 Hello and welcome, my name is Shannon Kemp and I'm the Chief Digital Manager of Data Diversity. We'd like to thank you for joining the latest installment of the Data Diversity Webinar series, Data Insights and Analytics, brought to you in partnership with First San Francisco partners. Today, Kelly O'Neill and John Miley will discuss the importance of effective communications in analytics. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them by the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share or highlight the questions by Twitter using hashtag DI-Analytics. As always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and additional information requested throughout the webinar. Now, let me turn it over to John and Kelly. Oh, excuse me, sorry, let me just take a free moment here. Now, let me introduce our speakers for today. John is a Business Technology Thought Leader and recognized Enterprise Information Management Authority. He has 30 years of experience in planning, project management, implementation, implementing information systems, and improving IT functions. John writes and speaks on a variety of topics and enjoys sharing his expertise on strategic planning, data governance, and practical technology applications that solve business problems. Kelly O'Neill is the founder and CEO of First National Francisco Partners and Information Management Consulting Firm. She is a veteran industry leader, speaker, author, and trainer. Kelly is passionate about helping companies leverage the value of data, empowering them to derive insights for that informed decision-making and improved results. And with that, I will turn it over to John and Kelly to get today's webinar started. Hello, and welcome. Hello. Thank you, Shannon. And hello from me. Great. Well, I think John and I are really happy to be talking to you about communications. It is one of our mutually most favorite topics because it is so important to the success of any program. And as always, we go through the attendees. It's nice to see some familiar names there. So thank you, everyone, who is joining regardless of where you are in the world. So what we're going to go through today is hopefully helping you better communicate all of the hard work that you are doing and all of the business benefits that you're providing to your organization. You know, communicating is hard. And I think that that's one of the reasons that we like talking about this is because we like helping people to navigate through that complexity. It's not just sharing information. And those of us in the data world, we're so used to talking about the actual data and we really need to remember that our audience may not have the same interest in the data that we do. And so we need to come up with some strategies for communication that really play into them as an audience actually and getting them to buy into our story and what we're trying to convey to them in a way that is meaningful to them and therefore will result in some action. And so that's what we'll go through today. We will talk about how to convey business outcomes and actions effectively. We'll talk about increasing your organization's understanding of data because of course data literacy across the organization is highly variable. And then we'll share some thoughts around visualization and data storytelling. That's become a really big thing now is showing how the data graphically tells a story and then weaving it into a line of communication that has some tension and some narrative and a result and an action. And all of this is really thinking beyond just the data. All right. So anything to add, John, as we jump in? As you segue to the next slide, this is a really relevant topic for folks that are starting to get into the data science or just kind of up their game with analysis or sophisticated business intelligence. Because the folks that have fun doing analysis and reports and working with data tend sometimes to not be aware of what communication really is about. And we've found that this is a really important topic, which is why Kelly and I like it so much. So back to you, Kelly. Great. And you'll see some additional quotes in our section separators to just both give you something to inspire you and maybe make you chuckle. So anyway, we're trying to incorporate that since it seemed to be successful last time. All right. If we're going to talk about communication, we can start from the very basics and just some communication 101 principles. Just like anything else, anything part of your program, your project, whatever it is that you're working on, it always helps to start with a plan and to take time to plan out your efforts. We do find that creating this plan about regarding communication tends to be something that is forgotten about because it's not necessarily recognized immediately as having a tremendous amount of value or having as much value as actually doing the work. But in fact, if you're not adequately communicating about the work that you're doing, how it's impacting the organization and how you're making a difference, then sometimes nobody knows all of the great work that you're doing anyway. So we always encourage people to make a plan. And this is important because it helps you to understand the effort and the resources that you need in order to do so. So that's really critical when you have so many things on your plate and you are trying to optimize your time. Creating a plan helps you prioritize and ensure that you are addressing the most important aspects of that communication plan. And we've got kind of some rhetorical questions here in terms of is a formal communication plan needed? Well, it doesn't have to be onerous, but yes, a plan is helpful to keep everyone on track. And, John, please jump in as we go. I was just going to touch on these a little bit. Yeah, go ahead, Dutch. I'm going to talk about the email one. That's one that really gets to me when you're ready. Well, I do think it is all about your audience and making sure that when you are communicating to them that you're not just using a channel that's appropriate, which is the email conversation, but you're also including the information that is important to them and excluding information that potentially is not. We are all barraged with more information than we can consume. And so customizing your