Thanks everyone so much for joining us. I think I can speak for both of us, and we're really excited to say to see that we have almost four hundred folks that, are excited and interested in learning about data strategy today, which is surprising but exciting for us. I know kind of speaking for Jason and I, this is what we talk to clients about most of the time, and it consumes kind of an abnormal amount of our brainpower at times. So we're excited to share, what we're seeing in the market today with you all and and share some successful strategies that we've seen. So, what we'll do today is we're gonna talk a little bit about, Interworks in case you have never heard of us. And if that's the case, we're excited to have you here. We'll get a little bit into the role of data strategy at organizations. Spoiler, it's not just for data engineers, or architects or DBAs. And we'll debunk some pervasive myths that we're hearing or seeing in the BI space. So, and and we'll discuss some actions really that led to some positive outcomes for organizations that we work with on their data strategy and walk through some case studies that we've seen. And then finally, at the end, we'll give you all some screenshotable slides in case you're looking for some high level themes to get you started, things to pass to your colleagues who may not have been able to join. And they're just nice visuals to to remind you of what we talked about today. So if you're sitting here and wondering who are these people, this section is for you. I'll start with a little bit about InterWorks. We are a people focused technology consultancy that works across IT data and analytics. Really what that means is that we seek out clients, that we have a trusted advisor relationship with or people who are excited to get into the weeds with us. And that really allows us to do our best work. So we're excited to solve problems. We're excited to, let everybody, you know, unblock their tech and IT and analytics problems so that they can do the thing that they're most excited to do at their job. And really what that means, is is working with the clients that we're excited to work with. So that that might be big names. Some of these logos you might have seen before. It also could be small names. It it's not really about the size of the company or what they do, but more about, who they are and, and and, again, having that trusted relationship with us. That also means working with the best partners. And so you'll see on the right hand side, obviously, this is not an exhaustive list of the partners that we work with. But as you can see, we do work with a broad range of partners that allows us for the best solution for our clients, that's custom fit to whatever the client's needs are. And last but not least, who are we, which is a very existential question. Jason, do you wanna introduce yourself first? Yeah. For sure. So, my name is Jason Hain. I am a data engineer here at InterWorks. I help advise clients on all of their, you know, back end needs and help with implementation as well. I've been here at InterWorks for about two and a half years and within the data world for, you know, well over, like, three, three and a half now. Based out of Detroit, Michigan. So, they, like, been heartbroken the past week and a half after the lions lost, but we're doing alright. And I'm really excited to talk some data strategy with all of y'all today. Oh, gosh. I forgot about the lions. We should talk about that later. No. No. No. No. No. No. No. No. No. No. No. We're done. Maybe we don't need to talk about it. On that note, hi, everyone. I'm Emily Miley. I'm a strategy lead out of Portland, Oregon. I've worked in the data space for about five or six years now. And and really what a strategy lead for Interworks means is that, I could be working with a client to evaluate the various BI tools in the space, and help them determine which tool or tools, spoiler alert, could fit their needs. This also might mean talking to stakeholders across an organization to provide recommendations about getting started with the data strategy, where to start, how to prioritize, how to, roll out a plan and and build on each each element of the plan, and that's kind of what we're here to talk a little bit about. So let's get started. Mhmm. Right. So let's talk about data strategy. So in this section of the webinar, we're really gonna break data strategy down into three core components. It's gonna be what is data strategy, why is data strategy important, and what are some common challenges that we see customers face when designing and implementing the data strategy for their organization. So first for what is data strategy, according to AWS, a data strategy is a long term plan that defines technology, processes, people, and rules required to manage an organization's information assets. In other words, a data strategy looks at all of your data related resources, within your organization and defines the use cases for each of them moving forward. And when it comes to those use cases, I think there's a really good way to categorize these, that can be used in, like, a day to day, in, like, a day to day sense. And that's according to Harvard Business Review, there's really two core components to your data strategy called offense and defense. I like to kind of think of this as, like, a game of chess. You know? You, implement a strategy based on the pieces on the board that is either gonna be more offensive or defensive. And in regards to your organization, you know, your business playing field, you need to look at, okay. Are we gonna be aggressive with our data, or are we gonna try to be more defensive with it? And the way that those are kind of defined is for offense. This is gonna be a strategy that's more based on flexibility and growth. It's gonna be focused on supporting business objectives such as increasing your business revenue, your profitability, and your customer satisfaction. It's gonna be something about operating more efficiently. You're gonna want to have more reliable decision making, and you're gonna be trying to target increased revenue streams. Again, this is more so the key term here is growth, and that's gonna be using your technology processes people and your controls to nurture growth. And some industries that we see this in pretty frequently include, like, the retail sales space, marketing space, digital product companies, and so much more. Essentially, if you're trying to sell something, you are gonna have more of an offensive strategy, to start. And on the other side of the coin, we have