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Naturally, Black inventors in the US were the first to express such possibilities. The creation of speculative art with distinctly Black aesthetics started in the middle of the 20th century, as writers, singers, and others with a worldview rooted in the troubled past and present and a variety of other cultural assumptions than white hegemony.

In 1993, the style was retroactively given the name “Afrofuturism.” Afrofuturism is defined broadly as “visions of the future — including science, technology, and its cultures in the laboratory, in social theory, and in aesthetics — through the experience and perspective of African diasporic communities,” according to sociologist Alondra Nelson, who insists that it “should be a big tent of expanding borders of the possibilities for Black life.”

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In the publishing industry, people of colour are underrepresented. Despite a century of fighting the gatekeepers, our books only make up fewer than 6% of the titles that are published each year. We are also excluded from the national discussion, beginning in elementary school, as a result of this systematic exclusion, which is obvious in its effects. People who reside here are taught to view US life as being almost entirely white by textbooks, libraries, schools, TV, and movies. Literature and intellectual life are undoubtedly designed to obscure us, and that goal contributes to the promotion of the racist white hegemony that is currently evolving towards white nationalist fascism.

I struggle with the effects of this omission in various circumstances as a Mexican American author who works both for children and adults. The view of the future that the publishing industry has created in science fiction is one of the most heartbreaking for me. The vast majority of the corpus projects the current injustices of white fundamentalist capitalism forward in time, making our current erasure permanent and neoliberal homogeneity (despite the superficial racial diversity that is occasionally espoused) firmly white.

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Practically speaking, it can be difficult to establish the practise of metric reviews because you need to persuade other functions that it is worthwhile of their time. If you are the data representative, start by discussing the concept with the team member who is in charge of the team’s overall work and who is most interested in keeping track of developments and working towards a common objective. The CEO, a general manager (such as the head of operations or the head of sales), or a product manager could be the one to do this (ie PM, engineering lead, etc.)

If you are the one who is directly responsible, bring up the concept of a metrics review with your data partner. Ask the rest of the team to commit to giving it a shot for at least a quarter if you and your partner are in agreement. Friction is also decreased by starting the exercise during an already-concluded team meeting.

Your metrics evaluation doesn’t have to be exhaustive to begin with. Start off with a 10-minute period every other week. In the initial sessions, the data representative should be prepared to set the agenda and remind everyone of the objectives. (This is our primary success metric. I’m displaying this section to you because (I’m displaying this associated metric because…) Get feedback on the best set of metrics and cuts to look at during the first few sessions.

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To be clear, developing familiarity involves asking questions, and insightful inquiries can result in significant new learnings. But not every question is worth the time it takes to research and respond to it. It’s simple to ask a dozen questions just because you can and you’re naturally curious, but by pursuing follow-ups, you can unintentionally be adding hours of labor. Instead, try to raise questions that will likely result in a decision being changed.

What is my estimate for the most optimistic answer, and what will I do if that turns out to be the real answer? is an excellent litmus test for whether a data query is worthwhile asking. What, on the other hand, is my estimate for the most pessimistic response, and what will I do if it turns out to be accurate? Your inquiry is probably not worth answering if your responses to the two questions are the same.

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Every industry experiences some degree of seasonality and volatility. For instance, weekdays are when more people utilize corporate apps, and weekends are when more people play video games. Let’s say you anticipate a fall in sales in January because you believe Januarys will be worse than December. How significant would the decline have to be for you to become concerned?

Knowing the regular patterns of your organization is crucial to rapidly spot when you might need to act because something is wrong or when there is probably a measurement error. For instance, if one of your team members reports that a recent experiment increased Key Metric X by 20%,

But since you are aware that X is really tough to move, you would be suspicious and look into it more. A refined sense of what is typically improves forecasting and long-term planning throughout time.

When done correctly, the weekly metric review process fosters team cohesion, develops a team’s strategic acumen, and speeds up the identification of problems.

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