Still on the housekeeping section, I've seen a few hands being raised along the way. Is I don't think there's any way that we can open up the mic for you. So as Vicky says, if you've got a question or a comment on it, just just pop a quick message into the chat or the q and a section, and we will try and, get back to it. Vicky, if you're okay to monitor the chat a little bit and, interrupt me if there's anything that's kind of pressing and needs needs to be addressed. Otherwise, I'll get to things at the end. Very good. Right. Well, Vicki gave you a quick introduction, but, I will do the the the full one. My name is James Austin. I am the services director for InterWorks EMEA. I started out with a degree in maths and computer science, but I really got interested in data and BI through running my own business. It was a business repairing games consoles on mass, and there are thousands and thousands flowing through the factory. And I got super interested in the sort of management reporting, tracking parts, and returns, and repair success rates, and things. And, that passion led me to become an analytics and data consultant back in twenty twelve, and, I've been delivering, running big, small projects, data related mostly, but, projects ever since both for clients and internally within InterWorks itself. So I come with a little bit of experience. Some of you on the call may have more than that, but hopefully, the, the data perspective will be, new, and, you'll be able to take something valuable from this webinar. Today, we are gonna be discussing project management secrets for success, and this is part of the, Mastering Data Initiative series. There are others that can be found on our website. Today, we have five topics that I'd like to share with you, and, then there's gonna be some time for q and a at the end. So firstly, there's some interesting trends in project may project management related data in general, and, I'll walk you through some of those. I'll share how we here at InterWorks approach project management, a little bit about how we report on project data, some of the tools that we like to use. I'll then take a few minutes to share some of the learnings, being an effective program manager, and wrap up with some practical things we're seeing in the AI space, so anything relative to data management. A lot to get through. I will try to keep us on pace, but, I have the lovely Vicky keeping me on track if, if I'm running too long. So project management trends. And as technology evolves, so does the project management framework. And, you know, sometimes to look at trends and to look forwards and see what's happening, it's often helpful to look backwards first. So let's take a quick look and see how we got here. Full disclosure, this is a revamped version of a slide from a PMI article published last year by Shane R and Dov De Beer. I found it interesting to see this progress. Hopefully, you will too. The timeline highlights the evolution of some of the bigger updates in the PM framework. So all the way back in the sixties, companies began to understand the value of organizing and coordinating work. And then through the seventies, into the eighties and nineties, we added more process areas to the framework. We increased practices related to teamwork or risk management, and developed orchestrations to handle multiple projects. But I think some of the most exciting, and feel free to disagree, but, I think some of the most exciting developments came since the two thousands. We realized that one size doesn't fit all, and we started using an agile methodology. We learned to work remotely, obviously, during COVID, but also benefited from global companies that have an offshore model and some of the learnings that, that were taken away from that. And we emphasized that developing a strategy for products was super important. Organizations realized it's not just enough to get the job done or within time or on budget, and it's actually the outcomes have to be tied to a strategy, a business goal. For example, maybe we are looking at increasing our market growth or we're establishing a data observability capability, or we're improving data literacy. And the good news for professionals like us is that the demand for structure, for project management, and, you know, therefore project and program managers is massively trending up. Couple of couple of sort of general stats for you, but, PMI forecasts that we need twenty five million PMs by twenty thirty. And just in case you wanted to know, according to PwC study of the UK, which was commissioned by APM, there are roughly two point three million full time equivalent PMs currently employed in the UK. So over two million out of, you know, twenty five, it's a big slice and gives us a really good indication of how big the UK services sector is. According to Mordor Intelligence, it's a brilliant name for a company, the PM software market is expected to grow by sixty six percent up to twelve billion in that sort of same time. And according to PMI, sixty two percent of project management professionals work remotely as of last year. McKinsey reports that ninety percent of companies have embraced a hybrid work model related to project management. And, you know, it's a lot of stats. Apologies. But, all of this is validation that project management is here to stay. And, I mean, personally, I look at this and wonder what trends resonate the most. And, you know, I'm seeing that projects are more complex. They tend to include multiple work streams. So for example, data