acQuire Connected episode 42: How to build a robust data governance framework
In the latest episode of acQuire Connected, we sit down with Yulia Merrill, President of DAMA Perth and a leader in data governance, to explore the critical role of data governance in modern organisations.
Yulia begins by explaining that, unlike a physical asset that can be touched, moved or locked away, data can be duplicated, shared, and even stolen, without the original version ever physically disappearing. And if data goes missing, it can be nearly impossible to recreate. These same attributes that make data unique to work with also make it complex to manage. This is where data governance is critical.
What is data governance?
Data governance is the framework that ensures data is managed responsibly, securely and effectively throughout its lifecycle. This means establishing roles, responsibilities, policies and standards that dictate how data is created, stored, shared and used throughout a company.
Why is data governance critical?
Companies rely on accurate, trustworthy data to make confident business decisions. Without proper governance, data can become unreliable, unsecure and costly.
Yulia explains that while technology enables us to collect and create as much data as we want, it doesn’t mean we should. She references Rahul Telang, professor of information systems at Carnegie Mellon University, who highlights that as much as 90% of data in companies becomes “dark data”. This refers to data that is overlooked, underutilized, and even ungoverned. Collecting data that isn’t required not only creates significant cybersecurity risks but also adds unnecessary maintenance costs.
The impacts of poor data governance can be felt both in the short and long term:
Short term: poor governance practices such as fragmented and inconsistent data stored in outdated spreadsheets, disconnected systems or paper records, leads to wasted time, missed opportunities and conflicting reports due to duplicate records, creating a reporting and operational nightmare.
Long term: poor data quality erodes trust in organisational systems, stifles innovation and further increases the risk of regulatory penalties and reputational damage.
Good data governance safeguards data assets, ensures regulatory compliance and drives better outcomes for organisations. By managing data responsibly and staying compliant with laws such as the Australian Privacy Act and General Data Protection Regulation (GDPR) in the European Union, companies can unlock the full value of their data while maintaining trust, transparency, and long-term value.
How do companies get on top of their data governance?
A robust data governance framework doesn’t have to feel overwhelming or intrusive. Many of us are already practicing it in our roles and personal lives. Whether it’s saving a document in the right folder, cleaning up your inbox or double-checking a report for accuracy.
Yulia shares, “For organisations, effective data governance is about embedding those [data governance] practices into everyday operations in a way that feels seamless and natural to people. It’s not just about processes; it’s about having clear pathways to address data issues and empower teams to work smarter.”
Here are four key steps to building a robust data governance framework:
- Establish data leadership – Create a clear structure of accountability, with data owners and stewards responsible for managing specific datasets and maintaining standards.
- Build a data framework – People, processes and technology are the cornerstone of any successful data framework, and culture is the glue that ties it all together. It’s critical to equip teams with the right tools, standardise processes and ensure your technology is set up to support your data in a way that is accurate, accessible and trusted.
- Empower data owners and stewards – These roles complement leadership by focusing on operational execution, ensuring alignment with standards and acting as go-to experts for data issues on the ground.
- Lead change management – This is often the most challenging step. Yulia shares three key lessons for effective change management:
- Communicate the ‘why’: ensure everyone understands the purpose behind the change, whether it’s migrating to a new system or implementing governance policies so that people see the benefits not just the workload.
- Engage stakeholders early and often: their input can identify potential roadblocks and accelerate company buy-in.
- Lead with empathy and clarity: change isn’t a one-off event. It’s a continuous process that needs clear communication, empathy, and responsive support to build buy-in.
For a more comprehensive view of data management, explore the DAMA wheel of knowledge.
To learn more about building a robust data governance framework, listen to the full episode.
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Read the full transcript here:
Intro (00:00): Welcome to acQuire Connected, the podcast that is your compass in the world of data across environmental, social, and governance.
Jaimee Nobbs (00:11): Welcome to the acQuire Connected podcast. I’m Jaimee Nobbs, your host for this podcast, and today we sit down with Yulia Merrill. Yulia is a leader in the information management and governance space and is also the president of the Perth chapter of DAMA. In today’s episode, we have a great chat about why data governance is so critical, the impact it can have on companies when done well and when not so well, and then what steps companies can take to build a robust data governance framework. I thoroughly enjoyed today’s conversation and I hope you do too. So let’s get into it. Would you be able to start off by telling us a little bit about yourself and the work you do?
