have the pleasure to speak with Andrew Andrews, data governance manager at ANZ and vice-president of DAMA Australia. DAMA is the Data Management Association. And if you haven't heard of it, it's a global data management community. It's a non-for-profit organization and DAMA Australia, as a chapter was incorporated in 1981. And the overarching mission is to improve and promote the field of information management and help practitioners become more knowledgeable and skilled in their profession. For many years, they’ve been organizing conferences, workshops, and webinars.
I was introduced to Andrew by James Price, who is the director of Experience Matters, whom I met for the first time here in Sydney, not too long ago. He appears in Douglas Laney’s book Infonomics, which I was reading. And so I reached out.
And that’s when I learned about the leader's data manifesto and dataleaders.org, an organization that encapsulates a lot of the concepts that we've been talking about here on the podcast. So I volunteered to translate the manifesto into Italian, and that's how Andrew and I met. It’s a big world but with a tight knit community.
Today we'll talk about challenges in data: from data management to data literacy. And I have a feeling that a lot of the topics we'll touch on today are going to be evergreen and so I'm really excited to do it. Andrew, thank you for taking the time and welcome to The Data Project.
Andrew: [00:02:00] Thanks Loris. I'm thrilled to have the opportunity of speaking with you, a like-minded individual, who cares about the human aspects of what we do with data. I'm very passionate about communicating these concepts with the general public. So it's a really great opportunity to have this discussion with you today.
Loris: [00:02:04] I've been looking forward to this episode because there's an aspect of data that I’ve never had the chance to explore. I found that recently just the fact of being active on LinkedIn and sharing my ideas, right or wrong, it's been enough to attract a lot of people. And you are an example. We're here in this virtual room now because of that act of sharing.
I'm really passionate about this because I think that everyone has fears, right? We tell ourselves stories all the time and we have strengths and we have weaknesses. Well, at least we believe we know what they are and I am no different.
And I've told myself a lot of stories in the past. But what I came to realize is that there is a huge gain associated with just letting go of those stories and just meeting people and sharing ideas. We need to do more in the data space. How do you see the gap?
Andrew: [00:03:14] I think the more we communicate and exchange and debate and have robust discourse, whatever you want to call it, to bounce around different subject matter areas. Starting from ethical discussions, privacy, confidentiality, and the protection of the protection of the individual versus the good to humanity, those sort of conversations, that's at one end of the spectrum.
The other end of the spectrum is, how do you create a better data model and how do you improve a database? And the whole data spectrum is huge. And it's mind blowing when you think about the complexity of data governance, data management, and the impact that it has on the world.
Because if you think about it this way, any organization, any community has assets, it has people, it has expertise in people. It has financial resources. It's got physical and virtual assets and data is an invisible asset, which should be more visible. But without data, nothing happens. Any exchange of any kind results in some sort of data to be recorded, whether it's subjective and iterative data information or quantitative data, data is everywhere.
And it's our job, as in communicators like yourself, myself, and other communicators around the world, to share their knowledge and interact and have debates and have conversations about informing people about data and what it means for them and what it means for humanity: to improve it and uplift its quality and make it more useful for people.
I think there's a lot of academic research to be had around legal frameworks using data and data ethics. And we know that's happening already, but I think there's a whole lot more that needs to be done. But that assumes that you have a standardized set of data.
Loris: [00:05:30] Let's talk about that.
Andrew: [00:05:33] Because I'm involved with DAMA, my passion is to help humanity and the community to understand that without well-managed and well-structured data, it's not useful. So our job in DAMA and the DMBOK, the data management body of knowledge, is about creating this baseline academic framework in which to manage data using a structured approach.
And the textbook is 600 pages. It's great reading if you're into it, but it's for people who really want to practice managing data formally, and I think we need to do more of that. Firstly, we need the community to be aware that that's what you need to do. And then organizationally, when an organization collects and manages data, you should apply the same principles internally.
So my job in DAMA is to help create awareness and having conversations like these is exactly the sort of stuff I'd love to have more. As a community, as a set of practitioners, we need to have more of these conversations that talk about data literacy, and that literacy means having an awareness of data and then knowing what to do with it when you know about it.
