Fuelling PETRONAS' Data-driven Transformation

In this episode, Datin Ts. Habsah Nordin, Chief Data Officer of Petroliam Nasional Berhad (PETRONAS), discusses how the company has leveraged data to transform and grow its business in the last half-century. She provides an overview of how their data transformation started, and their continued investments in various projects, including Enterprise Data Hub (EDH) and Data+. Datin Habsah also shares about PETRONAS' preparations for upcoming technologies like AI, exemplified by the introduction of J.AI.

PETRONAS, Malaysia's integrated multinational petroleum corporation, has a presence in over 100 countries globally and this year marks the celebration of their 50-year legacy in the oil and gas industry.

Transcript

AIBP Intro: 0:02

The AIBP ASEAN B2B Growth podcast is a series of fireside chats with business leaders in Southeast Asia focused on growth in the region. Topics discussed include business strategy, sales and marketing, enterprise technology and innovation.

Irza Suprapto: 0:28

Hello, and welcome to the next episode of the AIBP ASEAN B2B Growth Podcast. Today we have a very interesting guest with us. But before I go on to introduce our guests, just a general reminder, you know, the AIBP ASEAN B2B growth podcast, we sit down with leaders in Southeast Asia who are driving growth within their organizations, most of our guests represent organizations which are native to the Southeast Asian region. And as I mentioned earlier today, we have Datin Habsah Nordin, who is actually the Chief Data Officer of Petronas to those of us you know, as part of Southeast Asia, especially if you're based in Malaysia, you must be very, very familiar with Petronas, one of the largest organizations, storing organizations and for those of you working within the oil and gas field, obviously, you would have heard of Petronas as well. But I think our guest today will be able to do a better job of introducing the firm and as well as herself. Hello, and welcome to the show, Datin. How are you?

Datin Habsah, PETRONAS: 1:25

Irza, thank you so much for having me here.

Irza Suprapto: 1:28

Thank you. Thank you for taking the time to be with us. You know, before we start, maybe you know for international guests would you like to give an overview of the firm you're working for currently, PETRONAS.

Datin Habsah, PETRONAS: 1:38

Okay. PETRONAS is national oil company of Malaysia. It is established in 1974. So pretty much this year we are celebrating the 50th anniversary of Petronas. Congratulations. Yeah, so Petronas service in operations around the world we are operating in more than 100 countries and our group of companies is more than 250 around the world. And I think for Petronas itself it is highly integrated value chain, we do have all the way from the exploration of finding oil, gas and so forth into selling all those our products to the market. And we are also venturing into renewable energy.

Irza Suprapto: 2:26

Thank you Datin. Now well, Petronas is it's an interesting company. And there's a whole history that you can learn about them. I think what's more interesting is obviously our guest today Datin Habsah Datin can you tell us your role within Petronas? What do you do within the organization and also how long you've been with the firm?

Datin Habsah, PETRONAS: 2:45

Okay, so I am now holding the positions of the chief data officer Petronas, this has been the post that I am holding since April 2020. So actually, when I started this role, it was already the movement control order. I think most of us remember about the COVID days, right? So when I came into the office, pretty much I came into the office, virtually I knew that with all my teams this, around 100 of them, we have been online working together for two years, actually, prior to the movement control order has been disbanded. So I can imagine, and you can imagine this pair, right? Coming into a new setup, having a new team in a different environment in a different setting that's designed to there's an arts to it. But there's also I would say a very enriching experience. How exactly that we started this journey about data transformation. How do we discuss about the strategy? How do we do our daily cadence on every single project that we are doing? But one thing for sure is, the team is very excited. We want to see changes, we want to see changes, how does the data transformation be able to powering up the digital success for Petronas? Because we believe that the one of the component of success for digital is on its data. So I think that's how I started but when I came into the data space was actually in early 2019. That was a time with so I've been entrusted to look after and manage technical data for Petronas for the formations of or I would say that the focus of data and Petronas is always centered on the technical data because we are a technical organization. So the formations of a data team and the centricity of managing data started in 1976. And the focus was always on the entering data, subsurface data and so forth. And I think we see that evolutions of the data but I think what's more important we see that there is a need to orchestrate the whole data management better and that's why the formation of enterprise data in 2020. So I become the first person who actually driving the whole transformation of data for Petronas.