Yulia Merrill (00:52): Sure. So my name is Yulia Merrill and I’m a leader in data services and technology with a passion for turning data challenges into exciting opportunities. My journey began as a programmer. Over the years I’ve worked on projects modernising systems, developing data quality initiatives and more recently diving into the risk space to mitigate data privacy and artificial intelligence risks. So that’s certainly something that if I look back at 20, 25 years ago, would I ever be in data risk? That’s not something really that crossed my mind back then. It was all about coding lines and how to write efficient code. And so over the years I’ve had the privilege of leading multidisciplinary teams across many sectors like healthcare, banking and utilities, and collaborating with various stakeholders that included accountants, doctors, nurses, asset engineers, data scientists, data engineers, just to name a few and delivering impactful data initiatives. As the current president of DAMA Perth, I’m deeply committed to advancing data governance practisces and mentoring the next generation of data leaders.
Jaimee Nobbs (02:26): I’m really excited to talk to you today about some of the data governance frameworks and getting them set up, particularly with how much experience you have across different industries. I think it’ll be really fascinating. In terms of data governance, are you able to explain what data governance is and why it is so critical across any industry that people work in?
Yulia Merrill (02:49): Yes. Well, before we get into data governance, let’s take a moment to think about data. Let’s just look at it a little bit more in detail. So data is truly unlike any other type of asset. Physical assets are tangible, they can be touched, moved or locked away. But data, it’s intangible. It can be copied effortlessly. Yet once lost, it’s often nearly impossible to recreate. When data is lost, trying to recreate it, it’ll take more effort to bring it back, and even more intriguingly, data can be stolen without the original ever physically disappearing. And so this unique nature of data makes managing it a complex and critical challenge, and I think that’s where data governance comes into play. So data governance essentially is the framework that ensures data is managed responsibly, securely, and effectively throughout its life cycle. It establishes the roles, responsibilities, policies, and standards across the organisation that dictate how data is created, stored, shared, and used.
In other words, the data lifecycle. So why is data governance so important? Well, in today’s data-driven world, businesses rely on accurate, trustworthy data to make informed decisions. Without proper governance, data can quickly become unreliable, insecure and even a liability. And data governance isn’t then just about control; it’s about empowering organisations to unlock the full value of their data while maintaining trust, transparency and compliance. I’ve recently read Forbes article which revealed that 60 to 73% of data within organisations is never used strategically. Can you imagine that? And that’s because we are able to bring whatever data we want, and that’s where acts like Australian Privacy Act and GDPR come in to kind of, whilst technologically we can, legislatively now there are some boundaries to that. And then we’ve got also research from Carnegie Mellon University, which found that 90% of data becomes dark data. And what that is, that is an overlooked and underutilised data, and this represents both a lost opportunity and a significant risk. And so good governance, safeguards, data assets, it ensures regulatory compliance and it drives better outcomes for organisations. It’s not just about setting rules, it’s about unlocking data’s full potential while fostering trust, transparency, and importantly long-term value.
Jaimee Nobbs (06:02): I think that’s a fantastic explanation of not only data governance, but also some of the risks around data as well and why it is so important for a lot of businesses. You mentioned dark data. I haven’t actually heard that term before, which is it’s quite a fascinating one because I think that is something a lot of companies are aware of,some peoplewho is they’re collecting a lot of information but not necessarily using it. Is that the case across multiple industries or are there particular industries where you see that more than others?
Yulia Merrill (06:34): From what I’ve seen, it is certainly something that has been across many industries, and that’s why there is a way to make sure that the data that you’re collecting or creating is for a purpose. And this is where it’s that whole software development lifecycle, for example, and you start with business requirements. So collecting data, the data that you collect needs to link to a business purpose. Is that well-defined at the beginning of the initiative? And absolutely, as I said before, technologically there is nothing that’s stopping us from collecting and creating as much data as we can. But when we actually do do that, not only do we increase the risk because that data, the unused data, becomes this absolute sweet spot for cyber criminals to go and grab it and then sell it on the dark market. But also what organisations need to start considering is the cost of maintaining that data are increasing.
