OK. Thank you for coming today to all the survivors. I guess I know it's been a very long week. And welcome to my talk. Composability is the software, as compounding interest is the finance. So I'm Bill Mulligan, and I'm a community pollinator at ISOvalent, working on both the EBPF and psyllium communities. Now, as someone once said, that is much smarter than me. Compounding interest is the eighth wonder of the world. He understands it, earns it. He who doesn't pays it. Now, if you don't remember high school math that much, compounding interest is when your money is making money.  Now, we all, this is the business value track. And the question is, what is the compound interest of software? And if you haven't guessed from the title already, I would say that composability is the compounding interest in the software industry. But this is a long, complex word. What actually is composability? Let's start with the standard definition. So going from Wikipedia, composability is a system design principle that deals with the interrelationship of components. A highly composable system provides components that can be selected and assembled in various combinations to satisfy specific user requirements. Now, I guess the part that I'd really like to point out here is the selected and assembled, because that is really what we're going to focus on. Now, the question is, do we see composability in the cloud native ecosystem? Do we see things that we can collect and assemble into exactly what we want to satisfy our specific end user needs? This is a CNCF talk. So of course, the landscape is going to be here. And I would say, yes, that is where we have composability in the cloud native ecosystem. It has grown and grown and grown to a massive extent, because there are so many pieces that can be selected and assembled into exactly what you need as a business. And not only that, but we can see that it builds business value. One way to track that is just the number of acquisitions in this whole space. In 2016, we had two companies. In 2017, we had five more acquisitions. In 2018, we have a whole list now. Now we have half a page. 2020 is now the whole page. And 2021 is almost the whole page by itself. As you can see, there's a lot of value being created in the cloud native ecosystem. But why exactly is that? And I would argue it's because innovation builds on itself. Just as in compounding interest, your money is making money, the innovation is making more innovation. And we can see it through the course of the development to cloud native computing from non-virtualized hardware all the way to cloud native. And another thing that we like to say, if I've seen further than others, it's because I was standing on the shoulders of giants. The standing on the shoulders of giants is the innovation building on itself, building on projects, being able to select and assemble them. Now, why exactly is composability compounding in software? Let's start with one project and look at the lifecycle of this project. Every single project is going to have a lifecycle in terms of the investment that people, the community, companies need to make into it. And it changes over the lifecycle of the project. In the beginning, you're making a lot of investments into the project. And later on, you get to reap the rewards of that. And there's kind of four different phases that you should probably think about. The first is the launch phase, then is commercialization, maintaining it and sustaining the project. In the initial launch phase, you need to do things like get the word out, be visible, making sure you're releasing the code, show momentum, attracting user interest, adding contributors, creating the brand. And this is what gets the project initially off the ground. And it's a lot of work to do that. In the next stage is the commercialization. You're still seeing a very high-level investment, but there's a lot more momentum behind things. You actually have products using the code. You have production users. You have a diverse and stable contributor base. There's a steady release cadence that people can understand and rely on. The features aren't coming as quick, but it's good because it provides more stability for the project. And there's a beginning of the push towards conformance and interoperability. In the maintaining thing, you need to do things like having an established community with products, a conformance program, interdependency, and long-term support releases. This is when the project needs to continue, and people need to be able to rely on it. And finally, in the sustained phase, we have things like making sure that companies continue to support the upstream work, providing a long-term home for the project, and making sure that things like security notices and updates are continuing to do it for people that are still using the projects. And in CNCF, when we talk about end-user-driven open source, this is the lifecycle that we're talking about, where we have a project that has end users that are using it in production. These end users create feedback. They create issues. They understand what the use cases are, what the edge cases are, and they feed that back into the project so that we drive the project forward. Now, this is what creates a virtuous cycle in a single project because this feedback from the end user makes the project better itself. So for the project, once you have the developer community and the technology, the end users put it in production. They have things that wake them up at 3 AM. And finally, they have participation through feedback. Maybe it's issues. Maybe it's even a PR into things that they're looking for. Now, if we're not looking at just a single project, but a whole ecosystem, like for instance, Cloud Native, it's not just a single lifecycle we're looking at. It's really a whole collection of life cycles that we need to look at and understand to understand why the composability is important. So let's add a second project. Now, the question I'd ask is, where do we put the curve? Do we need to make the same whole investment all over again? But no, it's not the same investment twice. Now, also going back to some high school math, what it's more similar to is the area under the curve rather than the addition. You can build on top of the existing community momentum and tools and life cycles that you already have so you don't need to make the whole same investment twice. And this is why ecosystems like Cloud Native become so valuable. In terms of the composability of projects, when you need to be visible, you already have an existing audience to tell your story to. That's why if you look in the sandbox, there's so many projects there because there's so much momentum and there's such a community around Kubernetes and Cloud Native right now. In terms of releasing code, you can reuse tooling for things like prow. For showing momentum, you can leverage existing projects momentum. Think about how many tools here that you've seen in the past couple of days building on top of Kubernetes. Attracting users, there's already users. They have problems. There's gaps in what is available. And they are looking for solutions to their problem. I mean, just go walk around the vendor showcase right now. For adding contributors, people have feature requests and gaps that they have. So people are naturally attracted to solving their own issues. It's the whole virtuous life cycle that we saw before. And in terms of creating the brand, you can relavage the existing brand. Think about how many things we've seen with either a K or some type of C themed to build on the Kubernetes ecosystem brand. For the commercialization, there's already an existing user base who are using it in production and they need to solve their issues. Think about simple things like debugging or security for Kubernetes. How many companies do you know in just those two spaces? For the diverse contributor base, the contributors are once again moving across these projects. People that have already worked together in the past are able to work together in the future. I know if you saw me before this talk, I was talking to some of our great ambassadors. And it's because we know each other and we can work together. And that's a really important thing, this trust in the community. As the rate of new features slows, you start to see some of these gaps becoming new projects. These things that people want and need may become projects by themselves. And once you have conformance and interoperability, you really have a stable base for other projects to build on top of. I mean, think about how many things are built on top of Linux because it is almost a universal computing platform now. In the maintain phase, we have an established community, but also contributors and users can move to these new projects. The stable core is allowing new projects to build on top of that. And you have security policies and bug tracking so that you can track all of these across the ecosystem. So looking at this kind of at a high level of where these fall into, composability compounds the ideas and identity of a community by helping things be visible, creating a brand, showing momentum, and creating a diverse contributor base. It compounds people and community by showing momentum, attracting interested users, adding contributors, and having an established community. It compounds tools and integrations through a stable release cadence, conformance and interoperability, and a security policy that people can rely on. And it compounds companies and profits by having products that are using the code, production users, conformance and interoperability, and long-term support releases. So the composability and software isn't just about the code, but it's about all the other things around the code, too, that can be reused across an ecosystem of projects and products and companies, the ideas and identities, the people and community, tools and integrations, the companies and profits. So let's