Customer Roadshow

[Title: Your Business Optimised. Telstra and Telstra Purple logo.  

Hi, we’re Telstra Purple 

Oliver Camplin-Warner, Head of Telstra Purple 

Kim McIntosh, NSW Associate Delivery Manager 

Becca Duane, Executive Manager, Government, Quantium Telstra Joint Venture 

Amanda Pitcher, Principal Consultant in Design 

Prashan Jayasundera, Managing Consultant in Data 

Haroon Munif, Principal Consultant in Digital] 

Kim McIntosh: So for the next 40 minutes, we're going to talk with our new Head of Telstra Purple for 15 of those minutes, and then we're going to spend the rest of the time geeking out a little bit and really getting into the nitty-gritty of how you get your data primed for AI success. 

A lot of it's going to go probably way over your head. We're going to get very technical.  

[Slide: Data Primed for AI Success. Modern Data Platform Preparation & Techniques for Optimate Performance] 

That's totally fine. If you take anything away today, it'll be the last slide with our email addresses on it. Just take your phone out, take a picture, and we'll get going. 

But first off, I want to get to know you a little bit better. 

Raise your hand, everyone. We're going to start with our hands raised. I know, right? I'm making you think this late in the day. 

Keep your hand raised if you were not born in Australia. Oh, keep your hand raised. I know everyone's like what? If you were not born in Australia, your hand is raised. Yeah, born somewhere else, your hand is up. OK, refresh. All the hands up. 

Keep your hand raised if you're in a technical role at your place of work. Alright. Alright. 

So I'll send it out. Last question. We had like half up. Last question. Everyone raise their hand. Keep your hand up if you can hand on heart say your entire organization is aligned. That's what I thought. That's what I thought. We're going to tap on that later. We're gonna tap on that later. 

But first, we're going to bring Oliver up to the stage. We're going to get to know Ollie a little bit better. 

Oliver Camplin-Warner: I haven't done anything yet.  

Kim McIntosh: I know he's only been here for two months and two months in this room. Two months into the role, in one word, tell me how are you feeling right now?  

Oliver Camplin-Warner: Pumped.  

Kim McIntosh: Pumped. That's a good word. That's a good word. 

We're going to ask Ollie a few questions, humanize him a little bit, not get too technical with about eight questions if you've got time for that. 

Oliver Camplin-Warner: Sure. Can you give me the questions ahead of time next time? 

Kim McIntosh: No. I've given him a few and then I changed them all. But that's fine. 

Hey, Roger. How are you doing? Welcome.  

Oliver Camplin-Warner: Welcome. Are you just going to embarrass everyone who's late.  

Kim McIntosh: I am. I am. I'd say there's a seat right here. There's a seat right here. 

First question. 

Oliver Camplin-Warner: Sure. 

Kim McIntosh: What's your favorite sports team? 

Oliver Camplin-Warner: The mighty, this is where I lose the entire audience, the mighty Brentford FC. Anyone? Yeah, hello! So for those of you who don't know the mighty Brentford FC, they're a really, really bad soccer football team in London. Like, really bad. I've supported them all my life and somehow we managed to fluke it and get into the big boys league. We're now playing like Man U, Chelsea and we are winning. It's amazing. So, that's great. 

Kim McIntosh: What was your first job? Your first paid job?  

Oliver Camplin-Warner: Tough. That's what it was. A telemarketer. Hi, Kim. This is Oliver. Would you like to do a survey on McDonald's?  

Kim McIntosh: At dinnertime? 5:00?  

Oliver Camplin-Warner: And then you'd hang up. And then the rule was if the customer hung up, you had to phone them back. I'm like what? It's like, no, no, they might have just lost connectivity. They might have been with Optus. Like you have to... I didn't say that. I didn't say that. You might have to phone them back just to check. Hey, you didn't want to do the survey? OK, bye! Yes, you did. You did. 

Kim McIntosh: iOS or Android?  

Oliver Camplin-Warner: Might get a few boos. Sorry. Android.  

Kim McIntosh: Who's Android? Who's iOS? Oh, alright.  

Oliver Camplin-Warner: I had a presentation for the grads the other day. Super cool. And I showed them the advert that I saw. I was a grad at IBM, and I showed them the ad that I applied to, it was in like the Financial Times in London, and it had a picture of a PalmPilot on it. I remember seeing that as a grad like this is 25 years ago going, I just want a PalmPilot, like, so cool. OK. You still got one. 