Jaimee Nobbs (07:46): Yeah, that is an interesting point. I’m sure there are people that think collecting data potentially that they might use in the future isn’t such a bad thing. I’m thinking companies perhaps that are collecting a lot of environmental data they might be reporting on in that environmental data set in the future, but I guess it’s a bit of a balance between the security risks and the financial implications of holding that data now if you don’t necessarily need it for the future.
Yulia Merrill (08:17): Yes, absolutely. If you know that environmental data, for example, there is future legislations that are coming through and you know that there may be more likely than not that additional environmental data will be required, then there is a value for that to collect it. But it’s more so around personal information that we need to be a little bit more considerate, actually quite a lot more considerate than the industry used to be before. And it’s not just about collecting it, the other side of it is retaining it. And that’s where a lot of organisations, and if you look at the data breaches that have happened over the last several years, they’ve really targeted big organisations that had some retention periods that were not necessarily with limited timelines. And that’s where, for example, in Western Australia we have the state records office act, which has a retentional schedule, and we’ve got Australian Privacy Principles and Australian Privacy Act that sort of helps and puts guidelines and actually regulations around collecting data. So it’s that at the beginning, at the end, we have regulations in place that help organisations design the purpose of data and then also design the systems with those regulation requirements in mind and to be put in place.
Jaimee Nobbs (09:54): It’s interesting to hear how the legislation is being put in place to create those, I guess boundaries for companies. It sounds like we still have a fair way to go in terms of data governance and doing it well and how we host the information, how we manage it. I guess it sounds like we’ve still got a bit of improving to go, but in terms of government, it’s nice to see that there are guardrails in place to make sure that companies are doing the right thing with that data. You’ve talked about how hard it is for companies to get data back once it’s missing. If we look at things like duplicate data or outdated information, are there any impacts of having that on an organisation,S either short or long-term?
Yulia Merrill (10:45): Yes, absolutely. So fragmented and inconsistent data can absolutely have significant short and long-term impact on an organisation. So for example, let’s consider an energy company managing contract and asset data and data is stored across outdated spreadsheets. And yes, spreadsheets are still a thing, and I think spreadsheets are going to be here for a long time still. We’ve got disconnected systems and we’ve got paper records. So teams may waste days reconciling information to verify lease terms, for example, or equipment availability or maintenance schedules, which is even more scary, leading to operational delays and missed opportunities. And similarly, a retail company, we can take any retail company and with duplicate customer records, they might be sending conflicting promotional offers which can frustrate customers and erode brand loyalty. And in the long term, these issues can escalate. So duplicate and missing and outdated data leads to poor data quality.
And over time it erodes trust in organisational systems and resulting in missed opportunities for innovation, regulatory penalties as we’ve discussed, and even reputational damage. So you’ve talked about environmental data, so it’s probably a good scenario to consider as well. If a mining company submits incorrect environmental impact data reporting due to missing records, it risks projects, delays, fines, and even sometimes public backlash. I’d also like to shine a spotlight on another important fundamental aspect that probably does not always get a mention, but works hand in hand with data governance, and that is application governance. So this is again leading towards that duplicate, missing and outdated information. And why is application governance important? So for example, if a company decides to customise an off-the-shelf product, that can lead to breaking architecture, data relationships, it introduces formatting issues which lead to missing and incompatible data. Application governance ensures that the systems generating, storing and processing data are aligned and standardised. And this might involve implementing master data management tools to synchronise, for example, customer records or deployings data catalogues to make information searchable and reliable. So by prioritising both data and application governance, organisations can maintain consistency, reduce risk, reducing risks of duplicate missing and outdated information, and that way they ensure they remain competitive in an increasingly data-driven world.
Jaimee Nobbs (14:08): The application governance is a fascinating one because when you think of data management, you potentially think of just one tool, but I guess having that application governance also ensures that it can interoperate with other systems across the business. So that one application isn’t risking the security or the integrity. Integrity of data. Yeah, exactly. So I think it is interesting from an operational perspective that companies should not only consider their data governance from within one system, but across applications at that kind of higher level as well.