Irza, AIBP: 5:13

Perfect, so April 2020. For those of you overseas, I think Datin mention movement control order. This was the MCO in Malaysia. So Southeast Asia basically went into lockdown sometime around March, April 2020. Different countries in Southeast Asia. So Datin started her role right at the start of the COVID lockdowns in in Malaysia and COVID lockdowns in Malaysia were quite severe, whereby, if I remember correctly, only one person can leave the home to buy groceries.

Datin Habsah, PETRONAS: 5:44

I remember when I had to leave, you know, and I thought that am i at war all those, you know, the distance we need to keep among each of us, and then we need to put on proper gear. And I think there's a lot of I would say, you know, shops being close and so forth. And there's a dedicated path for you to go to the shopping mall. That is a different experience altogether.

Irza Suprapto: 6:09

It must have been amazing starting your new role that so Datin you mentioned you started April 2020 in that role. Previously, you mentioned you were working with technical data from as early as 2019. Can you give us an idea about how you got there? Where were you before that. How was your career trajectory moved and what what you've done previously before PETRONAS as well.

Datin Habsah, PETRONAS: 6:30

Okay, so maybe I start when I can, I'm a I graduated from Case Western Reserve University in computer science. So I joined Petronas in 16, November 1995. So I came into Petronas. I actually was placed in an IT department, I spent one and a half year in IT department. And after that I step out and step out into a marketing division. In fact, in the marketing division where I was assigned, it was in a company called Petronas Dagangan, Berhad. And I was actually the only female and the first female that was assigned to a commercial and home fuel department. I even have a picture to give an evidence of this, then was the starting point. So I learned about the business and I feel very empowered. I feel very energetic how I see what I do if my job like I was the experts in pricing, and I can see how pricing actually contributed in winning contracts, how it contributed to improve market share, and so forth. So right after that, one, once that I got that flavor, I wanted to do something bigger. So I went into a strategic planning. So I spent about 10 years in strategic planning. And right after that, I went into internal audit, there was you know, we I got assigned to internal audit, and that was a field and a platform for me to learn about the entire business of Petronas. In internal audit, where we do audit, we do many scopes of audit area, it goes from procurement, to finance, to planning to operations and so forth. So I understood about the whole Petronas setup and the core operations and so forth in that two thing in the two years thing where I was in, in group internal audit. And right after that, I came into a division, it was a newly formed division, it was called Research and Technology Division. That was the division where the vision of that division was about to create a center of excellence on engineering, on research on technology development, and I've been in this this particular division for quite some time in a multifaceted role. And one thing that I have discovered is every single places that I moved in, I love that new challenge because I feel that there's something new I'm learning. You know, to me learning is always constant. And I think what's more so exciting is I get to work with a different team, I get to see how my team grew, I get to see how exactly, you know I make an impact. It doesn't matter whether you are in a stewardship role, you the shaping your role or your delivery role. There's always the connections to the impact that you can bring to the organization. So I think that's what gets me motivated. But one of the key tasks that I was interested in 2016 was I'm part of the transformation team and I was alone assign this was actually a very secret project actually. I was assigned to become like the, I would say the designer of the division. So I designed the whole operating model the division, the vision, it was actually a congre is actually is The congregations of a few departments in Petronas. And when we form it does close to 5000, employee of that division so you can understand the scale and the size of it. And it has multifacets of I will say expertise from project management, to research, to technical services, and so forth, including also managing the technical data. So that was a feel the term of what I've learned over the years. And I have prescribed that and deliberately undertake what I've learned over the years into what I call as my transformation adventure, or I would say my transformation piece. And I think, noticing that as part of my strength. And when Petronas wanted to see the changes in data, well, I still have this conversation with my Senior Vice President at that point in time. He strongly believe that I can make this happen and that's the reason why I am happy to accept the mission critical. And that's why I'm here in enterprise data. And the whole context about the management of the data in Petronas is how would the transformation be felt, how the right strategy is being developed and applied and delivered. And how I make sure that there is the culture hack within it within Petronas. You know that that data is the responsibility and accountability of everyone. So it's no longer about the person in IT. You have to manage the data. It's not longer that is all about how do we as an employee of Petronas, 50,000, over close to 50,000 of us we feel accountable. We take that accountability seriously in managing data because I think many people misunderstood when it comes to data. The first thing in their mind is all about technology investment. Actually it's not the biggest challenge about transformation is data. It' the culture hack. You build the right culture for data. So I guess that's a give us a little bit of snippets of what I'm doing.

