On Thursday, June 27, 2024, in Kuala Lumpur, the Alation Executive Breakfast brought together some of the brightest minds in enterprise data management today. With the theme "Empowering Excellence in Enterprise Data Management," this event was an exceptional gathering of industry leaders and data professionals, all united by a shared passion for harnessing the power of data to drive innovation and excellence.

The discussion was moderated by Sid Sharma, Regional VP Sales, Asia, Alation, and featured insights from Mel Valerio, Head of Data Governance at Maya, (a Filipino financial services and digital payments company), as well as Khai Chiat Ong (“KC”), Data and Analytics Partner at PwC Malaysia. Together, they explored the intricacies of building a successful data culture, strengthening data governance, leveraging active metadata, and advancing organizational data maturity. 

What follows is the audio recording from the panel and a recap of the key takeaways (some of which have been edited for clarity). The full transcript is also included for those who would like to listen and follow along. Let’s dive in!

Data governance is a journey

Mel Valerio shared insights into the initiation and evolution of the data governance program at Maya. The journey began with a strategic approach to standardizing business terms and data dictionaries using Excel as a unified template across all business units. This early method ensured consistency and prepared the foundation for the future. As the company expanded, particularly with the growth of its digital bank, the need for a more robust solution became evident. The company has transitioned to advanced data governance platforms, capable of managing the increasing data volumes and complexity with greater efficiency and precision.

A platform that supported automation was essential. Due to the high costs of experts’ time, “I don't have a lot of resources to do manual work,” he explains. “So I want them to really focus on strategy, really focus on automation. And we need to have the right tool in order to do that.” Valerio’s journey highlighted the importance of having a robust foundation for data management and governance.

Establish clear data roles

A significant factor in Maya's success was the establishment of clear data responsibilities. This ensured that everyone, from technical teams to business units, understood their roles and how to collaborate effectively. The creation of a self-service platform for data-related issues further streamlined communication and problem resolution.

“We do have a common vision… and a framework [so] that, every one of us knows our roles and responsibilities,” Valerio shares. “At the same time, the business knows exactly, who to talk to or who to reach out to if there are some data concerns.” Valerio’s team created, a self-service platform they’ve called the data analytics intake process, where “everyone from the business, teams, any department or any business unit [is] able to raise a concern… then data governance [can] triage who should be the rightful owner [and the] actions to be taken.”

Critical Data Elements are key for an AI and data-driven culture

To realize Maya's goal of becoming an AI-first company, Valerio’s team has focused on data governance to ensure responsible and ethical AI deployment. He shared how they focused on identifying critical data elements (CDEs), maintaining high data quality, and building a metadata repository. 

“Since we are very highly regulated by our central bank, (plus the fact that the data users are the primary data users, [as well as the] data science and AI and data analytics teams), we, identified, which are those critical data elements that will affect their models, dashboards, and insights,” he explains. “So we started that way and put all those in a metadata repository. So from ther, everyone will see, what's the meaning of terms like ‘past due’ and all of that?”

Secure stakeholder and cultural buy-in

The discussion highlighted the critical role of senior stakeholder support in driving data governance and AI initiatives. KC from PwC Malaysia emphasized the importance of aligning digital and data strategies with business goals and fostering a culture that drives data value. Successful organizations have strong senior leadership backing and a culture that integrates data practices seamlessly into business operations.

“The most successful strategy… is when the buy in comes from very senior stakeholders,” he says, pointing to the CSO, CEO and board as key supporters. He also emphasizes the need for a “quick win” to generate momentum, sharing that “there must be some pilot win, use case, or value realization that convinces the stakeholders. So that is the duty of the technologist to make it happen and make it real.”

“Culture eats strategy for breakfast,” he adds. “Ultimately the culture has to move as well. Also remember the people factor. The people factor ultimately will determine how the organization… puts data and strategy on steroids to help them move further.”

New organizations can build a robust data foundation

KC pointed out the varying stages of data maturity among organizations in Southeast Asia. Some have abundant data but struggle to utilize it effectively, while others lack sufficient data but are eager to advance their capabilities. What’s more, new organizations have the advantage of designing their data architecture from scratch. 