Loris: [00:06:44] And tell me a little bit about your story. How did you get into data management?
Andrew: [00:06:50] My career has not turned out the way I thought it would. It was the mid-seventies. I was in grade 5, and my dad bought me a 15-in-one electronics kit from the local handy store. I was starting to wire up circuits, and then I got myself a soldering iron. Then I started making digital clocks. You know, I went to the local electronic store, bought myself some kits and started making some electronic clocks.
So that was the mid-seventies. And then at my high school here in Adelaide, the South Australian education system had a program where they had an IBM mainframe available for education purposes. So it was an IBM 360 mainframe with optical mark cards. They got the kids in each school to write programs using optical mark cards.
So there's a big textbook for this. There's a textbook you can buy that actually records the history of this computing center. It was the Angle Park Computing Center. Long story short: my high school maths teacher at the time said to me, "Andrew, would you be interested in joining our computer club?" We're talking like 1976.
I started writing code in optical mark cards and there are many column cards, and I started writing code in BASIC, programs in BASIC language, beginners all purpose symbolic instruction code.
Loris: [00:07:54] There you go, you remember the acronym. That's impressive.
Andrew: [00:07:58] So I started learning how to write code in BASIC and another language called APL. APL stands for A Programming Language. APL uses Greek symbols as operators and being Greek, I sort of had an affinity to the language.
So in the mid seventies, I started writing code in APL and BASIC. And there was a summer school in the Angle Park Computing Center where we’d spend a month just hacking away at writing code. They'd have an open house for people to come in and write, and start hacking away at the terminals. I had a HP 29C Reverse Polish Notation calculator, and it was all a lot of fun in those days. So that was my beginning in technology.
Loris: A technical beginning.
Andrew: [00:08:26] I started as a coder. Yeah. So then I did a math science degree at Adelaide University, in 1981-1982. Learned how to write code formally through my course. And then, I also did a couple of years of math stats in my course. And then I started as a programmer in the Department of Finance in Canberra, early eighties.
And then I came back to the bureau stats, the Australian Bureau of Statistics, the south Australian office in 1984. And I started writing code in a product called SAS. It's a global statistical analysis platform and ABS still uses it today. And I started writing BI platforms in SAS back in the mid eighties.
Loris: [00:09:18] I wasn't even born by then. I was born in 1987.
Andrew: Long before you were born, Loris. So, I started writing statistical systems back then, built graphs, charts, and a whole bunch of stuff. Started teaching people how to look at analytics back then. So that's how I got the bug: because I started writing SAS code, mid-eighties.
Loris: [00:09:46] When did you realize that management of information was actually a real problem and you wanted to focus on that aspect as opposed to just writing code?
Andrew: I think what happened to me was that by the time I got there, I think it was the early nineties. I worked in South Australia, in a government organization called WorkCover, which is a workers comp injury management of people who have had injuries at work.
So in the early nineties I started working at WorkCover. And, what I did was I helped build two things. Firstly, I worked and helped build an online transaction processing system with an SQL database at the backend.
But the other thing that I did was, because of our new SAS, I wrote the strategy which created the SAS platform for WorkCover back in the early nineties here in Adelaide. I wrote the strategic plan to migrate the data into SAS from mainframe to a Unix platform on some pizza box computer, and ran a team.
I actually ran a team of four people writing SAS code all day, every day, doing analysis. So that's when it started for me was in the early nineties where I was responsible for a team to write statistical systems to do reporting.
We did actuarial reporting. We did compensation reports. We looked at the patterns around injury to the claims and what the progressions were in terms of people's long-term prognosis with an injury. And we used statistical data to work all that out.
From a management perspective, it's been nearly 30 years doing it in an applied setting. And then from there I sort of branched out into other insurance areas, education and now I'm in banking and before that was in, in education and other sort of settings.
Loris: [00:11:40] With 30 years of direct experience in the field, how do you feel the field of information and data management has changed? From 1990 to 2021, or let's say to 2019 before the pandemic, and then I'm interested in the last year and whether there's any difference before that.