Irza, AIBP: 12:10

That's so you've been with Petronas since 1995. And you mentioned that Petronas is celebrating their

Datin Habsah, PETRONAS: 12:17

50th anniversary this year.

Irza Suprapto: 12:20

So you've you've been with Petronas for half of its existence more than half of its existence.

Irza, AIBP: 12:28

You know, nowadays with with different people joining organizations, people stay for maybe one two years, and they think oh, I've been very long with a firm, but Datin you've been with Petronas for a very long time. And I think you need to have been with them for a very long time in order to experience the entire supply chain, the different departments. And you mentioned as well, it's 50,000, nearly 50,000 employees and $70 billion business 100 locations globally. There needs to be some understanding of the internal workings of the firm, I guess in order to be where you are today as well. That's true. Let's go back to the data piece. Since you're the title of chief data officer as well. Let's go back to the fundamentals. Right. Okay. Can you just briefly share with us in your words, what is data? And can you share some examples of instances where data has helped or can help in generating actionable and relevant insights for Petronas as a firm?

Datin Habsah, PETRONAS: 13:19

Okay, the principle of data is not just numbers and statistics. In fact, it is the driving force that fuel our decision making processes. Data provides that foundation upon which we navigate the complexity of the industry, driving business performance, operational efficiency and unlatch new growth opportunity, you need to actually depends on the data for you to download the insights and for you to make the informed decision making. I think that's the part that I think has been needs to be a constant reminder to everyone. So you imagine with Petronas is such a very large organization. So we have an operations around more than 100 countries. We recognize the importance of the data as our strategic enabler so that we want to become a data driven organization. So one of the things that we did first thing that we did, right, and I think I will need to qualify that we did right, and I think that's most important, is how do we double up and bring about the data liberalization program, because that's very important to unlock the value of data is to allow for the data to be shared, and the fundamentals of that we need the mechanism how you share the data across and that's why we formed that program. We call it data liberalization agreement, data liberalization program. So the data liberalization program by its principal, it ensures the default accessibility from all the data cross its subsidiaries within Petronas to the Petronas corporate. So one of the mechanism is we developed what we call this group Data Liberalization agreement. And we executed that agreement with 227 subsidiaries around the world. And I think that's what's more important is when we have, we call as a data liberalization that we need to have a mechanism, a platform how you want to liberalize data. And then we actually set forth in terms the development of Enterprise Data Hub. Enterprise Data Hub is actually our data platform. It serves two function. The first function is for data liberalization across Petronas, the second it actually function as the advanced analytics platform. And with that, also, because EDH is part of our data factory, we need to actually also develop the right data marketplace facilities for the users. And that's where we give birth to our data plus, so to your questions, whether we talk about, you know, how do we ensure that the most important things about building this whole data platform and data plus is not just about the cutting edge technology, but I think what's more important is the fundamentals of the data governance and practices, because when we design, we need to design it right. And I think that's where we anchor upon in the way we manage our Enterprise Data Hub development. I don't know whether I answer you not.