modernization might include strategy and data and analytics and integration, some change management, some training, some support. There are lots and lots of facets to a modern project. Secondly, everyone is asking for a project construct. No matter how small the company, I think the value of project setup is well understood these days. And here at Interworks, we often find ourselves being an extension of the client's PMO and COE, COE, Centre of Excellence. You know, bringing the analytics expertise, accelerating those objectives, and, becoming part of their team as much as bringing our own methodology into it. So a couple of trends that we can relate to in the data space. We talked about number one already, but Forbes says this is a newer trend. Personally, I think it's been around for a while. It's focusing on the bigger goal of maximizing value, so not just about the project delivery. Now agile for the entire portfolio means reacting to changing business goals and making that iterative process. Number two here, I completely agree. The shift is driven by the complexity of the project. We always value technical skills, and there is a strong realization that interpersonal skills or emotional intelligence are equally important today. Remote work will obviously stay. It's likely gonna grow as well. And combining I think we have to combine that traditional and adaptive method to provide the, you know, good balance for structure and flexibility as projects do get more complex. And the other point around this is finding the right collaborative tools that make asynchronous work effective in your organization. On the last bullet point, there is always automation that you can apply to project management. An example is, you know, getting automated burn reports. I'll show one later, for internal discussions or client status calls. According to the World Economic Forum, seventy five percent of survey companies are looking to adopt big data, cloud computing, and AI technology over the next five years. So, you know, that was over over that was last year the the survey was done. But so over the next four years, we're gonna be seeing seventy five percent of of companies moving in that direction. There is a lot of ground to cover for us. I've talked for a little while, so rather than let you guys sit quietly in the background hiding behind the Zoom screen, I'd like to invite you to give your opinion. I'll do this a couple of times throughout this, webinar. So I've got a quick poll for you, if I may, just to give Vicky a a a start point. I'm here. Launch the poll. Can I move the screen as well or is that locked? There we go. I'll put it up on screen as well. You guys are firing into this, but, I'll give you a few moments. Yeah. Your participation is always welcome. It certainly gives us a little bit of a nod which way to move the webinar towards. So it's quite interesting, to see that there is no clear winner here. I will share the results with you shortly. Just gonna give everybody the opportunity to participate. We tend to see it's slowing down a little bit. So we're nearly there. So if you do want to hit that button and let us know which trend you can most relate to, that would be great. Hey. Thanks, Vicks. It doesn't order it for me, so I'm gonna have to figure this one out. But, which trend could you most relate to? Agile, looks like it's just got the top there at thirty three percent. Then remote and outsourced teams being a a heavy trend, then all of the above, at twenty six percent, and having an SME as a PM only down there at ten percent, and then there there was there was another as well. But, that idea of remote outsource teams, agile, or maybe a combination of all of those, if you're seeing it, we are seeing it too. Honestly, it's a it's a common one. Great. Thank you very much for that. Appreciate it. We are going to move on to our second little section, which is around data use cases. So these are a couple of use cases that we see, Interworks here. And, I'm gonna talk about, you know, just how we approach that project management, and, we use the sort of case studies to, try and walk our way through that. Case study number one, a strategy development. So we mentioned a couple of times a theme around aligning your projects to a strategic goal. And but, you know, what if you're still trying to figure out what projects meet your data strategy goals or, you know, what can you put in place to get to those strategy goals? Let's say your chief data officer comes to you and says, I need a roadmap for a modern data stack so that we can start leveraging AI. Great. But where do you take that? And, our approach here is to essentially structure a management consulting engagement rather than a data one. The objective is to develop a strategy and create that road map from the client or stakeholder vision. And this can be done with an assessment of current capabilities, looking at the current landscape, and then using that with the end goal to develop a multi quarter, maybe even multi year implementation plan. And then we're providing, you know, key inputs such as, ROM or staffing requirements. For those of you not familiar, ROM is rough border of magnitude. SWAG is scientific wild ass guess. Yeah. I like those. This is typically how we describe the initial scope to our clients, and note that these are building the inputs for creating a project charter and a project management plan. So that is the whole purpose of the engagement. We would describe it with an overview section. Now this is what the