look at this thing again, except for instead of this time looking at a single project, we can look at many projects as the ecosystem as a whole. So the developer community and technology around all of our projects is being used by end users who put it into production, have problems with it, and feedback, whether that's into the existing project or creating new projects on top of that to grow the ecosystem and expand the cloud-native landscape. These solid building blocks allow the innovation to move up the stack so that we can tackle new challenges and solve new issues. But so far, this talk is really just focused on the theory of this. You've seen some graphs that I just colored by myself. What does it actually mean in the real world? And can I actually prove this? Well, the simple one is where we are right now, KubeCon CloudNativeCon. I found this on the internet. The website for the first KubeCon in 2015, as you can see, is a very simple and small thing. If you haven't seen, it's expanded a little bit since then. We now have co-located events on Monday and Tuesday. They're covering everything from security, wasm, telecommunications, to GitOps. As you can see, it's not just about Kubernetes anymore. The innovation is moving up the stack and the challenges are moving up the stack to expand into all these new categories, too. Going back to 2017, this is also another slide from a CNCF presentation. Looking at the storage landscape, a lot of people had problems with running data on Kubernetes. And pulling from the slide deck from that era, it's hurting the consumer experience, limiting in cloud native use cases because people are afraid of running data on Kubernetes. And there really is a lot of value by being able to run those stateful applications on top of Kubernetes. And so we need to have a way for applications to be able to make use of those storage resources. And so what the community did was come together and look at this. At the time, the volume plugins in Kubernetes were entry, which means it was challenging to add support for new volume plugins. Because all the vendors that wanted to do it had to align with the Kubernetes release cycle. All this third-party code created reliability and security issues, especially if you weren't using any of the plugins or you wanted a different storage vendor. And a lot of the storage systems weren't available. So for the maintainers, it was creating an almost impossible burden where they couldn't test or maintain any of the code that they were shipping to the end users. So if we want to make a composable system, we need to provide components that can be selected and assembles to satisfy user requirements. And so the ecosystem came together and created the container storage interface. And it provides a user experience where end users have a single integration method to work with common storage operations. So this is five years ago. And what's happened since then? We see the container storage interface has become really the standard for exposing these storage systems. All the vendors can implement and update what they're doing independently. And it's removing these third-party code and dependencies from Kubernetes itself. For the ecosystem that's resulted in over 110 CSI drivers, which would be almost impossible to maintain entry, reduce maintainer burden, increase security, and a massive expansion in the number of potential Kubernetes users. If anybody's worked with businesses before trying to adopt Cloud Native, everyone has their own unique requirements. Everybody has the same until you have that one thing that's different. Oh, we can only run this on Dell. We can only run this on pure storage. This is all that we have in our data center. This is all that our Cloud provider is providing. By creating a pluggable system that people can select and assemble exactly what they need, we've increased who is able to use Kubernetes and the value of the whole ecosystem. In the same way, at around the same time, we also had the beginning of the CNI. So the CNI is the container networking interface. And it was also a simple interface to standardize how networking is done in Kubernetes. And we're really seeing that today. As I said, I work with the Cilium community. And this standardized interface is allowing people to choose and swap their networking as they need. Before, a lot of people couldn't run Cilium yet because it's based on EBPF, which requires a newer kernel. And a lot of people didn't have that kernel available to them. Now as we've gone five years later in the ecosystem, that kernel is more available. More people have access to running EBPF programs. And they can switch easily from something like Flannel to Cilium. And they can take advantage of EBPF on top of that. Cilium itself is also a composable project for observability, networking, and security. And people can select and assemble the things that they need out of Cilium to solve their unique requirements. For instance, on Monday at the Cloud Native telco day, we had Daniel Bernier talking about SRV6 and some of the telco-specific networking use cases. We also talked to customers that are running edge data centers and production manufacturing lines that are trying to mesh their clusters together. These are both great use cases, but they're very different requirements. But by making Cilium composable, they can choose the pieces that they need to satisfy their unique requirements. And also, EBPF itself is also making the Linux kernel composable and programmable. You can load in the programs that you need at runtime to modify the behavior of the Linux kernel to meet your specific requirements, whether that's something like XTP, the Express Data Path that speeds up networking, or other things like Tetragon that's providing security enforcement. Whatever your unique requirements are, you're able to do that now with EBPF. But it's not just about standardizing and creating interfaces. It's also about bringing the community together. In observability, there used to be two projects. Open, well, there was a lot of projects. But two of them were OpenCensus and OpenTracing. These two have combined into the OpenTelemetry project, which is now actually the second fastest moving project in the cloud-native ecosystem behind Kubernetes. So by bringing the community together and assembling them into a new project, we were able to accelerate the development in the massively important observability space. We also have the API Gateway, which was actually just announced earlier this week by the Envoy project, where Contour and Emissary Ingress are going to be coming together. And pulling from the blog post that they just had, I didn't write this beforehand, was it's reducing duplicative efforts, layering value-added functionality on top of Envoy proxy, and more potential users, allowing more use cases, feeds a virtuous cycle of more potential customers, and more support for the core Envoy project. Exactly what I was talking about, and this is a blog post, but written by someone else. But this is on the project, the technical, the code side. What about the businesses? I would like to go back to the cycle again, but it's slightly different this time. We have projects leading to profits, and these profits are fed back into creating new projects. We still have, on the project side, the developer community and the technology, the code. But these are then transformed into products, whether that's an enterprise distribution or a combination of multiple products together into something that companies can sell to the markets, whether that's a specific vertical market or a platform. And these profits allow companies to participate back in the ecosystem, sustaining existing projects and creating new ones to meet their customer needs. So composability compounds, ideas and identity, people and community, tools and integration, and companies and profits. If you'd like to learn more about getting involved in the cloud native ecosystem, I recommend checking out tag contributor strategy. And also, if you want to learn about how companies grows, this is another example of composability in the cloud native ecosystem, where they have guidance for projects for things like a community ladder, a runbook, and different ways to help people get involved. You should also, if you're interested in the business side of cloud native, check out the cloud native glossary. This breaks down a lot of confusing terminologies or terminology that may not have met before into specific business use cases, into what it is, the problem it addresses, and how it helps. It's also now been translated into seven different languages. I'd also recommend checking out the CNCF landscape guide. You can find this on the cloud native landscape. This is some great work done by Catherine and Jason from Boyant, a great shout out to them. And if you're interested in more generally in the business of open source and how open composable ecosystems make money, I would highly recommend Open Source for Business by Heather Meeker. This is probably my favorite book that I read last year. So thank you for coming today. Any questions? Cool, go catch your flight then. Thank you.


Real chili is what? It's a meat sauce. It's got meat, salt, fat, and spices, and that's it. All right, what about chocolate? Not like a Hershey's bar. So it's like dark unsweetened cocoa, which adds like bitterness and some kind of like complexity to the chili. It doesn't have a place in traditional Texas chili. Tomatoes. Tomato paste. Whole tomatoes, crushed tomatoes. I'll welcome it in my book. I think not at all. Banes. I'm for it. The beans are just fine, but you gotta put the beans in. It's a separate thing that you put in the chili over the top of. It's a preserved dish, so if you add anything into it that can spoil, you're basically ruining the chili. Don't think of it as chili. Think of it as, man, this is gonna taste really good. I like the part where you said don't think of it as chili.