message to that recipient is incredibly important to make sure that they're actually consuming the information that you want them to consume. So giving them three bullet points that are meaningful to their business and their job and their personal goals is much more effective than giving them the entire background of the program and the project and everything that you've done for the past, say, four quarters. And email is an option for communication. But what do you think of that, John? Well, a couple of comments here. One is a lot of folks think that communication is asynchronous. So you send out the email or you announce the thing or you say, we've put all the documents and share points, read and tell us what you think. And then you go, oh, gee, nobody is engaged or anything like that. And that's because you haven't communicated. And you might say, well, what do you mean? I sent out the stuff. Communication has not occurred until you know or have measured a response. And hopefully it's the response that you anticipated, which is, yes, I got it. I understand it. I liked it or whatever. Many of us in the technology realm think if we just push send, you've communicated. You have not communicated at that point. Email tends to be the biggest culprit with that. And then the last thing, in terms of the next bullet point, supporting your message, and then I'll hand it back to you, Kelly. Why research? Why case it? Why do you have to support your message? Well, if you're going to be data-driven, you will eventually show somebody something that is new and different. And they will go, is that really true? So just become, get used to the habit of whatever you say that the hypothesis is your message or the direct recommendation is your message. Have your evidence in your hip pocket right away. It will be asked. We'll just get used to that. And that's what I had to add on to that with Kelly. Great. And as John said, communication really should be bi-directional. So sometimes a way to get action or to ensure that that person is understanding what you're trying to communicate is to reach out and ask for feedback. And so they may have not read it. They may have not seen the video. They may have, you know, what have you. But if you reach out looking for feedback, then it might trigger them to say, oh, yeah, actually, I do need to do that. So if the bi-directional nature isn't happening automatically, reaching out for feedback, asking to improve, to make sure that things are relevant to them as a consumer is a great way to ensure that you're creating that bi-directional feedback process, even if it's not happening automatically. So that the communication can happen, not just, you know, like John said, the email getting sent. Anyway, just some communication 101 principles. But they're so important to start with, which is why we started this webinar, to make sure that you're always considering what the basics are and why they're important. In order to make this practical and actionable, we have a very high-level sample of the components of a communication plan that are effective and that you might want to consider. Most communication activities involve both qualitative and quantitative information. So some of the quantitative might be the data, and some of the qualitative might be along the lines of the storytelling that we're going to talk about a little bit later. But it is important to understand both the qualitative and the quantitative and how that's important to your audience. How often does that person want to be informed? And ensuring that you understand the frequency of that key stakeholder or of that group is important to ensure that you're either not over-communicating or under-communicating. Again, when you're trying to reach out for feedback and optimize your process, this is a great question to ask. Is this frequently frequent enough? Am I giving you enough information in order for you to make decisions in order for your team to be effective? Or if I'm not communicating enough, let me know. The method. Let's be creative and look beyond the email. And the last thing to consider is truly the owner. It is important that the messenger is as important as the message. And we find that quite frequently. It's not just the one person who might be responsible for the program delivering the message. But maybe if you've got a really critical stakeholder who is a little bit on the fence in terms of their support, you might want to leverage your network and have someone who has a personal relationship with that stakeholder try to deliver the message. Because, again, what you want to happen is the communication, not just the email, for example. You want to make sure that people are consuming things. So the messenger is as important as the message. John, anything to add? Yeah, while you're moving forward, I'll just tie you that one up and just say that a communication plan is a formal artifact. We have many other aspects of that. These are the most important ones. And as you can see, it's not just a matter of listing out a bunch of things like we're going to send an email or something else. You need to really, truly think through these things. That's it. Ready for the next one, Kelly? Excellent. OK. So now we're going to talk about growing an organization's understanding of its data. And there's a couple of different ways that we're going to think about this. And this is really this ongoing process to improve data literacy within your organization. So John, I'm going to let you talk through the first few slides in this section. Sure, I'll just push the buttons. And weigh in when you can. Just interject here, OK? I'll just keep it plunging along here. If you're going to talk about, one of the part about communications here, and we're talking about communications within the context of analytics, that's OLG. That's what the webinar series is about, right? It means you're always going to say, well, data is an asset. Well, we found it really important to define what does that mean. Every organization is a little different. Some organizations don't want to hear it. Some organizations seek to subtract. Some organizations say, yeah, please tell me what you mean by that. Whatever that is, you have to address that initially. Of course, now, if you do have some type of level of guess, go ahead. We're going to