feat: 🔗 Add new links to resources in Data Culture and Social Games

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Data

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Data/Data Culture.md

··· 17 17 - You don't hit a quantitative goal by focusing on the goal. You hit a quantitative goal by focusing on the process. 18 18 - Business Reviews are one of the best ways to get people to think about data. 19 19 - Value of clear goals and expectations. Validate what you think your job is with your manager and stakeholders, repeatedly. 20 + - [Weekly Business Review meetings are a process control tool](https://commoncog.com/the-amazon-weekly-business-review/). A tool designed to uncover and disseminate the causal structure of a business. 20 21 - [While the output of your team is what you want to maximize, you'll need some indicators that will help guide you day-to-day](https://data-columns.hightouch.io/your-first-60-days-as-a-first-data-hire-weeks-3-4/). Decide what's important to you (test coverage, documentation missing, queries run, models created, ...), and generate some internal reports for yourself. 21 22 - [Data teams should be a part of the business conversations from the beginning](https://cultivating-algos.stitchfix.com/). Get the data team involved early, have open discussions with them about the existing work, and how to prioritize new work against the existing backlog. Don't accept new work without addressing the existing bottlenecks, and don't accept new work without requirements. **Organizational [[politics]] matter way more than any data methods or technical knowledge**. The hard bit about becoming data driven in business isn't the technical bits. It's the political bits. 22 23 - Including data people in meetings causes happy accidents! ··· 52 53 - Good use of data is, ultimately, a question of good epistemology. ("Is this true? What can we conclude? How do we know that?") Good epistemology is hard. It must be taught. 53 54 - **When things are going well, no one cares about data**. The right time to present data is when things are starting to go bad. Use your early warning detection systems to understand when it looks like it's gonna be time for data to step in and save the day and then position data as a solution in the context of whatever meaning makes sense. The stakeholders are decision makers and they don't have a ton of time. They're looking to make decisions, they're looking to solve problems. 54 55 - [So much of data work is about accumulating little bits of knowledge and building a shared context in your org so that it's possible to have the big, earth shattering revelations we all wish we could drive on a predictable schedule](https://twitter.com/imightbemary/status/1536368160961572864). 55 - - A big purpose of data is knowledge. Knowledge is "theories or models that allow you to predict the outcomes of your business actions". Insights may originate from data but are confirmed through actions. 56 + - A big purpose of data is knowledge. Knowledge is **"theories or models that allow you to predict the outcomes of your business actions"**. Insights may originate from data but are confirmed through actions. 56 57 - You won't have the best allocation of resources in a reactive team. Data teams need extra [[slack]]. [Balance user requests with actual needs](https://scientistemily.substack.com/p/product-management-skills-for-data). 57 58 - Do weekly recaps in Slack in to highlight key items, company-wide progress toward north-stars, improvements in certain areas, new customer highlights. All positive and fun stuff. 58 59 - How can we measure the data team impact?

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