Kim McIntosh: Next question. 

Yeah. AWS or Microsoft. 

Oliver Camplin-Warner: Cannot. 

It's like saying Oliver, Florence, or Felix which is your favorite child? Clearly, it's Florence. I can never, ever say that. Like, it's not a joke. Can't say but I love them both dearly.  

Kim McIntosh: There you go. Good answer. The audience doesn't want to answer that either, or do we? No, I won't make you do it. OK. 

Now, on the theme of Telstra Purple, that's why we're all in this room. This is getting down to the gritty. It's very easy. What inspired you to take your role in Telstra Purple? 

Oliver Camplin-Warner: Yeah, cool. 

So for the last five years, I've been running Telstra International out of Hong Kong and Singapore, which is one of our best-kept secrets. We are in 31 countries around the world with the largest provider of internet connectivity in Asia. It's just this amazing part of the business. But I got a phone call from our new boss, Vicky, and I don't know if you've met Vicky, heard from Vicky, like she's a God. She's so good. And she started talking to me about Purple. And basically, what she said is there's just this incredible opportunity. Every customer I'm speaking to, it's just... And we've got Purple, which is great, and it's doing incredible things today, but it's kind of this big. And moving forward, I think it could be that big. We have today 2,500 people, some of whom you're going to see later today and they are amazing.  

They do incredible things day in and day out. Then you look at kind of what they're doing, so that whole purpose piece, the impact they are having, you put those two things together, it's a thing of beauty. I don't know if you know where Purple the name came from, but it's a mixture of people and purpose. And we didn't pay a marketer 7,000 million billion dollars to come up with that, one of our team came up with it as well. So, I just love the opportunity here. It's such a strategically important part of the business for us and as a team, we're going to have a lot of fun moving forward and for me, I think Australia needs us. There is just that bigger opportunity.  

Kim McIntosh: Thank you. OK, we'll keep you. Keep you. Thanks. 

How would you, and we get this question a lot, how would you characterize the relationship between Telstra and Telstra Purple and how does that relationship play out for our customers?  

Oliver Camplin-Warner: Yeah, it's a cool question. I've got quite a bit of feedback on this both internally and externally. I think we've confused our customers a little bit. They're kind of like what is this Purple thing? Like are you part of Telstra or are you not? You kind of seem to be side by side but not always. We are part of Telstra, right? We are part of Telstra, we're fully owned by Telstra. And for me, there's just a magical opportunity to leverage what Telstra has been able to achieve over many, many recent decades, right? You look at the connectivity journey that we've been on, the way we support customers around the country today, like we've got an opportunity with Purple to work with the connectivity teams, with the other parts, but we've probably lost our way a little bit on that. So yeah, it's one of the areas I need to lean into with the team, with yourself, just on how do we kind of tell that story a little bit better.  

OK.  

Kim McIntosh: Thank you. Two more questions. 

Oliver Camplin-Warner: Sure. So we need to get to that. They're the really good ones. 

Kim McIntosh: I know. They're amazing people. We're so close. 

Two months into the role, you've had some time to look outside the market. Look inside? What is your unique view on Telstra Purple's placement in the market and how does that best serve our customers? 

Oliver Camplin-Warner: Yeah, I think the really unique parts. 

So one, the one-stop shop. Like there is a lot, and I'm not sitting here today and telling you like all the various parts and kind of deep diving into it. You'll see some of the different parts up on stage later. 

But whether it be cyber, whether it be IoT, whether it be cloud, we are deep in all of those areas so, you know, sometimes you have to go to a variety of different organizations to get what you need. 

It's cheesy but it's a one-stop shop. I think the second piece which is coming out really loud, from not only the government but from enterprise as well, is sovereign. 

So, we're Australian and bloody proud of that, and we're a trusted sovereign player. So, that's something which I think sets us apart a little from some. I think then the other thing is just the people again, it's back to the people and just the talent that we have. 

We've been on a bit of an acquisition path in recent years, so we've got about ten acquisitions we've made already. We've got some other ones coming online pretty soon. But we've got some amazing people, incredible skills. And again, I think that sets us apart. 

So, for those of you that you work with people, I thank you. For those of you that don't, give us a go because, yeah, you'll hear from some of the team a little later. 

Kim McIntosh: You will. You will. 

Last question. 