engagement will entail. There's descriptions of what to expect, the approach, the milestones that are likely to come about. And then finally, down in the bottom right, we've got the outcomes or deliverables from the project. In terms of those deliverables, the output is likely to be a brief. It's a slide pack, thirty to sixty slides. Descriptions of both the current state and future state of, that that vision. We will build a road map and recommendations for those vision elements. There'll be an implementation plan, both priority, level of effort, staffing. There's gonna be architectural diagrams. There's There's gonna be lots involved in that. But ultimately, it's a it's a readout. In terms of building those deliverables, we have put together a sort of a notional timeline. And I'll say that the scope is somewhat bound to eight weeks. Works well when you're setting expectations during the kickoff call to make sure that that, time period is is very fairly compact. And interesting on the left hand side to notice that mix of both traditional and agile methodologies. I think that kind of, scheme in terms of the time frame works well. And then if you are the project manager in something like this, there's a quick cheat sheet to, kind of keep you keep you on track. For an engagement like this, you're likely to be dealing with all levels, all the way from the c suite down to an analyst. And at the start, you're gonna expect to play a lot of Tetris with your calendar trying to get all the stakeholder interviews in play and, and make sure that you get that initial input. Lots of upfront structure and then lots of inputs with the remote participants. There's some change management tactics that's gonna help here creating, you know, a stakeholder assessment matrix and then identifying the groups of change agents and resistant groups to, to to to identify which which you're gonna have to work with where. Group facilitation. It's an interesting tactic to bring everybody or as many people into the same session at once so that everybody hears the same thing. And you'll find that people often correct each other during the sessions, you know, throwing in information that maybe some people didn't didn't know or didn't understand or, you know, is not entirely accurate. And we often see in these group sessions, there's a lot of moments where, you know, there's a there's a little nugget shared that not many people in the group know about. You're gonna have to expect some serious deep dives into some of the domains, and you're gonna need some SMEs by your side to get that done. And overall, these things are genuinely fun. It's, an interesting exercise, and you get a lot more back than actually what you asked for. And getting more than you're gonna be able to deliver is fine. Right? You just, build a parking area and, save those up for another day. Just a quick interruption, Aus. Sure. You know? So just a quick question. When we're talking about the traditional method, is that the waterfall or something else? Yeah. Much more waterfall methodology in that in that case. I can quickly go back. But, yeah, the the the the phased planning of this thing is, yeah. In my, in my view, that's, that that's waterfall, and then the sort of agile mix in the middle, which is the actual session developments and things. Thank you. No problem. Okay. Moving on to use case number two. And this is a fairly rapid fire proof of concept, proof of value, proof of technology, depending on what you wanna call it, or building a minimum viable product. Lots of acronyms going on there, and there's lots of flavors to these kind of technical engagements, but the rapid style of engagement is, of course, a good option for organizations. It allows them to experiment and add new capabilities and sort of approach it in a new way. And at the end of it, you get to make an informed decision. Right? And you spot those potential issues, validate the business value, and you can often lower risks and, you know, provide valuable information to understand that post production requirement. So it's a popular exercise. We do a lot of these. And, again, sort of description wise, this is how we would tend to describe it. I'll give it a moment to sort of, read through the sections there. But, again, these are inputs to create the charter and the project management plan. In this case, just like, use case number one, there's an overview section. We tell them what to expect, the approach, the milestones for the project. We describe the deliverables. And then additionally, in the bottom right hand side, we identify the dependencies and the considerations there. Ultimately, for the, for for the first one, for the SDR, we are very much working in, hand waving world where we don't need full access to systems. As you move into proof of concept, you need to actually get hands on to the data and, and into sort of adding new technologies. So there are always gonna be, dependencies going on there. We will often include a diagram like this to try and describe the the target state, where we're trying to get to. And, often that will promote discussion, around the the architecture pieces if necessary and, sort of all the pieces that are needed for the project or or proof of concept. In this case, it's got a got a Tableau reporting layer on the end of that, but, yeah, the the, we're we're talking tool agnostic here. In terms of the deliverables, they're fairly straightforward, really. There's obviously going to be the the technical solution, but alongside that, you would want a