Welcome, my name is Shannon Kemp and I am the Executive Editor of DataVersity. We would like to thank you for joining today's DataVersity webinar, Emerging Trends in Data Jobs. This is the March edition in a monthly series called DataEd Online with Dr. Peter Akin brought to you in partnership with Data Blueprint. Now let me give the floor to Megan Jacobs, the webinar organizer from Data Blueprint to introduce our speakers in today's webinar. Megan. Thank you. Hello and welcome. My name is Megan Jacobs and I'm the webinar coordinator here at Data Blueprint. We are pleased that you found the time to join us for today's webinar on Emerging Trends in Data Jobs. As always, a big thank you goes out to Shannon and DataVersity for hosting us. We'll be back in just a few moments after I let you know about some housekeeping comments and introduce your presenters. We have an hour for the presentation followed by 30 minutes of Q&A. We answer as many questions as time allows, but feel free to submit questions as they come up throughout the session. And to answer the two most commonly asked questions, yes, you will receive an email with links to download today's materials and any other information you're requesting during the session within the next two business days. You can find us on Twitter at the link section. We have hashtag DataEd on Twitter, so if you're logged on, feel free to use it in your tweets. Submit your questions and comments that way. We'll keep an eye on the Twitter feed and we'll include answers to those questions and our post-session email. Thank you so much for the opportunity to introduce you to our presenter, and Peter will be presenting our special guest for today. Peter Akin is an internationally recognized data management thought leader. Many of you already know him or have seen him at conferences worldwide. He has been in 30 years of experience and has received many awards for his outstanding contributions to the profession. I'm also the Founding Director of Data Blueprint. He has dozens of articles and eight books. The most recent is Monetizing Data Management.  Kind of a ripe off of a red eye coming back from San Diego where we had a group that we were working with out there. So welcome, everybody. This is a really fun opportunity. I get to work with two of my favorite people in the world. Let me introduce, first of all, Eva Smith, who I've known for at least 10 years at this point. Eva is located up in Seattle, Washington. Eva is working with Edmunds Community College and, in particular, supervising a grant that is focused on how to help people get more into these careers. And she's going to talk to us about some of the career, about some of the success that she's had with that grant on this. So Eva, welcome. Thank you for taking the time on this one. And Mehmet Orun is Director of Sales Solutions for Salesforce.com. And he is a, first of all, phenomenal stylist and dancer, but he ran a data management group at Genentech before this, and we've had lots and lots of conversations around these areas. And really just being able to pull together and to work with us is great. Megan, before I let you go on this as well, thank you for two years of excellent service at Data Blueprint. This is not goodbye, but again, congratulations on your two-year anniversary with us as well. Thank you. So what you're seeing here are the sort of general foci that we have to bring to this. And Eva, I'll just turn it over to you for a quick second and let you articulate a bit on this, and then I'll turn it over to Mehmet and then we'll dive in. So basically, we're talking about how do we enable intentional development of people who are interested in getting into this career, but also once you're in the career, how do you get into this career? And this is something that we've been talking about in data management. And so I'll turn it over to Mehmet to talk about a little bit more about the data and the professions. You know, on this has been, as a consultant, as a professional, I had a chance to work with many, many individuals, persons in career development are both hands-off of mine. And I always, you know, after ways of better supporting people, we're not sort of to the profession to understand what it is that we do and how to do it better. And through brainstorming both with Eva and Peter, we noticed that we have commentary points of views that come from fairly different points of origin and also being a part of the conversation today. Thank you for joining us on this. I've been concerned really for the last 10, 15 years about raising the profile of data within organizations. And you'll see on the slide I like to call it our sole non-depletable, non-degrading, durable strategic asset within organizations. And if that's the case, we really do need to play around with it and focus particularly in this area. So it's a slide that we kind of put together to talk a little bit about conveying time to get into. So you can see sort of three different perspectives. And again, particularly congratulations on getting a grant in that area because it's been hard to get the governing organizations to look at this. It's really much of a case of they don't know what they don't know in this. Eva, did you want to talk a little bit about this? Well, as I mentioned, I work in education or have been working in education. I've been in treatment for 20 years or more, but in the past 10 years in education, we work at community colleges. In particular, we have people from mid-career, points of view and changing jobs or even younger people who are interested in moving into these kinds of careers. And so we're particularly asking ourselves how do we recognize information and programs around preparing people for work and field. And some of the areas that we're looking at are, you know, physical positions that also what happens when you get into mid-career positions and you actually get into this. And so we're talking about how do you actually see people fall into this career and how do we work through these different titles and roles and their scopes in some different programs. Anything on this one? So one of the things we are hoping you're going to get out of this is reasonable frameworks you can have for yourself or for your organizations depending on, you know, what role you play, what developing people in particular skill sets, through a given role, switching roles, getting into the field, and you know, hopefully these, you know, four symbols will serve as placeholders if you go through it. What you can see here is that there's a concept around working with data and again sort of duties and tasks that are there. One of the graphics on this chart that might not be familiar to some of you though is the circle in the bottom right-hand corner of the screen. This is what we've called for years and years the Dama-Dembach Wheel and what we now have is a codification if you will whereas before the year really 2009 when you talked about data management it was very difficult for people to conceptualize it. So lots more on that particular topic that we could get into it and maybe in the Q&A section. I think you were really looking to hit this particular question on it as well which is why is the data, what is the data profession and why is it so hot? There's a pretty strong interest in this session and I think one of the reasons for it is, you know, data is not being talked about in the back of IT departments anymore. You know, hot business review and some of the well-published business journals have all been writing about data and big data and the idea of the data science is the next big thing. What is the data profession, however? Data science is hot in general. If you have experience on data making, the best use of data making, designs that are going to take advantage of data all of these are being really sought after and there's a person how people can get additional skills and abilities to do these jobs better. And one of the things that is one of the newest titles or all that is emerging, my hypothesis to you is it is not the only hot field. Now, the only thing about the data world is it's still not that well understood. In fact, if we were to look at the definition of what a scientist is, because this is where it most appears to be, I want to move on to the next slide, please. Now, there are some common definitions that I was able to pull up online, whether a scientist is someone that focuses on research or if someone that focuses on taking research academic principles best practice of turning things into something that can work. Let's look at the work in the field of data as someone that uses the information. Here's an example of a historical example when the telegraph was put in place. If you took a link to look at the work it turns to come up with the alphabet. It took mathematicians to see how life can be transmitted. It took engineers to make the device work and then people observing telegraph operators in order for the information to be transmitted correctly and then the equivalent of economists to figure out how to monetize it or not monetize it, which is why the technology evolved. Now, the data analyst on the other side was looking at the insight I pulled these other definitions from. Data is described as a much more comprehensive role as if it's the person that can do everything. And in reality, this I would say much more of a description of the data professional. And the information is composed of many, many other types of roles. And in the context of IT, some of the IT roles may relate to each other. I'm going to look at this as a spectrum of interlevel roles evolving into more linear roles. In the starting field, and if you think about your typical IT project, whether it is for reporters, whether it is for innovation, application development, whether it's the nation of people in a business analyst, data analyst, developer type of roles. If you think about what an IT department does in general, you also have people like support specialists that actually talk to and use those applications more than most developers ever would. So they have a better sense of the type of information that may be needed or how information may need to be presented. So when you look at the career paths and we'll get into some of these in a much deeper level of detail, there's a natural progression people can evolve into if they evolve in their career or if they have an experience for education. I think that I will follow the cues of this slide. Let's talk about how once you decide that you want to be in the data profession and you understand there are different roles, what are the different ways you can get into the field. And Eva, who is the educator of our set of three, is going to take the lead in this area. So Michael, how does someone actually get into this profession? And that's one of the questions that I get constantly from people who are mid-career, changers, or students. It goes to how you get into this thing. So one of the things that we've begun to give is actually what are the different attributes related to this? So one of the things that you have here, you have another profession or another job? I'm really interested in stability and once we take the good work that you've done and Eva does this from working, and we should point out not only is Eva in the educational world, but she's also been a practicing professional for a long time as well. So she's well qualified to comment on this. So when we look at this, we've also got to go back to the college and university accreditation bodies and tell these folks that this stuff is important because we have a case that they don't know what they don't know. So from data management professionals, when I've asked my colleagues in this field of how do I get into this career? You know, it's a little, you know, one of the things I just fell into this role is much the commenting. An opportunity came along in my company. Another data professional that I knew, hey, said I'm in and you'd be great in this field and you've heard me. So a lot of us have found the data management association or other professional association and found a home there and said, oh yeah, I love this stuff. Or I took a class. And, you know, for folks that are in IT, they've kind of had, you know, that it's important and really wanted to make a difference in this area and began to try to find a way to improve the quality of data and the use of data in organizations. What's not difficult and is really talking about this in high school. Nobody's really talking about it. Most of the college, in terms of a profession, most college programs have a database design class or some class on database theory. Those are in computer science, some of the business programs as well. And it's not that