Andrew: I think people perceive people who manage data as geeky, a bit nerdy. They see us as academic people who do things because they're necessary, but they don't see the exact benefit. When I was managing data, we had a very direct imperative that we had a piece of legislation to make sure that we comply with. So we produce data to comply with the legislation.
The one thing we had to make sure that actuarially we weren't sending the fund broke. We were managing our funds well, and also obviously we're there to make sure that people's lives were protected because they were injured in a car accident. So you're balancing those parameters as well. But that's a very specialist function. I thought.
Loris: [00:12:56] And easy to explain right? The value is right there.
Andrew: The value is there, but the actual function of doing it is pretty specialist. And people saw that as basically a backroom function that wasn't really that visible to the ordinary community.
So that's how I've explained most of my career is the fact that it's a function that needs to get done, but it's done by a group of specialists who don't interact much with the general population. But then like what you said with COVID things changed.
So every day with COVID in the last 18 months now, since March last year, everyone is now focused on the question of how many cases are there every day? What's the infection rate? What's the mortality rate? Like there's a whole conversation every day about data.
And, you see newspapers are constantly referring to the John Hopkins dashboard, which has now become globally famous for being the benchmark of measuring infection rates around the world and what's going on around the world.
So data has now become a central thing for everyone. We all now use the scoreboard of infection rates as a daily sort of go-to every morning, when you wake up and watch the news, you want to see how the infection rates are around the world. We now live our life around the metrics that we've got through the pandemic.
As bad as the pandemic has been for humanity, the experience has actually created a huge awareness of the need for high-quality, timely data, and especially data that relates to the individual. What it means for each person and for communities.
The world has changed irrevocably, from now on, it will never be the same, for many reasons, but a lot of it will be about data as well.
Loris: [00:14:11] Yeah. I'm trying to decompose the problem of creating awareness around data management and information management. And as we said, COVID raised awareness quite a bit. Like we went from, "Oh yeah. You know, who cares about management of data" to, "Okay, my livelihood is directly related to how well we know the number of cases or potential proximity data as well, close to someone that resulted positive" and all that stuff.
But that's one level, that's almost the general public level, but then within an organization, when we actually have to do our work and we're under the pump and there are deadlines and we need to juggle between hundreds of Excel sheets, the reality can be quite different. And even if we understand the importance of clean data, life almost gets in the way. What do you think? What is it that we are missing now in order to change this?
Andrew: [00:15:24] Loris, it's a really good question. And this directly speaks to the maturity or lack of it that we have. You know, we value money. We value currency and a balance sheet or a physical transaction is data on essentially currency. And we have global frameworks and global standards on how we report financial transactions.
So we value financial data at a hundred percent pristine. It's got to be absolutely accurate. You've got to account for your debits and credits, and they have to basically match to the cent for this data to be valid. So we have very high levels of precision around financial data. Because we value money.
We value currency. Then, from there we value health data. I think in terms of priority, we value health data about the same or probably at the same level. The maturity of health data is getting better, but it's not as precise as financial data, but it's getting there.
Education, I don't think is managed well yet, as well as it should be, but it needs to improve so that we can improve the literacy of our students and the outcomes.
Loris: [00:17:00] Exactly the relevancy of the stuff that we teach.
Andrew: And then you've got everything else. And we have not made the recognition that data on everyday things is just as important as data that you record on financial transactions. And that's big. Is that because we value it less? Absolutely. Can we associate it to anything real? It may be difficult to create the connection between the data point that you create to the real life human that's associated with that data point. And it's more abstract.
So with money it's not abstract. You have a transaction, there's a dollar, you spend a dollar, you bought something for it and you need to account for it. If you recall a piece of data on a form, like a date of birth, and you haven't put the actual date and you've put all zeros on there. And then the operator decides when they enter the data from the form into a system, they have to get past the validation rules with dates, so they might put 11, 11, 11, right?
Instead of going back to the source and saying, “give us the real data”, they're just faking it. And then that data point gets associated with some other transaction and then creates an outlier in a report somewhere which then needs to get corrected. And organizations are not mature enough to realize that the higher quality of data coming in means a better quality outcome at the end.