call data an asset. Or we're going to at least use it as a metaphor. Then you need the inventory of what that asset is. If you're going to be analytically driven, you're going to get really serious about engaging analytics into the ebb and flow of your organization, you've got to have that inventory. Of course, that inventory produces and satisfies business requirements, artifacts like KPIs and things like that. We find a lot of organizations have a real gap between how they measure themselves and what does everybody believe that they measure themselves with. And a lot of times, and boy, I've hit on this one before and it's just my nature. I'm going to hit it on again until we fix it, is that what we call business requirements in our data field, which is delivering a really awesome report on time, is not a business requirement. That has nothing to do with that. That's a technical and an operational requirement. So you must have that. Because if you're going to have, again, you're treating something as an asset, there's some things that goes along with that. Then you have to increase the awareness of this. Let's face it, most operating, functional, large areas in large organizations or even small organizations have their own data capability. If you do not promote yours and make them aware, they will focus entirely on what their own capability is. You have to grab their attention. And that's part of a maturity thing, is folks, we have to understand something. We've declared data as an asset. That means we all have to pay the same type of attention to the same types of things. It's no longer a departmental issue. And then lastly, once you've achieved all this, then you can really start to say you're using data to anchor your decisions. If not, you're doing one-off things. Now, there's nothing wrong with being one-off. You've got the sandbox, you do an analysis, you go, wow, look at that, 42. We have to do something. But you have not exploited this asset to its maximum extent. Ready to move on, Kelly, unless there's something to add? I will move on. All righty then. So growing your data literacy is similar to learning to fly a plane. And those that know me know why Kelly gave me this slide to talk about, because I am on a weekend basis a flight instructor. That's a hobby of mine. And I teach people to become pilots. And years ago, I realized I was able to transfer some pretty significant things from my hobby to my day job. And now, one thing that's transferred more than anything is how people learn. And I've discovered that data literacy is very similar to the progression of steps someone learns to solo an airplane. Without going into learning theory and all of this and taking far too much time on this slide, there is a process that people go through to learn something new. And it starts with basically understanding the facts, which we would call rote, all right? And rote learning would be in an airplane. It would be the controls move a certain way. And the airplane should do a certain thing if I do that. In the data field, I would know a fact about something in the organization. We have 100 of those. But as you know, with data, I have to have understand a little bit about the nature of that. And then I start to learn to apply that data. And then I get to a spot called correlation, which is I can take what I know. I can understand all the context which came from application. Then I can add in experience and previous or planned events. So when I can put a sense of time and wisdom on here, now I have correlation. In the aviation business, I cannot solo a student until I witness correlative behavior with them. In the data literacy world, an organization is not data literate until they are able to look at the data, understand the context of it, and then apply all those other things that are going on. And then they're making a solid data-driven decision. So correlation helps anchor a behavior that is that decision. Standard learning model. You can almost use it as a maturity scale or kind of a qualitative or ad hoc assessment vehicle. And that's important as we go forward here. So let's go to the next slide then. I also wanted you to go through this slide because I've been trying to zoom in on these photos and see if those are actually you. No, the FAA has not taken pictures of me, but I would be happy to supply pictures of me if I have several. Anyway, so let's apply this to an example, all right? So wrote, here's a report. We shipped 100. We all know that we do reports, right? That is wrote, OK, we sent 100. Now, of course, anyone on the business side is going to, you know, we will deliver a report and we've been delivering ports since the 1950s as we shipped 100. Of course, now it's like, well, what does that mean in a modern data world? In 1960, you're glad to know you shipped 100 yesterday because you didn't know before. Understanding level. Now, we were supposed to ship 120, weren't we? So now I have an understanding of what that data, it has some meaning to it. And I'm starting to put some context around that. Now, some folks will say this is when data becomes information. That's a really nice philosophical argument. And those of you that know me, it's great to talk about, but it doesn't help me move forward. So now I have application. If I was supposed to ship 120, why? So now that I understand the context of this, now I'm starting to apply it to a problem. And a lot of us, our organizations are stuck there. And by the way, that's not a bad thing. But the reason you want to go on into the analytics is what you're thinking of is there's more to it than this. I need a bigger picture. And analytics and Hadoop and all these technologies we've talked about like data lakes, those give us tremendous technical capabilities to go to a correlative level of data. So the analytics will tell us, well, this is the third time that's happened. We haven't hit 120 yet this year. Is the forecast too high? Or maybe the forecast is right and an external thing has changed. We have to adapt. Now we're being correlated. Now we're being data-driven. So you can see how this applied to communication. My communication has to understand where people are before I send out the message. If I send out a message that says, we think the forecaster is too high. The market has changed. And your audience is still