Fall 2017 — Week 02 Process Journal
One of the main takeaway points from this paper, is that Kling and Star raise an important issue in the definition of Human-centered: the architectural relationships — e.g. if the system architecture reflects a realistic relationship between people and machines (or processes, or any other type of technological innovation). As technology continues to evolve, and humans adopt behaviors dictated by the technology they incorporate in their lives, the design of these relationships becomes a responsibility of designers, who should also factor in the way their designs will shape behaviors, interactions, and society. “HCS designers recognize that computer systems structure social relationships, not just information. ”
The productivity paradox is also a relevant point, where the productivity measures after investing and introducing computers (and technology in general) might not be directly observable because of the social processes that need to adapt to said technology: even if computers help on a simple, clerical process, training the users to use a system will negatively impact productivity on the short term.
Regarding the processes associated with design, use, and analysis of Human-centered Systems, the authors also highlight the fact that technology by itself will not solve social justice. The example they use (placing computers in inner-city classrooms will not per-se increase literacy) is also extremely important to consider. A large-scale government project in Mexico to bring computers to remote areas was abandoned because of several factors:
- Hardware placed in remote areas would fail or be damaged, and the necessary repairs would take a long time.
- The software used for the application was based on Microsoft’s Encarta technology, which generated licensing costs to the Mexican government, as well as a high barrier of entry to add new and refreshed content.
- Licensing content was not done adequately which led to copyright trials
- How can designers/developers develop empathy to accurately represent the functional relationships between the potential users of technology and the technology itself.
- What are some of the ways to evaluate productivity benefits that include the productivity paradox after a new technology/process/solution have been introduced?
This chapter defines Entrepreneurship by adding that the creation of a new enterprise “serves society and makes a positive change”. Although the premise is true in an ideal environment, in practice there are many counter-examples, such as financial firms that aim to profit off financial illiteracy (payday loan businesses), as well as small firms that made it their business model to manipulate entire industry sectors to profit off debt (debt collectors that buy debt by cents on the dollars). While this might not be the most typical situation, entrepreneurship does not always imply societal benefit, it could be rooted in one individual’s benefit at others’ expense.
Among the types of capital that exist in the entrepreneurship space, Intellectual capital and knowledge itself are essential. The chapter mentions that “knowledge is one of the few assets that grows when shared”. Besides completely agreeing with the premise, the example offered in another reading of less startups starting in stealth mode is quite relevant to the current state of the tech industry (as of 2017). Given that many technological barriers have been reduced, such as the ability to spin thousands of servers, virtually limitless data stores, or infrastructure that lets me create machine learning models in minutes, knowledge and data are the new key assets. Data should now also be considered capital because of all the opportunities to derive value from it — from tailoring technology for users, to interconnect industry insights between enterprises.
The breaking apart of entrepreneurial capital into entrepreneurial competence and commitment was extremely interesting… it resonates with real life examples where founders might not be well versed in specific fields or skills, but due to their high level of commitment, learn and become functional enough to provide value in the field or skill necessary.
Highlighting sustainability as the 6th wave was also very interesting, as governments and societies realize that sustainability is also good for business, and the right long-term strategy to continue driving economic growth and development.
- Rather than “pulling straws”, how can the duties/responsibilities be better split in a situation where there is no interest from team members to learn a new skill (e.g. marketing, sales, support, finance, etc)?
- How can a small team with homogeneous skills accurately evaluate the tradeoffs of removing potentially useful talent to cover a skill gap (e.g. move an engineer/designer founder to marketing)?
The distinction made early in the article that start-ups are not smaller versions of large companies is spot on. Even teams that operate as a start-up in large companies are not start-ups; they are adequately funded, and have a large safety net under which they can operate, while true start-ups do not.
Towards the end, the comparison made in education is quite interesting: “The first hundred years of management education focused on building strategies and tools that formalized execution and efficiency for existing businesses. Now, we have the first set of tools for searching for new business models as we launch start-up ventures”. These sentences not only highlight current industry trends (where even large companies try to emulate start-ups in parts of their organization, or their culture), but the radical shift that the education system will make: tools to learn, adapt, implement, validate and iterate quickly regardless of organization or context.
Besides listing several key attributes of a design thinker’s personality profile, Brown centers on prototyping and the value it provides — from validation of concepts and ideas, to user testing and the ability to integrate real feedback into the product/service/solution in a short amount of time. Brown lays a process framework consisting of a cycle that iterates through inspiration, ideation, and implementation.
There are several interesting case studies presented in the article:
- Shimano collaborating with Ideo to design a bicycle that would be both useful and practical for adults in cities.
- India’s Aravind Eye Care System and how they transcended the delivery of ophthalmic care to the transmission of expert pracitve to populations that have historically lacked access to it (including preventive care and diagnostic screenings).
Towards the end of the article, Brown mentions that “As more of our basic needs are met, we increasingly expect sophisticated experiences that are emotionally satisfying and meaningful. These experiences will not be simple products. They will be complex combinations of products, services, spaces, and information”. This is an extremely accurate prediction of the things we’re starting to observe in late 2017 — in large affluent areas within the United States, services such as food delivery services that leverage multiple experiences are combined: I can use an app that knows my personal preferences to find a local restaurant that matches my tastes, and easily request someone to automatically go to the restaurant, order food prepared to my preferences, and deliver said food. Machine learning and user profiles are transforming from a competitive advantage, to the expectation for new services. Solutions like the one previously mentioned rely heavily on the experiential innovation based on human-centric design to be successful.
Reid Hoffman has a conversation with Mark Zuckerberg in this episode of his podcast (Masters of Scale) and shares his most commonly given advice: “If you’re not embarrassed by your first product release, you’ve released it too late”. He then expands to say that it does not mean a start-up should launch something woefully incomplete or not functional, but something functional (conceptually the MVP), and then adapt to feedback or the way users and customers use the product to either develop the next versions, or pivot based on user feedback.