Algebra is the foundation for trigonometry, trigonometry is required for calculus, for example. Logic is needed for discrete math, required for automata and compilers, and so forth. The stack is also how technology is described: physical, logical, and conceptual layers, for example, or layered abstractions in networking protocols. The systems lifecycle, on the other hand, is how we tend to structure industry guidance. We plan and design, we build, we run.
A different form of the stack fallacy is seen when practitioners assume that systems can easily be decomposed through layers business, data, application, technology.
Smashwords – Chasing String in the Digital Era – a book by Jaffer Ali
The lifecycle narrative is far too prone to promoting waterfall thinking , anathema to the current Agile and Lean Product Development approaches redefining the digital profession. A complex system designed from scratch never works and cannot be patched up to make it work. Henrik Kniberg created a compelling visual image showing how systems scale: through ongoing, iterative re-design and elaboration of fundamental characteristics Systems evolve and scale iteratively [ 2 ]. What would be the simplest possible thing that could work? How would we iteratively evolve their understanding, based on practical topics?
Scaling seems to be orthogonal to the other narratives Three narrative dimensions. Hence the overall structure of the book:. A key focus of the book is explaining what practices are formalized at which level of growth. Note that this would be a testable and falsifiable hypothesis if empirical research were done to take inventory of and characterize organization scaling patterns. If we found, for example, that a majority of organizations formalize governance, risk, security, and compliance practices before formalizing product management, that would indicate that those chapters should be re-ordered.
This does not mean that GRC is not a concern , but they have not yet instituted formal policy management, internal audit, or controls. The presence of product management at an early stage in the book Chapter 4 is intended to provoke thought and debate. Product management is poorly addressed in most current college computing curricula as well as the de facto industry standards e. Yet formalizing it is one of the earliest concerns for a startup, and the imperatives of the product vision drive all that comes after. This book however is not a complete dismissal of older models of IT delivery.
Wherever possible, new approaches are presented relative to what has gone before. Why is a Scrum standup or a Kanban board effective, in terms of human factors?
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Of course, the person may cease evolving their understanding at any of these stages, with corresponding implications for their career. This book does not cover specific technologies in any depth.
Many examples are used, but they are carefully framed to not require previous expertise. This is about broader, longer lifecycle trends. There is benefit to restricting the chapters to 12, as a typical semester runs 14 weeks, and the book then fits well, with one chapter per class and allowing for an introductory session and final exam.
Of course, a two-semester series, with two weeks per chapter, would also work well. Each half of the book is also a logical unit.
The chapters have been re-ordered and refactored many times, and based on further research may evolve further. With three chapters in each section, the book can be covered in one intense semester at a chapter a week, although expanding it to a two-semester treatment would allow for more in-depth coverage and increased lab exposure.
This required new thinking. How could students learn IT management at scale in a lab setting? A hands-on component is essential, as IT management discussions can be abstract and meaningless to many students. Ten years ago, the best that would have been possible would be paper case studies, perhaps augmented with spreadsheets.
But new options are now available. The power of modern computers even lightweight laptops coupled with the widespread availability of open-source software makes it possible to expose students to industrial computing in a meaningful, experiential way. There is great utility in the use of lightweight, pre-configured virtualization technologies such as Vagrant, VirtualBox, and Docker. The initial course used a central server, but even that is not necessary.
The class can be taught with zero computing budget, assuming that each team of students has access to a modern laptop recommend 8 gigabytes of RAM and gigabyte drive and a fast Internet connection. See the dm-academy resources on GitHub for the current status. Some may question the inclusion of command-line experience, but without some common technical platform, it is hard to provide a meaningful, hands-on experience in the first half of the course. Currently, digital professionals require hands-on technology skills; barriers to developing them being much lower than in years past.
The initial course assumed that the students are at least willing to learn computing techniques, with no prerequisites beyond that. Not even a programming language is required; the Java currently used as a sample is minimal. Truly beginning students will have to work at the Linux tutorials, but all they need to master is basic command line navigation; this is possible with a diverse student body, some with no previous computing experience.
The labs for the second half of the course mostly use games, experiential paper-based classroom exercises, GUI-based software, databases, and office productivity tools.
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The emergence model is intended as the learning progression for either traditional semester-based education, or industry training. Labs tie into the emergence model and encourage the students in hands-on exploration of fundamentals, as if they were starting their own business or initiative. As they progress, they remain grounded in the basics of applied technology.
Even the most highly scaled topics, such as Enterprise Architecture and portfolio management, become more real when the students are reminded that the systems in question are simply larger and more numerous versions of the examples they see in the lab. This is a survey text, intended for the advanced undergraduate or graduate student interested in the general field of applied Information Technology IT management.
The book is grounded in basic computing fundamentals but does not require any particular technical skills to understand. You do not need to have taken any courses in networking, security, or specific programming languages to understand this book. However, you occasionally will be presented with light material on such topics, including fragments of programming languages and pseudocode, and you will need to be willing to invest the time and effort to understand.
This book makes frequent reference to digital startups -— early stage companies bringing new products to market that are primarily delivered as some form of computer-based service. Whether or not you intend to pursue such endeavors, the startup journey is a powerful frame for your learning.
Large IT organizations in enterprises sometimes gain a reputation for losing sight of business value. IT seems to be acquired and operated for its own sake. Market revenues arrive, or do not, based on digital product strategy and the priorities chosen. Great features, but your product is unstable and unreliable? Your customers will go to the competition.
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Thinking about a startup allows us to consider the most fundamental principles as a sort of microcosm, a small laboratory model of the same problems that the largest enterprises face. Verne Harnish, in the book Scaling Up [ pp. See Organizations cluster at certain sizes [ 3 ]. Of 28 million U. However, this is not a textbook or course on entrepreneurship. It remains IT-centric. And, the book is also intended to be relevant to students entering directly into large, established enterprises. In fact, it prepares the student for working in all stages of growth because it progresses through these four contexts:.