Yulia Merrill (14:51): Yes, data lineage, whether it’s in the business processes or that sort of data integration and data flows need to be designed between different systems. And this is where we’ve got the solution architecture playing an important role as well. And this is where we’ve got business having an absolutely critical role playing in delivering and delivering any solutions as well, that business defines their requirements, business defines their business rules, and then it’s up to the development cycle to stitch that data from that business concept all the way through different systems to get the outcome for the business to make decisions.
Jaimee Nobbs (15:38): How often should companies be revisiting those business rules?
Yulia Merrill (15:43): That is a really good question. Okay, I’ll probably start with an easy one. So rule number one for me when developing a system is first of all considering regulatory requirements. And so as soon as there is a change or there’s going to be change in the regulatory guidelines and requirements, that’s where you go and you set up the review of the business rules and how they have been implemented across the different systems. Secondly, how often do you review business rules? I suppose when you start noticing or when the organisation realises that its strategic direction is changing, what does that look like? How is that strategic direction reflected in those business rules? And again, how do we go back as soon as we recognise that the organisation should really start reviewing the business rules and start implementing changes to ensure they remain strategically viable?
Jaimee Nobbs (16:50): So it is a recurring thing at any time there is changes to legislation, for example, or changes or new pieces of software coming into a business. It’s something that you kind of need to stay on top of; you can’t just set and forget it.
Yulia Merrill (17:08): And another important one is that when you expecting to get an output from a system and you’re not getting what you were expecting, so that’s where you also go and you review your business rules to make sure that they have been defined properly and that there is no inconsistencies in the interpretation of the flow between different systems.
Jaimee Nobbs (17:32): It’s an interesting one. It seems like it must be hard for companies to have to stay on top of that when it’s not necessarily part of their business’ usual day-to-day role. I’m sure it can be hard for companies to stay on top. Do you agree with that or do you think it should be something that once set up is easier for companies to manage?
Yulia Merrill (18:00): It’s definitely something that needs to be included in their program of work and just really consistently reviewing the validity of the application and how the rules are reflected in that.
Jaimee Nobbs (18:17): So if we flip this, what does a company with a robust data governance framework look like in your ideal world?
Yulia Merrill (18:27): Okay, so another excellent question, and let’s go ahead and imagine a company with a perfect data governance framework. I’ll reference this cinematically, and it’s like a sci-fi utopia where everything just works. So no one is frantically searching through outdated spreadsheets. There is no mysterious file named finalco capital letters final underscore version three floating around. And decisions happen faster than the speed of an email thread. Imagine that kind of company and where every piece of data is neatly catalogued, it’s easily accessible and ready to fuel innovation. But let’s actually bring that back down to reality. A robust data governance framework doesn’t have to feel overwhelming or intrusive. In fact, many of us are already practising aspects of data governance in our roles and even in our personal lives. So anytime you save a document in the right folder, clean up your inbox or double check report for accuracy, you’re already practising data governance whether you realise it or not.
And that’s the exciting part for me personally. So for organisations, effective data governance is about embedding these practises into everyday operations in a way that feels seamless and natural to people. It’s not just about processes, it’s about having clear pathways to address data issues and empower teams to work smarter. This might involve establishing policies for managing contracts, ensuring seamless integration between applications. It’s setting up data council or a committee to oversee funding and resource allocation or enabling self-service analytics with trusted data. So ultimately, data governance is more than just a framework. It’s a culture of trust, importantly, accountability that ensures everyone in the organisation can rely on data with confidence and better decisions and drive success.
Jaimee Nobbs (20:52): That’s a fascinating point actually. It doesn’t have to be this big overwhelming policy change if done right, you potentially don’t even notice it. It’s just the way that people operate within a business. I think that’s, and in their home life, like you said, it’s something that we kind of already do, but perhaps don’t consider it data governance because I think there’s a connotation around data governance that it has to be hard, it has to be difficult to manage, difficult to implement. So it is interesting to hear that when it’s done well, you don’t actually need to even notice it’s happening necessarily. I think that’s fascinating
Yulia Merrill (21:33): And doing it well as we are all doing it already, it’s not a roadblock, it’s just really making sure that we are a bit more cognisant around our responsibility around data and what it can lead to if it’s not managed. Right.