“You start fresh, you get your chance to define the data architecture you want,” he expands. “You get the chance to then build the platform that you want. You get the chance to influence. Then that becomes a data-led company.” Understanding these differences, based on organizational maturity, is crucial for tailoring data strategies to each firm’s unique context and challenges.

Organizations should leverage existing technologies and resources to accelerate their data initiatives. It’s important to align data projects with the organization’s vision and capabilities, rather than aiming for unrealistic maturity levels. Collaborating with external partners can also help in propelling the organization forward.

To grasp data’s ROI, look to time savings and regulations

The discussion emphasized the importance of measuring the ROI on data. It was noted that ROI can be both tangible and intangible. Tangible ROI includes cost savings from avoiding regulatory penalties due to data quality issues, while intangible ROI covers aspects like reputational risk.

“Let's say if we do a data governance [project]...” KC begins. “How long does that currently take? …the infrastructure is also very important. For example, you have good data technology, and so on. But you also need a good data model…The actual model that fits your organization makes your data easy to find, easy to mine.”

Effective data governance, where data is well-documented and understood, helps organizations avoid costly mistakes which include regulatory issues. KC points to governance frameworks as delivering clear time savings when he says, “The fashion of how governance is implemented… equates to how effectively you’re using your data. Effectiveness of it equates to time-saving. Time-saving equates to actual saving. And the hope is that you get your data right and regulatory penalties will not come your way.”

Conclusion

The Alation Executive Breakfast in Malaysia was a remarkable event that highlighted the pivotal role of effective data management in driving organizational success. With expert insights, attendees gained a deeper understanding of how to build a successful data culture, strengthen data governance, leverage active metadata, and advance their data maturity journey.

As organizations continue to navigate the complexities of the data-driven world, it is clear that a strategic approach to data management is essential. By fostering a culture that values data as a strategic asset, implementing robust governance frameworks, and embracing innovative tools and practices, enterprises can unlock the full potential of their data.

Full Transcript: Malaysian Executive Breakfast

Sid Sharma, Regional VP Sales, Asia, Alation [00:00:10] Well, now that the, awkward break is over, and there's food here in case he's getting hungry. So we'll try and, you know, get the show going. But, really a big thank you to everybody for showing up today. Really appreciate everybody, that have come here and been part of this with us. It's always great to see so many folks together. And I'm really happy to see everybody here. Just to quickly do introductions. I'm Sid, I'm the rep for Asia at Asian. And I'm really excited to be here with everybody. I let KC and Mel go next just to do quick intros.

Kai Chiat Ong (“KC”), Data and Analytics Partner, PwC Malaysia [00:01:00] Hi, everyone. I'm KC. I'm, partner of PwC. I lead the data analytics team.

Mel Valerio, Head of Data Governance, Maya [00:01:07] Good morning, everyone. I'm, Mel Valerio, so I lead the data governance team in Maya, it's, one of the fintech, companies in the Philippines.

Sid [00:01:17] Awesome. Mel, actually, do you want to tell us a little bit more about Maya? So that everybody understands context as we go through questions later on? 

Mel [00:01:25] Yeah, sure. So, Maya is for one of the leading, fintech companies in the Philippines. So, we, we have this vision to really revolutionize financial inclusions and services, in the Philippines. Right. So Maya offers, a wide range of, platforms, including the, digital payments, online banking and, a lot of our cash management, systems. So we really would want to make sure that, with our advanced, technology and, and AI solutions and to be a data driven, we'd like to have a personalized, a lot of personalized, experience to our, our customers. So that's what Maya is doing right now. In the Philippines.

Sid [00:02:11] That's, that's awesome. And then just if anybody's curious, Maya also gives you 14% interest on your bank account. So if anyone's wants to move money to Philippines, these are the people to move their money to. But we're super, super happy to be here. And I think, you know, while we were discussing what to kind of talk about, I think where our heads went to is that everybody here is in a at a very different stage of your data governance and data overall journey. Right. We've got companies who are here who are highly regulated. We've got, companies here who are more new age just because they were born in cloud. And then we have got companies here who could have possibly, who would have to work with so many other, smaller functions, teams or departments in their organization that it becomes harder for you to work through so many of these barriers?