going, what were we supposed to ship again? You haven't communicated because there's no way to get the response that you've anticipated. So let's add to that, Kelly, or push the button and we'll move forward. Yeah, absolutely. All right, so back to you, Kelly. This is a lovely series of how to craft your message. Yeah, and so again, as we think about how do we walk people through their involvement in a program and their interest in improving their own data literacy and getting from rote to correlation, there's also a concept of getting people to buy in and participate. And as they increase their data literacy, they will also presumably increase the concept of being data-driven because they will see how it benefits their own job, their own professional career, et cetera. And so one way to get them through this journey is through a communication process that helps to align with their beliefs and link those beliefs to the behavior that you want to see from them. And we talk about this as a way of first articulating the vision of what you are trying to accomplish. So what is it within your organization that you are trying to accomplish and why are you communicating about this in the first place? What does that, why are we putting together the program? So that's the purpose. Slightly different than the vision statement, it's the why are we doing this? And then those two things should be compelling and evocative and get people to believe that this is a program that they personally should be invested in. Following from that are the picture, the plan and the participation. And the idea is that you're starting to build this story and build this image of what the future state is going to be like and why they should be involved with it. So that picture is a representation of what the future will look like and why it will be better. The plan is how are you going to get there? And then the participation is what is that individual's role in this whole process? Again, why should I care about this? So as we think about this, this is just another way to get people engaged and be interested in moving from rote to correlation so that you can start to increase that data literacy and increase the concept of data-driven. We're going to look at each of these sections individually because I know that none of you can see this very well except maybe that top section. And we'll talk a little bit about why the contents are important. So from a vision perspective, this vision should be short, memorable and pertinent to the organization. Leveraging data for a competitive advantage was very relevant for this organization because they had a competitive nature. They were in a competitive industry. They had a marketing and sales-focused culture and their goal was to be the best and to be able to serve the best and be recognized as the best in their industry. So pulling in that cultural component of competition makes this vision relevant to them and data becomes a tool in order for them to execute the other aspects of their job which are inherently trying to promote their company, their products and increase their sales. Then if we think about the picture, sometimes it's good to put it into an actual graphic. So this is what it's going to look like. In this case, we did kind of a before and after. So we looked at what are people doing now that's creating frustration, lack of productivity, additional cost in the organization, et cetera and what is it going to look like after so that they can start to create their own picture in their mind where that they start to identify with the improvements that will be achieved within that future state. The next is when we go through the picture, it's also a little bit about what the purpose or sorry, what the behavior is that you want to see. So not just what does the future state look like but how do we expect people to behave in that future state? And we use guiding principles. I'm sure if those of you that have heard John and I speak before and I actually see a couple of folks that we've worked with, they will recognize that guiding principles are a great basis of fundamental understanding to actually align people's behaviors. So this is an aspect of the picture in that it articulates expected behavior that will be seen as part of that future state. So we're accountable for the data we produce, we're responsible for the data we use and share, those sorts of things that articulate not just what the future state looks like but how are you expected to behave. Just a couple of other examples. I know you guys will all get the slides so we don't need to go through this but again, driving to expectations of behavior. And then wrapping this all together in this infographic, we have the purpose on the bottom of the infographic so that the vision and the purpose created essentially bookends for this client. And again, the purpose does go back to the culture of this organization. There was a financial institution so currency was relevant for them and they're embedding how data can be used to improve their organization. So the ROI of all decisions should be easily measured which of course needs data allowing time for real-time simulation, excuse me, and optimization. So really looking at how data not only benefit the organization but ensures that they are able to measure and track that benefit. So again, just a way and a method of communication to get people bought into what you're trying to bring them along and what journey you're trying to get them to participate in. Okay. Anything to add to that before we move on, John? No, oh, looks good to me. Okay, great. All right, well now we're gonna move into a fun section about data visualization and storytelling. So John, why don't you go ahead and get started since I've been blathering on for the last five hours? No, no worries. Run out and get some coffee or something, yeah. Oh, I'll just take a few minutes here. And we, this one is gonna be kind of quick really. We had a talk on visualization a month or two ago, right? So real quick review, why visualization? Well, the quantities of data that we're talking about nowadays, you can't even look at that dump of the data and make any sense out of it. So obviously we have to somehow reproduce it in an easy to communicate fashion and get there quicker. You have to see what's important. You have to