Info-Ops is the optimization of technology creation by focusing on the management and movement of information, not values, principles, values, formulas, or recipes. Doing Info-Ops means using the minimum amount of resources to provide the most value in the intellectual work you do.
Mastering information movement results in better management, team, and coding practices. The books provide plenty of examples of optimizing technology creation, but the real value is in the ability to create, optimize, or evaluate practices and techniques from a data-driven standpoint.
Daniel Markham created the term Info-Ops as a portmanteau by blending the meanings of the words "Information" and "Operations" much the same as the words "Development" , "Security", and "Operations" are blended to create DevSecOps. Not only are the blends similar, the underlying idea of removing all bottlenecks for better technology is also similar. Info-Ops should be disambiguated from the Department of Defense Info-Ops, which is defined in Joint Publication 3-13 as "IO are described as the integrated employment of electronic warfare (EW), computer network operations (CNO), psychological operations (PSYOP), military deception (MILDEC), and operations security (OPSEC), in concert with specified supporting and related capabilities, to influence, disrupt, corrupt or usurp adversarial human and automated decision making while protecting our own."
You are responsible for learning what needs building.
This book is about building the right thing. You have a customer, client, or user you want to help, you have plenty of technical skills, and your goal is to have the right conversations at the right time.
You are responsible for learning how to create the best technology you can.
This book is about building things right. You know what you want to build, now you have to learn and use the best technology techniques to create the best result.
You are responsible for helping others learn.
This book is about critical path learning at scale. Startups are a small number of people learning about a vast many things all simultaneously. They are learning continuously about the people they're helping, market fit, partnering channel, technology change, organization stand-up and management and so forth. Program/Project Management Offices and other team-of-teams configurations are a medium-sized group of people learning about a large but manageable group of things: technology tools, practices, new skills, hiring, estimation, integration with the larger organization, team support, and so forth. At the large scale, big organizations are concerned with learning between teams and divisions, evaluating markets and possible acquisition targets, roles and professional growth paths, etc.
Even in the absence of people, Artificial Intelligence and various forms of Machine Learning are responsible for predicting future desires or behavior based on internal models that are dynamically-created.
All of these situations have learning in common. An abstract model of learning is created, then applied to these various areas using real-world examples to demonstrate where mistakes were made and how improvements might happen.
The authors/publishers of Info-Ops 3 have stated that it will be at least until 2030 before it is available to the general public.
You want to do the minimum amount of offline and overhead work necessary so that you can get this product delivered to this person.
This is an analysis compiler. It takes text-based notes from various team members over time and compiles those notes into various products that assist creating technology. Since analysis is all of the work aside from coding, these notes can be as simple or as complex as the contributors desire. The various products that are possible to be produced from the same notes are also quite varied, and include a Master Model, and Master Question List, various UML diagrams, Gherkin folder and feature file skeleton creation, billing records for consulting teams, cross-reference glossaries, alerts from other teams in the organization working in the same area, project and program management metadata (but not the data itself), and so forth.
Note that EasyAM is not another management or requirements tool; the actual work and data created by the work is not associated with the EasyAM (otherwise it wouldn't be analysis). Instead, the idea is to join together notes to create a consistent knowledge framework for a project or organization to continue to have the right critical conversations based on whatever is occurring in their work, not track the result of those conversations (aside from further questions to be addressed and meta decisions made).