Then there’s the Repository, where we check in and we share the models and we allow the teams to collaborate on those and publish them out to the team server so that multiple stakeholder audiences that are engaged in a project can actually see the artifacts that we’re creating from a data perspective as well as the other things that we’re doing in the project delivery itself. So let’s talk about the Agile Data Modeler for a moment. There are different ways that we work with developers. To fully embody the spirit of how you really want to approach data modeling – whether it’s in agile development and other things – is you need to realize that data model, if done correctly, becomes your full specification for what that data means in the organization and how it’s deployed in the back-end databases. A key bene t of Anchor Modeling is that changes in a data warehouse environment … Focus on the important things first and as you work your way through it, then you’ll get to consuming and trying to understand the other information from outside. Deep Reinforcement Learning: What’s the Difference? Data governance is, let’s call it, a very modern concern and I think, really, there’s a lot of work that, let’s say, should have been done historically on data governance. And Ron, thank you very much for your time today, you’re always pleasant to have on the show – a real expert in the field and it’s obvious that you know your stuff. How do you know how to utilize the data in your applications? So one of the important characteristics that we can have is when we are doing a data model, we can divide that data model into different views, whether you call them subject areas or sub-models, is our terminology. So why should Data Modeling be left behind? Dez Blanchfield: It does, indeed. Very briefly, ER Studio is a very powerful suite that has a lot of different components to it. The data modeling in SAP BW changes due to these new possibilities. Painful experiences sometimes lead to powerful lessons learned and many lessons are hard won. My name is Eric Kavanagh, I will be your host. Every part of 99.9% of all applications is either manipulation of the data or some kind of presentation of it. And you can turn that around in a very quick fashion. As the agile development started and we started with this concept of self-organizing teams, if you drank the Kool-Aid a little bit too pure and you were on the extreme programming side of things, there was a very literal interpretation of things like the self-organizing teams, which a lot of people interpreted to mean, all we need is a group of developers that can build an entire solution. We're going to find out how you can stay on top of things in an agile way. We take that database that they have been working with, bring it into our modeling tool, compare to the models that we have and then resolve and push out the changes back to them so they can refactor their codes so they are following the proper data structures that we need. What I’m going to cover very briefly is, I’m not going to go into scrum in a lot of detail; we just had a really good overview of what that is. You have data elements, which have keys, element definitions, aliases, synonyms, specific physical formats and then we have this metadata layer. Software developers tend to think that the data model is a living outgrowth of their work, while data modelers tend to think of the model as a static design with a more static and strategic approach: that the data model must be created up-front based on user needs and fit into the enterprise data model. In an environment where related data is changing frequently, like a stock trading application, embedding data that changes frequently is going to mean that you are constantly updating each portfolio document every time a stock is traded. After week 21, that is when the data modeler came in, refactored the data model based on what there was to fix a number of things and then started modeling as part of the project team going forward, the changes as that project was being pushed forward. But a very interesting observation and question from one attendee who writes, sometimes agile projects have the data modeler not having the entire long-term picture and so they wind up designing something in sprint one and then having to redesign in sprint three or four. In practice, the actual metadata is part of the data universe. Like I say, you have to lose the – sometimes there are data modelers that have that traditional gatekeeper attitude where, “We’re here to control what the data structures look like,” and that mentality has to disappear. I have formulated four principles which, in my opinion, are crucial for agile SAP BW modeling: I had a full data architecture team, so I had data modelers from my team embedded in every one of the other application area teams, and we were working with a combination of in-house business experts that knew the subject matter, that were doing the user stories for the requirements themselves. Having said that, data is only one part of the overall picture. This is a third post in a series on Agile Data Warehouse Design, a 2011 book about data modeling. That’s not necessarily the case, that is it in a purest development perspective, but quite often – especially at the beginning – we may have something like the sprint zero where we are focused purely on standing things up, doing things like getting our test strategies in place. And as they start to embrace that and see that they can have the productivity, their inclination to embrace agile even more increases. I know you spent some time going through examples and you’ve been showing some screenshots about how you actually roll some of this stuff out. July 14, 2015. We’re Surrounded By Spying Machines: What Can We Do About It? The start of data modeling is to grasp the business area and functionality being developed. Can you give us any sort of insight into some success stories that you’ve seen where you’ve gone into an organization, it’s become clear that they’ve got a slight clash of the two worlds and you’ve been able to successfully put this in place and bring large projects together where they might have otherwise gone on the rails? Whether high, medium or low for these types of things for these different constructs within the organization. Data Modeling in an Agile Environment By Techopedia Staff, November 16, 2016 Takeaway: Host Eric Kavanagh discusses the importance of data modeling in agile development with Robin Bloor, Dez Blanchfield and IDERA's