technical feasibility assessment, you know, something to prove that business value. There's gonna be an implementation plan because the proof of concept alone is not enough. You can't just build the thing and drop it. There has to be that step beyond of if it's, if it's the right path forward, how do we roll this out? How do we scale it? So there's gonna be some sort of priority, level of efforts, and staffing requirements, some architectural diagrams of how we can scale up, and some training involved. Again, we've got a little timeline, very similar in this sense. We've got, kind of a four to six weeks engagement here. Again, that sort of hybrid approach. Slight difference here. The the development is likely to go right up to the end and incorporate as much of the feedback as possible. And, again, that's that sort of traditional agile mix. Let me I think I've seen a couple of questions popping up, so let me have a quick look. Oh, No. It must be in the QA. I can't get to those Vicks. You might have to read them out. That's absolutely fine. So, yes, we will be sharing the recording afterwards. And how to distinguish a deliverable and a milestone? Oh, interesting. Okay. Yeah. The deliverable side of things, we are talking about the end result and, the the the deliverable is the output, but there's likely to be multiple, multiple milestones along the way to get to that. I mean, often you're talking about trying to show quick wins and, you know, instant gratification to try and encourage people along. So the more of those milestones, the better as as we move on, and then those milestones will work towards a deliverable. Not sure if that's, if that fully answered the question, but hopefully. Okay. And then moving on in terms of sort of the the project management push in this sense, we are gonna need some serious heavy lifting from a tech team here. Proof of concept, you need to be ahead of the game in terms of the technologies that are in there because it's about rapid deployment. So there's gonna be some heavy tech development, but it is a short engagement, all to six weeks. So bring in those subject matter experts alongside the, the the the technical lift. Again, there's that hybrid methodology. And, important to communicate that timeline early. We typically use between naught and forty five days. Naught's a little ambitious perhaps, but that kind of forty five day limit. And definitely a heads up that there's likely to be some compliance requirements for getting into the systems and actually, you know, installing things and getting, getting things up and running. Super, super important to build clear evaluation criteria. If you don't know what success looks like, then the whole thing is never gonna never gonna pass. And at the end of it, you're probably gonna need to estimate the expected annual cost for these new technologies and the maintenance involved in the, you know, the the the training that's gonna be required. So there's sometimes some sort of costing element to this as well. Okay. So those were our two use cases. We're gonna move on a little bit into, the the tooling and the data that flows out of a project and, how we would typically, push that forward with it, in InterWorks. So here's a couple of screenshots. Hopefully, they're not too small, but, let's take these one at a time. On the left hand side, we've got an automated burn report. This is a client focused one. The the great thing about this kind of thing is once you've got the data in there, you can build out reports for multiple personas. You can have the internal operations view. You can have the project manager view. You can have the client lens and, be able to sort of shift subtly shift those metrics that you're, you're displaying. But the the key to that is getting that automated feed of the data. In this case, we've got some data coming in from Salesforce. We've got some data coming in from the time entry system, and then puzzling that together and sticking Tableau on top of that for the for the front end. I love burn reports. They're great. On the right hand side, we've got a, a program management effort. This is, querying a Jira database and, you know, again, visualizing a Tableau and, you know, you've got the the filters and, you know, different different sort of cuts of the data. But at high level, it's the summary of the scheduled, the in progress, the completed, and the backlog tasks. So showing where those things are sat. And being able to sync all that data in from different systems and bring it on demand all of the time, I mean, it just means that you've got that instant planning tool plus that instant status, you know, report to, to push out to the stakeholders. And no reason you can't share these reports with the stakeholders and make sure that it's all nice and live. Feeding in data or acquiring data from the project is obviously gonna require some diligence in terms of entering the data into the system, so you've got to be using a, you know, a fairly regimented PM tool to to be able to do this kind of thing. But once that's set up, the, the automation is fantastic. And then more widely, some of the tools that we enjoy using. On the top left is a tool called FigJam or Figma. This one's another sample timeline that you're able to just kind of work and, and let that develop as the project moves forward. And the great thing about Figma is it's a whiteboarding scenario. So you've got complete freedom to include things like timelines, but then also bring in a wireframe of what the what the deliverable is