Sid [00:03:04] So I think one of the things that I wanted to get started was to to ask Mel a little bit about how did that journey start when when it came to data governance, at Maya? and from what I recall, it started about 3 or 4 years ago. So what did the vision at the time look like? What were you trying to achieve at the time? And then I think through the conversation we can talk about where you've ended up at now. 

Mel [00:03:27] Yeah, sure. So, just a bit of trivia. So I started data governance, in my, way back in August of 2021. So almost three years. So yeah, three years already in Maya, but I thought of myself that I'm already there for ten years because there's a lot of things to do, in as far as data governance is concerned. So I started the data governance program in Maya, and, I'm not feeling embarrassed that we started using Excel. So, we are the center of “excellence” in my, when it comes to consolidating all of our business terms, data dictionaries and, doing the data domain. So that started, the data governance. You have to find the or introduce the new rules, data rules and do the data domain mapping. And at the same time make sure that, all of those things are in place. [00:04:19]So we used Excel at that time, however, we expanded from, Paymaya doing a lot of our wallet transactions to become a digital bank. So meaning to say we expanded the roles, expanded all the services offers Excel cannot sustain. Definitely, because there's a lot of, data types, a lot of the volumes increase and all. So, we do have a lot of challenges updating. And at the same time, where are those Excels linked to another, data types or another data sources? Because we, we also built our lakehouse infrastructure from the traditional, data warehouse to data Lake to Lake house right now. Then it all started a lot of our integration issues and all that. 

[00:05:04]That's why we, decided then explore that, we have to find the right fit, data governance solutions that can really handle all those things together. And at the same time, I don't have a lot of resources to do manual work. Because I do have a lot of specialists, but they are way above the salary range of doing manual work. Right? So I want them to really focus on strategy, really focus on automation. And we need to have the right tool in order to do that. So we started exploring identifying which are the data governance tools that are in the market that of course, number one, our, our evaluation criteria will be its technical capabilities. And of course, not to mention the the ease of use of the, of the tool because it, it doesn't really make sense for us that are only the data teams or that technical teams can be able to make use of that tool. But, we would want to be a data driven and make sure that we empower and democratize the data to our, employees, to Maya employees. So those are part of, I would say, the factors to consider in order to identify the tools that we are currently using now.

Sid [00:06:19] Awesome, awesome. And I will pick up on some of the things that you said as follow ups. But one of the first things I picked up on, and probably, is a follow for you and KC for you as well, is you obviously work with a lot of companies in Malaysia as well as the entire Southeast Asia region. PwC obviously being such a big, company has, has access to so many companies in terms of having these conversations and consulting them on their data journey and data strategy. What are you seeing right now in the region in terms of what's the change that's happening? Where are companies right now? You know, where are they doing well, where are they getting stuck? And probably pick up some of the things that, you know, Mel said, where would you say they are in that journey right now?

KC [00:07:04] Okay. I think first question is, when we look at the, the the people in the room, right. If you think about the C-suite or if you attend a board meeting, can anyone just show me of hand if the C-suite or the board have not brought up the worth of AI or genAI in the last six months? I assume that laughter is definitely yes, that's right. But I say, what does that mean, right? When you when when the board these are very easy work to pick up and it's very aligned to our national agenda investment that's coming through to Malaysia and specifically also the our major fund houses in Malaysia commitment towards our AI journey. Right.

But if you peel the layer of AI you go down, the next topic is data. So from my experience with two, 2 or 3 different types of organizations in Malaysia. One type of organization is I would have data, but how do I use it. Right. So that's that's one level of maturity, right. The second organization is I don't have data, but how I'm going to use it. Right. And and that's real. That's real because the hygiene of collecting historical data specifically models. Right. [When you talk about AI, ML models, you talk about features and features requires a depth of, basic, fundamental data, right, for proper depth in order for the features and for the model to work correctly. Right, so you can fit into that. So that's the second organization. Don't have data but want to move forward. The third organization is the best. Best organization is I'm brand new. I can start fresh right. It's like now I can redesign my whole architecture. So Malaysia as of now, and I have to be careful that some regulators in the room as well. There is three digital bank that launch public launch for public launch, not soft launch. Public launch means, not as good as 14%, but around 3.6%. Daily rest. Daily rest. That means every day you get recompute, right? Better that our traditional commercial bank of 2.45%. I'm not promoting any color does just they are not promoting any color. It's your choice, right. But it's a good time as a consumer to, to to use some of these facilities. So if you are a million bucks, put it in, but IPFs, you better so that this the third phase, right. Which is essentially you start fresh, you get your chance to define the architecture you want. You get the chance to then build the platform that you want. You get the chance to influence. Then that become a tech led company, right?