defensive strategies. And this is focused primarily on a system of controls. So this is gonna be more so ensuring compliance with, you know, regulatory agencies using analytical, applications or even just, like, processes to detect and limit fraud or to eliminate theft. Really theft just to really mitigate risk, to your data assets. And some industries that we see this in are gonna be more so your, your finance financial industry, your insurance industry, your health care, government agencies, anywhere that you have more sensitive data, or you are going to be audited by a regulation industry. If you have PII, you're gonna have controls in place. And, you know, when we talk about these two, these two, like, core components to a data strategy, just a little teaser alert. Like, this is not something that you're one hundred percent in one boat or the other. Don't put all your eggs in one basket. You wanna have a balance that suits your business needs today, and over time, you adjust that balance as you start to see the fruits of your labor. Right? As you start to see, oh, like, as a maybe someone that needs to be defensively oriented early on in their data implementation. Well, once you sort of have those controls in place, you can reassess them over time, but now you can maybe start shifting to an offensive strategy. And that's something we'll talk about a little bit further on in the presentation. Yeah. So with that, you know, what might be a good place to start? And, really, the foundational question that you should be asking yourself anytime you wanna start using data assets is what business problem are we trying to solve. Right? What is our North Star? Where are we trying to go? If you don't know where you're going, you're not really gonna go in. And once you know what you want to solve, then you can start answering the questions of, okay. Well, do we have the data and the resources to do that? Once you identify those re those data sources or lack thereof, then you can start to determine, alright. Well, how what do we have to do with this data to make it consumable to actually achieve the end goal that we have in mind? And with all of this in mind, it's really, do we have a consensus within the organization, around, like, data and metric definitions? So that's a little teaser too of, you know, having some organizational alignment. And all of these questions really lead into a slew of other questions. You can even tell right there, like, I was starting to ask second and third questions, but we even see these come up pretty often. You know? What else? So what control systems need to be instituted if we're doing a more defensive strategy? Do we have the technological resources and softwares to actually implement the processes that we want to create? You know, you could have an organization that has all the data in the world and the a great vision, but if you don't have the technological resources, if you don't have the people to execute that vision, well, then you might have to think about, you know, hiring external help or bringing in additional full time employees and making that investment. And that's how you kinda get into another question. Well, how much time, how much money do we have to dedicate to x and y, and how should we prioritize these items? And you can kinda start seeing a theme develop here of, you know, how aligned are we? How can we get aligned? And these are some, again, very good, like, preliminary questions to get you on the path to developing a data strategy. So next, I believe we have a poll for you guys, and you should see something pop up on your screen, and it's, does your organization currently have a data strategy? And, Emily, I'm curious what do you think the results are gonna be here? Yeah. So it's hard because I saw it pop up, so I'm watching them come in live. Right? I it's it's much more than I thought, which is exciting, actually. I think, it's it's really encouraging that we have more people than I expected. I think that what we're also seeing more and more in the space as we talk to our clients is that people will have elements of data strategy. Right? So they'll have one element, maybe decent documentation, maybe their data pipeline is, like, like, pretty solid, but there's the coordination between all the things, all the people, all the processes, all the technology. Sometimes it's just that last I don't remember what the curve is, but it's like the first eighty percent takes twenty percent of the effort, then the last, you know, twenty percent takes eighty percent of the effort. That's obviously very reductive, but I I think that that's sometimes I wonder if, like, some folks have they have some of it and they just need help getting to the last, the finish line. But, yeah, more than I thought. I mean, we have fifty five percent of folks have data strategy in some capacity. Maybe a full fleshed out data strategy. Who knows? And, actually, before we move on from the results, I see Kimberly bringing up a good point. There's a gap between having a strategy and executing a strategy. And that's something we definitely see quite frequently because, I mean, I don't know how like, you've seen all the charts too of, like, even, like, meta corporations. So, like, you think, like, your Metas, your Googles, and they're on that chart of, like, execution versus vision or even for, like, BI tools. It's like, alright. Like, what do they want to do, and how good have they been at doing it, which I think is just an interesting thing to say. Yeah. Absolutely. That's a great call out. And that is, especially in a lot of the engagements that I tend to do, that is one of the largest issues is, like, how do we how do we actually get this plan to work? How do we get buy in at all levels? How do we create urgency? How do we, you know, use change management as a way to, like, actually implement, step by step? So I won't get ahead of myself, but that's that's a really, really good point, Kimberly. So with that and, actually, this is kind of a nice segue. So with all that said, we understand that data strategy really is complex. So maybe you have a good understanding of, like, the framework that you wanna be using. But I think what we like to think of as data strategy is it's not just the technology and the processes, which I think sometimes can can work independently of people. It's also it's the people that, create priorities that can push initiatives through. And, you know, I'll kinda basically give the consultant answer of all of this stuff depends. Right? So what we're gonna talk about today, and