Ron Huizenga. Because it’s interesting that, even today, when you look at data in organizations, we have so much data in our organizations and generally speaking, based on some surveys that we’ve seen, we’re using less than five percent of that data effectively when we look across organizations. When people think of data models, they often think of a data model as being a picture of how the different pieces of information tie together – that is just the tip of the iceberg. The real premise of agile is that you’re basing things off of backlog, of stories or requirements. It was an unfortunate outcome because the reality is that agile is not limited to developers. And there’s quite often this misconception that data modelers will slow projects down, and they will if the data modeler doesn’t have the right attitude. First of all, a full participant in the sprint planning sessions, where we are taking the user stories, committing to what we are going to deliver in that sprint, and figuring out how we are going to structure it and deliver it. If you look at this vertical bar on this particular chart, this is showing cumulative defects versus cumulative objects, and I am talking about data objects or constructs that were created such as the tables with the constraints and those types of things, if you looked at it before the data modeler was introduced, the number of defects was actually exceeding and starting to build a bit of a gap over the actual number of objects that were produced up until that point in time. Painful experiences sometimes lead to powerful lessons learned and many lessons are hard won. When I say databases, I mean not only the relational databases that we may be using, but in today’s architectures where we have big data or NoSQL platforms, as I prefer to call them. Stock zaza may be traded many hundreds of times in a single day and thousands of users could have zaza on their portfolio. Dez Blanchfield: That’s a great takeaway point and I totally agree with it and I’m sure Dr. Robin Bloor would as well. Agile development methodologies ignore the value of data modeling. This methodology is more flexible than traditional modeling methods, making it a better fit in a fast changing environment. To summarize, data architects and modelers must be involved in all development projects; they really are the glue that holds everything together because as data modelers and architects we understand, not only the data constructs of the given development project, but also where the data exists in the organization and where we can source that data from and also how it is going to be used and utilized outside the particular application itself that we are working on. So, typically when we’re data modeling we’ll make sure that we’re applying proper naming conventions to all the artifacts that’s getting generated out in the DDL as well. Dr. Robin Bloor: Well that’s impressive. Well, there’s an article in 1986 written by a couple of gentlemen whose names I tried desperately to do justice to, Hirotaka Takeuchi and Ikujiro Nonaka, I think it is pronounced, produced an article that they titled “Moving the Scrum Downfield.” They introduced this idea of a methodology of winning a game of rugby going from this scrum activity, where everyone gets around in one place and two teams essentially lock heads in something called a scrum to try and get control of the ball and play it down the field to get to the try line and touch the ground with the ball and get a point, called a trine, and you repeat this process and you get more points for the team. Fundamentals from a data modeling perspective that we want to have is, always have a baseline that we can go back to because one of the things we need to be able to do is, whether it is at the end of a sprint or at the end of several sprints, we want to know where we started and always have a baseline to know what was the delta or the difference of what we produced in a given sprint. A perfect example of that is, I know we’re talking IoT and sensors, but the same type of problem has actually been in many organizations for many years, even before IoT. Over the past three years, we have been working in parallel on data models with hydrologists of Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) and Berkeley Water Center (BWC). Thus, the data model needs stable interfaces and consistent for a longer period of time. At a high level AM is a collection of Core Practices, depicted in the pattern language map below (click on the practice for information). our. We understand the complex data relationships and it is paramount to be able to move forward and also from a governance perspective to map document and understand what your full data landscape looks like. Dr. Robin Bloor: It doesn’t surprise me, that particular aspect of it. We need to make sure that the changes for those frameworks or services are checked in at exactly the same time. 0 Comments. New, much more flexible data models and process models for development are possible. Data model represents the business in a fundamental way, so how are all these new data sources changing the game? Dez Blanchfield: Thank you. We had a couple of other people just asking specific questions around how does this all tie back to the tool. So every time we see things like the backlogs, the requirements and user stories, as we’re going through we need to look at what are the development pieces we have to do, what are the analysis pieces we need to do, how about the data design or the data model, what about things like the business glossaries so we can associate business meaning to all of the artifacts that we’re producing? Agile Robots AG is a high-tech startup based in Munich. If all of your data is tagged with this level of granularity, it guarantees interoperability and data can be mixed and matched to build r… Ron Huizenga: Sure, and in fact the one project which happened to be a pipeline project was the one that I alluded to where I showed that chart with the defects before and after the data modeler was involved. So we can extend our metadata in our models to give us a lot of other characteristics outside of the data itself, which really helped us with other initiatives outside the original projects and carry it forward. From here we will take more questions. But if you look at the IoT we can understand mobile more than we used to, although it's introduced new dimensions: the dimension of location with mobile. Let’s talk about data models in this context very briefly. F V These days when