So where Malaysia is at this moment, from my experience working with a number of clients, let's just say financial sector is one, one very huge sector. They are in the conundrum of between the first yep, a lot of data, but how to use it. And also the second I don't have data. The second case happens in for example, ESG, sustainability reporting. Where do I find my last three years worth of data for the purpose of reporting or forecasting of my risk model of in the Klang Valley, which side of Klang Valley will be flooded example in the next 20, 30 years. And that's true, right? By the way, he assures me. Okay, okay. So that's one, right? And the second part is growing the top line, which is talking about sales. Right. Do I have enough customer trend to understand my customers profile? So as a result you're stuck there. But what they have very good data is existing. Right. They have feel a lot of transactional data, payment data. So they can fine tune the operations. But to up tune that's always a challenge. So therefore they need to now discover what they have. Link. What. They use and then move forward from a business per day. Think we are stuck somewhere in between there? Yeah, just to share.

Sid [00:11:23] Yeah. So that's that's actually super helpful. Mel, to do some of the questions, some of the things that, KC shared, where would you say, you know, Maya is right now? I know, you and I spoke about AI governance at some point. But I'm very curious. Right on that AI journey. Since KC brought up that topic. Right. Where do you see Maya right now? And and according to you, right. While trying to implement some of these AI models in production, trying to get the organization ready. What have been the observations, challenges and learnings?

Mel [00:11:56] Yeah, for the last three years that I've been working in Maya, so we really accelerated our, data driven journey that, we immediately set our data foundation at the onset. So there's a lot of challenges. Number one, very siloed data. Fragmented data. Right. So, when you have fragmented data, data quality, issues come, come in. [Then what we immediately did was to, to have a list of our data asset inventory, make sure that everyone knows, from the business perspective. And the technical perspective knows where to, get the data, where to find the data. And what's the meaning of that data? That from there, we, we provide, very good tracking and dynamic monitoring of our data quality. Profiling. On on those are critical data elements. 

[00:12:46] So when we say critical data elements, number one, since we are very highly regulated by our central bank, plus the fact that, the data users are the primary data users, data science and AI team, data analytics team, we, identified, which are those critical data elements that will affect their models, their, their, dashboards and insights. So we started that way and put all those in a metadata repository. So from there everyone will see, what's the meaning of this past due and all of that? Because I always remember that this past year, because in mango, everyone is actually asking, what's the meaning of this past due or. It depends. It depends. It depends. It depends on the calculation. It depends on the department. It depends on who is using it. So that's what we immediately said on the landing perspective.

 [00:13:35] Then from there, for data governance, right now we are very serious in our, making a, the company to be an AI first company. We start that from being an ambient AI to ubiquitous AI. So we, we are deploying a lot of AI solutions in Maya, making sure that, of course, our vision is to really have a personalized to our, to have a good customer experience. So on a data governance perspective, I'm already starting to have an AI governance framework, making sure that, you know, whatever we, deployed or develop are really, on the perspective of being a responsible and ethical AI, solutions and all of that. So that's where Maya at right now we are, on on the journey of AI. But of course, on data governance perspective, we I just want to make sure that, we are not just deploying for the sake of our being an AI first company, but, we also have are responsible to, to everyone, especially for our consumers.

 Sid [00:14:39] Awesome. That's that's super helpful. And and, KC, you just mentioned a little bit about the type of companies that you've seen. You know, Mel's spoken about where they are and kind of maybe, I won't say they had the the advantage of being fully new, but somewhat had a little bit of that. And that helped build the right culture in the company as well. From, from your perspective, you know, when you speak with customers, when you, you know, consult, and somebody says, hey, this is where I am. I've, you know, our company's been existent for such a long time. We are in that first company bucket where I have the data. I don't know how to get started or I some I'm good at certain areas, and I think that's what our observation at Alation has been. Whenever we work with certain companies, they are very good in certain areas and in certain areas is where people get lost. So how do you can then help these companies to get going? In terms of finding their way from there. Right. So what are some of the things that, you know, you've done on that front and, and what would you advise to companies in terms of how to get started on that side?