filter through a lot of noise. Sometimes you have to visually filter the noise as part of the visualization. You can, someone with the right context or again, correlated behavior can look at something and see something on one visualization that nobody else has seen, right? This has happened in all kinds of pursuits. I think the most interesting is finding asteroids and the new planets and stars. There are amateurs. As many new celestial bodies have been discovered by amateurs as there has been by pros because they've looked at the data differently. So that's kind of a thing to keep in mind. So, and it helps your recall. It's easier to remember a picture and what the heck, it's more fun, right? So let's go on and just take a quick example here. Sure. All right, let's. So, and this is one we used before. So you have a bunch of data and you go, oh, yeah, I have a bunch of data. A part of communication is making it see important. So here's a visual that we've used before of the cheerful topic of deaths in the Grand Canyon. And as we can see, there's some spots that, heck, I'm not going hiking there, or that could just be a place to be more careful when I enjoy the view. So obviously that one little picture there which also shows some really safe places. All right, that's where I wanna pitch my tent over there. And it's kind of an upper left-hand corner. And that's where I get the understanding. Now, in one page here, we have history. This is from 1869 to the present, right? So on one page, I see where a lot has happened over the years. I see where a lot has not happened. It makes me, maybe if I'm looking at this, I might wonder why some of these areas are safer than others. Is that something I can apply to my experience or is that something that is just the way it is? But obviously visualization helps me really, really dive into understand and that is what this is all about, is understanding. Kelly, anything to add to that or we'll move on? Yeah, I think that the visualization aspect of it can help to get the participant or the recipient along this path of road to understanding, et cetera, to correlation more quickly, because it helps to do that for them to a certain extent and helps them to see what are those links that are created, what potential questions would be answered and therefore helps them through that process so that they can practice that process and engage that muscle themselves. So I think it moves people through that process more quickly and teaches them how to do it. Yeah, and then this just triggered a thought and I could have said this at any site, but just before I forget it, there's a lot of data support organizations that will do the left side of this and provide the tools to do the center part, but they don't ensure that the understanding has taken place or they don't ensure that it's being used in a way it's intended. That means communication has not occurred. Okay, so let's go back to that. So someone pushes out a comma separator file and goes, yes, we've done our job. We have met our service level agreement. That's not really data literacy. That's not being a data organization. That's just dropping off milk at the door and moving on. For those of you that remember the milk used to be delivered to your door, I'm sorry, I just really dated myself with that one. And anyway, so part of this cycle, I know we're talking about visualization here, but this is prime evidence of why communication and understanding that it's more than just sending out a message is really, really important. Because if not, you could be delivering, you could be doing all kind of stuff, things on the left side of this page and having no use from it all at all or not receiving the intended benefit you had thought. And that makes all the activity on the left side of this page including infrastructure and labor and everything to be a sunk cost with no benefit. If you think about it that way, visualization and communication pretty important things. Okay, ready to move on Kelly. Absolutely. And I think visualization is a component of our next topic, which is about data storytelling. And so this I think is a great way to think about as you are crafting your message and crafting that the communication content that you think about it in the context of a story. So we've talked about a learning process to increase data literacy. We have talked about a communication approach to get people bought in to change beliefs and therefore to change behaviors. Sometimes it's good to just think about this in the context of the plot of a story to get people to go along the journey with you. And it does compliment the topics that we discussed earlier and it's a way of crafting that information so that when you're talking about possibly your vision and your purpose, et cetera, that it's in the context of a story that may be more memorable. And so there's lots of work out there around the benefits of storytelling and how to create a story when you present, when you talk, when you send emails, et cetera. But essentially it's thinking about the first of all, setting up the conflict. Well, in our world that conflict is the business problem or the challenge that we're trying to solve. And then why is that important? So what are the stakes at play? And that's the risk to the business if that business problem is not addressed. So the conflicts, the tension, the stakes of why this tension is worthwhile to continue to watch or observe. And then who is impacted? Who gets involved in those stakes? Who needs to help resolve those stakes? So who are all of the characters and the players? And then finally, as you get to kind of the resolution of the conflict, there's ultimately this concept of transformation at the end of the story or at the end of the communication, where as you identify who's involved and who is impacted, then you can talk about what that future state looks like that addresses the conflict, that ensures that those stakes are minimized, that ensures that the characters have their roles to play and are doing so efficiently. And therefore that the business outcomes and the opportunities are addressed in the way that you like that you are planning. And so again, when we think about sharing analytics, putting together that whole story about why the work that you've done is important and how you can build up the