many degrees. And as we'll talk about in a bit, that is changing. It's not that many college degrees. They're in data management specifically. So early on when we were looking at building curriculums at Edmond's, we took a look at the Department of Labor Occupational Handbook, which is one of the most current counselors in high school and college send students to look for the types of jobs they might be interested in. And just to add a point to that, Eva, if the federal government doesn't measure it or keep track of it, it probably doesn't exist in a lot of people's minds. So very important point that we put things in place there. That is absolutely true. And in fact, what we've also found in working with the college environment is that for those that are relying on training aid, for those that are reliant, who have been late and are getting worker retraining money, they look at those kinds of government publications as ways to identify high demand areas to fund and not somebody's qualified for funding for certain types of programs. So another part of this is that in the past 10 to 12 years in data management, we're trying to take a look at the elusive data management career. This worked down a while back in 2006 on a survey at an UDW conference about what is a typical data professional. Back in 2006, the boy was approximately 45 years old, had a lot of experience and typically began in the mid-20s. So it really became clear that this is something that occurs partway through somebody's mid-career and requires quite a bit of experience. Recent studies, however, and in recent publications bring more interest in this area. 2013 demographics began to change and a lot of that is because of the big data conference and a lot of attention on big data. And in academia, we're hearing a lot about big data. And so this sort of fueled more interest in this field. And it's nice to take a look at the left and the left because, you know, the things that we often sort of compare to are the perpetrators. So for example, you train for throughout your college career. There's definitely a path for it. And a computer designer, there's another example you train for your career and there's a path for it. So colleges can develop programs around that. For us, it's been difficult because there isn't a clear path. And so there's kind of a gap there in between the time that you graduate from college and the time you actually get into and there's some sort of a gap there. So... Not quite. So people have been in classification and in data management, we know a lot about classification. A lot about classification when we're thinking about what we want to do in our career. People aren't coming to careers and things and asking IT departments to put them in as data professionals because the IT departments don't recognize it as a career. And consequently, it's a chicken and egg situation. That makes sense. Yeah. I think that's one of these notions. So for both of you on the phone that have answers, this little picture may be much more familiar to those of you that do not. Mary was one of the first non-typical Disney princesses because she actually used weapons and made her own decisions and pushed back on us. So she challenged the idea of what a princess is or is supposed to be. Similarly, I think we talk about what a data professional is or is not supposed to be. And we strive to understand the meaning of titles. We strive to understand the meaning of words because studies do matter in human conversation. If we think, for example, a data modeler is someone that's going to take six months with designs that we are never going to implement, and I think we're in some bubble that sometimes can have negative content in that sense and sense on purpose. You know, to evolve as much. So part of what we're trying to do is totally understand in order to enable individuals to be as effective as possible in the organization and to be able to have the right set of skills and the right set of abilities. The other part is to recognize that while these are roles that are broadly defined in their nature, people may be having different business titles given that they are broader responsibilities or better off the work culture that they are a part of. What you're going to see is while we have these titles that Eva mentioned earlier, they are not reflected by the well-managed, long life cycle taxonomies either. I'm going to move on to that as well. Another advantage that we have is we have varying sizes and shapes of IT departments. So a smaller IT department is much more likely to have a blended sort of an approach than a very large IT department that may have very specialized components. If a data person, what I would say is we need to differentiate what a role is supposed to do and what title may mean. They are related but different things. Eva, you alluded earlier that there are some challenges with the U.S. federal government classification of the professions, even though it's evolving. Yes. Like I mentioned, of early years, we talked about the programs that Edmunds, in particular, developed in the program in data management and we were looking at what the different rules and types are out there. And back then, when we were looking at FDU, everything was classified into database administrators and computer systems analysts. So it was really sort of difficult and still is sometimes to explain to people what a data management professional is. So, data management association actually did begin the Worklist Department of Labor to look at some of those roles. And now, today, this is a snapshot from the O-Net online. You can look at database architects, critical data managers, data warehouse specialists. These intelligence analysts are showing up as categories within the O-Net. And as you can see, database architects have a bright outlook, in which is which probably because of the folks built up the financial outlook that's what they look for when students ask to be in the profession. So, we're beginning to make some progress in this area. Making, you know, sense of that is part of what I think in the profession to try to help people stand and have quite a ways to go. As you can see, this is the directly from the O-Net type of sense not expectation for which data collection is put away. They are still in the process of looking at database architects are. But you can see there's a reasonable definition for that now. And part of this is that if it's not there, it's not gonna happen yet. We don't have a professional, we have a tier group that's doing this. And again, Eva, you put it a lot of your time and effort to make sure that happened. So, we now can start and we need to carry on. Correct. So, we're actually looking at and this is one of the things that as Peter mentioned, Eddie recently received a grant to put it in place actually certification programs that are self-paced and online and they're IT programs. So, one of the programs is data-focused and it's probably brought in a group of professors in the field in a focus group to talk about skills and abilities of each of these different areas in IT and in data management. But in the data management one, we collect some information. One of the that we ask the focus group attendees was, what are the preferred attributes that are required of a typical data professional and you can see the ones that jumped out these are the abilities that somebody would be good in this profession typically works in this profession would be to make them successful. So, you can see that capture is important, organization skills, communication skills were once that popped out as a potential and then also talked about the knowledge areas that would be necessary for somebody who works in this field and so, we're very familiar with the data management by knowledge as well. So, some concepts, technical structures, principles and framework were all areas that that came out as a knowledge that somebody in this area would need to have. Of course, we're continually asking those questions of how does one get that knowledge as well. These are things that people need to be able to be successful in types of things that they do in their work. So, organization and playing is pretty consistently across all of the professions and tools and those could be a variety of different tools and one of the talks that quite a bit in this focus group was facilitation, communication, modeling skills came out of high areas that all areas management type jobs and then we added this together into what a typical work profile would be. That was pretty important that the work we understand in terms of developing training programs or sort of the types of programs. We understand what the typical work is for these types of work. And these results that came out, project management, modeling, data analysis. Now I also want to point out this is not a scientific study. This is just you know within a facilitated study with the data management professionals in this area. But it did give us quite a bit of information to start with and also again very closely with the work that we've done with the understanding of knowledge and the certifications that the CDNC certification. In this before and the DIMBAK had not existed prior to 2009 so it was difficult when we talked to people for us to point to anything to read which is generally one of the first things they look to. So you can see here we derived it and modeled it after the PIMBAK and there is a DIMBAK. And the same thing QP that as we went through the discussions in the focus group it was pretty clear that the process also domains and intersects in many ways. So we actually in order to work in and progress through this people go these other people and and really Mehmed you know he's from a career perspective but also he's very interested as a professional manager within salesforce.com to make sure he's got people who can build that company and continue to grow the really interesting things that salesforce has been doing at this point. In the details this is I actually want to tell a story a real story about that we resonate with most of your listening whether you're a manager or an individual contributor. Four months ago and it got me going on some of the details you're going to see in the next few slides is I had a individual contributor who was an incredibly effective data analyst and those type of people I managed in my past I had a good sense of that I wanted to be able to make a case for them to be promoted and also managing other individuals and they want to know how am I doing and what do I need to be at the next level and my philosophy has always been we should always help develop you to be able to perform at the next level and if you can then we can make an effective case of making the adjustments reflect the nature of the work that you do. The challenge with that approach is well you need to have a clearly enough defined criteria for the most right what they can do and it is not how many languages you know it's not even how many you have been in a given field just fields as arbitrary as trying to make code complexity by the number of lines of code versus how well something may actually be written and how flexible the the I started working with the organization also on how to define career paths for the most defined roles in our field look at the knowledge they're supposed to have the type of skills they need to have they need to display in the way they work and we have how the theories ended we put together careers for the data analyst as a role on data steward and data architecture tracks also some good good feedback from Dama international board members as well as our HR department we love this to other managers that you know lead professionals and use the material to coach other individuals on what they would do in their career in general even if they just wanted to get into the data analyst role so today based on real corporate implementations and I hope they would help you also so we'll get into the role and then we'll get into understanding the expectations and how they will evolve over time so the role is making sure people understood what a data analyst is supposed to do what a data analyst wants to start with a common definition and also say that what are common academic backgrounds that drive people to this and any other academic backgrounds that can get trained because they are passionate about data they understand these trends they want the project that is for possibilities and a data analyst is supposed to do in the role let's talk about what we can expect given there are different levels of experience and there are different needs for different levels of experience can go great especially if you have large organizations you're going to need to have people