then the examples that we'll give, the things that we've seen are places for you to get started. They're questions for you to start thinking about. They're not meant to be prescriptive if these are the things that you have to do because it does depend on your organization. It does depend on the strategy your organization wants to take. But we can help you at least hopefully talk through why some of these things are important. So you can go to some of those people, in this data strategy and and try to get those initiatives, executed. Yeah. So, you know, we've kind of talked about what is data strategy, and, you know, why is it important. And, really, what it is is, you know, your organization, if you have data, you've made a large investment in, you know, cultivating that data. And in order to fully leverage the value of it, you need to, you know, maintain a secure and controlled environment that can be that's accessible and reliable to the end users. And it's also clear, because you don't want users to have, you know, accessible data that they can't really make sense of. Right? They're supposed to be making business decisions and not deciphering the data that they have. And, you know, the ability to diagnose your organization's needs, you know, your business needs, it'll help lead to a reduction in your technical debt. Like, if you have a clear cut picture of what you're supposed to be doing and you take the time to implement it properly, then you're gonna build long term solutions that will serve you today, but they will also serve you five, ten years. And, you know, with that being said as to why it's important, you know, there are some common challenges that are faced by organizations today, when implementing their data strategy, when executing it. I actually had a client that, was attempting to automate data processing and consumption processes, and they faced a lot of problems that we find to be pretty common in today's marketplace. The first of them being that when we got involved with the client, they were really only defining the technology they wanted to use and the source systems to be processed. There was no north star. There was no guidance as to what purpose this was serving. So once we got in there, first question I asked, I was like, so what are what are we doing here? We defined the questions being asked and defined the answers that were supposed to be given. Once we kind of worked through that, then we went on to a second problem, which was, you know, do we have the data to solve our problems? Right? And this this client didn't really identify and prioritize all of the applicable systems, to solve these high importance business problems. Once we asked the question, we realized a lot of the systems they wanted to bring in and start tying together, weren't even what they needed. So we ended up having to actually present a solution of, you know, auditing the data systems they had in place, both, like, internal and external vendors. And, you know, we wanted to see the data contained in each source and understand how it all tied together. So then that enrichment process could run a little bit more smoothly when we got to the third challenge, which was what do we need to do with the data to solve our business problems? And at the time, it was just, oh, well, right now we're doing manual processes where we have Excel workbooks using Tableau prep workflows to then feed into a Tableau dashboard. If we automate that, then that'll solve our problems. And it's like, well, it will make your life easier as a data practitioner, but is that solving the business's problems? And it's like, no. So to answer the second half of that question, no. It's not. But it still is useful. So, like, yes, data processing does need to be automated. However, like, a system of controls needs to be implemented to ensure the security, reliability, and so now that we've kind of, like, talked about some of those common drawbacks and what exactly data strategy is, let's walk through some of the common misconceptions that we see in the marketplace for data strategy. These myths that we typically encounter, two three of the top ones are gonna be more data always equals better insights. Spoiler, it doesn't. AI can replace human driven data strategy. Spoiler, it won't. And the third one is a single unified platform solves all data challenges. That's up for debate, but most likely not. So for the first one, the myth is, you know, more data always equals more insights. And the reality of this is, you know, I think we've all heard it probably a million times in our lives, equality over quantity. And that could not be more true here. Having terabytes of data or petabytes or whatever massive amount you wanna you wanna say, it means little to nothing with without proper curation, without proper context, and without proper relevance to answering your business questions. And some things that you should, like, really take away when thinking about, alright, like, more data does not equal better insights, is you wanna focus on the data points that answer the business questions being addressed. I'm really gonna say that a lot here, and I really wanna drive that home. As a data practitioner, I've seen too many times people just trying to use data for the sake of using it. Again, make sure everything has a purpose. And once you understand its purpose, make sure it's controlled from end to end, and you actually have data governance practices in place to ensure that the data is gonna be reliable and consistent for a long period of time. And you wanna make sure that this data, again, is not confusing. You wanna make sure it's clear and concise so your end users that are actually consuming this can spend more of their time making decisions using the data that you've configured rather than trying to decipher the hieroglyphics that your data have come to them as. So with that, I believe Emmy Emily actually has a real world example for you guys, of the client that we worked on. Thanks, Jason. So we we worked with an organization that we really wanted them to emphasize and understand that, you know, if they were to prioritize cleaning, of their data, sort of documentation, cataloging, analyzing existing data objects like pipelines, metrics, agreeing on a single source of truth, for their end analytic system. We worked with them, and and I think what we saw is this allowed them to actually take inventory of their existing environment, agree on those metric definitions, and really just bring in best practices before bringing in additional data. So we're not