And we haven't got organizations to that level of maturity yet to have that recognition. So I think we, as a society, have not progressed to quantify the impact of poor quality data and what it means. And on the flip side, what are the benefits of having higher quality data depending on the use case? So each use case is different. Each industry is different.
Loris: [00:18:59] Yeah, I'm thinking we definitely have an example now globally of the impact of good quality data. And COVID, as we mentioned before, is an example. But in most organizations, I believe they don't even have an idea, an experiential perception of what their life would be like if they had data accessible almost on tap without going and asking a hundred different people and if they could trust it.
I've been doing an exercise for my own business. I'm trying to use these principles of data management and information for my own activities, the podcast, as an example, and I'll do more in the future.
I'm planning to release short videos on how I manage the different parts of the project. And I think it's interesting because compared to six months ago, I improved my backend a lot more. Six months ago, I only had a cognitive understanding of the impact of good data and easy to find data sets.
Now I have an actual understanding. For lack of a better word, I can make a distinction between the cognitive and the actual, the cognitive being, you think about, like, yeah, it makes sense. Similar to what you said before, a dollar spent it's because you bought something, it makes sense at the rational level.
But there is an aspect that is beneath the layer of cognition: how does it actually feel to work in an environment where you don't have to second guess or chase infinite threads? You just have the information that you need at the time that it's needed. I'm not trying to say that this is obviously a silver bullet because this is true for one person.
I'm running a consultancy where it's only me so it's a lot easier. But in an organization with thousands of people surely there's a different level of challenge. But I guess this is sort of an intro to a question for you. What is it like to implement a data management program in a large organization where you've got thousands of people to coordinate?
Andrew: [00:21:08] That's a very fair question. Very, very insightful question, Loris. I think that the biggest challenge to implementing a data management uplift program is how do we improve data practices? The challenge all depends on the culture of the organization.
Because for example, any organization of a particular size, and especially an organization that's been around for 50 years maybe they might have 10-15 definitions of what a customer is.
They may have a different definition for who and what is a customer. And the context may vary depending on the product type or the service type that you're selling. And they might have different departments that provide different products or services, and they all loosely interact with one another and they all talk about a customer.
But when they talk about a customer, they're talking about what they believe what a customer is, not what is a customer, because the organization hasn't come to a conclusion as to what a customer really is, a central definition of a customer.
If you're the chief data officer for this organization, you can't just come into this organization where you find 20 different definitions of customer, let's say, for example. With silos all over the place operating differently, not unified in their thinking about what the customer is or who the customer is and how we interact with the customer.
As a chief data officer, you've got three challenges, in my mind. One of them is to arrive at a common definition, which isn't going to be easy and have that agreed across the whole organization. Second thing is, you've got to change people and their practices to rethink, and relearn, and unlearn what they've learned about how to manage a customer.
Loris: [00:23:12] Which is the hardest part.
Andrew: Because they now have to change the definition of the customer because it's now a corporate standard. So there's an educational uplift, and then you've got the next challenge which is actually practice.
You've got to change practices to move people along into operating differently in a consistent way across the organization. What I'm really trying to say is the problems that we have, I'm not establishing the rules because we can set the rules up any way we want.
And it's probably easy to set up the rules. I think the whole impact around our industry, in the data practitioners industry, is changing culture and changing practices. And if you have the right culture, then you can change practices.
So 90% of my work is about thinking about changing processes and practices and the human aspect of uplifting people from moving from practice A to practice B and what skills they need to do that. I think that the biggest challenge in our world is moving the individual and the organization towards a better position.
Loris: [00:24:31] Andrew, I think this is the biggest challenge, period. I mean that changing management and changing culture is extremely hard. It's one of the hardest things because we are creatures of habit. So the motivation has to be there. That sense of urgency has to be there. And people need to understand. We need to help people understand how the change is going to improve their life directly.
And that's hard though, because there's many people in larger organizations and each one has different priorities and a different workflow. So it almost requires a human-centric design thinking applied to each one of them. Going there and going like, "can you show me your workflow? Can I see how you actually do what you do?" And I'm not trying to criticize you or anything.