KC [00:15:44] It's very broad. It's actually a very broad topic, but.

Sid [00:15:49] I'm asking for some free consultation.

KC [00:15:53] This session is recorded, so whatever I'll say today is of my personal opinion. Anyway, just just to share a bit of perspective is because the, I mean, from a, organization perspective, when you look at, challenges that they face or let's not call it challenger, let's call it the vision that they want to achieve. Right? Let's start with the vision before talking about the challenge. Right. You have to start with the vision. What they they want to achieve. Right. Or what they can potentially achieve as well. Two things can and potential. There are certain things or some organization didn't realize they can actually go even further than what they have today. Yeah. Just just to share. First it has to you have to start with a very clear set of vision. Right. And the vision, from a digital data perspective, cannot be siloed. It has to be very aligned with the business vision. Right. In a way, it should be able to to propel or accelerate or put it on stage. Right. Puts the vision on steroids. Right. Legal stay right. So that, you know, you can actually excel further right. For example, let's just say digital bank, very aggressive

From a product development perspective, the sprint is usually two weeks or four weeks per sprint, right? But think about it. Why the digital bank could achieve this as compared to a traditional bank is because the digital bank knows the asset that is in hand. They know the data is in hand. They have the people to be able to do it. And notwithstanding the technical, technology people in this box, the technical and the digital people and the business people, there's no boundary is one, right? So when you do a product development, it's not that outsourced call source to another function. It's actually a co-development. So with all this assets data assets that's known. Right. So in in other words you could accelerate the product pushed up two months. sorry two weeks, two weeks, two weeks change. Interest rate change. Let's now call multi-currency example. Single currency multi-currency done in a month or two. Right. That's extreme speed as compared to any other iteration. But obviously you have to comply to regulators ruling. Yeah, yeah. So all this can be done from a quicker manner because they know their data. They have the capability, they have the business buy in. So I think when Mendelsohn shared the four DS just now, those are very important point. But fundamentally at the bottom there's a C, right. CS a bit before D because that's the culture and the change the people element as well. Yeah. At the bottom, I think this is a very old saying. Right. Culture eats strategy for breakfast. Sorry. You guys are eating better than that. So culture is strategy for breakfast. So ultimately the culture has to move as well. So remember the people factor. The people factor ultimately will determine how the organization put the how the organization put data and strategy, on steroids to help them to move further. That's an element on that vision of, okay, because technology, enabler technology can boost, but technology cannot stand by itself, right? I mean, that's just a bit of, background. So [the most successful strategy that we see that we are able to advise our client is when the buy in comes from very senior stakeholders. Right. The likes of CSOs, the likes of CEO, even the push from the board members right that monitor this closely, that gives that boost. Yeah that boost. But in order to convince this boost, of course there must be some pilot win use case value realization that convinced the stakeholders. So that is the duty of the technologist to make make it happen and make it real. Long journey if the organization set up faster journey if the organization is sort of, new right. New. So but new also you have other challenges as well culture integration and others. Yeah.

Sid [00:20:21] That's that's super helpful. I think, Mendelsohn talks about, you know, the, the framework of PPD right people process and technology. So we spoke a little bit about technology already. We spoke a little bit about process, that Mel spoke about. And now you kind of brought up the people factor of it. I think in my experience, having worked with some of the Alation customers, and this is I'm not saying this just because Mel's sitting here, I was still say this behind his back, but [00:20:46]one of the best, I think data culture that I've seen is at Maya, in terms of how they've set up the team in terms of how, the data strategy flows. So so, Mel, why don't you talk a little bit about how, you know, you were able to achieve that? And I know it's not just something that was totally on your shoulders. You know, your CTO, Alfred, and the entire organization kind of took this on, where you brought business in. It was never really to say, you know, we are the tech sides here, and the business side is here, but you brought them in. So can you talk a little bit about how you did that, and how you were able to take them along on this journey?