gonna look like or bring in some additional task lists. And, again, we can share that with stakeholders, get live feedback, they can add comments, they can stick thumbs up. Yeah. Big big win being able to build that collaborative forum. Bottom left, a little bit more of a use useful common one, using Slack for messaging, Slack groups, and, just being able to get that instant communication going. A couple of the other screenshots are something called Basecamp. Obviously, there's hundreds of other PM tools, monday dot com, Jira, and so sort of able to bring that kind of Kanban methodology into something nice and nice and easy. But it's a far, far richer experience if all parties have access and can contribute asynchronously to solve some of those hybrid working blockers and, and get everybody sort of up to speed at the same time. Fortunately, the days of group emailing a static excel file around to a big group is, is is gone, or at least well on the way to going. If there's other tools that you as a group use, that that you're interested in, you know, what what if you've got a favorite, PM tool or a sort of a whiteboarding tool that you use for this kind of thing, pop pop pop it in the chat and, we'll we'll have a look at those later. I don't don't have a poll on that one, but, useful useful to talk about. Okay. Moving forwards, we're looking at what makes a project manager successful. And, you know, we've we've seen some use cases and how a project manager might be sort of being encouraged to work, but there are different ways that we can see success and build success within a project manager. So I've got another poll for you guys. I don't want you to sit completely in the background. And that poll is sorry, Vicky. It's alright. I'm here ready to process. Thank you. So what PM ingredient is the most important in your mind? Is it PM experience and knowledge, maybe certain certification alongside that? Is it data subject matter expertise? Or is it soft skills, communication, coaching, cheerleader? You'll notice that I haven't given you an all sec an all option here. Yeah. And it is completely your decision. So your personal experience, please base it on that. There is no right or wrong answer. We will, of course, be judging the answers, but not too severely. I promise. So just gonna leave that open for another moment or so. And, of course, we will be sharing the results with you. But from what I'm seeing, there is definitely one clear winner, and I completely agree with the majority here. So I'm going to close those poll results, and I'm going to share the responses so everybody can see visibility of that. So if you wanna have a look at that, Oz. Awesome. Thank you, Vicky. So, yeah, no surprise really, but the soft skills is a clear winner up there at nearly sixty percent. So that idea of communication and, you being being being the cheerleader, I think is absolutely right. That, it's a huge part of a modern data project. Just second is PM experience and knowledge, and lastly, data subject matter expertise. So let's dive into those in a little bit more detail. And, you know, being a successful PM is a super challenging task. The the skills to become a PM do not come all at once. It's a slow build. They develop over time. You've got to oversee multiple projects. There's gotta be different size projects, different complexities. And, slowly, I think we work our way towards the center of this diagram. I don't think we come there straight away. In fact, I think often people start being a project manager with one of these topics. So that might be, on the sort of top. You've got the PM experience and knowledge. Maybe somebody's coming from a pure PM background, and they are gonna be successful. Right? They're gonna be able to run a project. But if they don't have one of the other two, I wonder how successful it's gonna be. So that PM experience, that's gonna bring the the leadership and the the problem solving and the the risk management side of things, what methodologies to apply when, and, and and give the structure and the framework. We've then got the SME side of things, subject matter experts, or at least SMK, subject matter knowledge. You you can always bring in an SME into it to sort of fine tune it. Fortunately, at InterWorks, we've got a huge depth of technical skill across data and analytics and, enablement and design. We've got a huge number of data data subject matter experts, and we can bring those in. But you need some recognition of, that SMK, understanding the knowledge, at least, of the space. You know, it it gives you that ability to be able to push back a little bit, not just take what a team member is saying at face value, but be able to push back and say, you know, are you sure that's right? Is that that doesn't sound accurate to me. And without that expertise or that knowledge, you're probably not gonna have the confidence to push back. And then finally, as you guys all identified, the soft skills, the communication is huge. And that's communication with both the project sponsor and the stakeholder, and with your project team and the team members. And, you know, just little things like, following conversations on with a paper trail, using AI on some of the, the meetings to grab the summary and share that on afterwards. You're also gonna be, you know, supporting contributors, removing blocks and making them successful. I mean, that partially falls into the PM, experience section as well. But, then we move past that and maybe you're gonna need a little bit of coercion as