Jaimee Nobbs (21:54): That leads nicely on to my next question in terms of the data governance framework. Now, you’ve already mentioned the cinematic journey that you went on for people that weren’t at acQuire Connect Tech Summit this year as a keynote speaker, you referenced Mad Max to talk about data poorly governed, and then Star Trek was the other
To talk about the utopia of data governance. Yep. So at acQuire Connect, you talked about a four-step process that companies can take to build a data governance framework, and I’d really like to go into those four steps. So to start off with, step one was to establish data leadership. Step two, build a data framework. Step three, empower data owners and stewards, and then step four, lead change management. If we look at step one to begin with establishing data leadership, what does that look like in a company? Is it just one person being accountable for the data being collected or is data leadership across the entire organisation?
Yulia Merrill (23:01): I certainly touched on the four key steps. Of course, these are by no means the only steps to consider. There is a wealth of other areas to explore. Just take a glance at the DMA wheel of knowledge areas for a comprehensive view of data management. But yes, for now, let’s focus on my streamlined approach to fast-tracking data governance implementation. Yes, so step one, establishing data leadership. So data leadership is not just about appointing one person to be accountable for data. It’s about creating a clear structure of responsibility and decision-making. So at the top, it might include Chief Data Officer or CDO or a similar role that aligns data strategy with business goals. It’s a very important step to make sure that your data strategy is aligned with business goals, but effective data leadership extends beyond that one person. It should include a council or governance committee representing key business areas, ensuring that data initiatives are prioritised and aligned across the organisation. So again, let’s take an example of mining company. We might have a leadership group overseeing how production data integrates with sustainability metrics and financial reporting. Each leader in this group ensures that respective areas are represented, creating a culture of shared accountability for data quality and importantly, outcomes.
Jaimee Nobbs (24:43): So it’s accountability on all across the business. It’s not leadership as in one leader of it, it needs to be accountability across the organisation.
Yulia Merrill (24:55): Absolutely.
Jaimee Nobbs (24:56): Okay. And in terms of, we’ll just jump to step two, building a data framework At Acquire, we talk about the importance of people, process and technology in our solutions. So one can’t work without the other. You need the people and the processes in place for technology to be able to use it well, the fourth part of the framework that you mentioned, the data framework that you mentioned was culture as well. How do you see those four parts working together?
Yulia Merrill (25:28): So as you rightly pointed out, the people processes, technology, PPT is basically the corner, a stone of any successful data framework and culture is the thing. It’s that glue that ties it all together. Really, it just ensures that data is seen not as an afterthought, but as an asset that drives decision making. One of my favourite quotes by Peter Drucker is that culture, eats strategy for breakfast. And so you can absolutely put us all the effort, all the funding that you can into people, processes and technology. But if you do not have that mentality, that culture that appreciates data and sees it as an asset, it’s certainly going to be very difficult to maintain a robust and good data framework. But people, we all know that people are the heart of any organisation driving the management and execution of activities across the data framework.
So equipping them with the right skills, knowledge and tools is critical to success. Key roles such as data owners and data stewards bring expertise and accountability, responsibility, ensuring a deep understanding of the business context surrounding the data. And so these roles function within defined data domains, which are another cornerstone of effective data governance. So establishing data domains. So essentially it’s logically grouping your data into these domains, create clear lines of accountability and responsibility, fostering collaboration and clarity among business teams while ensuring data is managed consistently and effectively. You’re probably hearing me through this conversation mentioning and talking a lot about collaboration, but that’s essentially what it takes to ensure that we do have a successful and sustainable data governance, really. And then if we move down to the next level, and it’s the processes and processes are, it’s important to have standardised processes in how data is created, accessed and maintained.
And so this is where we’ve got, as I said, we’ve got the apps, Australian privacy principles and let’s say wa, state record, office retention schedule, kind of guiding some of that standardisation around creating and retaining data. And so it’s creating that consistency. It helps create consistency and reduce errors for the organisations. And then the people that we have delegated and formally nominated as data owners or data stewards, they need to use these processes to address and manage data across the organisation. And so that includes data handling on a daily basis, kind of questions. It’s participate, these people, these data owners and data stewards participate in organisational projects. It could be anything from data migrations or implementing new systems, just making sure that data lens is represented in the organisation across the main channels in innovation, delivery. And then technology enables scalability and efficiency. So with tools like data catalogues met, data report repositories and analytics platforms, making data easier to use and trust. And then this is where we can then overlay those standardised processes as well into tech to ensure that again, there is that consistency and that we’ve got tech that supports us as an organisation and now goals in delivering on robust data governance.