we talk about data design, we talk about modern agile data design, and my view is that it wasn't so long ago that developers and data experts worked alone; they were in their own silos and pieces of design went from one silo to another. I also create a delta DDL script using the compare/merge again from start to end of sprint. Because it leads in nicely to this, just to wrap up, because we’re going a little bit over time now, and I’ll hand back to Eric. That means some things fall off the wagon doing a given sprint and you pick them up in later sprints. I’ve been involved in projects where we were replacing over a dozen legacy systems with new business processes and designing new applications and data stores to replace them. Every time we do a change, what we want to do is we want to model the change and what is very important, what has been missing from data modeling until recently, in fact, until we reintroduced it, is the ability to associate the modeling tasks and your deliverables with the user stories and tasks that actually cause those changes to occur. Dez Blanchfield: One of the things that comes to mind out of that is that, you know, a lot of the conversations I’m having on a daily basis are about this freight train coming at us of, sort of, the machine-to-machine and IoT. The model building or data science agile cycle needs to be decoupled from the software agile cycle (as described below) in order to produce working software that is also insightful. Len Silverston, a well-known expert in data modeling and best-selling author of The Data Model Resource Book series, argues that doing so will seriously impact the quality of your software. X When we work with an Agile process (in this case, Scrum), there is a tendency to assume that everyone can work with everything. For today's topic, it's an oldie but a goodie. This article was published in 1986 in the Harvard Business Review, and curiously it actually got a lot of attention. How Can Containerization Help with Project Speed and Efficiency? To set up the best in class agile data warehouse environment, you need to focus on its foundation: the development of a robust testing strategy and tools. Agile modeling (AM) is a methodology for modeling and documenting software systems based on best practices. Our experience is that the data modeling should be run closely in line with the user story creation; that the data modelers need to be working in lock-step with the business analysts, so that the user stories and data model are ready at the start of each Sprint. Data model is the starting point for designing and developing of data warehouses architectures. This has led to large, inflexible info objects. And how do we enable the concept of the agile data modeler in our organizations, in terms of, how do we engage the data modelers, what’s the participation of modelers and architects during the sprint, what are the types of activities they should be engaged in, and, as a backdrop to that, what are a few of the important modeling tool capabilities that we utilize to really help make that job easier? Data is a corporate asset and need… We’ve all seen these images that I got on the right-hand side here of walls and whiteboards full of Post-It notes and they were served as Kanban walls. And that’s it! Database code generation and it goes on integration and then modeled data around them and then more quickly access to control security around the data, the integrity of the data are we moving the data around as we’re retaining its integrity, is there enough metadata around it, should sales see all the records in the table or should they only see the address, the first name, last name that sends you stuff in the post? There were some that weren’t there and, interestingly enough, a lot of those were pieces of information that came from the legacy systems where, after the end of the project scope itself, that was still being documented as a carry-forward set of artifacts, as it were, outside of the project itself, because it was something that needed to be sustained by the organization going forward. I know that, I would say, probably in the last 15 years in particular, I’ve seen much more of an adoption of people recognizing that they really need to embrace quicker delivery. The Data Model Resource Book, Volume 3, Indianapolis, IN: Wiley Publishing, 2009. What we can also do is we can also use this compare and merge capability at any given time, if we are taking snapshots on the way through, we can always compare the start of one sprint to start or end of another sprint so we can see the full incremental change of what was done in a given development sprint or over a series of sprints. Then I’m going to go into a bit of a wrap-up and just talk a little bit about some of the business values and benefits of having a data modeler involved, or the way I’m actually going to tell the story is, the problems of not having a data modeler fully engaged in the projects and I’ll show you that based on experience and a defect chart of a before and after image of an actual project that I was involved with many years ago. There’s always a little bit of prep time, so quite often we will have a sprint zero or even sprint zero and one. Len Silverston, a well-known expert in data modeling and best-selling author of The Data Model Resource Book series, argues that doing so will seriously impact the quality of your software. It is associated with the task, we can have multiple changes per task, like you would expect. We also want to make sure that they don’t feel like they’re being restricted. It is like manufacturing; I came from a manufacturing background. And to do that you need to make sure you’re firing on all cylinders, and everybody is well synchronized in terms of what their deliverables are, and you had those frequent resets to make sure that we were completing our deliveries of all the necessary artifacts at the end of every sprint. I have heard some people say âthe business doesnât know that a database existsâ; that just makes me want to laugh. As a strategic enterprise resource, there is a heavy reliance on a data warehouse to be trustworthy, well-governed, and appropriately valued. Optimizing Legacy Enterprise Software Modernization, How Remote Work Impacts DevOps and Development Trends, Machine Learning and the Cloud: A Complementary Partnership, Virtual Training: Paving Advanced Education's Future, IIoT vs IoT: The Bigger Risks of the Industrial Internet of Things, MDM Services: How Your Small Business Can Thrive Without