I'm literally just trying to apply my full curiosity to your workflow and identify bits that could be made faster or better or more accurate. It kind of depends on who you're talking to. I had a direct experience of doing this in one of the last organizations I worked for and I ran into two different types of people: the ones that were keen to improve. They had this open mindset about it.
And those that saw it as a threat, no matter how hard I try to explain that I wasn't there to try and quantify their efficiency. In the end you know, if you look at a workflow, you try to map what you do and how to improve. Obviously, there's going to be an impact on productivity.
But this second category of people perceived it as something that was getting in the way. They just wanted to do their job and be left alone. And to me, it's an open challenge, how to communicate with those folks, because people that are like me and you, they like to learn, they like to challenge themselves. They're easy to get involved with. The other ones I still haven't cracked.
Andrew: [00:26:46] Okay. Firstly you need a strong executive team who need to keep communicating the messaging that the old way is legacy and that change is relevant and it's coming. So there's a mandatory imperative where executives need to reinforce the message that changes are necessary.
Loris: A must have.
Andrew: And I have to send the message saying, "Get on board or get out of the way", I'll be as blunt as that. Be part of the change or be left behind. Second thing, and that's something that you and I as practitioners, we're not in control of that messaging. The executives are in control of that messaging.
What I admire about Amazon, Google, Facebook, they do a lot of things, but fundamentally they're data organizations. They've been built from the ground up as data organizations. So if it moves there's data that's collected. So they're data organizations first and then everything else that they do is secondary. They're effectively data organizations. So the culture is that data comes first in those organizations and look at where they are in terms of their share value.
Andrew: [00:28:19] All the high tech companies of the world are data organizations first. And so the rest of the organizations need to learn how to become that. And so what I think is going to happen is that more and more a really smart CEO, a really smart board, will set up a parallel organization to the side of the main legacy business and set that up completely differently. But under the one umbrella with the main corporate board, maybe.
And actually set that business up from a greenfield, a data-centric business, and eventually grow that business and essentially consume the main business over time with a new team.
Andrew: [00:29:10] If you're even thinking about the individual company, but if you had, say you were in the plumbing business and you had a traditional plumbing distribution sort of mechanism. You got people doing transactions.
Loris: Pieces of paper or phone calls. Yeah.
Andrew: And all that. And let's assume you just put an ERP in and the ERP is not running at a hundred percent.
Loris: Enterprise resource planning.
Andrew: An enterprise resource planning product. You know, you've got something that if I was a CEO and I wasn't seeing the outcomes, I wouldn't go out and change the system. Some people might say, "Oh, we don't like the technology, throw it out, start again." You'll get nowhere that way because you'll have another set of problems with the next system.
What I would do, if I was the CEO, I'd have my set up like a parallel business, which is completely digital, which involves a team of people who actually really want to do this and have that running in parallel with the main legacy business. And eventually the new business will consume the old business.
And you'll find that every success story you have with online trading or selling products online, any business that's actually transitioned from an old business to a new business has actually done exactly that. They'll set up their online business team separate from the main legacy team. And get that firing up.
And eventually then the old team will transform into the new business eventually over time. Because that's how you get the new culture into the old culture or the other way around. So you find the smart businesses, don't try to change the structure. They create a new structure next to it, and then let that transform naturally organically over time.
Loris: [00:30:27] That's actually a brilliant way.
Culture, as we know, is the stuff that we do without thinking. When you ask, why do you do that? That's how we do stuff around here. That's culture. So to get to that level of automation that we’re thinking of, that's a long road. And there's going to be some trial and error. And if you don't have the resources and the time frame to make those mistakes, you'll never get there.
And I'm thinking particularly, scale-ups and startups and smaller organizations that maybe established a viable business model, they fund it, but they're under the pump. They gotta burn cash and they have to go from Series A to Series B to Series C and so on. So in that really high pressure environment, it's really hard to have these conversations because as we know, data culture and data management are like long marathons.