Irza Suprapto: 16:37

You have the I think I'd like to, you mentioned the design of the for example, the platform or the ecosystem. You also mentioned I'd like to just connect the dots with what you mentioned earlier with regard to you know, you had to design the digital transformation team within Petronas. So I think this is the problem a lot of Southeast Asian enterprises face whether you're a large enterprise or you're you're a small medium enterprise in this part of the world. How does the designing process work? And how do you come up with, say, an MVP for this enterprise data?

Datin Habsah, PETRONAS: 17:12

Okay, so before I go into in zoom for the MVP for enterprise data, let's, let's look about what this was that the what they call it data transformation looks like, okay, I think for me, right? I work on the basis, I am that big picture person. And as a big picture person, what's important to me is what's my end state looks like? What am I free? What am I making this equivalent? What does this transformation looks like for the company? Right? So what I spend the first time when I came into, you know, my position is understanding and picture that I had in mind. So that's the part where we develop the so called our data strategy, but the data strategy cover a little bit later, when we actually finalize our data roadmap, because the data roadmap actually emphasize and prioritize what will be the enabling success enabling foundational blocks that we need to make it right, we need to design it right. And we need to operate it right. So that was the first starting point. So started with the roadmap, defining the strategy and so forth. The second thing is, we want to make sure that we have the right data framework. So that is on the component of the data governance, so the data governance piece is very important to me, because it's at first in terms of new practices, in terms of new thinking, because governance in most cases is being seen as strictly on the context of control. It is strictly on the context of risk averse. But here we are, we want to liberalize the data, right? So how do we strike a balance between the liberalisation as well as direct control? So I think we need to find that sweet spot. So when we when I anchor and look at all this whole governance is to make sure that we have that sweet spot. And then when we talk about the liberalizations analytics as bringing the value from the enterprise data, how we started with the MVP, when the we started with the MVP at the same window, we also have a few of technogy digital projects. So when we started, the MVP was more about building the data pipeline ingestion, and be able to serve it to our technology to project but we have the understanding, what exactly would be the requirements of any technology digital projects in the future, and that we studied, and we actually concluded and finalized, close to 22 technology stacks within EDH and EDH to full completion, we actually took three years, three years to build, and three years to operate. So during these three years with many technogy digital projects, and use cases come in the picture, that's also giving us the opportunity to test the design and to test the outcome of enterprise data. So I think if I think we have even had a conversation we got no, we asked, How long does our you know, our the bill of EDH, similar, EDH will take place in many company, the Gartner actually came is within you know, five to 10 years is depending but we did it within three years. And I think Irza, is nothing short of concentration, passion, and discipline. Because I think, you know, I've been having cadence, a daily cadence for many, many years, as early as 8:30. You know, so that's how the so called intensity that I've worked to make sure that we be able to see the pace, we'll be able to solve whatever the issues that we face. And I think that's important, because if we wanted to make sure there's a success, I think, as a role model, as a leader, we need to understand where do we go deep? And where do we empowe? The empowerment is very important, because that allows for the organization, the team to actually start to actually discover new things, and they need to problem solve, they need to conceptualize that's very important. But it's also very important for the leader to actually provide the right steer. Because, you know, I'm saying that we're not sure of challenges, but with the right steer, we'll be able to focus was very important for the organization. Yeah, I mean, not not project is short of challenges, right. Everyone knows that.

Irza Suprapto: 22:00

Exactly. And to do it and the time that you did, I think that's something quite amazing. You mentioned earlier that culture had internal culture hack the 50,000 people at Petronas, I'm sure you've got support from both people, the management as well as the people involved in the project. But you also mentioned working with over 20 different technology stacks. And for a company like Petronas, I'm sure you have a lot of ideas around or you have a lot of legacy systems that currently exists or existed when you took on the role vs the new technology that's coming up. Every year we see not every year almost every day, we see new technology that's being enterprise technology that's being used. How do you how do you empower your team, for example, internally or yourself? How do you work with external vendors? How do you evaluate them? How do you look at what's most relevant for you to actually spend your resources on?