Mel [00:21:20] Yeah. First things first, because we have a very good, support from our senior stakeholders. So that's the the right start for us to, establish our or start our data governance program. So we have this, very clear and defined vision of our data strategy. Then everyone really knows their, data roles and responsibilities. Ownership and accountabilities are very critical. So, as head of our data governance, and we do have also different, data heads, like a head of data engineering, head of, data science and AI, head of our data analytics and business intelligence. So what I did is I, we do have a common vision, and we know exactly our, I, I did, very data role, a framework that, every one of us knows our roles and responsibilities. We know, we know, immediately our functions and we know how to function at one and how to function, independently. So that's what we did in Maya. And at the same time, the business knows exactly, who to talk to or who to reach out if there are some data concerns. And also as a process in a data management, a process for data governance. So we created, a self-service platform where in everyone, we call it the data analytics intake process, where in everyone from the business, teams or any department or any business unit can be able to raise a concern on that one, then data governance will be the one to triage who should be the rightful owner of that particular, actions to be taken. So in essence, we have this very good. I would say not perfect, but a very good data culture in, in Maya, where everyone really understands the value of our data. And for us, I don't want to say that I don't have any challenges. You know, influencing everyone in our organization, but, we do have, a good, we call it D2E, the data driven Everything committee, data governance committee where all the C-suite, executives are the committee members. And we actually, for me, I can be a bad guy in that committee because I really summon or call out those that are not complying for our data governance. So, it's a good start in a way. But right now, they are the one who's reaching out becomes our a good problem right now because they are the ones reaching out. We have this issues. We have this our product data we have to deploy. What are the data requirements you need from us. So it's the other way around. No, we are not the one who's chasing for them that you deployed the product. You haven't provided us the data requirements. Now at the onset initiation phase, they will be the one who reach out to us that, we have this a product, we have this PRD. We have this, broad, what data requirements you need from us so that it will be part of our design and development? So that's very good, because those are the things that we are now consolidating and put it in our, tool, that data governance tool that we are currently using so that now they appreciate all those data requirements, all those data documentations that they're providing to us. And, they are now reaping all those things because they can immediately see, oh, I don't need to create another data dictionary is already there. I just need to update and add those things so they are not reinventing the wheel. They are now somehow utilizing what we have in Maya. So those are the journey that we have. And I could say that. Still a long way to go, but I think we are in the right direction.

Sid [00:24:57] Awesome. You know, you brought up something that I'm pretty sure everybody in the room, you know, at some point, you're exactly. Or, you know, you yourself sometimes might have asked the question is that what's the what's the ROI on data? Right. And I feel like anytime that question comes up, for me the answer is, wait, why is that even a question? But it is still a question, right? So I think from that perspective, maybe maybe both. KC Mel, if you guys can share your thoughts in terms of just ROI of valuable data, right. How how do you see that today in a world where, just off the top of my head, right. You've got you've got the AI journey where everybody's jumping on that and, you know, none of us can sleep on it. You cannot really say, oh, I will do AI in 2029. Because who knows what's happening then? And then you also have a world where, most of the countries, at least in our region, are, bringing very stricter, laws that, you know, data protection, privacy. So while there's so many changes happening around us. Right. How do you figure out the ROI on valuable data in the right hands? And kind of how do you go down that road to, to measure it and have everybody understand, in your organizations. Why why it means what it means.

Mel [00:26:13] I can start so there will be some, tangible and intangible, ROI. I will say, you know, number one is, of course, how we are a regulated, regulated bank. And, let's focus on data quality. Okay. If you do have a lot of data quality issues and you are reporting, regularly to our central bank, of course, there will be a lot of penalties. Not only the penalties, penalties will be 100 bucks or whatever. Right. But the reputational, risk is always the critical partSo those are the things to tackle. And of course, data is nothing if you will not leverage or monetize it. So that's why our, data science and AI are really very greedy and hungry with the data. So they keep on asking, those kind of questions about, where was the data? Our data are really interested in all. So instead of asking them one by one for every department, they're just looking at one, repository, then they could see all those data. So they can be able to immediately create, AI solutions based on the business use cases that they are working with the business units. And, for us, in data governance, we we are the abstraction layer between the business and the technical teams, making sure that, we are all aligned with our vision to the business strategy. So I could say in as far as data governance is concerned in this, for us, that the value of data that we are managing and governing, I cannot say, we are earning or some, but it is indirectly because of the other business units are working on those data. Right? if we do have a lot of data quality issues, then definitely they set that, one record with one record will cost you times ten of the cost, because you need a lot of people to investigate, to do the RCA and all that. Whereas if you do it right at the first time, it will be very seamless. So that's the value of the data. And we can have those ROI. If you have number one fully documented and everything is actually fully understand those data that you have in the organization.