And I'm not saying that this is not a way to get around the problem of quick wins. Obviously we need to have that balance between the two, but what I'm trying to say is that the team composition is critical to get that going. And how would you go about setting up a team like that?
Andrew: [00:32:04] I've been very fortunate that I mentor startups as another part of my life. I love mentoring. So I mentor entrepreneurs and startups and the message I keep telling them is once you've got your team to a certain level, instead of thinking of being acquired or to get your investors to build your own business, why not think about getting acquired by a legacy business and moving your team underneath an old business?
I think that's where the industry is going to go. A startup team on a particular sort of business subject area will grow to a certain point, show some promise, and then a smart legacy business will buy them to absorb the new team into the old team or into the corporate structure, but to the side and build, and then use the support that comes from the bigger business to support them growing out the digital platform in a particular area or in a particular product or service.
And that's where there's going to be a clash of cultures and that has to get managed and all sorts of things like that. I honestly think there will be more businesses, there'll be new products, new services incubated outside of a traditional business and a smart CEO or board will acquire the startup, bring them in, and actually use them to leverage change.
That's one thing that's happening. I'm not saying anything new. This is actually happening. But my biggest disappointment is that startups themselves do not have good data management practices.
Andrew: [00:34:18] A startup is more interested in making sure that they're actually able to generate the minimum viable product and get it out.
Loris: And attract funding.
Andrew: Yes, and attract funding. But underlying all of that, what about the database structure and the metadata standards that they've set up for themselves internally? I'm sure if you did your due diligence on any startup, most startups, and said, "show me your data model, show me how well you've normalized your data, show me your metadata dictionary that says this is the way of capturing your data point." They wouldn't have it.
Loris: No, there's no time to build that.
Andrew: [00:34:58] So there's no time, right. But you know, as a startup, you know your most valuable asset is the data that you build. Because any startup will say, "Oh we're a smart company. We're a data company."
Loris: Where's your data? In a bunch of databases and nobody knows. Without any structure or any ability to be cross linked with.
Andrew: And I might be a bit controversial with what I'm saying here, but it's actually the truth. I think there's going to be a need for startups to positively embrace the principles that I've got with data management and data governance, and make that a fundamental part of the building of the business.
Loris: [00:35:48] And part of the dynamic is to have boards understand this, too. So that when you go in front of an investor, you have data quality as a centerpiece of the conversation around, "Are you getting your series B or your series C. Show me your best practices." Because it all comes down to money. We understand money really well.
But we've been talking about climate change for 30, 40 years, nothing has happened. And then a Swissre publishes a report saying that the impact is going to be $10 trillion by 2050. And everybody goes like, whoa, that $10 trillion is a lot of money. Okay. Now we gotta do something. Okay. So it looks like we understand the dollar value.
Andrew: [00:36:30] So I really admire Doug Laney with the book that he wrote that you read. I admire that because it actually takes the principles of valuing data as an asset and brings it to the real world. And that's why I think more startups should be reading Doug's book because if you're building a startup, you should build your database so that you're actually building the asset. The asset is more than just the product that you deliver, the asset is the database that you've created as well.
Loris: A hundred percent. So, Andrew, I'm gonna ask you the last question for today and it's a bit of a personal one. What do you wish you had known 10 years ago? What are the top three things?
Andrew: [00:37:21] Okay. 10 years ago. I woke up one day and I said, “Andrew Andrews is not in the data business. Andrew Andrews is in the people business.” I would have accepted revelation much earlier in my career. I would have said, your humanity first. Your work is about improving humanity, it just so happens that you do it with data.
So I actually had that mindset much earlier, about the human aspect of using data and creating using data. So that's why I like talking with you, because you've already got that mindset now. So that's something I wish I would have learned a lot earlier, or recognized a lot earlier.
I think the other thing that I would have done earlier is spent more time with executives and helping them understand and uplift their data literacy, rather than assuming that they understood it. I don't think executives understand it well enough.
And it's certainly something that our MBA courses around the world could spend more time on: teaching data literacy to executives and managers and also about the need for good governance of data. So something I would have done earlier, I would have spent more time with executives and educated them in what data literacy is all about.