Datin Habsah, PETRONAS: 22:49

Okay, so I think there's two parts to this right. When we build enterprise data hub, it was totally built a new data platform. And it serves to function the data liberalisation as well as the analytics platform. But at the same time, there's many other data platforms in Petronas, a service on function where it has its own operations, you know, operation or processing related, or it could also even serve domains, a single domain specific for even for analytics to different with EDH it is intended for across analytics. So those are the two different things. Now, when we take a look at how we want to build EDH our mind is designing it, right, designing it right from the framework of data governance, that's the first thing we need to understand. And once you understand that, then we go into the technology strategy. Okay, one thing just to give you a perspective, for EDH running on dual cloud environment. In a dual cloud environment, the first thing we need to understand we need to decide which of the cloud will provide a become our central kitchen. And from that, we depict that, okay, now, because of the central kitchen, we want to make sure that the technology are native to the cloud provided, I mean, that's one of the strategy that we did. But I think at the end of the day, we also want to bring also the other technology that can bring pace, especially when we actually processing a lot of big data. Because in PETRONAS we have lots of data right and some of the data are actually comes in seconds and minutes. So we need to bring the right technology stacks and the technology stacks that we bring, we go through what we call as technology evaluations, TEV process that will be has its own processes, it gets deliberated and so forth. And once that has been decided, we work with the vendor as a partner to me. I don't see vendor has vendor i see vendor as a partner because they have to share the same success as what we envision for PETRONAS, I think that's one most important thing from a relationship building. And I think what I feel that I appreciate, and I, my appreciation to the so called my partner, is when they see the amplitude that we wanted. And then if we found any blockages, they come together with us and solve it together. You know, since it's no longer about, you know, I will solve it upon your request, or what do you want? Do you know what you want? You know, sometimes, right? We, we, maybe it's either too many options, or we didn't have many options, you know, these are the two things. So it's good to actually bring the partnership into what we call both success is a common success for both companies. So I do appreciate that that kind of relationship with my partners. I don't want to call vendor even though it's vendor, but I just feel that partner will be best suited.

Irza, AIBP: 25:58

Yeah.

Datin Habsah, PETRONAS: 25:58

Company. Yeah. For a company that share the same sentiment of success with Petronas.

Irza, AIBP: 26:03

So the shared ownership by the partners slash backdoors in your program, right, nicely put us. Yes. And let's talk about I mean, you've done a lot of work around this EDH to enterprise data hub as well as data plus, can you give us some use cases about how it's being used today within Petronas?

Datin Habsah, PETRONAS: 26:21

Okay, I think there's a lot of use cases. But I think maybe one of the contexts in us one of the one of them, where they call it our business division in the downstream it has many companies. And I think what's more important as the Senior Vice President of the business, he wanted to make sure how he gets the real data update informations on the performance of each of the companies, right. And this is where they give birth to what we call as a project we call a pivot descriptive analytics is actually is a monitoring dashboard. As simple as that because people are the first thing that we on boarded into EDH. And the reason why I wanted to take that as the use cases. So it allows for all the data comes from all the OPU. And with that, each of the OPU, the CEO, the, as well as the Senior Vice President of the business division, in this case downstream, they able to understand what's the insights, what's going to be the intervention that they need to put in. And then that allows for a more meaningful and strategic conversation right, with the so called OPUs, and the CEOs within businesses. And in fact, that allows for the report to be get faster, because it's real time. Most of the data that is actually that we ingested into the hedge the data, it gets updated every five minutes. And some of it is every 15 minutes. Right. So is, is almost that you get your real performance on the ground. But actually, you have it within a single place across all the OPUs. And that allows for understanding about the real opportunities, and be able to even understand what's going to be the potential blockers. And this allows for the values that we see even if we were to be cutting in terms of some of the performance improvement, where in just two, just doing a report where it used to take for the planners, 10 days, it has reduced to four and a half days. And then there's also the validation of 55% improvement in process efficiency. And they calculated that because of this whole process of all data is now in one place. There's the 470 man hours savings per year. And they can trust the data because we also do a data quality profiling on all the data, so you can trust the report. So I think what's more important things that maybe become a constraint and challenges in the past is no longer that because informations data, it comes in, in a single place in an almost instant manner. And you can make decisions faster. So I think that's kind of a basic, i would say a representation of many parts of Petronas that has embraced a descriptive analytics is just the first steps of looking at analytics into actions and more all of it is being powered by the enterprise data.