an IT Team. Ron Huizenga: It’s just the nature of agile that you’re not going to get everything absolutely right in a given sprint. Agile Modeling (AM) is a practice-based methodology for effective modeling and documentation of software-based systems. You’re saving them from having to do that hand coding, as it were, of the data structures and letting them concentrate on the programming or application logic that they’re most proficient at. And one of the projects that I was involved with, we took this to an extreme – if the build broke we actually had attached to a number of the computers in our area where we were colocated with the business users, we had red flashing lights just like the top of police cars. Take it away, Robin. And that’s actually part of the spirit of agile, is: work with it – you’re going to be doing prototyping where you’re working on code in a given sprint, and you’re going to make refinements to it. What’s going to be considered master data management? Esp. You are currently not logged in. And like I have talked about, some things are originated by the data modeler, other things are originated by the developers and we meet in the middle to come up with the overall best design and push it forward and make sure it is properly designed in our overall data structures. Modelers are generally tasked with implementing data at the conceptual, logical, and physical levels while accounting for an Enterprise Data Model as well. And the other part of that is, don’t do it because you can, in terms of consuming all this external data, make sure that you have a use case that supports consuming this external data. Working with databases requires very specialized skills, particularly around data, and builds an experience. I get asked to help teams increase the performance of their database (hint: indexes, query tuning and correct datatypes, in that order) or to help the scale it out for increasing workloads. Maybe even longer than that, maybe 35 years ago. The actors in this particular data cast include the likes of data architects, molders, the administrators, managers of the database infrastructures and the actual databases themselves all the way through to business and systems analysts and architects, people who sit and think about how the systems and business operate and how we’ve gotten to flow data through these. You know, things like collaborating across projects between data experts and software developers, single point of truth or single source of truth for all things around documentation of the databases themselves, the data, the schemas, where the records come from, owners of those records. Thus, the data model needs stable interfaces and consistent for a longer period of time. In the Agile development process, data modeling has a role in every step of the process, including in production. That’s, kind of, my opinion. The thing that I wanted to say, because what we normally do, is me and Dez talk about different things and I just thought I'd give the general overview to modeling, but there is a reality to this, that's now becoming apparent. Are Insecure Downloads Infiltrating Your Chrome Browser? So from a data governance perspective, we also need to be looking at, what we are defining here? So we really need to get a handle on what that data is. All those are elaborated continuously during the sprint rather than going a few months down the road and finding out that you’re going to come up short and you need to extend the project schedule. As we’ve, kind of, alluded to earlier on in the presentation, is that it’s imperative that we have data modelers and/or architects fully engaged in the agile development processes. of time. The interesting thing about metadata is that metadata is entirely how data gets its meaning. 26 Real-World Use Cases: AI in the Insurance Industry: 10 Real World Use Cases: AI and ML in the Oil and Gas Industry: The Ultimate Guide to Applying AI in Business. What we do now is we need to have an iterative work style where we’re incrementally developing components of it and elaborating it through time where we’re producing usable code or usable artifacts, I’m going to say, for every sprint. On the contrary, I see data modeling partially as a team effort; team members other than the data modeler can contribute to the development of the model. Please log-in or sign-up to see the video. So we're looking at about 30 years, is it 30 years? Hacking Database Design. Dez? # That’s a team sport now and hence my picture of a bunch of people jumping out of an airplane acting as a team to play out that visually interesting image of people falling through the sky. What I’m going to be focusing on today is going to be a few things that we’re going to see out of Data Architect and because it’s really important that we have the collaboration of Repository-based aspects of that. This data modeling course highlights the differences in the lifecycle, purpose, roles, and approach for data modeling for NoSQL in an Agile development environment. So as we are building up the model we are also showing it in these different sub-model perspectives so the different audiences only see what’s relevant to them so they can concentrate on what they are developing and putting forward. Len Silverston, a well-known expert in data modeling and best-selling author of The Data Model Resource Book series, argues that doing so will seriously impact the quality of your software. I’ve just asked his permission, which he’s given, to ask it of you. And then those roll up to the overall model and the whole structure of sub-models to give different audience views what they need to see. Could you maybe just give us a scenario of, I mean, there’s two places I can see this being a perfect set: one is new projects that just need to be done from day one, but I think invariably, in my experience, it’s often the case that when projects get large enough that this is necessary in many ways, there’s an interesting challenge between gluing the two worlds, right? A key feature of Anchor Modeling is that changes only require extensions, not modifications. That's really what you're trying to do, right? What are your experiences of data modeling in Agile projects? We may be working with multiple databases or data sources simultaneously in the context of a given application. However, this division of labor can be a challenge; data modelers must participate in the development sprints and make the importance of the data clear. 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