Oliver Camplin-Warner: Sure. 

Kim McIntosh: It's a fun one. 

Oliver Camplin-Warner: OK. 

Kim McIntosh: Blue sky thinking. 

Oliver Camplin-Warner: Slightly scared. 

Kim McIntosh: OK. 

Kim McIntosh: If you could build your dream home, dream home, doesn't need to be on this planet, what are two things you would need to have in it? 

He doesn't know I'm asking this question so it might take a minute. 

Oliver Camplin-Warner: Well, it's funny because I live in Singapore, as I said earlier, I don't know if any of you have lived there, but you live in like tiny little apartments, like really small little apartments and that's been cool. 

Like we've loved it, but I'm looking forward to moving. We're in the process of moving back to Australia, moving to Melbourne and space. Not like up in space but I actually just want space. 

I've got two little kids, you know, my favourite, but I want the space for them to like run around in. And then the other thing is just the family space as well. So an area where we could come together and just spend... I've spent a lot of time on the road in the international gig and actually just reconnecting as a family would be probably my two. 

Kim McIntosh: Good answers. 

That's a really good segue. Thank you for giving a good answer for that. 

Thank you for your time. We're going to let you sit down now and we're going to introduce... 

Oliver Camplin-Warner: Thank you. 

Kim McIntosh: Thank you, Ollie. 

We're going to introduce our speakers in just a minute, but I want to wrap that up and explain why I asked that question, why we're here today, actually. 

[Slide titles, Why are we here today?] 

So, we hijacked the title of our session today and we called it 'Getting Your Data Primed for AI Success'. 

And in the market, there's all these buzzwords, you know, AI and ML and what do we do with that? How do we get there? Intelligence that is built artificially, AI. When we break it down, machine learning, learning built through a machine. How do you get set up to actually hit play and use those tools? And that's what we're going to talk about today. 

So the reason I asked Oliver what he would want in his dream home. 

There's a lot of work that goes behind the scenes to get to that stage of having a home and having those features. You need to have a mortgage broker. You need to have insurance. You need to have an architect. You need to have a builder. You need to have a plumber, an interior designer. All of those people need to come together before he can enjoy those two spaces with his family. 

Not only do you have to have all those people in one room, they need to be aligned. They need to know what they're building, who they're building it for. Ollie is using that space, it needs to be right for him and his family and his kids. They can't build it for anyone else. 

And then you also need to know where you're getting all of the tools that you need to build that house so that he can, at the end of the day, have the outdoor space for his family and use that one common space in his house with his family. That's the purpose. He's the user. We need to build the house for that. 

We're using that analogy to talk about AI today. So what is that whole data space, that data house, look like so you can have fun with this AI ML space that is moving so fast? And we're really going to focus on the how today. 

So I'm going to be shutting myself up very soon. 

I'm going to introduce our speakers. We have the troops here today, four speakers. 

[Slide showing, what we’ll cover] 

First, we have Becca who is from the new Quantium Telstra Joint Venture. She's the executive manager in the government space. She's going to tell us three lessons on how you can get alignment in your business to get on the same path to commit yourself to this journey. 

Second, we're going to hear from Amanda Pitcher. She's our principal consultant, primary consultant too, in the design space at Telstra Purple. She's going to go deep into human-centred design, and that's really focusing on how you find the value that you're going to bring to the business and also ensure the technology you bring in is the technology that the users are going to use. If you bring in technology users aren't going to use, you're going to waste it. Becca's got a good stat on that later on. 

Last, we're going to hear from Haroon and Prashan on the technical side. They're going to go very deep. If your eyes go rolling in the back of your head, that's fine. That's why Telstra Purple exists so we can help you out with that stuff, but they're going to dive deep on the building blocks and how you build that house so you can switch on that light switch for AI and ML. 

We cool? Are you ready to move on? Sweet. Becca, over to you.  

Thank you. 

[Slide titled, Becca Duane. A mindset for AI Success.] 

Becca Duane: Thank you and good afternoon, everyone. 

So there's this good saying in football and it goes and it goes winning is not everything, winning is the only thing. 

AI is winning but just not for everyone. 

According to Gartner, 85% of AI projects don't meet expectations. 

What can be done to get the best? 

From our experience, a lot of this matches up with the mindset, and this is what I want to talk about today. 

Three Mindsets for AI success: Alignment, Engagement, and Continuous Improvement. 