saying that more data isn't helpful. But sometimes, you know, before you start targeting all the source systems that you may or may not be connected to and the legacy reporting systems and trying to kinda compare numbers, we find that sometimes, like, this can help you create a smaller, well governed space that's flexible and a good starting point that allows them to have a process in place so they can scale and they can grow when they do decide to integrate more data. And I think that gets to Jason's point, which is, again, like, small starting small is great, and then slowly building off of those successes can be a really, really good place to start. Yeah. Thank you, Emily. And that brings us to our second myth and probably the the heat term that gets everyone's ears perked up. And it's, artificial intelligence. AI can replace human driven strategy is the myth. The reality of this is that AI is meant to complement human driven strategy. At look. At the end of the day, we as humans are training these tools, and we are the ones that are prompting them. So, you know, we are not a tool to AI. AI is a tool to us. And we have the domain knowledge. We have the strategic thinking to actually develop a sound data strategy, and we can utilize artificial intelligence to aid in automating manual processes and identifying meaningful patterns, again, as a tool. And the key takeaways here, again, you know, based on what I just said with the we kinda provide the inputs. We do the training. We do the prompting. Human intelligence is necessary for ensuring that the correct input's given to AI and that it lacks bias in its responses. There needs to be an expert on the human side of things to actually make sure, hey. This this makes sense. You know? And by doing this, humans can actually leverage AI to aid in their decision making process. So I'm not saying it's obsolete. I'm saying it's, again, it's just another piece to the puzzle that we're gonna be creating. And with that, Emily has another example. Yeah. So and like Jason said, this is a hot topic. So, you know, surprise, surprise. It's not we don't just have a single organization that, you you know, is somebody that we're talking to about this. I think it's very common, right, nowadays to say, like, how can AI help us? How can we use this to get ahead? And I think that, you know, we have seen some organizations that really believe that it'll solve all the problems. And so I have a couple just sort of broader examples based on the conversations that I tend to have. So, you know, if you're in marketing, if you're sitting here in the call and you're in marketing or sales or customer service, like, could I I help you do sentiment analysis and segmentation and lead identification, churn risk assessments? Like, yeah. Maybe. Do you need a sales or marketing or customer service human being to train the models, validate the segmentation and outputs, handle some biases that might exist, and provide strategy, like a business strategy alignment. Absolutely. Right? So those things can both be true. You need both again, like Jason said, the AI can be very helpful, can bolster some of the work that you're doing, improve some of the processes, but you still need that human compliment. I think another example that we've seen, like, if you're in manufacturing or if you're in supply chain, could AI help you manage traffic or optimize budgets or predict maintenance for, like, your fleet or your equipment? Yeah. Baby. Do you need a human being to provide contextual oversight, handle some of those edge cases, manage some of the complex decision making like prioritizing goals, determining root causes of supply chain failure, anomalies in the data? Absolutely. Right? So where we're seeing things right now, and at least for the foreseeable future, is, again, AI is meant to complement the human driven strategy, not replace it. Yeah. And with that, we move on to our third myth, which is a single unified platform solves all data challenges. Yes. It would be really nice to have one tool that can handle every single step of your data ingestion, you know, storage, transformation, and consumption processes, but that is unrealistic. I mean, in this sort of data renaissance that we're experiencing in, you know, modern society, there's a lot of tools that are a lot of that are good at very specific things. You get one tool to roll them up. My wedding ring actually says one ring to roll them all inside of it. So it's not really Sweet. That's amazing. It's us. So, like wow. That threw me off for a second. Let me take a breath. Sorry. No. You're good. That's just the wrong one. No. So, like, it would be nice to have one tool because then, you know, you have one bill to pay. You have one software that you're gonna be familiar with. But in reality, there's a lot of tools that fit specific needs. Right? So, yeah, we could simplify your architecture, but it's difficult to find that single tool to address all of your needs. And the takeaways here, you know, be prepared to identify and integrate tools that address specific problems. If you have, complex and processes, then maybe you're gonna have a tool for that, and you have different you have complex transformation processes too. You might have a different tool that you use for that, and that's okay. When you're selecting these tools, you wanna focus on, you know, the small scale problems that you're dealing with. So you're short term. Right? But you also wanna focus on long term scalability, reliability, and cost. Don't be afraid over time. Like, I think it's really good to pick something that solves your business problems today. But don't be afraid over time to, like, reassess your organization's tech stack based on what your business needs at that point because, yes, you are making an investment in the technology. But if that investment is no longer serving your business, then you actually could be losing out on opportunities, and it might just be better to bite the bullet and move on to something else. So with that, we have had a client that had a bunch of different technologies they use, and Emily's gonna break that down for you. Yeah. And I I'll actually, spoiler again, say that this is also a very common conversation with a lot of clients, whether it's, hey. We have a lot of tools. Are these the right tools? Or, hey. We have one tool. Should we have multiple tools? So I think just the concept of what does multiple tools look like. Do I have the right strategy given the tool the needs of my users and the tools that I have? Are these fitting my strategy