Jaimee Nobbs (29:38): That is a lot to consider. It’s a lot to consider for a company, ensuring that the people are skilled in the processes, in the technology, ensuring that the processes is set up to support the people and the technology to ensure that the culture is set so that data governance isn’t an afterthought. They do really, they are all really interconnected. It seems like quite a, it basically takes into account every aspect of a business. Now, you mentioned data owners and stewards, so that was step three of your streamlined version. How does that differ from the data leadership team, and what are the ways that you can empower someone that is a data owner or a data steward to do that role effectively? I guess that’s a two-prong question there.
Yulia Merrill (30:28): Yeah, sure. So empowering data owners and stewards. So data owners and data stewards, they compliment leadership, but they focus on operational execution. So operational execution sits more within the responsibilities of a data steward. So while leadership sets the vision and priorities, data stewards, the hands-on champions, data owners are typically senior roles with accountability for specific data sets like customer or asset data or sales and so forth. And they really ensure that it all aligns with organisational standards. Whilst data stewards on the other hand are the day-to-day runners, sometimes I like to call them, and they’re subject matter experts essentially. They monitor data quality, they address issues. They are someone on the ground that people can go to and ask for advice on how to address certain data issues and questions dilemmas. And just really they ensure that data governance processes are followed. And when I’ve referred to domains before as well and how data also sort of flows between different applications and whatnot.
So data can flow through domains too. And this is where data stewards then play that collaborative role as well. And this idea of working groups between data domains can be stood up if there is a query that impacts on let’s say customer and asset or sales domain. So they’re the ones that basically would bring the relevant data stewards or subject matter experts between those domains and figure out a way to resolve an issue. Just think about this, how often does this happen in an organisation? I bet you these conversations happen already. It’s just that people don’t have the data steward role formally allocated to them. And again, it kind of goes back to that non-invasive data governance. And really empowering these roles involves providing them with the tools, training, and importantly authority to act. So for example, again, just pulling out an example, a data steward in the mining company might commonly validate geospatial data for accuracy, ensuring that operations can proceed without costly errors.
Jaimee Nobbs (33:11): It sounds like the data owners and the data stewards are really the frontline, so they need the authority to make the calls, but ultimately the data sits with them and they work with the data. So they are really that frontline. And then the leadership are really, like you said, setting the strategy and the vision. So thank you for clarifying those two. In terms of the fourth step, this is probably the most, I would say the most difficult for a lot of companies to begin. So it’s looking at lead change management. If companies are looking to integrate or to migrate to new systems, bring in new technology, whatever that may look like for companies that are struggling with implementing effective change management, how can they navigate this? In your experience, how have you seen companies do it well or do you have any advice for companies that are just looking to start that change management in their organisation?
Yulia Merrill (34:10): Yeah, so look, change management is where the rubber meets the road, so to say, and it’s often the toughest step. Companies do struggle with change management. There’s three probably key messages that I’ve learned through my experience. And that is, number one, communicate the why. So just make sure everyone understands the purpose behind the change, whether it’s migrating to a new system or implementing governance policies, people need to see the benefits, not just the workload. And it’s quite one of the frequent questions that I used to get, and I still do when implementing data governance and data management frameworks. And that is what is going to be my workload. And as I have alluded to, there is really no more workload on people when implementing, let’s say data governance for example, is because that’s already something that they’re doing. And really communicating the why is important, engage early and often is another important strategy.
So involve stakeholders from the start, especially those directly impacted, so their input can identify potential roadblocks and it certainly accelerates, thereby in most of time. And then really provide continuous support. So change isn’t a one-time event, it’s a process. It’s continuous. It’s also, it should be evolving. So provide training, clear documentation and responsive support channels to ensure users feel confident in adopting new practises or technologies. For instance, let’s consider a company transitioning to a new data integration platform or a new system that’s been implemented to carry out financial processes. So ensuring that teams receive hands-on training and ongoing support can make the difference between a smooth rollout and a widespread frustration. And I think importantly, leading with empathy and clarity, companies can turn resistance into enthusiasm.