KC [00:28:19] KC. I think the ROI bit, other than what Mel mentioned. Right. There's some a bit of calculation that can be done. There's a logical way. For example, let's say if we do a data governance, if you want to do a project right, you want to help a business. How long does that currently take? Right. Example. That's a measurement, to find data. How long does it take the turnaround time. How long does it take? Let's just say if we know all the data set is well connected. And that's not all magical. Let's just put it that way. Right? It's not all magical if you know your data, but the infrastructure behind also is very important. For example, you have good data warehouse lakehouse technology, so and so forth. Right. But you also need a good data model. Model. Right. The actual model that fits your organization then is easy to find, easy to mine. That will be a saving right. That's human time saving. And as you know, people time course is not going to be cheap. Who in the last 12 months never ask for a raise. Didn't expect as the won a race. I don't have a salary race in my salary? Anyone? I mean, it's very kind of you if you did that right. Helping to stop inflation as well as the positive path. But you see, cost is going to increase, right? Expectation of people's time in producing X amount of productive hours will increase. And if you use this to still do the manual way and so on and so forth. That is actually one ROI. Okay.

[00:29:57] Another simple ROI for regulated the entities in Malaysia is the fines that comes from the regulator. And over the last few years, there are a lot of fines, banks bank specifically that came from the regulator because of error in reporting. Example. Very simple stuff. It's not simple. It's complex stuff, but very simple. It's because if you get that foundation right, then you get the reporting, right. A just a bank typically has about 100 to 130 regulatory reporting that needs to be submitted on the where there is a weekly, monthly semiannual annual basis, but 100, 230 depending. And if you get some of these data set wrong I say, oh, by the way, I need to adjust my last month's number. And you know, you can't hide because the regulators at the back run a lot of analytics and stuff to to compare against bank. And then subsequently you will see fines coming up and how important this became. Is MAS Singapore over actually last month maybe 2 or 3 weeks ago somewhere. And then last month or 2 or 3 weeks ago, somewhere about there, did, thematic study of data governance of all their selected banks, specifically these Ipsa, banks, bottom systematically important banks. And that is a finding to talk about, governance, how governance and quality is treated. So this is my word. How I interpreted the whole document is, is very interesting, for people like me. Right. You can read it as a bedtime story. How I interpret it governance currently is implemented like a framework, right. But it's not implemented in a practical, practically usable manner [9.2s] in in a way, yeah. Because like if everyone's aware culture is aware awareness is is there. Yeah. Like what? They know where the data is. The owner has the right call then is actually in a more practical, practical manner. Right. So the fashion of how governance is implemented right then equates to the effectiveness of how you use it. Effectiveness of it equates to time saving. Time saving equates to actual saving. And also, with the hope that that's the hope that you get your data right and regulatory penalties will not come your way. And the regulatory penalties is real, real in Malaysia. Right. Specifically.

Sid [00:32:42] So it's not 100 bucks.

KC [00:32:43] It's not 100 bucks. It's more expensive than 100 bucks. Sorry. What denomination? Euro. Okay. Still more expensive. Okay. So so so that's the case, right? So but also another point. Imagine the cost of the business if a decision is made, the wrong data. Just just think about that. Especially AI, ML model, right. Bias model and all those things. Yeah. Touch the PDP privacy then that's another type of fine that you talk about.

Sid [00:33:17] Awesome. I think we're almost at time. So probably I think I would want to wrap this up and maybe I think one, you know, last question, you know, as, as folks go back, what is what is maybe that one question that you'd, you'd want everybody to ask, you know, themselves or their teams or just kind of think about, in the coming weeks, after our chat today, and I know the question is not, you know, when are they going to reach out to PWC? But but more around their data strategy. Right. Like what's probably one question that you think everybody should be asking, you know, their teams or, you know, amongst, their executive team.