appropriately? I think one of the things that we've seen, we worked with an education company, that had a bunch of different systems, and we brought together all their source systems, into one final kind of, like, BI interface. And I think this is another thing that I wanna like, a sub myth that I wanna debunk, which is the idea that having multiple systems means that we create, like, a swivel chair experience for our customers or our users. And I don't think that that has to be the case. So what I mean by swivel chair is the idea that, like, you have something over here. You have a system over here that has this metric or number, and then I have to go over here for this other thing. And then this isn't, like, some sort of, like, web interface that I have to, like, dig through a bunch of folders, and then this one lives on so and so's desk on a sticky note. Right? Like, we don't necessarily have to have that experience as our multi tool experience. And so bringing together all of those disparate systems, making sure that they work together using things like webhooks, data integration. And and and the other thing I'll say too is, you know, when we first started working with this client, the answer wasn't clear. So we at InterWorks use, design thinking a lot of times to sort of figure out, like Jason said before, like, what are the actual business problems? What are the challenges we're facing? Let's not, you know, put duct tape over things. And so we worked with the client, very closely to figure out what they actually needed to get a successful data and analytics implementation for their consumers of their data. And what that looked like was multiple tools integrated all together into one final nice looking interface, that they their end users can can access. So, kind of next step is is we'll talk a little bit about what we have seen drive results, at clients that we've worked with. Yeah. So I've kind of been hinting at it a little bit throughout the, webinar, but, you know, I think the foundation of all good data strategy and, I mean, I guess, like, all good relationships even in life is, you know, you wanna have alignment and a clear vision as to what's gonna happen. Having everyone understand your business problem and, you know, what data is needed to solve it and what this data means and what needs to happen to this data and how all of your assets are gonna be invested is the foundation of any data strategy. If your team doesn't understand it and doesn't feel to be a part of it, they won't be engaged, and it won't be as successful of an execution. So you need to really, again, identify the purpose of your data assets. This Is it gonna be something where we're gonna be trying to drive growth within the organization, or is it gonna be something where we're trying to control our processes and make sure that, you know, they are secure and they are reliable and they are accessible? So with that, we have another question for you. Knowing this offense defense categorization, you know, which one does your organization primarily fit under today? And an add on like, an add on question as we're getting the answers in or like, which part of the organization do you fit under? Right? If you fit under a group that's particularly offensive and maybe your organization is a bit defensive, but you need to think a little more agile agilely. I don't like that word. A little more agile and a little bit more, like, how can we go out, and and take some risks even, right, as opposed to security access controls, things like that. That might be, that also might be an answer. It looks like, Jason, we're pretty split down the middle. What did you think? What do you think about that? That's a fifty fifty there for a second, and now we're at fifty one forty nine percent. I I think this makes sense because, you know, this offense defense sort of framework, up fifty fifty. Look at that. This offense defense framework is is something that's gonna be kind of on a scale. Right? And the balance of that scale is going to change over time, And it really depends, again, on your organization's needs that day. Like, again, like, let's just take an insurance company, for example. You know, day one, their top priority is gonna be protect protecting PII and making sure that they have, like, proper security protocols in place. But, you know, after that's all done and those controls are, you know, fleshed out, then they can start thinking about marketing. Because at the end of the day, yes, insurance has sensitive data, but they still have to sell insurance. So it makes sense that this would kind of come in had an even go out. A perfect fifty fifty. I'm curious if the audience did that on purpose or if the to humor us or if, if that's really it. Right. Yeah. So what are our key takeaways with that? You know, again, your data strategy must be business driven. You wanna answer your business questions. That's always priority number one. Once you do that, then you wanna have some cross functional collaboration between your team so then everyone can feel as if if they're a part of the process and they are engaged in it. And you wanna tailor your data governance and performance metrics to your specific use case. So you know you're going back to bullet one and you're answering those questions. Right? And with that, another pillar of success is data governance, and I believe Emily's gonna talk to you all about that a little bit more. Yeah. So, you know, the way that we're thinking about data governance is this idea that it's a framework for managing the availability of your data, the usability of your data, the integrity, like, trust in your data, and the security in your data. I I do wanna, again, have a little sub myth to debunk that governance doesn't always have to be as rigid or stifling as sometimes the concepts may suggest. I think that, you know, we're finding that governance can really help aid with your company's business goals and therefore your data your data strategy goals, like things like data accessibility, data quality, data security, which tend to be shared goals across the organization even if sometimes it takes an extra step to tie that to a business goal. I think that sometimes what that means is implementing people and processes that assign ownership and stewardship of data objects, for accountability. Right? That might mean spoiler, like, thinking of data as a product. This also could mean something like a data catalog technology, or framework, something like Alation, Calibra, something that helps with data discoverability for people at