Jaimee Nobbs (36:37): So the three pieces of advice, just to make sure I’ve got this right, is explaining the why engaging often and early and often, and then leading with empathy would be the three key things to look at when you are implementing any change management in an organisation. Is that correct?
Yulia Merrill (36:57): Yes. Yes, absolutely.
Jaimee Nobbs (36:59): I think they’re very easy to understand, perhaps more difficult to implement, but I think they are really tangible things that people can take away from that change management and your experience in doing it as well or seeing how companies do it. So let’s talk DAMA now. DAMA is a, from your website, non-for-profit industry association dedicated to advancing the profession of data management. How does an association like DAMA support its members and who is a part of an organisation like this?
Yulia Merrill (37:35): So as the lead of the DAMA Perth chapter, my role is to build a vibrant and supportive network of data professionals here in Perth and supporting Australia nationwide. So DAMA supports its members by providing access to resources like the DAMA DMBOK framework, professional certifications, webinars, and workshops to enhance skills and knowledge. We also create opportunities for networking and collaboration, enabling members to learn from one another and tackle the evolving challenges of the data industry together. So DAMA welcomes anyone with an interest in data management, whether you’re a seasoned professional, a student starting your journey, or just an enthusiast eager to learn more. Our diverse community includes data analysts, data stewards, engineers and leaders, all working to advance their careers in the field of data management as a whole.
Jaimee Nobbs (38:44): And where do people go to join?
Yulia Merrill (38:49): So dma.org au is our website. And have a look around. There is benefits page as well that I usually ask members to go and have a look at and see if it is something that you’re interested in. And we’re happy to have anyone on board.
Jaimee Nobbs (39:06): I’ll link it in the show notes because you did reference the DAMA website when talking about the data governance wheel, I think it was. So I’ll link it there if people want to go have a look as well. And my last question for you today is what are you most proud of being the president of the Perth chapter? Are there any particular initiatives or projects that you’ve done that have been most rewarding for you in that role?
Yulia Merrill (39:32): So I’ll start off by mentioning a DAMA Australia mentoring programme. So it is a nationwide initiative. Over the past 12 to 18 months, DAMA Australia has worked really hard to strengthen DAMA Australia mentoring programme, and it’s been incredibly rewarding to see the impact. So this programme, which now runs through Mentor Loop Group Loop’s, feature connects certified data management professionals or CD mps with individuals in the broader data community, enabling to share knowledge and professional growth and focus data management topics. The success of this programme was recognised when Demo Australia won Mental Loop Mentoring award for the most impactful mentoring programme for 2024. It’s a testament to the value of building connections within our community and just highlights how mentoring can drive meaningful change both for individuals and the industry as a whole. And secondly, I’m proud of how we’ve created a platform for people to share their knowledge and lessons learned, whether it’s through our chapter meetings, webinars, or panel discussions, we’re fostering an environment where data professionals can exchange ideas, showcase innovative solutions, and learn from real world examples and their experiences. And one of my favourite moments is when members tell me that they’ve applied a lesson or an insight they heard at one of our events to solve a challenge in their own work. And it’s just a reminder that sharing knowledge not only strengthens the individual, but also elevates the entire profession. These initiatives are about more just events, programmes, really. They’re about building a community where people feel supported, inspired, and really equipped to advance their careers and contribute to the evolving world of data management.
Jaimee Nobbs (41:50): Sounds like you guys are doing some really great work. So yeah, for anyone that’s interested in learning more about DAMA Australia, DAMA Perth, I’ll pop a link in the show notes, but I think that’s all we’ve got time for, so let’s leave it there. Thank you so much for sitting down and chatting with me today. I really enjoyed it. Yeah.
Yulia Merrill (42:12): Well, thank you very much for your time and for the opportunity to be on your fabulous podcast.
Jaimee Nobbs (42:20): Thank you for listening to this episode of acQuire Connected. If you enjoyed today’s episode and want to support the show, please share it around or feel free to leave a review to stay up to date on the next season of episodes dropping. Please hit the subscribe button on whatever podcast player or app you’re listening to this episode on. Thanks for listening to the Acquire Connected podcast channel. Find us at acQuire.com au.