Mel [00:33:57] I think it's not about the question. It's about to reflect. Just finding your sweet spot when it comes to, using the data. Right. When I say, finding the sweet spot. Of course, data is, more of the the center of our people process and technology, right? So don't just focus on that, on people. Then forget about the process and technology. So don't just focus on the process. Then you forget about the people and technology and don't just focus on the technology, but you don't have people in the process. So always find the sweet spot, make sure that it is aligned with the, what is your business strategies, the company strategies, how you now use the data when working with those people process and the technology. Right. So those are very, very important. It's a lesson learned also from us that we don't need to get a very high tech, you know, tool in the market which doesn't fit in our overall tech stack. So that's our, challenge before the integration part of it. So always find the sweet spot that can actually move all things together. And technology is your enabler. So make and, leverage on those things. So sorry. It's not the question, but somehow you have to reflect.

KC [00:35:10] Yeah. Technology. One of the best analytics technology that we can all have today is Excel. It's still going to stay. Okay. Okay. This going to stay? But, what? I think I resonate, with Mel on finding a sweet spot. Not all organization will be, ideal organization. Maturity level five. Bla bla bla as your end state because even though you can be a level five maturity assessment, so on and so forth from your data perspective, but is the overall organization even rating as well. Right. So make sure the sweet spot like what Mel has mentioned is identify clear strategy. Define then value delivered. Right. That's that's very important. Then the expansion according to where the the business actually landing. Right. So making sure that you aligned making sure that there is outcome, making sure that it fits your organization purpose but still drive towards the similar, similar level of vision. So the one question is what is your current organization vision and how do you as changemaker, in this room to be able to accelerate that for organization? And what can you do today with what you have and think of tomorrow and what you can achieve I think that's that's and also don't go all maturity level five Malaysia don't have enough talent to support everyone. Technologies data scientist, data engineers. Right. They don't have enough talent. So your cost another talent? Yeah. Like there. I mean, this will be a cosmic factor that impacts just a share. So try to go to what's your best but leverage of available technology sometimes. Right. Available time to help you accelerate as well. Because there's a lot of investment that came into Malaysia over recent years, as you guys know. Right. Leverage of that to be able to help you to accelerate as well. Yep. Okay. And, don't be afraid to share knowledge. Us even via always here. Not not not not purely just consulting and stuff, but we are here to connect and help everyone to propel to the next level. I mean, the Malaysia, landscape. Yeah.

Sid [00:37:46] Awesome. And, yeah, don't don't take all the talent because KC wants to hire them and then then come back to you. No. I really appreciate both of you and all the thoughts that, you know, you've shared. I definitely I go with KC's, you know, last, last point. We are here to help. We are here to, you know, share stories and give any kind of, guidance, that we can, you know, even at Alation, we've now worked with 700 companies globally. Lot of them are government defense companies. Lot of, you know, companies that have faced similar challenges that a lot of, you know, companies in Asia are facing now. And we are happy to, you know, give access in terms of helping you learn from that journey and helping you learn from their use cases. You know, our our team, like our CEO, everybody kind of visits, Asia quite a bit. This is a very important region for us. And and we are here to help as well. Along with obviously PWC and, and our other customers.

[00:38:41] Right. So, I think we'll go back to our tables. Please feel free to have questions for for melon and, KC, you know, we guys will keep working around, and, we can have that. And I really appreciate everybody for coming out here, and spending time with us and really hope to see everybody, more in the, in the coming months as we keep coming back to, KL it's not that we are far away. We're just in Singapore. So, you know, you let us know and we'll be there in like three hours. And then we can meet you. But I really appreciate everybody. So thank you so much for being here. Thank you again. Thank you.

KC [00:39:20] Discussion. 

Sid [00:39:23] Yeah, I think I think we can just go back to our tables and, you know, obviously eat the food. I know it's kind of gotten cold, but but perhaps I think, on the tables, we can have, you know, discussions around some of the, you know, challenges you're facing. Any, you know, inside that you heard today, which was interesting or, or any insight that you might have. Right. Like, we've got folks here from insurance and so many other, backgrounds. I'm sure you've done some things in your own world that, you know, we can all learn from. So. So I think let's just go back to that.

KC [00:39:53] Thank you.