your organization. This could mean like a data lineage platform such as Monte Carlo, talking through something like a semantic layer with your data engineers and maybe your analytics engineers, which is kind of a newer title of the space. I think that, you know, some of the things like tying some of the questions and, again, those business problems to data governance. So, you know, if you're hearing, man, I'm concerned. I can't trust the insights on this dashboard. I get emailed this once a week, and it doesn't match this existing reporting system we've had for thirty five years. That might be a data governance issue. Obviously, there's multiple things underneath the data governance that that's causing the challenge, but it's a holistic thinking about people, process, technology, that might be a data governance issue. If you can't seem to coordinate a single source of truth, across your organization, that might be a data governance issue. So all of these concepts are becoming more and more important, and it can be helpful helpful to start talking with your organization, and with people at your organization about which elements would be most helpful to start thinking about. And, of course, there's there's plenty that aren't listed here. And so we have another poll for you, which is I'm curious who all, feels like their organization has a a data governance plan in place. And while we're getting the answers to that, I'm gonna take a look at, the comment, which is it sounds like, Pam mentioned I've heard some some refer to governance as enablement to avoid some of the negative negativity associated with the term governance. Yeah. I I have also seen some kind of reframing of what that looks like. And I think I mean, in the best way I can say, that's, like, whatever works. Right? If you can get the people and the process and the technology and and everything to work together and people to agree on on metric definitions and an ownership of different parts of the pipeline, like and we call it enablement. I think that's great. But I do I do see sometimes that governance has, some connotations with it for sure. Yeah. Jason, how are you feeling about this poll? Oh, this is incredible. I was yeah. I'm surprised too. We, we were talking earlier, and I was like, I feel like I'm always hopeful for things. I'm like, this is the results I would hope for, but, my hopes can be dashed quite frequently. So, this is this is great to see, because I mean, we're talking about, like, let's take AI, for example, or even just, like, predictive analytics. Right? Those aren't possible if you don't have proper data governance protocols in place and you don't have reliable data because then you can't rely the predictive analytics that are being fed to you. Right? And I'd like, I said this here earlier, and I really think it's true. And, like, a lot of times, the only way that you realize you need better data governance is by trying to consume results from one of those, one of those tools, realizing they're not reliable. And I mean, like, alright. We gotta take a couple step steps back. But, no, this is this is great to see. Yeah. Sometimes the burden of proof falls on the analyst and the data engineers that are like, look. I told you. Yeah. Yeah. No. That's that's very true. But I'm excited. I'm I'm I'm impressed. I I think much like the data strategy, though, I imagine, you know, at least from the conversations that I'm in that similar to data strategy, you might have a governance plan, but it's actually getting compliance. It's getting people to implement and actually follow through with with the data governance plans. And that that is a challenge for sure. And so if you're feeling that, you're not alone. Governance as a pillar of success is is I think that, again, you wanna define organizational policies for data ownership, quality, usage. That could mean documenting your models. That could mean working to streamline and fully automate given all the things that we talked about automation are true, what Jason was saying earlier. And it could mean, you know, streamlining, automating, and deploying your models into production. It could mean creating systems where you have the ability to implement feedback and iterate official efficiently so people feel like they're excited to use the processes and the tools that you have in place. And all of these things aid, in your overall strategy, data strategy. Yeah. So next up, another proven reality that helps drive results is that, you know, iterative approaches to your development process are going to pay dividends extremely well in the long term. You know, implementing a data platform doesn't really happen overnight. And your first time building something isn't gonna be the optimal way to build it. So it's gonna look way different from the last time you built something. Or I guess there's never truly a last time, but the most recent time you built something. Right? So you wanted to develop a system to identify areas of improvement and build upon those insights. And I think a really good analogy in my life right now that can be similar to or adjacent to developing these sort of business processes. Like, I'm in the process of moving into a house with my wife, and we're not taking one weekend to pack all of our boxes because that's a really big picture and daunting task. We're taking a few weekends and a few weeks to actually package things up and slowly move over. We're iterating over the process day by day, week by week. So then when we get to our actual move in date, it's not like we have the entire apartment to go in there. No. We just have part of it, and then, you know, the whole picture has been painted. And I think that's a really good thing to do within, again, your development processes for your data platform. So with that, we have another poll question for you, and you might have an idea of what it is. But does your organization refine data processes as you learn more about your business? And we got a question in the q and a. While the poll is going on, Emily, do you think we take this now, or do you wanna wait for the end? I think we could take it now. I mean, we've been asked what wireframing or flow diagram tools do you use to capture your scope or data flow, etcetera. Depends. I'd say, like, we have an internal tool that we use to actually diagram a lot of this stuff. But we also I mean, we've used paid tools before, something like a SQL DBMS. We've used something more manual and, something more manual such as, like, just sort of, like, anything and everything, at least, I've used in my