that are good at critical operational work things that are supposed to happen happen and being able to perform some rapid analysis when it takes place I see a lot of Harvard business reviews being written on the impact of the operation and the organization making sure there are no breaches etc. however they are essential for developing for the higher levels we are having a lot of conversations on what the data analyst can or cannot be for a company the idea you may catch is yes I'm absolutely classifying the scientists as available because the levels you're in as you can read in these descriptions we are expected to fulfill part of the results for the organization it is very well the scientists and in earlier career also start often as a lab technician understanding how to conduct experiments how to do experiments focused on making sure that their measurements are in fact going to be accurate precise repeatable to continue the aspect if you understand the role if you understand the expectation for a level then it is really important to know what are the scale and the expectations that comes that as well we'll go on to the next one and just look at what an operations analyst look like will contrast it to a senior analyst for operations analyst Peter an important attribute for operations is discipline execution and consistency but you know people in these roles are able to repeat things the way they are supposed to be they are going through the escalation process rapidly and they are also keen enough to identify opportunities for additional ongoing improvement and for level roles they are going to be learning the way to be you know learning by cycles now if you compare that to a senior they are not focusing on a repeatable task they are actually being given problems to solve and what's expected for a senior analyst once you give them a problem they should actually figure out where to find the answers to solve the problem but you need to find the statistical analysis it's going to be the best way to come up with clusters maybe they are going to need to have a combination of Excel and Excel or R and you know top-load tools matter a lot less how they approach the problem is going to matter a lot more and you need to have a lot of protection on these career ladders at Enterprise Data World for those of you interested and for managers it is important to be able to describe what work would expect to be able to do at a given level sample B I need or sample data call to own and deliver to display and here are the behaviors evolving in our career just to make it that we are starting to talk much more about abilities years of experience and how many technologies someone actually knows one slide behind however you'll see content again as part of the distribution so the the points that you're making is that subtly I think you're saying we've already got a data scientist category out there we've been operating on it for a while and there are people that have developed these knowledge skills abilities trying to articulate it perhaps a bit better Peter and if you look at some of the development programs that are evolving because there are development programs here in Silicon Valley that will take people who have a hard science background because they didn't know how to hypothesize how to look at data and they get additional technical skills in this they have the ability but they don't have the tools people who have been doing more traditional data analysis with reporting type tools they're wanting to understand the importance of more complex analysis and the coaching we provide to them is much more about additional abilities about being willing to take risk being able to you know fail and you never recover quickly these are the phrases I often use so they are comfortable being able to come with alternative ideas evaluate them on their own and they've been doing for a long time you know analyzing the data using level controlling it or you know on building systems to deal with the data I fundamentally believe there are six core types of things we can do for some time and that we will continue to do which is what gives us the longevity to be adaptable even if technology changes some of the work that we've done one of the things that was so fun to put this together was that Mehmet and Eva had sort of come to their conclusions independently of each other so we kind of you know wow you've got chocolate and I've got peanut butter kind of thing and then added sort of maybe the smore in the middle of it the marshmallow stuff just to completely identify these individuals at conferences and things they say they spend 80% of their time because they don't have the knowledge skills and abilities that Mehmet and Eva have described to us so far and so you know basically the organization throw into a thing and say here's a problem that we need you to go solve and they say great where are the knowledge skills and abilities at managing that particular process any comments on that? Absolutely and again we need to start building those skills if we want to look at it from a holistic perspective you know we are building those skills from a very age actually and begin to build great gaps there between college and the point where people actually level of profession in terms of building the underlying skills and sort of things that are successful as Mehmet pointed out and so you're going to go ahead I was just going to say I'm sure every one of you watched the President's State of the Union every year but two years ago President Obama actually did mention data management as a career role that was important for transitioning in there and we invited where did that come from and it was a group of community colleges as Eva has related to us that had gotten together in career field we think we can make a difference here so it's a community college based initiative if we can't get the four level colleges to pay attention to this but Mehmet are we transitioning to the more senior roles here at this point and I think that's great and I think we are going to probably have many more late night deep type conversations on our teams people profession in general at the upcoming physical encounters too session coming up at the enterprise data world conference on this and Mehmet is going to present these concepts in more detail we're also going to throw birds of a feather session so some of you have ideas around this please look to that as an opportunity so this this next person here my friend and colleague Ken Shepherd who I've met in the last year and become quite good friends with he's introduced me to something called is that it's based on something that Elliot Jock who put together was at order and it sounds academic but it's actually quite practical and the energy that I like to use it it is that if you're in production research and understanding things one of the books you may or may not have come across is something called the goal and the very insightful way of describing the theory of constraints which actually relates back to Rick and the selfish gene concept interestingly enough but we don't have time to go there on this one on this but requisite principle get patterns of language obviously moving from the concrete very low levels to higher and higher levels of abstraction and as and even have both described looking at the ways in which we expect the various knowledge workers in these situations to approach a problem and a junior person we're going to expect them to be closer with techniques and a more more concerned with looking at problem solution development this requisite order I'm going to present to you just very very briefly in this chart it has seven levels and it's out at the very bottom with what we call a frontline operator somebody who is not typically thought of as management and that they have a very prescribed function operate this machine perform this task interface with these customers their planning horizon is really going to be less than a quarter in most instances the second level up then is a line manager and their focus here is on an operational function that is not as prescribed but certainly not as vague as we're going to see at the top of the chart and their planning horizon is a little bit less than a year in this particular situation a director a department manager and again here the purpose is on evolving the department or optimizing the department with a two year planning horizon the fourth level is a VP or a general manager where they're taking care of an entire organizational unit and looking around in this case two to five year planning horizon then we get to a five which is a president a managing director they're in charge of a business unit they have a five to ten year planning horizon and executive vice president at level six for a multi-business organization with a ten year to 20 year planning horizon and the CEO in a 20 plus year focus on this those of you that have read Jack Welch's material know this is the type of thing that he was thinking about when he finished his career at General Electric so one of the things that did with this material was to try and put together a set of professional work in the data management community and you can see we've got again the front line of sort of a data steward down there at the bottom these are not hard and fast and what we're talking about here are data management management career titles now that's the last thing of course we want to confuse the HR directors with but this is for really making big efforts in IT organizations smaller IT organizations they're still going to need combinations of these functions and we can look to requisite theory to pour some guidance in these areas on how if you have a small IT shop much less a small data shop how you can use these theories in here so again a steward a manager a modeler the director the chief data steward you might have a two year planning horizon maybe a two to three year career field and again just working our way to a chief data officer which we won't have a level seven in our thing because the officers probably always are going to report into the level of the organization somewhere around that so this presents us with yet again a series of challenges and and it's going to be a little harder to get some energy around this because there are a lot more people in small shops and big shops but it can help and purchase as Eva did earlier with the work with the labor department put these into some categories it provides some advice some guidance for people who are trying to work on this in here thoughts around that and things that we need to do clearly is look at curriculum development clusters and things like that in that type of a concept we've talked and Mehmet put together a wonderful world here for us what is happening in the educational career field as you're working towards these? So as you mentioned and both you and Mehmet have played out I mean there's definitely we need to look at this from a past perspective in terms of thinking about development capabilities or the competencies for people who might eventually want to end up in this field but also you know easily go all the way back to thinking about developing skills and competencies in high school and early you know problem solving that help prepare people for these kinds of jobs but in the next 10 years the Native Management Association in particular has been actively involved in a program we are beginning to really mature as a profession but in addition and you can see in credentialing and those kinds of things that happen in a real professional really establishment like a lawyer or a doctor or an accountant so one of the attempts that one of the attempts that Dama has been involved has been creating a Dama curriculum framework back in 2005 a group of us got together who were mostly transplants from industry education who found that there really wasn't a good guidance and you can move on to the next slide Peter but we'll move through these next few pretty quickly so this was then published through presented at the IS Education Conference and published in the fall and just as a bad job clusters and at that point we really were beginning to acknowledge a recent search on that that I just did there are other curriculum models developing so you can see this one that I found based on frameworks for data management curriculum and it's not exactly the same as the Dama framework but again different ways of looking at that the other we're seeing there are more master's degree programs and certificate programs coming up that mentioned that have been a good place for these kinds of things because we have the flexibility to be able to quickly develop programs to meet in the needs sometimes before universities have a harder