So the first mindset is on alignment. 

To make AI work for your organization, you need to make sure that you rally up your team and those who are invested in their success. 

Building new capabilities is very challenging, and you need to have a long-term vision. 

Measuring those benefits for success and linking that to your organization's vision. 

And a good way to do that is to focus on value and be specific. 

So, for example, instead of just saying we think this chatbot will increase customer satisfaction, actually, you've got to do a lot of groundwork, and you can use analysis to be more specific. 

Like this chatbot will increase customer satisfaction scores by 10% because it will reduce wait time by 20%. 

And so that's on alignment. 

The second mindset is on engagement. 

And here we're talking about engagement with the user as well as engagement with the experts. 

And it's important to do that along the way from the very beginning, from brainstorming, identifying the solutions, all the way through to delivery. 

And I know that that's very easy said than done but, again, not a lot of this happened in practice. 

And so to bring Kim's analogy back, it's like when you're building a house; if you build tech without the user, you're building a house without knowing who's going to live in it. And if you do it without the experts, it's like building a house with no architectural plan, both of which you want to avoid. 

So remember, engage the experts, engage the users, and you have this beautiful house at the end. 

So you'll hear from Amanda later on human-centered design more and also from Haroon and Prashan on some of the technical advice. 

So the third mindset here is on continuous improvement. 

So the reason why this is particularly important, as Ollie mentioned before, is that actually AI is moving very fast. 

So what we're seeing on the ground is that actually, it's very hard to keep up with this, and so a good way to do that is by breaking down your biggest strategic goals into smaller milestones, putting that into the roadmap and sharing that and communicating that across the board. 

Again, I know this sounds very simple, but a lot of the organizations that we work with actually find this very, very challenging. 

And that's on the people side of things. 

On the process side of things, sorry, that was on the process side of things, on the people side of things, another thing to remember is just make sure that you do celebrate those milestones because it is these smaller wins that actually help fuel the positive momentum. 

So there we have it. 

So three mindsets for success: alignment, engagement, and continuous improvement. 

Now over the next minute, I'm going to throw a whole bunch of resources at you, and feel free to take note of them and explore them at your own pace. 

[Slide showing, Lora Linsey’s 4 pillars of value framework.] 

The first one I really like is called the Four Pillars of Value Framework. It's particularly good for pinpointing what kind of value you want to be driving and also what kind of narrative you want to be bringing to your senior sponsors. 

[Slide showing, The double diamond approach.] 

Next, we have the double diamond framework, which Amanda will go into more detail, but it's a bit of a go-to for user design and also problem-solving. 

[Slide showing, Quantium framework on GenAI: When to license vs build bespoke?] 

This is the Quantium framework for Gen AI. That one's good to keep handy if you're interested in GenAI solutions to know when to license versus build bespoke. 

[Slide showing, A clear and simple roadmap] 

And lastly, here's a little neat template to help visualize and break down those project and capability milestones. 

So there you have it, three mindsets for AI success: alignment, engagement, and continuous improvement. 

[Quote showing on screen] 

And so I want to finish this off with an inspiring quote for everyone. 

"Continuous improvement is better than delayed perfection. Focus on delivering value and driving organization adoption, and let the journey toward excellence be ongoing.” 

So these final words, everyone, are the wise words of ChatGPT. 

Thank you. 

[Slide titled, Amanda Pitcher. Human Centred Design: What, Why, How] 

Amanda Pitcher: Hi, everyone. 

My name is Amanda, in case you missed it at the beginning, and I'll be talking about human-centred design, what, why, and how. 

So first of all, what is human-centred design? 

Design thinking, lean UX, UX, CX, service design, all of these fall under the human-centred design umbrella. 

[Slide showing, What is Human Centred Design?] 

How do you avoid becoming a statistic, one of those 85% of companies who have implemented an AI solution that has failed? 

Hint, human-centred design. 

Human-centred design is the way to remove risk from the implementation of any solution, technology, AI, or otherwise. 

It's essentially a way of going from point A, where you don't know what the right solution is, to point B where you do know and you've implemented it. 

And as Becca introduced, we like to refer to the double diamond methodology because it's pretty straightforward. 

It's got four phases of activity all starting with a D, discover, define, develop, and deliver. 

And there's a reason for the diamond pictures because in the phases we go wide of the opportunity or problem space in the discover phase, we do research starting with people, their context, their process, and their culture. 