experience. Emily, do you have, like, one in particular that you like to use a lot? I have a totally unsponsored plug for Figma and FigJam. I love those tools personally, but I do a lot less of the kind of, like, in the weeds, like, actual data processing, than Jason does. So I think for those kinds of processes. For, like, wireframing and flow diagrams in general, though, I really I really like them. But like Jason said, they are manual. So that's a thing to to think about. And it's good to see. Everyone refactors their data process. Well, a lot of people refactor their data process. Seventy percent. Yeah. And if you and if you don't if you don't, it's okay. Just give it a shot. You'll enjoy it. Cool. So, yeah, takeaways with this, you know, build piece by piece rather than shooting to complete the whole thing in one in one shot. Right? Taking those small steps to build the bigger picture is gonna be a lot more sustainable rather than trying to take down the whole thing in one shot. You wanna end up utilizing your end user feedback as you, you know, create these smaller wins to then drive your decisions further in, you know, future iterations. It's gonna show you, alright. Like, now I have a better understanding of my business. There's a little more nuance to this business problem. How can I, you know, leverage my data to answer it a little bit better? And, again, you can identify these changes to business needs and update the systems as you continue to iterate over, over your processes. And we talked a little bit about it earlier, but this is the same with, like, leveraging your BI tools. And Emily's gonna go into that a little bit more for you. Yeah. So this will be the last kind of driving results, and then we'll share those screenshotable, takeaways for you all. So thanks for sticking with us. We're almost there. So for leveraging BI tools to extract insights, really what we mean by that is is that you can use the tools that oftentimes organizations already have. So, like, BI tools, you can use them to increase data adoption allowing users to self serve metrics and information ad hoc. And it can do a good job of of surfacing clear insights on well structured data, which can help you kind of build on that momentum, as you start to move towards more complex, strategies with AI or data strategies. And, you know, I think sometimes people forget that, like, you can also use, like, BI tools to detect errors at various points from source system to insight. And you can use some tools as a way to kind of track how your data strategy is working, and and automate processes like that. So we have one more poll for you all, and this is I'm I'm just curious. So this is a multi select, so much like we have, with have talked about with the multi tool approach. I'm curious what tools folks are using, at the organization and, again, can be plural. If you didn't see if you don't see the ones that you use here, feel free to type it in the chat. I'm really curious what else you're finding that you use. Yep. Cognos is the one I forgot to put on here, but that's a good call. Yeah. Super interesting. I'm curious. Yeah. Jason, what do you think about this? It's pretty expected. I think there's some some major players in the game. You know, there's some that we might have accidentally left out as well, but I was kind of expecting, you know, some some mountain tops and then some, some hills as well. That's what I'd say in these, in these results. Yeah. Yeah. And I think, you know, I think what's what's interesting too is that what we're also seeing is that there's just a lot more shifting. So similar to kind of that, like, the renaissance, like, data renaissance, if you believe in that. Right? I think that there there are a lot of a lot more tools coming into the space, and people are looking not only to to talk through, like, oh, what do I have right now? But, like, what could I have? What should I have? And so I'm I'm really just curious about that. Tableau totally nets out as the top winner, which makes a lot of sense. I think a lot of folks know us from Tableau. Okay. Real quick. We'll do takeaways, and then we'll give those screenshotable, assets for you all, and then you'll have some quest time to answer questions. So, again, BI tools can be really big key components for bringing insights to various parts of the organization. I think the thing to be really clear about is that some folks are talking about BI tools being outdated. I don't believe in that, and I don't see that being the case right now. I think they're they're really important for current success and building on future success as you get ready for advanced strategies like tech, like utilizing AI. And so and and there's a lot of conversations which we, like, don't have enough time to talk about in this meeting around what if my BI tool has AI, and what does that look like? Should I how do I feel? How do we feel about this AI offering that's within the tool suite that we have, and and what about this other one? And so, those are all things that are connected to BI tools already, our built in functionalities, and and and also very valid questions to be asking with your organization. So now we've got those screenshotable assets, that Jason will walk us through. Yeah. So what are some lessons that we've learned, you know, here today and what we've kind of learned from some, you know, innovative leaders within the, within the BI space. First and foremost, again, I said I'd say it a lot, and I will continue to say it a lot. Sorry. Excuse me. Organizational alignment is gonna be crucial for effective data utilization and data strategy effectiveness. You wanna make sure all teams within your organization are aligned in your mission and your use of your data so then you can actually optimize the return on investment that you're getting from your data. Lesson two would be that, you know, data governance or as was brought up in the chat, maybe an alternative term, data enablement, helps build trust, with your end users. So by managing your data properly to reduce risk, provide a foundation for growth, and it allows business users to know, like, hey. We have a source of truth here that we can build things off of. We know this data is reliable, and we know when we use tools to, like, you know, try and derive insights from this data, that's gonna be trustworthy too. And lesson three is, you know, leaders who take on an iterative agile approach, they often see better results than those trying to overhaul everything at once. You know? Build on your successes, generate some