time being nimble and having programs in place so as I mentioned earlier Edmunds is a grant to work on self-paced programs that are designed for working professionals that lead to a certification and the certified data management professional is one of the areas we chose to work on and we'll see how it goes the next thing we've seen is more online career guidance I thought this was particularly interested interesting because Dana is specifically called to go to find out more information about how to become a data management analyst so it shows where this is evidence that all the work that we've been done to build the profession was not in to is starting to be used is starting to be recognized and make its way into those areas that can actually I got a call from the government recently where they wanted to have a number of CDMP people who had 20 years experience with the CDMP that was a terrific thing but it's also a physical impossibility since the CDMP has only been around for a couple of years at this point there are about 1600 professionals worldwide that have been related with this you're talking to two of them on this particular call yeah so that's quite the interest I know in the Seattle chapter we have a lot of a lot of interest we run to work the year just to get people certified and and so this is it's it's growing but we do have a special certification it's a data management professional it's it's based on data management by the wheel of knowledge it is really kind of put a stake on the ground related to what's the scope of management work and what are the functions and the kind of things that you need to know in order to be successful the principles so the knowledge pieces of it the skills necessary and of course the attributes are the things that the individual who is drawn to this profession brings to the table so all of those the exam to the body of knowledge and as we're we're continually finding it so the data management body of knowledge is currently in the second version you can add new functions the data integration interoperability was in response to the data community saying hey this is a very important function that we need to call out and of course we continue to refine data modeling and design have really been a refinement of the data development piece so there's work Pat Coopley and Deborah Henderson are working on related to getting any data management body of knowledge with new version published again volunteers who are trying to push this noodle up the hill and one of the reasons we wanted to do this webinar was to hopefully entice some of you that are listening to come and participate and we're getting ready to move into the Q and A section here in just a second but again just to recap a couple of things that are coming up Enterprise Data World is our big conference is going to be in Austin this year Austin's a great music city and we'll have some music and hopefully we'll get to see Mehmet and Mabdant and Shannon do some singing down there and you can also of course see the other things that are happening at data diversity here in late July you can see it down at the bottom with the Global Organization Design Society here if they look interesting to you we'd love to talk to you more about it because we just are very much resource constrained and dependent on the really excellent efforts of people like Mehmet and Eva who have been working with us over the years on this so we're actually at the top of the here and really this is the part we've been looking for some time and talked to you all about what sort of questions we can you know if it's a question of how do you get involved reach out to us that's easy but more importantly perhaps you know just know what your thoughts are again it was an enlightening thing to me to see that Eva and Mehmet had sort of co-evolved in the same direction and that we are starting to see some movement around here so Megan we'll pull back over and thank you Eva and Mehmet as well that was an awesome presentation it's now time for Q&A time for you all to ask your questions so just click on the Q&A window feature it's at the top of your screen you should be able to submit your questions through that window if you come up so we can just jump right into it the first question is how and where break into the data profession field as a newly college graduate and is there a non IT discipline people like that that's great and first of all we did some library science up there on that and that was not an intentional slight to library science they're an obvious group that should come at this but Mehmet you do all kinds of hiring of different people and Eva you do all sorts of career advising so Mehmet why don't you go first and then we'll leave it open almost probably once every couple of weeks now in my current role but a lot more often in my teaching role but it's often what I tell people is you need to set your eyes on the prize and look at those areas that you're interested in and get your students in the door somewhere so if you're coming or or type of role where you're working with data in some way and really get to know that in the industry and then you'll look for opportunities on your job where there's more and more opportunities to work with data as a data stored for example in your area or in helping people to analyze reporting reports or view reporting and then from there you'll start to you know get involved with data and start to get your way into into the profession. David Lupram we typically will take on an intern or two each semester just to look for people who have a keen interest in this area Matt are you back with us? Hi I'm I'm I'm not sure if you're there there's so much data everywhere that you can't look at can't really got to getting paid in the I.T. it's a help desk I was steve you know level for escalations eventually many years back I was fascinated by inquires that were coming I think it was remedy that we were just switching to at the time to try to find out why do we get a lot of calls in certain times. And the question I wanted to answer was to improve processes. And the way I thought to look at it was through the – and I made proposals to my supervisor saying, okay, here is the data that I see. And you already have the spreadsheet at that point, right? So the generous side of the answer is specific positions versus being in the company and then going into the data role. Start playing with data in whatever medium that is accessible to you. Then you encounter someone looking to hire somebody. Data is a good path for your family and your curiosity, your ability to take risks and make choices because data is harder to develop people in. And before you catch me at an event and make that case, you're going to have my attention and even if I don't have something, I can try to – you know, if someone can. If you want to hire a company and then get involved in data, it's even easier because you have many more opportunities. And then you find a specific business problem you want to solve or you think that needs to be solved. That's the way, you know, it can be managed. I mean, in this state, I'm in a relationship with my finance partners in my organization as part of the budget. I am responsible for it. And one of the first things is what are the numbers you are trying to manage to? How do we have visibility to it? And how do we ensure that the information is good and it is timely and it is effective to minimize the risks and analysis and action opportunities everywhere? You were also talking specifically about the role of mentoring. Eva, I wonder if you have looked at that from the curriculum perspective. I don't know exactly what the mentoring context would fit there, but if we get a flood, we won't be able to mentor everybody, but certainly that seems like an important component. Well, you know, one of the things that I've done over the past is a lot of times I invite the students that are interested in pursuing this to our management chair, and that's not a really good opportunity for them to work with people in the profession. And often they will find somebody who is interested in mentoring them or they'll be actively involved in the chapter. And I think that it's really important for all of the people working in the profession to be willing to reach out and help somebody who is interested in this because they don't have enthusiasm there. I'm entering looking Mehmet up at a meeting and stalking him. I think that was an open invitation for some friendly students. And again, certainly, Paige, somebody else. Go ahead, Mehmet. Yes, I am doing this to promote a conference that helped me be a mentor in my career, but if someone is actually willing to take the time to show up at an event, whether it's Enterprise Data World or a different conference, and they are at their homework to actually want to have a productive conversation. Here's what I'm interested in, where can I go? I'm very privileged not to engage with that person because such people are so rare in our field. And who knows what's going to happen? I mean, I'm open to trying to step up my team at different levels of experience. You know, I have customers that are trying to hire people in different geographies. So absolutely. If you are doing that, you know, that's the idea of what you want to do beyond the generic question and I think that's all the answers, which is what I actually coach to, you know, people in college when I do career programs, when I apply at Career Fair from Wednesday. Megan just passed me a note and said there's a lot of questions. Megan, what's the next one up? Okay. Let me see here. What's the difference between data analytics and data analysis from your perspective? Matt, they've talked more about that. I mean, analysis is the concept of analyzing data, right? And analytics sounds like a particular implemented version of it.  Electrical business intelligence is finding out what is the information needed to drive the business operations more effectively, stop by data technology and processes. That's not the answer. I know there's a confusion around that. It's something that we'll continue to try and address. I also see analytics as a bit more tool focus perhaps than the analysis part of it. Eva, anything on that one? So, yeah. I mean, you are, I would differ from Matt on that. Great. Next question. All right, Megan, do you have any defined roles and responsibilities for each of the third level of data governance professionals? I should have one in about two to three weeks. Excellent. I'd rather plug to come see you at the conference or look you up on the Web on that he's not hard to find. It's just hard to pin down. I'm coming. Good. Excellent. Next one, Megan. Okay. The question is, is the data management a function of data warehouse, BI, or data quality? Neither on that one. Really, we can go to that one. So, what are the things we tell people and where we see projects get in trouble is that they're trying to go with more of a single solution on this. So, when I look at most data warehouses, they fail because they're trying to do a one-legged stool and they focus on the data warehousing technology and the software, of course, works very, very well. But if you put a warehouse in place without a governance and a quality component to it, the quality of the warehouse becomes very, very poor very quickly and it's very difficult to work within that. So, I very definitely see that what we really need again goes back to Matt's comment about holistic thinking. We want people that can put the right governance around this. And the question was specifically about metadata. Here's my answer on that. If you have a data governance group, the language that they should speak is metadata. So, take that and run with it, I guess. Yeah. And the question is, almost sounded like it should belong under one of the buckets. Each of those, as well as application development, have metadata associated with it. Is that a singular function? A whole hard to agree with that. That was my first reaction, too, is that we're dealing with, working with metadata in all aspects of the wheel, in the body of knowledge. But I think that, you know, it's one function and it's a function in terms of managing metadata under one of the buckets and working with it is important in data management. Okay. I hope that's that. Megan. The question is, we're not seeing much take up of the data analyst title. Data manager does seem to be exploding. What do you think? Does the matter of a hiring manager and somebody who participates in hiring manager activities, among other things that you do, what's the question of engineer versus analyst? So, I guess we're going to have Stephen that ask that question. Hi, Stephen. Purely for semantics, we are not measuring