Culture includes things like compliance, legislative considerations, security, and your business culture that you need to consider. 

In the define phase, we start to narrow in on the problems that need to be solved and how we can deliver value for the organization and the existing technical landscape. 

So we're focusing on what we need to do to deliver success and deliver value. Then we're going wide again in the develop phase where we're coming up with ideas. So we're looking at all the different technologies bouncing around different solutions before we identify the right one and we deliver it in the right way for your organisation and ultimately delivering success. 

[Slide showing, Value is about balance] 

Becca touched on the importance of business alignment, so making sure all the stakeholders in the business are aligned to the purpose for doing something. In human-centred design, we like to talk about alignment in three different layers which is desirability, viability, and feasibility. This is a Venn diagram you see everywhere. Desirability is what the humans need and want for a solution to represent value for them. Viability is what your organisation needs for a solution to be valuable and deliver success and align to your broader strategies. Feasibility is the tech lens. So, a solution may be technically wonderful, but if it requires you to re-platform all of your products and systems, it may not be viable for your organisation unless it's already in your strategic roadmap. So we like to look at what's your existing tech landscape and make sure we're choosing the right technology solution. 

Why do we do human-centred design? Why do we start with the people? As Oliver mentioned, at Purple, people are our purpose. But guess what? They're everybody's purpose because without people using your products and services, you don't have a business. So, it's people who are going to use your data and AI solutions, so why wouldn't we start with them? Those 85% of companies who failed in their AI implementations, they may have had a technically wonderful solution but it may not have aligned to the people who are going to use it. Tech solutions fail because they are not aligned to the needs of the users and they're not delivering it in the right way, they're not delivering value to the people whose hands it's going to be in. And people are great at avoiding using tech solutions that you just drop on them. We're very ingenious in going, "I'm going to find a workaround for this." And that workaround is introducing risk to your business, risk in data handling, manual data entry, sending Excel files via email, I don't know. So doesn't it make sense that we would start with the people who are going to use the solution? 

A lot of the time, what organisations have in terms of a picture of their users or their people, so a user could be your staff or your customers, but it looks a bit like on the left. So you've got a bit of a shake with them, you know their want, what they do, where they are, when they use things. You don't know their why. So in human-centred design at Purple, we do activities to build this fuller picture and we know about the gentleman with the glasses and the mustache who likes hiking, painting, karaoke, and the sunshine. 

[Slide showing, Defining problems, designing solutions.] 

Problem-solution fit is what we're ultimately trying to achieve in any solution we're implementing here at Telstra Purple. So for success, we need to understand the needs of the user and identify problems worth solving. We need to understand their current way of working and align to their behaviors. So what that means is we're not going to recommend a desktop solution for people who are mobile field workers using a phone or an iPad. We need to understand their context. We call this problem-solution fit and when you fail to achieve problem-solution fit, you end up with a technically wonderful solution that isn't solving a problem and has low adoption and ultimately fails. The other risk you introduce here is, as I mentioned, the risk of wasting time, the risk of wasting money, and the risk of low adoption and people not following process. And when you have that splintering effect, you can't control what's happening and, you know, we're not sure what happens there but, you know, data breaches, data hacks, all these types of thing. 

[Slide showing, Lifecycle of an idea] 

Finally, we're moving on to, now we're finally, how we use human-centred design. So I've talked about the double diamond which is a kind of methodology that we move through four different phases. This is what we call the lifecycle of an idea. So organisations come to us with an idea of something they need or want to do, and maybe sometimes they know the solution they want to implement. And this is how we identify what type of activities we're going to do with them. So, on the left is the infinite universe where every possibility is viable and feasible, but there's increased risk of following the wrong pathway and doing the wrong thing. And the activities that we do move you from left to right where the risk is reduced and we start to focus in on things worth listening to, we make sense of those things and we come up with ideas. 

Critically, we test those ideas to make sure that they are actually going to deliver success, deliver business value, align to the needs of the user, and are technically feasible. 

Then we develop MVP. So who has heard of an MVP? Yes, no, minimum viable product? We like to refer to it as Most Valuable Product because it's the first product that you're releasing, and it's your first chance to build that adoption and advocacy. And if you're releasing a product that doesn't align to your users, isn't delivering value, guess what? They're not going to use it, and you're not going to then be able to iterate out and build a bigger product. Product or solution, we use those terms interchangeably. And Becca mentioned continuous improvement, and I like to call that death and rebirth. Ultimately, everything ends in death if you are not continuously improving it. Why? It becomes redundant; other products come along that are better and do the same things in a better way, have more features and functions. So you want to be continually rebirthing your product, continuously improving it. 

