1 – Cones of Uncertainty Applied
Describing how cones of uncertainty applies to AGILE project management for software development Steve McConnel explains that no two projects can be planned the same because, well, no two projects are the same. McConnell offers some advice of “dealing with the cone”:
- Pad the estimate – inclusive of money and time, always leave some “wiggle room”
- Size the project relatively – by comparing the project completion process to a similar project, this gives folks an estimate of how much time and resources will be required to complete the project
- Be upfront and honest – rather than shy away from uncertainty, be transparent and offer ranges the uncertainty fits within
- Fund incrementally – model planned spending based on degree of risk, which makes it possible to adjust based on the resource restraints triablge moving forward
- Know the root cause – in project planning, whether the development cycle is even feasible is key; the fact that the developing company has a set annual budget, so embracing cones of uncertainty can prevent failure on the front-end
Applying the cone of uncertainty to a particular year, it’ll be a lot easier to predict, say, what the rest of 2021 will be as opposed to 2121. So let’s try it. Based on the cycle of the pandemic, it can be safe to predict that the pandemic will have a major impact on health, wealth, and community well-being. As access to vaccines increases, businesses will re-open, and new businesses will likely be opened to fill gaps based on previous closures. It’s also likely that socio-political movements, ranging from hate groups to “social justice warriors,” will continue to grow, galvanized by online spaces that make it possible for such communities to learn, grow, and organize. Lastly, it seems likely that economic recovery will continue — the implications this will have on social issues like conversations about universal basic income, increased minimum wage, and resources available to all families with children will exist, but the “boundaries of possibilities” remain uncertain how these implications will play out.
2 – More Project Cogitations
I’m interested exploring opportunities to monetize this podcast. Even if I do not immediately include ad space for this project, if there’s an opportunity to build an audience with the first eight-episode pilot, it could be good to continue the podcast and incorporate ad space. This could, at first, include promotion of other related podcasts. Such space could be donated, meaning I wouldn’t charge others for this digital real estate. This would help me build new partnerships, learn from other more established podcasters, and create the right sound and style for advertising on my podcast.
Over time, I may also explore ways to automate soliciting for advertisers. I may not have much time to build these relationships, and I know platforms like Podbean make it easy to market advertising space on podcasts. However, while this is definitely worth exploring, I’ll want to make sure that what I am advertising is aligned with my content and include products I believe in. For example, if I’m running a podcast about equity and justice, I wouldn’t want to advertise for financial institutions that invest in oil pipelines or engage in unfair, discriminatory banking practices.
And while I intend to launch with a 30-minute, “stories behind the data” format, I will explore continuing the podcast beyond the eight-episode pilot. I will try to get position myself as a guest interviewee or blogger to draw attention to my podcast, gather insights from audience members and other podcasters, and re-evaluate format and implementation based on these insights. For example, after the first eight-episode run, I could begin to bring on experts to interview, consider themes to run, or alternating the format from interview to storytelling over time.
I would say the key to the success of this podcast will be the partnerships I establish for promotion and advertising as well as the consistency and quality of the content I produce.
3 – More from Weapons of Math Destruction
We criminalize poverty, believing all the while that our tools are not only scientific but fair. (p. 91)
This next section in O’Neill’s book takes on how communities have invested countless dollars and hours tracking and responding to the symptoms of poverty — theft, substance abuse, and “broken windows” — while largely neglecting crimes that devestate people’s lives and perpetuate poverty. Her deep(-ish) dive explores how data modeling tools and related programs have been developed to reduce crime by nipping “saggy pants” in the bud (i.e., increasing police presence and response in low-income communities), while ignoring the root causes of poverty as well as the criminal activities that produce events like the 2008 Great Recession. Furthermore, “stop and frisk” policing, which is justafied by snake oil salesman “data science” further exacerbates narratives and the reprecussions of systemic, historical racism and “poverty-as-personal-failure” thinking. Without intervention — without a reset of what we accept as okay in how our police forces exert power as oppowed to improving our communities — “stop and frisk” will be replaced by a complete eradication of individual rights and protections as facial recognition and even DNA surveilence technologies increase in availability and affordability.
I appreciate the direction O’Neill goes at this point. A lot of the research that has been conducted over the past half century emphasizes relationship building. Rather than throwing more folks in jail cells, data science can (and should) be used to more intelligently strengthen neighborhoods involves police officers exploring why windows get broken in the first place, connecting residents to resources that give them a path out of poverty, and demonstrating what people can accomplish when they work toether to grow available resources instaead of focusing on existin g limitations.