Irza, AIBP: 29:31

Understood, that sounds like a very interesting, you mentioned that the name of the product, so to speak, is this pivot descriptive analytics. That's what you call it internally. Okay. And looking at this, you know, you mentioned it took three years to build. And now it's being rolled out how, and obviously you have, I think, a clearer perspective about you know, the hindsight, especially when it comes to the investments. When I say investments, I don't just mean financial investments, resources plus finances the time and effort that you put into this, especially building something new like this. You know, with the value of hindsight, how do you measure the ROI of you know, this EDH build out as well as, what metrics do you use to evaluate that success?

Datin Habsah, PETRONAS: 30:14

Okay, the metrics comes in multiple facets, right. Where you look at the use cases with the give that we we give birth to all the terminal digital projects, each of the use cases has a specific, I will say, value being identified. And the fact that EDH is powering our oldest, technogy digital, the value creation has gone beyond millions actually, but I don't want to quote number, but we have the number it goes into billions. And that's from the end state, whether you call it to the businesses, but I think within the processing of the EDH we also have seen how exactly it has increases the wrench time up to 60%, as well as the process cycle efficiency improvement up to 25%. And then when we actually bring a lot more critical data elements available, and then with the big data processing, it become more efficient the way we we actually bring this data. So can you imagine where we used to prepare a 1 million records in the past that will took us one hour. And with all the rights sophisticated and just text within EDH, the one hour has now reduced to only one minute. That's how fast you can actually process a 1 million records data. Yeah, so you imagine with that velocity veracity and suppose and the value is obviously become more prominent, because you have the ability to bring and manage a big data processing. And other things that we have done in EDH, where we also have cognizant. And really exploiting the cognitive services, the cognitive services is actually, you know, the machine learning that we have trained using where the text analytics, you know, we actually be able to extract data from engineering documents, from technical reports, and so forth. So you imagine this, you know, we used to at the plant, we have what we call this document controller. So document controller, the job is managing the document, and some of this document control in the past, they were tasked to actually extract and index the engineering data and the way they do it, they do it manually. Some of them eyeballing the document, you get the data. So, in the past, they used to spend like this one particular refinery plant, they dedicated two thousand of man hours, to manner extract and index to engine data by examining 15,000 technical documents. So the 2000 man hours is to actually supervise 15,000 technical documents. So by having our AI Cognitive Services, which is machine learning that we develop, it really have changed the way we work. So imagine that the man hours have now saved to 75%, because it is no longer a manual work. In fact, some of those work that was done in the past when people took days, it is now just in minutes and seconds. So you we have living up and we are bringing the organization efficiency at many parts of the organization. Okay, I'm just saying that the journey is not completed, we are not yet the end of the destination. There's still an ongoing things. There's a lot more, I will say opportunities to do better, but we are on the right start. We are on the right track. It doesn't matter right now to further amplify, and making sure that adoptions by the user to understand right now. They can use this machine learning where they can look the document and then we'll see get the same results. So this is also the other parts of culture building that we're doing.

Irza Suprapto: 34:17

You seem to be I think you've mentioned culture quite a few times today already. Yeah, let's talk about that a little bit more in terms of you know, when you talk about data initiatives, and culture when you talk about talent, like I think that's a very big part of what you look at Datin, but how does Petronas you know prioritize and allocate resources for these initiatives, both from a monetary perspective as well as what you mentioned from a culture or people perspective, I would say?