There's a lot of information here, and we're happy to answer questions at the end, but for now, I'm going to handover to Haroon and Prashan who are going to talk about how to be AI-ready in your organisation. 

[Slide titled, Harron Munif + Prashan Jayasundera. How to be ‘AI’ Ready.] 

Harron Munif: Thanks, Amanda. Mic. Alright. Next slide. 

How to be AI ready. Now, we've heard Becca and Amanda mention technology as one of the key enablers or factors of innovation to make AI a success. And from a technology perspective, there's a broad range of platforms, services, and tools that are required to make AI a success. But where do you get started? What are the foundational pieces or the building blocks to make AI a success? 

[Slide text showing, In the era of rapid digital transformation, a modern data platform stands as a pivotal foundation driving organisational success. It empowers businesses to harness the full potential of their data, accelerating innovation, informed decision-making, and enhanced customer experiences.] 

Now, as we are talking to customers, the customers we work with every day, we hear a common theme that a modern data platform is seen as critical infrastructure for breaking down data silos, accelerating decision-making, as well as setting up for AI success. Now, I'm going to start with talking about some key features and benefits of a modern data platform, and then I'll hand over to Prashan to talk through some sample modern data platform architectures. 

Let's get straight into it.  

[Slide showing, Key features of a modern data platform] 

Now, as I mentioned, we work with a lot of customers to help them build their modern data platform, and these are some of the key areas that we help our customers in doing that. Starting with data integration. Most organisations have a diverse range of structured and unstructured data sources. A modern data platform helps bring that together, so break down data silos, and provides a holistic view of organisational-wide data. 

Looking at advanced analytics, some use cases require advanced analytics type of work, and a modern data platform provides the right tools and services to be able to do that, which normally is really difficult to do, especially with legacy systems. 

Talking about real-time processing, some use cases within organisations need near real-time data, and this is especially true for solutions like IoT solutions as an example. And this is where a modern data platform provides you with data ingestion pipelines from streaming data sources. 

Looking at artificial intelligence and machine learning, we all know that machine learning, especially to train and build models, requires tons of data, lots and lots of data, and these are then used for your AI use cases. So, a modern data platform is basically that source of data which feeds the machine learning and AI use cases. 

Now, looking at scalability as an example, as demand for data in the form of storage and compute increases, a modern data platform is able to scale to meet that demand within your organisation. 

Security and compliance, very common. We have heard other speakers talk about it as well. Almost every organisation, if not all, have some sort of security and compliance guidelines. These, with a modern data platform, are easily configurable and implemented because of tools that are available and standards that the platforms adhere to. 

Last but not least, looking at cloud-native architecture. Now, a modern data platform designed for the design on cloud-native infrastructure is, as we all know, is able to benefit from cloud infrastructure. So all the benefits that you see from cloud infrastructure is basically inherited by a modern data platform as well. 

Now, as I mentioned at the beginning, these are the types of features that we are helping our customers with to build their modern data platform. 

[Slide showing, Platform architecture] 

What benefits is that providing to our customers? When talking to customers, these are some of the words that we hear. Agility, being able to make rapid decisions, quick decisions. Innovation, being able to experiment with that huge amount of data to create new products or enhance existing products and services. Customer centricity, understanding the customer to create personalised services or products. Efficiency, very obvious benefit. And last but not least, strategic decision-making, having a holistic view of the organisation with the wealth of data enables executives to make quick and right decisions. 

And this, again, helps with the key theme of having the data and being able to accelerate with everything that you do in your business. 

[Slide showing, Our Customer. Leading energy provider] 

Now, before I hand it to Prashan, I just wanted to highlight some work that we did with a leading energy provider, again, in the modern data platform space. Exactly to the topic that I was just talking about, customer problem was ageing infrastructure, legacy infrastructure, data silos, data in different systems. And as a result of that, generating reports, gathering information, you know, taking weeks or months. And based on the solution that we worked with and helped the customer build in terms of a modern data platform, the customer was, in the end, able to generate reports in minutes instead of weeks or months. And that's where the value comes in, is being able to get to the right information quickly so that your business is an agile business and able to make quick decisions. 