Also I would have maybe set myself to a change management course. I think it's Prosci, the organization that specializes in developing change management skills. I probably would have sent myself there and actually done a course in change management, and actually developed my change management ideas.
For example, how do you write a good job spec that's actually data centric? How do you change someone's job role and make sure that the role in data stewardship and subject matter expertise is actually valued as part of the job role?
Andrew: I mean, we don't do that. How many job specs have you picked up in your work in consulting? And show me anywhere in those job specs where there's the word data written in? It doesn't exist. Yeah.
Loris: All the job descriptions that I get are unicorns. We want someone that does the architecture, that does the modeling, that can report to the board, that can also lead a team of 10 people. I am like, this is work for a team, not a person.
Andrew: I would have spent more time in my earlier years to actually focus on the call face with businesses, line operations and helped with educating and culture. I think the other thing I would have done differently is maybe I should actually transition to outside of the data space and actually work in line management with my data skills.
Maybe I should have been an operations expert or something like that. Not as a data practitioner, but actually a business practitioner who is data literate. Maybe that's something that I could have taught myself. I should've made that career decision. I should've gotten into business strategy, and become a business strategist who just happens to be a data expert.
I might've made those decisions earlier in my career. I don't regret anything I've done by the way, but I think something I should have done differently or thought differently is become a business strategist, work and do more traditional business strategy with a data focus, so that I could have more influence.
I mean, the fact is that in our industry as practitioners, what's our circle of influence? It's only recently that the chief data officer has become a thing where they've got influence? Before that they had the CIO, the CIO's have no data influence cause they're mostly focused on the system, the technology, the infrastructure, making sure that service levels are maintained, making sure the help desk is managed well.
Loris: It has nothing to do with strategy and the business’s vision.
Andrew: I probably would've actually even pursued a chief data officer path. If I had a crystal ball 10 years ago, I would've said to myself, “I'll get myself on a trajectory to become a chief data officer”, 10 years ago, 15 years ago. I’ve actually foreseen that a lot earlier in my career. It's not too late.
Of course. I'm doing that work now, but that's something I should have done earlier. I think I should have actually focused in that direction. And certainly there's a lot of opportunities for cultural improvement across the world in different organizations. And I'd like to think that hopefully, I'm an agent of change in organizations to help uplift them. I think I should have spent more time being more proactive.
Loris: What kind of legacy would you like to leave for your children and grandchildren?
Andrew: [00:41:35] James Price and I have these discussions every day. Our legacy is to help uplift the recognition that data is an asset. As important as human capital, financial capital and other things that we define as capital that the data is a capital asset.
And the legacy I'd like to see is that board reports have a data set. The agenda on the board meetings is data. You know, a board meeting in any public company has as a regular agenda item the management of data. And what are the risks and issues we have with data. And data quality and incidents and all that.
I wanna see that infiltrated as part of every board meeting around the world. That'd be great for me. If we can leave that sort of legacy behind. That'd be good. And not just data. I mean, I think you'd have to involve things like climate change and you know, all the other non-financial aspects as well. I think that the way we make decisions needs to change and I think data should be part of that future.
Loris: I couldn't agree more.
Andrew: So that's the legacy I'd like to leave: a change in thinking.
Loris: Let's work hard then. I'm all in. I'm trying to look at the next decade too. And I certainly share a lot of the hopes that you have. I'd like to see more efforts to try and speak about data as a tangible asset. I know it's intangible, but try to create the framework as much as possible so that it becomes second nature. You have a conversation down the street while you're getting a coffee with a total stranger and they will go like, oh yeah, sure. Data is money. Data equals money. So if you care about your finances and you want to manage it well, then you should care about your data too.
I think we'll get there. And I'm hopeful and I'm optimistic. So I'm going to leave it on that note. Andrew, it has been an incredible honor and a pleasure to have you on the show. So thank you for the time, and I certainly hope we'll continue to stay in touch via the LDO and DAMA and a bunch of other activities we might do together in the future.
Andrew: [00:43:08] I think there's plenty of scope for that. Loris, thank you very much. It's been great. Thank you. Thank you. Cheers.