well. Maybe people aren't gonna naturally just get that done. Maybe it's slightly outside of their job role, and you need to, you know, bring a little bit of that soft skill element to make sure that the project keeps moving. And then building success momentum, you know, identifying small wins along the way. Some small deliverable that you're able to sort of tick off and get everybody on on on side, be the cheerleader, and, you know, show that there's momentum within the project. Lots to do. I think with any one of these elements, you're gonna have a hard time. If you've got two of these, I think you're gonna run a project, and and it's probably gonna be a success. If you've got all three of these, yeah, it's gonna be a pleasure working with that PM. I would like to be on a project with all three. And then we move into this sort of bigger picture of being a PM within a data project specifically. This is a a version of a slide that we share a lot. Quite often, the SVR that I talked about earlier produces a tailored version of this, but it just gives an indication of just how big the landscape within a data within an organization that the data landscape can be. There is a lot to it. And, when I talk about being an SME or an SMK, it's about understanding the pieces here and their purpose, not necessarily which buttons to push inside them. I mean, I won't go through every element of here, but but there's too much. But let's let's take that right hand side. So that's the interaction layer. That's how we get the users, picking picking the topic up and, and interacting with our data. And for us, this, interaction layer is usually Interworks Curator product. It's a web front end, you know, it's an attractive, customizable way to bring reporting, both self-service and rigid reporting, from multiple tools, and you can push that alongside other supporting content, bring it all into one place for the end user. And the output can be amazing, but you don't have to know how to configure it. You just have to know what the purpose of that sort of portal is. So, but, actually, if anybody wants any further information on Curator, honestly, please reach out. We'd love to show you what that what that is about. But, it was just a a minor point of this much bigger landscape. Okay. And moving on, we find ourselves in the fifth section. This is the last one. There's a little bit of a wrap up, that we're gonna move to as well. But we are seeing a significant uptick in clients getting ready for AI. We talked earlier about the seventy five percent move in the next four years or so. That is all beginning to start now. And, some of the types of projects that we're seeing in anticipation of AI are around this. So a lot of this is heavily skewed towards data assets, obviously, I guess. But, you know, making sure that the foundation is built and can be trusted is an area we're seeing an awful lot of growth in. That often starts with data architecture and data engineering work, building the the the the basis of where those pipeline where the data's coming from, the pipelines and everything. And then on top of that, we're finding that there has to be an element of governance in there or the AI is just not gonna be trusted. So we are seeing observability tools and data catalog tools become much more popular. Semantic layers, I think, have been around a little bit longer than, well, not around longer, more mainstream than maybe those other two for a while. There are stand alone tools to deliver that semantic layer for the end user, the so things like PBT. But semantics can also be built into a much bigger tool. And then once some of those elements are put in place and you trust the foundations, then you can start using some of those, BI tool pilots. That might be in generative AI, or it might be sort of agent driven AI. And, the the two are becoming more and more commonly baked into products. Often, it's just a case of turn it on and use it and see how it goes. But without that data foundation, without the solid understanding and trust of the data, it all becomes a little bit meaningless anyway. We are also seeing a good number of data activation projects. The idea of the data activation project is that you are unlocking the data, making it more accessible and more available to the masses in an understandable way. We are also seeing things that we we term Lego projects, but this is where you start mixing open source and off the shelf products to create that full custom application that mimics your data business sorry, your business workflow. And, the the sort of combination of open source and traditional BI, I think can work very well in a lot of organizations. It often brings down cost, it often speeds up the deployment. And the last one here is around pushing things into cloud, and that might be a case of moving the data into the cloud. Maybe you've got an on premise server at the moment, and you need to get that data pushed up into a a reporting layer in Snowflake or Databricks. Or it might be about the tooling itself moving from on premise up to cloud and moving into a SaaS platform rather than, something that you host. Lots of elements. Lots of different task styles of projects. Did I see a pro a question come in? See a quick question come in. So just a brief summary because we we have completely separate webinars on this together. But just a quick summary of what an observability tool and a catalog tool is. Apologies. Completely lost. Observability and cataloging. Yeah. So one is around, finding the data and discovering