content to get people engaged within your story is really an effective way to think about it. And I think just fundamentally, we do get drawn to stories. We love the fact that there's characters involved and that there is tension even as very small children, we do get excited around the tension in the story. But the storytelling also makes it more memorable. And so you could either craft your message in the form of a story or you could actually create a story. One of the things that we do when we think about some of the storytelling in our work with our clients is a day in the life where we talk about, this is what happens when the person gets up in the morning and then they go to work and they identify this conflict and the issues around the conflict and who do they work with. And so you create a story through a day in the life where you actually create this memorable event in someone's mind. And then stories also tend to pull people in and increase that engagement. John, anything that you wanted to add to the storytelling concept? Any examples? Well, I think the day in the life is a really good thing to talk about. But when we talk about the generic structure of a story, conflict and stakes, that doesn't mean when you go to work and do an analytics report you have to introduce tension and conflict. It's not a spy movie or anything, right? But the conflict does come from something as mundane as an opportunity where you miss it. So for example, people deliver data all the time to an organization and they hope something gets done with it. Maybe if you're in an environment where you feel like there isn't the support for your data analytics function or you've got too many functional areas hiring their own analyst at perhaps the expense of a better managed data area, maybe it's because there isn't enough of a story involved here that people cannot see that addressing one type of story because they perceive an immediate conflict or a problem might be creating an even bigger story. So you can actually use stories to help with other stories as well. We try very, very hard in all of our walkthroughs and stuff to basically tell a story with our clients. That's what I had to add to that one. Great, okay. We'll keep moving along so we have time for questions. Yeah. And of course we always love stories in cartoons format. So we had to bring in Mickey Mouse and Walt Disney. And pictures still tell a story in a much more emotionally evocative way than any amount of data. So let's talk a little bit about thinking beyond the data and why it's important to do so and remind ourselves that as much time that we spend in the data, that the outcome and the benefit is beyond the data. So, John, why don't you talk through how this learning approach creates that impact? Absolutely. And so one quick reminder here, we're getting very close to question and answer time. And for those of you that do have questions, comments, things to discuss, please start entering them in the Q&A section on your panel there. So here is what we reviewed earlier, right? Brode understand application correlation. So now we're doing analytics. We're doing what we would call a correlative behavior. Now, at that point, it's time to leave the learning model and enter the world of an operational model. Once you have attained a certain degree of awareness or maturity or literacy or whatever word you want, you have to make it real in an organization. Let's face it, if it doesn't stick or it can't be institutionalized or operationalized, it will always live in the realm of the ad hoc or the sandbox. A really powerful, powerful analytic result that has really made a difference will be operationalized or the behavior that it recommends will be operationalized. So once we get through this, we have a scenario where we have a resolution. We figure it out, the forecast perhaps is too high or the market has changed. That means we have adequately communicated the results and a realization has set into the organization. And we go, yay, now we know. Now what do you do? Well, you have to get out of directive, all right? You're in now a directive phase in aviation once my student has soloed, I'm going to teach them how to manage all the various types of events that they have to have correlated behavior and basically operationalize being a safe pilot for all the things that pilots do. So, but the directive here for the organization would be, wow, we need to keep an eye on this all the time. This surprised us. So we have an opportunity here to be a little bit more proactive, a little more aware. So let's operationalize this result. So this analyst becomes part of the business. So now we implement some alerts. We do the analysis on an annual basis. We alter the sandbox activities to maybe go to the next level. Now that we've got a root cause, what are some of the other things that we want to understand about this particular situation? Because now this new scenario, remember we've got a feedback loop now. We're taking these results, we fed them back into the organization, we've adjusted to the market change, we've adjusted to the forecasting, but now we've changed the operation. So now we have to start to measure something else, but we've learned so much and we are now correlative that the next step of analysis, knowing what to do will be fairly easy. We're now monitoring a business as usual analytical process. Now your data driven. We've operationalized correlative behavior. This sounds a bit academic, but think about boiling us down to the practical example. This is really cool. Let's do this all the time. Make it real, make it so. And then you put that into production. So you're gonna move from the sandbox to the productionized material, which then again frees the sandbox up for doing more new stuff. The one thing to avoid, we've hit on this before and Kelly, when she weighs in here in a second will echo this, is don't operationalize stuff in the sandbox because you're paying an awful lot of money for something that needs to have different controls on it, different scrutiny and is maintained in a different way. And the mechanism you're using for it is not amenable to any of those things. So you're setting yourself up for more problems, not less. Kelly, anything to add to that? And by the way, I just told a story if you were paying attention