data, rather we can write derivation logic, which could be a part of somebody's role. We are measuring someone to mean that it builds and the analyst is someone that analyzes. So, if you're looking at this purely from a title update perspective, I would look at the job description and I would look at whether the job descriptions are being written accurately or not. I get tapped sometimes internally from other groups and many of my customers try to figure out how to write things in general and I would just look at the meaning of the word. I'm still seeing a lot of requests for data analysis. Requests for database engineers or software engineers with a data focus. I haven't heard of data engineer as a hiring manager ending title at least in the Bay Area and that includes attending the data conference for the last 30 years. Eva, any insight there? Well, I think about the job description framework in terms of the different roles and engineering a place in the total view. I think that titles that go along with jobs are not necessarily descriptive of what their real role is and I think we see a database where we can put data architect, data analyst, data engineer, data evangelist, that's the new one I think we can start with. I think that part of the work that we all need to do and the challenge is to try to rein in some of these job titles. The IBA is also working on similar things with business analysis because they're all over the board and organizations don't really understand what those roles are. So the more we can provide information to help guide that, the better, I think. Excellent. I'll just add in that I agree with Mimette's focus on title because an analyst analyzes and an engineer engineers and those are distinct knowledge skills and abilities that we need to apply to these things. However, one of the reasons we in the data community have had such trouble getting IT to understand is because IT has not taught architecture and engineering concepts, period. They learn business concepts, they learn computer science concepts which are distinct from these and it's very difficult to implement any sort of IT solution without a well-engineered pile of data, if you will, or a well-architected pile of data and the two have to occur in the same way. An architect is necessary to put in place large, complex initiatives and engineers are necessary to implement the architectural visions around those. But I would also agree that, again, we've got to be careful with the title specifically. Megan. All of the next question is, how has the role of data scientist been aware that the field of operations research? I've been on this one because I've seen in the last five years about 100 universities take their OR and decision science curriculum and rebrand it as a data science curriculum. And that I think may be partly behind what the question is. I think they are similar and certainly complementary. But again, let's turn it over to Eva who's really looking at some of those pieces. Eva, how would you look at that? Well, I can only see from the perspective of the types of academic programs that are emerging. My concern is that maybe scientists is the new hot, you know, which is not such a bad thing because it brings us to this whole idea. And I do know, you know, whether we've really sorted out exactly what all computer science seems to be the one that's moving toward the data scientists types of statistics, a lot of the statistical and data skills area. And again, of course, the data seems to be driving a lot of that because there are people that at least see those as hot jobs. And so I think that, you know, I think it's all good and eventually we're going to sort it all out. Although it's really helpful. Do you have any comments on that? No. Data scientists. They take data solution into production, you know, plus years ago. So we definitely we use it for different purposes. We use it for operational intelligence. We use it for intelligence. We use it for internal data quality operations. You know, one of the things, you know, I've worked on is how to assess the quality in the huge environment with multi-sourcing, not calling it data, say, and the degree of research as well. One of the questions, I mean, I think in the AD, we had qualms and operations research really took it from management and science perspective. I think fundamental principles are the same. And the most that we can do, I'm all for it. The attention will help us get more energy behind it and more people joining the conversation. And hopefully we'll get it sort of. But I also have a problem in particular with the term big data because every definition I've seen of big data is absolutely not operationalizable. We identify big data techniques and big data technologies. And I'd encourage those of you listening that if somebody comes at you and starts babbling to you about big data, if you can take, can we have a different conversation instead of using the term big data, could you use the term big data techniques or big data technologies because we can't identify those. We can, in fact, put them together. But we absolutely can't identify big data in and of itself given the current definitions that we have for it. This is a little challenge we've lived for the last few years. Big data is a technology approach. Data warehouse is a technology approach. It may absolutely be the best implementation approach to answer the business problems. Back to the ability slide that, you know, effective resources for our business, for our customers is to figure out the right questions and the answer to those questions. And the answer may not always be 100% of the data, 100% accurate. We need to quantify the transparency of the information. We need to quantify the likelihood accuracy of the information. But this is what we are fundamentally about as data professionals. And one of the things I think that data, we are architecting solutions that integrate and deliver that data. Making sure that data is being secure and compliant to adhere to our organizational principles are important about our individual roles in our organization. As I mentioned before, big data, when I talk to any customer about their intelligence needs and if we happen to record or attend that how they are adhering to other things, that's what we can talk to the, you know, the idea of the sort of achievements about our application about problems starts with the definition of the business needs. Okay. Question is, as a liberal arts graduate who has many years in the data field, how significant do you think that the current emphasis on STEM is as a preparation for working with data? That was STAT, STEM. Yes. Okay. So as technology, engineering and math, we know we have a big deficit in this country and we're trying to address that. So they're all sort of moved around the STEM curriculum. My answer to that is particularly that if you're a college worker, you need to know something about data. And I'll just give a little shout-out to a book that's one of my favorites. It's called The Information by James Glick, who's this guy that introduced popularizing just chaos in the world. It's a terrific book and it gives you a little bit of background if you're not as familiar with this area. It'll help somebody who's been in the field to understand sort of the formal definitions and things like that. Again, Eva, comments on this. Well, so, you know, when we talked earlier, there are so many different aspects of data. And I think, you know, again, one of the earlier slides, we talked there, there's the side of working with data. There's also many other types of professions that, you know, involve working with people to collect information to make it and, you know, understand the definitions of data. And so, you know, really, I mean, as a liberal arts graduate myself, as well, I feel like those goals have been incredibly important. The communication skills, the ability to communicate with people to be able to recognize patterns is something that you learn in other areas as well. So, I think that there's room for everybody. I think that the current trend towards the data scientists is to put more of the STEM perspective because there's more math involved, there's more understanding of math, just like the scientific problem-solving types of skills that you learn in STEM. So, I think that you really, you really there's a place for you and for your skills, no matter what background you have because there's areas that you can apply that. First, if I recall, two daughters. Two daughters. There we go. Are they headed for a STEM career? Honestly, I focus on making decisions effectively, figuring out what it is that they want to make a decision on. So, I think that our daughter has much fashion design. She's nine. Her passion trust and another good friend of mine who is in fashion design. I did not realize how much math is actually involved in it because of making something work. There is the understanding of the flexibility of different types of subjects that they have to consider. There's the economics of it. So, liberal arts has been an engineering major academically. There are two daughters who I appreciate it. Another one that I've been in my career I absolutely appreciate the richness of it. It's about the subject of effectiveness, especially when it comes to K-12 education whether it's a bachelor's degree or a liberal arts degree. You wanted to comment on how we skipped over it fairly quickly as I was going through that. You know, you heard us, I think, with what you are doing in the role and the level in a framework that on a phone call we actually came up with in the past that in the moment of epiphany fundamentally we are passionate about one of the things whether we want to work with data or we like to work with systems. And if you think about your colleagues or systems that touch the data and then there are many different things people like to figure out. You know, some people absolutely love writing codes. It's not what I was passionate about. I like figuring out how systems worked. And, you know, if you were to look at this chart and look at the top-level words probably two that you're going to relate a lot more to than the other users you can think about the type of roles that can be most effective and if you would be following your passion and then you can look at the gestation for the roles that typically fall in that type of sense. Since it's epiphany all three of us say we copyright it at once and then we'll be protected, right? Megan, next question. I'll take five or six questions left. So, we'll go on to the next month and see how much time we have and if we don't get to any of them in the follow-up email answers. So, on to the next one. The next question is, I'm a role in IT group in capital markets. What is your recommendation on how to go about creating standards and processes for data quality and governance where none exist and is not in the current culture? That could be a good place to start on that where we do have a chapter on data quality. I have to apologize it was the last chapter we put in the dim block and that was a bad thing on our part because quality should never be last on that but it will certainly give you a framework for starting to look at resources and different approaches. That is probably more than we can get into here quick Eva and many other things to point the individual to. So, I'm going to pick a problem or a continuity and put focus behind that one and then the other great for personal knowledge if you're trying to make a case given your role and try to emphasize it people are going to need something that's going to resonate for them. And you know one of my earlier learnings is the quality of the architecture plans on the knowledge you have doesn't help you if you do not have a sense of urgency to move into a quick comment. I think that too as an analyst I think that is a really important role to begin to have conversations about data and the discussions that are having with the various areas of the organization and focusing on starting to raise the importance. It's a culture change in a lot of organizations I think and I've been there too I understand it's you have to start raising awareness and then begin to bring in a discipline I think using some of the tools and the other experts in the field there's a lot of information out there through various tools on the web and a lot of people in our community that are writing about this so you know sort of agree with the mat pick a problem and focusing it from a data perspective. Great Megan next one trying to speed up here a little bit. Do you see programming are coming back together again for a while many programmers seem to treat the data as a black box. I think the answer to that is no and