it and, having automated ways to cleverly link this data lineage and understanding sort of where it comes from, who's ultimately responsible for it, and, where it's where it's gonna go and end up. And then you've got the cataloguing tools, which are, much more about taking that lineage and making sure that it's very well documented and obvious and able to to to be used. So there's the sort of discoverability side of it, and then there's the cataloguing. That was that was the the the quick version and Very quick. Yes. Vicky's absolutely right. There's whole webinars on on that topic, which, you're welcome to dive into. Brilliant. We are moving into a little wrap up section. So, we will definitely have time for q and a at the end. But, before we do, I just wanna sort of move into the the the the role, the purpose, the story of being a a PM slightly. You know, what is the job? What does it enclose? What what does it entail? You know, what what do we have to be? And, so I wanna close out with a little bit of recognition. And, this guy on screen is, a guy called Gene Kranz. And he was very much known for his leadership teamwork, the can do attitude. He's an American guy. He received the Presidential Medal of Freedom, and you may know him better as he was the guy who was the NASA flight director for the Apollo thirteen mission. Ultimately, he saved the lives of the Apollo thirteen crew. This might help a little bit. I love this film. So, like me, you probably know the story from watching Tom Hanks, rather than, read reading up on it. But, well, actually, not Tom Hanks. It's Ed Harris in this place because he's the guy that played Gene. And Gene, like all program managers, was the guy working behind the scenes to make sure that the mission was a success, which in his case was bringing the astronauts home. Yours might not be quite so dramatic. But, you know, I wanna acknowledge that this can feel like every day for a project for a program manager. Maybe not quite to this scale, but nonetheless, you know, to be a project manager, you are expected to make sure everything is ticking along smooth. You've gotta be on budget. You've got to be on time. You've got to be that cheerleader. If you see risk and issues, you've got to be the one to step up and address those. And at the end of it all, when the project is a success, you've got to turn around and give the credit to your team, not take it yourself. So it's a tough gig. And I just wanted to say I I see you. I thank you for being a project manager. It's sometimes an unthank task, but it's a hugely valuable one to a lot of businesses. So one final poll before we do wrap things up and move into a Q and A. And that is, what is your organization's biggest challenge when managing data driven projects? I won't be there on the screen with you. Sorry. I was yes. They we we have made this one somewhat wordy, so I do apologize. So we might have to do some time filling, whilst everyone takes a moment to read through all of the options available. We could always put a bit more of, police on if you are out there. So, hopefully, people spotted the link. Every step you take, I'll be watching you. Sometimes, I'll I will Hopefully, not every breath you make. That's, getting a little bit far. But, Yeah. They they did a a funny one about dogs. You know, every snack you make, every food you take, I'll be watching you, which is quite apt. So wonderful. Quite a few of you are still participating, so thank you very much. It certainly gives us a lot of food for thought when it comes to putting content together in the future. So I'm just gonna take one more moment, and then we will share the results. So let's end the poll, and then I will give you all visibility. There we have it. Yeah. I must admit I didn't know where this one was gonna go. The other poll polls, I think, I was fairly fair fairly certain on the direction. But, this one is much more specific to you as an individual. And, the winner here, quite substantially actually, is challenges with data quality, governance, or compliance. It's a huge one, and it just shouts to the amount of groundwork and, effort that goes into making data reliable and, up to speed. So we have that as the top option. And then moving in, we've got, difficulty integrating data from multiple systems or sources, that kind of data lineage, data pipeline, bringing it all together into a centralized system. And then third, fourteen percent limited resources to scale data initiatives across the organization. So sometimes those pilots can be built. It all seems rosy, and then the rollout becomes suboptimal because we just don't have the resources. And then lower down the list, lack of in house expertise and the struggle to translate data insights into actionable decisions. Thank you, guys. Appreciate that. Probably thinking about it more clearly now that you've all sort of, spelled it out. The challenges with data quality makes absolute sense. Fantastic. Well, thank you very much, everybody. That is the end of this, webinar. I'm not sure if there were any other questions that have already been posted, but we do have a minute or two if you did want to submit, a question or pop something in the chat. Feel free. If you are interested in that SDR concept, the strategy vision roadmap, then there is a link on screen or a QR code on screen. But hopefully, you found this useful and, can take away some of those actionable items, some of those cheat sheets, and, put them into your own projects as you start developing them in your in your organisation.