Datin Habsah, PETRONAS: 34:46

Yeah, so actually, when we started our data governance, one of the things that we put focus and we institutionalize is the creation of the data rules in Petronas. Because it If you have a governance data governance framework, the actions for you to ensure the discharging or the execution of it, you need to have data rules, who ensures that when they look at business outcome, they also look at data outcome as the same priority. Okay, and when we created data roles we created in multifacets of our roles, we have the data owner, the executive data steward business data steward data, technical technical data, steward data analysts, and these are the people who had the highest level rank. These are like the Senior Vice President, Vice President, the CEOs of the company. And why we need to do that is because it is leadership by example, if you really want to make sure that your data culture is seen and felt that I will say advocate for change must happen from the top. Right. And then that's the first thing. So he mentioned that when we started the data roles, and we start to appoint all these personnel in their current capacity as the CEO of the company, or the vice president of that particular position. They also undergone the upskilling process of our data roles. So they converse the same thing. So if we talk about data quality, they understood about data quality, and why data quality is very important. So once we have that, then it's easy for us to bring the change within the organization. So imagine this Irza. In the early days, when we started the governance, we say that if we want to make sure that the data creates value, the first thing we need to solve our problem is, we need to make sure our data meet the right quality. So we design a data quality program. That was in 2020. And the whole execution in 2021. There was a time that we designed our data quality framework, we design our data quality metrics, we undertake a major data quality profiling. So that whole program actually cut across all businesses, when you have a data quality profiling, every single business now understand their data quality score. That's very important. Because the moment they understand their data quality score, then it becomes their accountability now to improve the score. This is not about competition on score. Actually, this is about understanding that the business is not taking action on their own. Because when we do a data quality profiling, and we do the root cause analysis, and some of the reason why we find that why we still have bad data is because the user, when they update the data, they update it wrong. So now you imagine those data quality focal the business, they interact with the user who updated those data, and actually educate that user to make sure that this will be the right way how you should update your data. So this whole cycle, and this whole internalization of data quality as a culture is become very evident in Petronas. The business are really like doing a marathon you know, they're just doing a marathon or data quality competition among themselves, which I'm happy to not because I am not doing the difficult job anymore. I did the difficult job at the point of designing at the point of training those data focal at the point of putting the right I will say ecosystem, but now is an easy job. Because as far as the focal of the data quality, they take it upon themselves to ensure that they are accountable for the data quality, they will work with the business user to make sure that is the right data is being updated. So I think culture, building data culture, you really need to have the art and the science of it. If you are going as any organization, if they really want to make a change the way they do and manage the data, they need to invest and strategize that change management right in each of the components of the data area.

Irza, AIBP: 39:35

I think it helps as well that you also the president of the Data Management Association in Malaysia, right, so you talk about best practices for data not just for Petronas. But for Malaysia as a whole and international standards wise, you make your subsidiaries your different business leaders sound like very, very change management experts. You know, it seems that there's very little

Datin Habsah, PETRONAS: 40:00

We need to advocate for change.

Irza Suprapto: 40:02

Let's talk about change you mentioned earlier as well, talking about continuous learning, you know, with new technologies coming up now, you know, you've seen all this data is the base, obviously for AI. Let's take a step back right just outside of Petronas. What what what do you think companies can do to ensure that their data strategy is, you know, if not keeping them ahead of the curve, or at least keeping them at the curve, you know, like keeping up with what changing globally, and you look at Malaysia compared to say, the world's borders are just becoming less prevalent? I guess, competition is everywhere. So what should companies do when it comes to data strategies, do you think?