there. Okay, Kelly, anything to add to that one? Yeah, I think that then another step beyond this is to think about the communication process of that action also. And so then how do you, when you're explaining to someone who possibly wasn't involved in the inventory process and the analysis process, some of the work that you've done to create a change in the way that inventory is managed, the way that shipments are happening, the way that you're optimizing the efficiency of your warehousing process, et cetera, then it's a way to tell that story and demonstrate value back into the organization based on going through that whole process and creating a change and an action that can then be sustained going forward. All right. Okay, to our wrap-up slide. So to kind of pull this all together, communicating in general and communicating about analytics and data in general is both an art and a science. And so one of the things that we always encourage people to do is rather than trying to do it all themselves, reach out to those artists within your organization to help you with that communication process. A lot of us in the data world tend to be more on the scientific side. So let's reach out to those marketers, to those salespeople and to those artists to help us with the messaging and to help us create those stories to ensure that we are communicating and not just providing data. Because what we're trying to do is drive understanding and action, which is really fundamentally important. And be creative. Consider using compelling visuals. Use video. Use channels that might be a bit unexpected within your organization. Anything to ensure that the communication is memorable. And incorporate the concept of storytelling with that data. And that will help your organization to really see value from the work that you're doing as well as to improve the way that you do communication or the way that you communicate in general. John, what would you like to add? I think that's great. We've got some questions coming in. Why don't I read them out? Kelly, you take the first stab and I'll add on. So here we go. Big data science, is big data science industry benefits specific or are there industries that can benefit the most from data science slash big data? And Kelly, you take the first part of that and I'll wrap that one up then we'll move on to the next question. Sure, interesting question. I'm trying to think if we've got another webinar that tackles that a bit more directly. Some industries are more data driven than others. Those laggards in terms of becoming more data driven such as some hard manufacturing and things like that are adopting things that create data more and more such as robotic manufacturing and things like that. But fundamentally there needs to be the creation of data in order to be data driven, except for the fact that you can be data driven within the environment in which you have. So the volumes of data may be higher for those industries that are inherently data driven like finance when just about everything is data versus steel manufacturing which is a very physical product with a manufacturing life cycle. But the concept of being data driven can be regardless of your industry how much data you have and how scientifically that data is analyzed to be the best data driven that you can. Yeah, so follow on with that. There is always certain industries that grab on the certain things sooner than later. What we've seen around data science and big data is of course industries that can spew out a lot of data. So social networking, social media as in marketing, things like that. And we're all familiar with the fact that when we use Google in exchange for using Google or Facebook, they're collecting our data. And there's a lot of us doing that and there's a lot of data being used. Other industries that have traditionally had lots and lots of data that they have to kind of mind through would be pharmaceutical and chemical, things like that. But there is now, and Kelly I think you can echo this there's nobody, there's no industry that I can think of right now but it's not now thinking about this type of technology. And then therefore saying, well, we're not in the big data or data science. We don't need to worry about communicating or architectures or all the things we've been talking about in this series pretty much everybody is. And I would say big data is no longer big because it's emphasis on volume. There's a case to be made now that big can be translated as important data and using important data to do different things with data that you've not done before. So industries that you might not have thought of like steel, it's a good one Kelly, steel industry kind of big, go make a bunch of steel, right? But it turns out now that what's happening in steel is they're starting to collect the data from all the various mechanisms and machineries and line controllers that move things around and collect the temperatures and the humidities and all of that. And you're starting to get an internet of things thing where this was years ago but I had a friend say the steel company $50 million a month by putting in some analytics and adjusting the flow of natural gas to a furnace. So there's a lot of things that are starting to happen here. I guess another way Kelly to look at this is there's nothing new under the sun in terms of what to do with the data but now that you can do it, it's time to dig out those old notes and see what you can do with it. I guess would be another way to do that. Any comments on that or else I have another question for you. Let's go to the next question. Okay, when or what is a data landscape? So someone was listening and wanted to know what's a data landscape? Well that's a good one Kelly, go ahead. Sure, yeah so a data landscape is kind of a way of thinking about this in again a pictorial and more visual way. And a data landscape is that combination of your inventory of what data do you have combined with where it's located. So where does it sit doing your data map if you will. And it's also a little bit about what is the topography of that landscape as well. So this could be considered a component of a broader data lineage exercise, but your data landscape is really drawing out what you have, where is it, and in general what does it look like without going to necessarily the level