OK, enough from me. Prashan, why don't you take us through some common architecture samples? 

Prashan Jayasundera: Yes, thanks, Haroon. Before I start my slide, next slide. 

Before I start my slide, I'll be like Kim for one second. Raise your hand, everyone. Keep raising your hand if you're scared about this slide.  

[Slide showing, Modern Platform Architecture – Azure] 

Anyone? OK. A lot of people are scared about this slide, so I will go through this slide with the analogy that Kim brought in earlier about building a house. 

If you're considering building a house, you need to consider having a proper architecture. This is a sample architecture that we are specialized in building. This is in Microsoft data platform. However, we are building on other data platforms like AWS, Snowflake, and Databricks. 

So, this modern data platform architecture, it really scares me as well. We're looking at that; it has several layers. You have data source layer, ingestion layer, process layer, enrich layer, store layer, and serve layer. 

However, if you're building a house, you might go through different processes as well, right? So, data source layer is your material layers, like you are finding materials to build the house. And without data sources, the data warehouse or the modern data platform cannot exist. 

With the data sources, you can have a streaming data sources like real-time streaming data that is coming through the streaming sources like data streaming and IoT devices. You can have unstructured files like audio files, image files, and video files, and semi-structured files, CSV, JSON, whatever the flat files. And the relational database, these are structured files like application database and some other database that you want to integrate into the modern data platform. And also some other Azure data services or some other services that you want to integrate. 

So how you integrate it with the ingestion layer? In this ingestion layer, we are talking about like tradies working in the house. Tradies will get all the materials and build the house. Likewise, these ingestion tools get all these materials and build the data platform or the data warehouse. 

So, the real-time data that is coming through the event hubs and IoT hubs and all other data sources can be ingested through the pipelines. That will be ingested into the specific tool called Data Lake first. Once you ingest it within the data lake, it will be transformed and load into the different kinds of pools in Azure, so in the Microsoft Azure has. 

After that, that is when you build the house however you might need to add some features like security cameras, some other features like landscaping, I don't know. Like, you know, you might need to find out some other features that you modernize your house. 

So with that, the features that Kim was talking about earlier is in the data platform, we need to identify how we can have AI features and ML features or the machine learning features and artificial intelligence features. With the data, when we build the data platform, we have the cognitive services which enable the AI models to consume data from the data platform as well as Azure Machine Learning Services will enable the machine learning models to read the data from the data platform. Other than that, the most important part is the Azure cognitive search. That is in preview at this stage with Microsoft, however, that will enable you to build AI applications such as ChatGPT within your organization. With that, you can consume your data to enable the ChatGPT functionality within your organization. 

Let's move into the next slide. 

[Slide showing, Modern Platform Architecture – Azure Databricks] 

I think let's quickly jump through the next slides because we are almost out of time. So let's very quickly touch on this for a few seconds. 

Yes, sure. 

So this is like a mansion when you are comparing with the other data architecture because if you have multiple data sources, then you can consider about this architecture, this is Databricks architecture. And however, the key difference is this data has stayed within the lake. I don't think that you are building a house within the lake, but here this architecture builds a house within a lake. Why is that? Please reach out to me, and then I will explain why the benefits of having Azure Databricks or the Databricks architecture compared to the other architecture. 

I will hand over to Kim so then she will wrap it up with this presentation. Thanks, everyone. 

There you go. 

Kim McIntosh: Thank you. 

I'll skip to the most important slide. 

If you missed anything today, take a picture, email our very intelligent consultants and they can dive deep into any of those topics, mindset, human-centered design, and the actual architecture that it takes to build a modern data platform. 

I know there's a keynote you need to go to. We've got about 60 seconds if anyone has any critical questions, we can answer it. Otherwise, grab those emails, get in touch, and thank you so much for attending our session. 

Oliver Camplin-Warner: Maybe I'll just wrap it up by saying a huge thank you to the speakers. And yeah, there's so much in Purple. There is so much here. We tried to give you just a little bit of a taste of what we have to offer and how we can help, but yeah, reach out if you'd love to hear more. I'm on LinkedIn. We're on LinkedIn. My details are all there as well, so thank you for your time.  

Upstairs we can have a bit of fun. So the closing session of the day, David's going to lead us through that. And then a bit of fun with a couple of external speakers.  

Thank you, Kim. 

Thank you, everyone 

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