Datin Habsah, PETRONAS: 40:44

Yeah, I think right now, people are just so in the hype of bringing AI to its full scale for the organization. Yeah. And it's not a secret that many organizations who fail big time on AI, and the reason why they fail is because they have never made the data ready for AI. Which means that they don't really have the practices, the fundamentals, on the data management, governance, and so forth. And I think that's the first thing that any organization needs to do before you want to put AI to a large scale, because you want to make sure that your whole ecosystem of AI, which is data dependent, is sustainable. Because once we look at many countries right now, around the world, they're putting AI act, there's a lot of AI ethics, you know, how do we make sure in terms of the fairness, trustworthiness, even the AI has to make sure that explainability so when you look about fairness, trustworthiness, how do you attain that the AI be able to address that is go back to the size and the sample, the data that you use. Is the data that actually the makeup of your AI, if you think about it, so I think that's where I think I always want to tell everyone, you know, everyone that's going on an AI journey, the first thing that they also need to do it right, and to put more focus is now on their data. And I think when you talk about data, right, which I think is going to be very overwhelming, especially if you are in a company that is so huge. And maybe, you know, the state of the data. And I think what's more important, a lot of organizations struggle with the data practices, the data management is not in a perfect world, we are really in in a storming environment. So I think what's more important, the strategy piece, where do you fix this? What do you want to fix this? So I think that's also what we can do. And you also have your AI, I would say strategy. You also coupled that with your data strategy. And I think that's more and more important in a lot of the conversation about the AI results is on your data quality. And when you look at the data quality, there's many components to it, when you look at data quality is actually the second piece of it. But in order to ensure there's a data quality, you have to manage your meta data, they must be the right data standards and so forth. And how do we make sure that you have put the right data entitlement for instance, or data security? When your AI is going to be processing personal data, for instance. How do you make sure that it is with respect and properly manage that the AI solution is trustable. And those are the whole components that we need to see from the lens of the data prior to the monetization of it on AI. So I think there is equally focus, but I would say that start with your data first. I mean, put it right on your steering wheel. And I think AI will just be at pace faster than what you can imagine.

Irza Suprapto: 44:04

That's just fundamentals right. Get the fundamentals right, and then before you build something on top of it. Thank you Datin and I think I have one last question before I let you go if you don't mind. You know, we started this conversation around, you know, the different roles you've played at Petronas, you've been through different departments. And interestingly enough, I think it was a couple of days ago that Datin you posted something on LinkedIn about packing and keep posted about what your next step will be. I think that's something we are all looking forward. But in the vein of looking forward, you know, what exactly within or what areas within data or are you most excited about what drives you? What forces you to continuously learn as well.

Datin Habsah, PETRONAS: 44:49

I think the first thing you know, I think I fall in love with data accidentally and the reason why I say accidentally is because very simple because I got to do the job. And when I got to do the job, I also have to upskill myself. So I learned about it at my started learning is from the data management dama. dama is my reference. And I think that's why I'm so passionate about it. And what I'm so passionate about is, I'm seeing what we envision as vision, we translate into strategy, putting into action, we're not seeing the impact. What I'm seeing more, I would say excited about is, is actually the results by the user, what the user testimony, you know, how it has helped them? How it can make things faster for them. I think that's what gave me really, you know, the adrenaline push, I will say, to see the impact. I will not I will say that the efforts is very challenging. I mean, you can attest that to my teams, they really are really hard working team very committed. But I think what's more important is the strategy must be right. I think that's what's more important, and having to spend five years in the data and AI think that's why I feel that it's about time that I need to move on to a different space. But even though I'm moving to a different space, data, and AI will still be in that space. It's just not doing I will say my role as the chief data officer as a full time job. I'm doing a different type of job, but it has that connected connections with the data and AI plus other things. As you know, I'll keep you guys posted what I'll be doing next.

Irza Suprapto: 46:44

I think we've had some clues from Datin herself, you know, she can't let go of what she says she accidentally fell in love with. So obviously data will be a big part of your next stage as well. Okay with that, I think Datin thank you very much for your time. I'm sorry, I think we've overrun by quite a bit. Thank you again for giving us the time to speak with you today. And for every all of our listeners, depending on when this episode is published, you should follow Datin on LinkedIn and you hopefully will be able to see what her next episode will be like. Datin Habsah Nordin thank you very much. Thank you.

Irza, AIBP: 47:28

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