I'm just going to say my experience is that people are really interested in making programming faster looking at very interesting methods around agile and things and the way to speed those up is to take the data out of it don't let those things start unless they fully understand the data requirements you'll be amused at the difference that makes. Unfortunately there are two reasons for it people talking more about data and the per short data is coming from lines of business more than technology in IT and but a lot of people that are in IT management many of them came from the traditional infrastructure or application developed on background versus data background I think it is going to be a slower transition not transition to a cure but what's going to happen is people are going to start realising who's being successful and who's being more successful and if we end up having more conference sessions on the positive deviant we'll see a bigger mind shift but people I'm brought into a conversation at a customer site and they want to understand how to make it work they typically ask the question who has done this well and they look at other examples of where is something to hold up a benchmark you mentioned specifically starting to report results and of course that's one of the things we're all looking for where can we put in some A and B experiments your organisation has multiple implementation projects going on you can try some one way and some another way and then come back to us and tell us collectively as a community what has worked and what hasn't worked and that's been a piece that hasn't been an area of research in that just got a quick note she said we can keep going because there's still a bunch of questions on this so Megan next question is do you view the skill of logical data modelling as a distinct skill set or can it be done just as well by B, A, D, B, A or data analysis sorry I'm going to jump in on that one and say first of all the type of person who does the work has not corresponded to the quality of the effort that comes out of it we've seen lots of these things where people actually invent data modelling on their own and come up with it if you're and this is a deep topic one of our also people here Graham Simpson did his PhD dissertation research around that very question and published it in a book and I'm looking on my shelf right now to see if I can find the type it came up on Amazon you'll see he's Graham Simpson and he also has a popular novel he's changed careers now on that but that's a very very deep dive into that specific question but again Eva and Mehmet what do you guys think? I have to say that so I learned data modelling back in the early days and when I was yes and that skill once I had that skill that skill has served me through and I continue to respond and improve I mean it's not something that you just tell me it's something you're constantly practicing but you get in so many different contexts as a business as a you know just just to help me understand a problem or help other people understand a problem so people who know me know that I'll start drawing up on the board and it do you think it's it's a skill that you can use you know all different areas in many different areas but no I mean it is a skill versus a role I think if an effective business analyst you should be familiar with how to understand information requirements that is a must have and then take that and put it in a model that's going to be effective based on the consumption or storage needs that's great to me as a modeler as a profession is someone who does that primarily but as a modeler to be able to gather business requirements do some basic data analysis understand the system implications of their models can you question one more time just to make sure we did address it is can you understand that the technicians are data analysts I think that's on it I'm sorry you're not I think that's not that jet lagged very well do you view the skill of logical data modeling as is the skill set or can it be done just as well by DBA our data So I think the answer to that was resoundingly yes from the three of us then. So I do think that understanding the difference between physical modeling a logical model and logical physical data structure is important. Is that dangerous? Yes, and understanding the difference and using logical modeling is important if you're in any of those roles. Great, good. Megan. I'll just have one more. How are the roles expressed in this presentation determined as roles as managed under technology or business? Yes. You've actually reported into a group called customer service. At the executive level, this is a group that is accountable for a customer adoption of Salesforce technology. There are different models than many other technology providers that after someone buys the product then there's still continuous engagement because they tend to use it to get the return of investment out of what they cut and sometimes it is adding more services to it. Sometimes it's understanding what our best practice is. Sometimes it's training. We have a holistic engagement model. My latest role is building up a data-centric consulting organization because we saw some demand in this particular field. We've seen a making sure we understand the customer's definition of success in our science school and none of the above and all of the above. How are you teaching it? When I was teaching it was in general in the security information systems and now the I.T. director for the college is using it. I'm actually practicing. We're doing a lot of data management with college in it among new colleges in the state. There's I.T. but it typically depends on, and this is the whole topic, I think, because the capability and maturity model of the organization makes a big difference, I think, in how people view and where people view data. So I think that that may, you know, where the organization has a lot to do with how the organization or industry I think sees the data. And one of the reasons Eva was giggling because she knows I have very strong opinions on this, I believe firmly that data should be treated as an asset and managed at the same level of organizational assets as the other assets of an organization, be they fiscal assets, be they human assets, lots of other types of assets that are reporting in, and that I.T. has not been well suited to implement data. And again, because of a lot of the reasons that we've talked about here, people don't know what they don't know, and I assume what we've talked about in college and university for the past 30 years is the data engineering, excuse me, data design and building and construction engineering is a technical skill that belongs in the bowels of I.T. and I have some research that we've published that shows it's getting pushed further and further away from the top levels of the organization so consequently people don't have an understanding of how it needs to be used and leveraged in these concepts. So in my mind it is absolutely a business function and I want to do that to correct and to help organizations obtain a strategic competitive advantage in the long run and I'm seeing that as working in most organizations. Again, we will have conversations around that at EDW in particular, they're running a chief data officer event there that will be very, very interesting to see how those pieces are coming together. We'll be presenting some new research that we've put together as well and again if you want to read more on that I've got a book out on the topic called The Case for the Chief Data Officer. Megan, that's the last question. So I want to thank Eva and Mehmet because you both put in weekends and nights on this including last night Mehmet in particular. So while I was flying back from San Diego he was working on some of the final refinements of the slides here but this has been just from my perspective a really, really valuable contribution here and I want to thank you guys for taking the time to do this and of course, Shannon, for letting us present these ideas because it's not often you can go to a source like this and say, hey, we want to talk about something that hasn't been talked about a lot before and the data version has just been wonderful in that sense to work with over the years. Well, this is a great presentation. You guys, this is just engaging. Everyone's really been involved and thanks to the attendees who hung in there over the time it's just been fabulous and we're always proud to have such engaged attendees and who can contribute to this presentation. We're to care about the conferences then, right? Exactly. We look forward to it. And you know, as the regular questions of course as if we will be sending out slides we'll also send a follow-up e-mail within two business days so by end of day Monday containing links to the slides, links to the recording and anything else requested throughout the webinar I know that DM Bach was mentioned many times. That's of course available in our Data Diversity Bookstore. And Mehmet and Eva, if you want to get Megan any information to share for your blogs and so on and so forth we'll get that out to everyone as well that was mentioned. And so thanks everyone for this great presentation. I hope everyone has a fabulous day. Thanks for this go long, Shannon. It was okay. There was no way we could stop it. It was just too good of a conversation. Alright, well everybody, we'll talk next time then. Thanks so much. Eva, thank you very much. Thank you.


The anniversary marks the midpoint between two decades of action. The action of our growth over the first decade and the action that we all must take over the coming decade in support of the United Nations Sustainable Development Goals to meet the 2030 agenda. In 2008, the leadership of the American Society of Mechanical Engineers posed a question. How can we use the internet to engage engineers in solving the problems of global poverty? Over the next two years, answers to that question seeded engineering for change. E4C launched in 2010 with the promise of gathering the world's engineers together with global development practitioners across the sectoral spectrum. Our promise was threefold. First, we sought to connect volunteer engineers with the people who could use their skills to improve lives in underserved communities. Second, we believed we could suppress the increase of wasted investment and technological failures we saw in global development. Our goal was to inform and guide the engineers, funders and everyone involved in the process of design and deployment of technology-based solutions. Third, we strove to amplify the good work of organizations whose proven designs had been overlooked. In our decade of service, E4C has evolved. We responded to the needs our members express, shifting from solely an online space for collaboration to an online space for knowledge sharing and workforce development. Today, our reach is broader than most of us would have imagined. We have more than 40,000 members and an online community of more than 1 million followers. Our roster of multidisciplinary experts has swelled to nearly 1,000 contributors worldwide. We now manage a research fellowship and deliver actionable research to organizations in need of engineering insight. Our solutions library has grown to more than a thousand essential technologies for underserved communities. We regularly host webinars and seminars with thought leaders in engineering and global development presenting their findings. And over the years, we have had the privilege of partnering with dozens of engineering societies, multinational firms, startups, non-governmental organizations, multilateral agencies and others. With our first decade in the Reh-View Mirror, we are looking down the 10-year road to 2030. The world has made long strides in lifting people from poverty, but work remains. Technology and the engineers that developed play a pivotal role in reaching the sustainable development goals. Mounting pressures from climate change, overpopulation, uneven support from governments and of course the COVID-19 pandemic all threatened to derail their completion. With those challenges in mind, we're all at the starting line of a decade of action.


Yeah, I mean, it was incredible. That was probably, well, kind of like I've said before, it was like better than I ever could have dreamed. And I think it's because of stuff like that.  But I don't think I ever really realized or thought about what it'd be like to just feel like you're part of the team and have guys like that that obviously feel like are larger than life kind of take you in and make you feel like you're part of the team. So for me, that was the best part.


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