momentum, get some small wins, and tackle one challenge at a time. As you start to piece those together, you'll start to see the bigger picture, and you'll start to see, like, oh, like, I really do have a comprehensive data platform here. And with those lessons learned, some key takeaways that we have around, you know, and some actionable strategies that we have for your data strategy is, you know, again, align your teams around a shared ambition. Invest in strong data governance principles. Use quick wins to demonstrate value and maintain momentum in your data initiatives. I know it can be difficult to convince people to make the shift to data driven, business. So take the wins when you can. You wanna balance your offensive and defensive strategies and reassess them over time so you can reconfigure the balance. You wanna prioritize scalable, flexible, and future proof excuse me. Data architecture and consider your data a product. We mentioned it before, and I wanna mention it again. You know, for your customers, your product is your product, but for your organization, your data is your product. Because, you know, you sell your your customers your product, and they get use out of that. Well, you sell your data to your organization, and they're gonna get use out of that as well. So, yeah, those are our key takeaways. So Do we have any questions? And go ahead and pop your questions in the q and a, if you don't mind. That's what we'll be monitoring. I'm also happy to go back to the other slide if we wanna take pictures of that one too. Yes. Give it a couple minutes. Yeah. That works. And I feel the need as a consultant to reiterate this. The the this slide is these are all things that could work. Right? So I think the most important thing is is making sure that, this aligns with what your your company needs, and and maybe not all of these are things that you need to do right off the bat. Looks like we have a question in the webinar chat. Which BI tools have you found to be readily more readily adopted by business users? I I think, you know, truthfully, I I think that a lot of the majority of the ones that I put up there on the poll are the ones that we see. Right? So I I see and and I'm so sorry. This is such a consultant answer, but it really does depend. I promise. Right? Like, if you have some needs that if you have some clients that are looking for, like, total self-service, ad hoc, on the fly, give me a metric, give me a number, when I need it, That might be a very different BI tool for their use cases, than somebody who's like, hey. I want a report to show up in this portal or in my email once a week. Or, hey. I go through all of these reports every day. I have them all bookmarked, and I just, like, run through them, and it helps me it helps me run my business. And so I think that it depends on the business users. It depends on the business users techno technology kinda acumen. Right? So if they're very readily willing to adopt more technical solutions or maybe they have a programming or coding or or analytics background, that might be a very different BI tool, than one that's super user friendly, and and really just, like, client facing or or or user facing, I guess. So sorry. That's not a real answer, but I would say that the the real answer is is, like, most of the ones that we listed there are some of the ones that we see the most in the space. Second one. Well, that's a good question. Org structure for data and analytics, centralized, federated, or decentralized work best. I'm so sorry. That also very much depends. I I personally and, Jason, I'm curious what your answer is. I have seen federated, and well managed decentralized work well. I think any of them can can work well. But I think that I I think that that that depends. And and I think, ultimately, the answer is, like, any of them depend on the organization that you're working with and and what the needs are and what you can actually support. So if you can't support fully centralized, with the amount of staff that you have and, like, the demand for the things that that centralized group needs to keep up with, that's gonna be really hard to have that kind of model. And then if you have decentralized but a lot of folks aren't talking to each other and you're really struggling to get that connection, that may not be the right model either. Let's see. Okay. What If, I'll go back to the to the Yeah. I was gonna say I'll go back to the chat because there's been backup here. What company job roles do you see that would be needed to help determine a data strategy? I have my answer, but, Jason, do I take this one first? Yeah. I think, like, the easy answer that a lot of people would give is, like, a chief data officer. And that is all well and good, but I think, like, the real thing that needs to happen first before you even really start getting, like, data people involved again, it's just asking what question needs to be answered. And for that, you can ask I mean, to start, anyone in your c suite and then anyone that has boots on the ground too to understand what problems your organization is facing today. Once you do that, then you can start bringing more data people into the fold. And that will really again, it's it's a consultant's answer, and it happens this is the third time in a row we're doing this. But if you need more technological resources, then, you know, you'd have to discuss budgeting for, hey. Like, do we need to bring on engineers? Or, hey. Do we have the money to get these technological tools to implement the vision that we have? And yeah. Emily, I'm curious from your perspective too what you think on that one. Yeah. So, I I I think realistically that when it comes to people that need to determine data strategy, I look to the organization to see who the movers and shakers are of that organization. I I think CTO is an easy answer, but I would also say the business need users. Right? Like, I'm talking to, CFOs are quite common for me. Managers that roll up to the CFO. I think, obviously, analysts, data architecture, IT. I'm realizing we're at the top of the hour, though. Sorry. I I would love to stand here and answer all these questions. I would be happy to answer questions if you don't mind reaching out to us. Interworx dot com has a contact button. You're welcome to provide some feedback there, or or check out with a or check-in with us. We're we're always happy to answer questions after the webinar. But thank you all so much for joining us. This was great, and we hope you have a good rest of your day. Yeah. Thank you everyone for attending.