of detail of a robust data lineage exercise. Yeah, yeah. Most cases don't know what they have, right? They don't know what they don't know. Most organizations we work with are surprised, pleasantly or otherwise, that they're scooping up information from so many places. Part of your data landscape is right Kelly, it's external data too, right, coming in. And then there's an agreement that your data landscape is data that's going out. So think of it as that 10 or 20,000 foot view looking down on the earth. It's that Google Maps representation of the satellite and you can kind of see the big chunks and where the roads are going and where the highways are going and things like that. I don't necessarily know all the details, but it's enough for me to get a sense of complexity, a sense of movement, a sense of pinch points, and things like that. Okay, we have another question that has come in here. And just a reminder, we've had some requests from folks that they would like to hear some more time for questions. So we put that in to this presentation. So if you do have a question about today's topic or something that's been really bothering you from something else that we've talked about, go ahead and submit back here. Here's the other one. What is a way that you can start small with improving data communications? Does it have to be super formal? Can I start smaller at a team level? My sphere of influence is not that large in my organization. So here's someone that recognizes an issue or problem Kelly wants to get started. What's your advice? So my advice when you are just starting with communicating about data is to think about the storytelling aspect first and do what you can to not use the word data. So make this a relevant discussion that talks about and aligns the work that you're doing in data to the business goals and expectations in a way that demonstrates the value that you're providing even within your team. So that you're creating this approach to your communication process that is more qualitative. And of course, as we said in the beginning, make sure you've got your research done and you've got your statistics. But when it's presented, presented in such a way that it is more like storytelling and it can get people through that learning curve and get people to believe and behave in the way that you want them to, even if it's within your small sphere of influence. I'm sorry, I was gabbing to the mute button. Agreed, I agree totally. In addition to the story, apply the learning model to your audience. So you're getting started and obviously you don't have a sphere of influence. Usually means that the receptiveness to the data is maybe being tolerated, organization is less mature, you're thinking it's gonna work or not. Think about the ability of the audience to digest it. If the audience is at a row phase and you've been delivering row and column information for a minute, a long time, giving them the results of a Monte Carlo simulation that takes 50 years of activity in your business and then correlates it with external sources is not gonna be able to be digested. So think about that. Or if you're in a situation where it has to be digested due to some business circumstance, you'd better get some help to convey that, bring in a guru, bring in an expert. It's one of those times when paying for someone from the outside really, really, really has a high return on it in a situation like that. Here's one more question really quick here, Kelly, then we'll have to wrap up. What is the outlook of data science? Wow, 100 years from now, what is next after data science? 100 years is a bit broad. Why don't we try 20 and then 100? Kelly, do you really think of crack of that? I mean, if we think about like how quick the data science came up into the forefront, I think it's really just five years. I think that one of the trends that we see, like I'm not a future predictor, I've got some friends that maybe I can consult that are a bit more psychic than I am, but I think that what we can anticipate is that the cycle of creativity is going to continue and the cycle of operationalization is going to continue so that this concept of data science being a new novel thing is no longer gonna be novel anymore. We've got educational institutions supporting it. It is going to become just another job. I'm so sorry to disappoint people who are hoping to get gobs and money because they're a data scientist, but ultimately over time, it's gonna just be a very important job and there's going to be something else that comes up that will be the next new thing and the data volumes are going to be getting bigger and there's going to be different technologies available to analyze them and to create the data, of course. But I think that that's just the cycle that we will anticipate is that there will be within the next five years, the, you know, call it commoditization of data science and there will be a next thing. So don't worry, there is a next thing. I don't know what it is yet. Well, it's already here. It's already here, artificial intelligence and machine learning. So the, and I'll just stop because we're almost at the amount of our time here. The takeaway from this is that anything, and we know this now for sure, anything that you can take a look at, operationalize the input, the process, the output, insert the algorithm and set up a series of conditions that can do it and then even with AI and machine learning, a series of parameters that you can take action on it means it is automated. So a lot of what we're doing now, which is we think is really cool and awesome, will be automated and the next big thing will be whatever couldn't be automated after that. And at that, Kelly, I'll turn it back to you for the wrap-ups and you and Shannon and everyone have a great day. Excellent, well thank you everyone for attending, Shannon, over to you to close it. Thank you, John and Kelly, for another fantastic presentation. Just appreciated as always and thanks to our attendees for being so engaged in everything we do. Love all the questions that came in today. Just a reminder, I will send a follow-up email by end of day Monday for this webinar with links to the slides, the recording of the presentation as well. Thanks again, everybody. I hope you all have a great day and we'll see you on the flip side in April. Peace out.