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Data/Data Culture.md
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Data/Data Culture.md
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- Understanding variation is the beginning of data literacy.
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- Create a single space as the central place to post [[Data Request Template|data requests]].
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- On the other hand, data analysis and data science are domain level problems and cannot be centralized.
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- Create a single space to [[Sharing Data Insights|share the results of analysis and decisions made based on them]].
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- Create a single space to [[Data Practices|share the results of analysis and decisions made based on them]].
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- Log changes so everyone can jump in and be aware of what’s going on.
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- Log assumptions and lessons learned somewhere. This information should loop back into the data product.
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- Make the warehouse the source of truth for all the teams.
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Data/Data Practices.md
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Data/Data Practices.md
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# Data Practices
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Some useful practices to keep in mind when working in a data team. They can be proposed as Data Improvement Proposals (DIPs) and discussed in the team.
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## Request For Analysis
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- It's an [intake form for data requests](https://www.caitlinhudon.com/posts/2020/09/16/data-intake-form).
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## Data Platform UX Survey
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- [Asking about the data experience](https://docs.google.com/forms/d/e/1FAIpQLSfufs_0zOGlFiE6oqrdZU7xCi399CBYbIlZkAMe15GTRRcPZA/viewform)
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- [https://locallyoptimistic.com/post/surveys/](https://locallyoptimistic.com/post/surveys/)
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- [https://docs.google.com/forms/d/e/1FAIpQLSc-z4yCYX5cpbPTMUInLYurxYgY1UXd7iJOMGI_DAGc-wB17w/viewform](https://docs.google.com/forms/d/e/1FAIpQLSc-z4yCYX5cpbPTMUInLYurxYgY1UXd7iJOMGI_DAGc-wB17w/viewform)
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- We need to know what works, what doesn’t and what people are using
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- Is all the data you want available?
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- Is it difficult to find the data you want?
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- Do you trust the metrics/calculations presented?
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- Do you ever get confused about what metric or dimension means?
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- Do you know where to go for help with data questions?
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- Do you feel like our data platform hinders your job performance?
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- Are you happy with our data platform?
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- Can these two resources request be merged with an existing artifact(Dashboard/Cohort)?
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- Does X resource needs to be tagged as "curated"? Does Y needs to be deprecated?
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## Sharing Data Insights
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[Sharing your data insights across your organization facilitates collaboration and mutual learning – increasing data literacy across the company](https://locallyoptimistic.com/post/share-your-data-insights-to-engage-your-colleagues/). It also helps remind folks that members of the data team can be strategic partners, creating opportunities for proactive brainstorming that can drive innovation.
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The aim is to answer the following questions each time:
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1. **What am I looking at?** A **short-but-informative title** can tell people immediately what data is the focus of the insight.
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2. **What should I learn from this?** or, Why should I care? Include the **most useful information,** and/or a **clear takeaway**. For folks who only have a few seconds to scan the message, it should be **easy to spot** the **most valuable** bit of the insight, the **reason** this exploration was considered worth sharing.
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3. **What caught my eye?** Share a chart or a related resource!
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4. **What if I want to know more?** A **link to additional information** can be valuable for people who have time for more than a quick scan and want to understand how you developed the insight, or do some of their own related exploration.
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5. **What if I have a question?** Explicitly **inviting questions** and responses is crucial. It’s the best part of sharing an insight! This is where you get to learn about things your colleagues know that you don’t, or what they’re curious about but has not yet risen to the level of becoming a data request from them.
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6. **What if posting this prompts a whole bunch of follow-up questions, or exposes incorrect assumptions?** If you have hit on something that’s interesting to a lot of people there likely will be questions that spin off, new ways to slice the data you’re looking at, or assumptions you have made that need to be corrected.
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### Slack Template
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```md
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![Chart]()
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💡**One sentence summarizing the insights.**
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📝 Some extra information that might be useful.
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🔍 Dig deeper on link.com!
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_Questions, concerns? Thread on!_ 🧵
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```
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Data/Sharing Data Insights.md
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Data/Sharing Data Insights.md
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# Sharing Data Insights
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[Sharing your data insights across your organization facilitates collaboration and mutual learning – increasing data literacy across the company](https://locallyoptimistic.com/post/share-your-data-insights-to-engage-your-colleagues/). It also helps remind folks that members of the data team can be strategic partners, creating opportunities for proactive brainstorming that can drive innovation.
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The aim is to answer the following questions each time:
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1. **What am I looking at?** A **short-but-informative title** can tell people immediately what data is the focus of the insight.
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2. **What should I learn from this?** or, Why should I care? Include the **most useful information,** and/or a **clear takeaway**. For folks who only have a few seconds to scan the message, it should be **easy to spot** the **most valuable** bit of the insight, the **reason** this exploration was considered worth sharing.
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3. **What caught my eye?** Share a chart or a related resource!
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4. **What if I want to know more?** A **link to additional information** can be valuable for people who have time for more than a quick scan and want to understand how you developed the insight, or do some of their own related exploration.
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5. **What if I have a question?** Explicitly **inviting questions** and responses is crucial. It’s the best part of sharing an insight! This is where you get to learn about things your colleagues know that you don’t, or what they’re curious about but has not yet risen to the level of becoming a data request from them.
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6. **What if posting this prompts a whole bunch of follow-up questions, or exposes incorrect assumptions?** If you have hit on something that’s interesting to a lot of people there likely will be questions that spin off, new ways to slice the data you’re looking at, or assumptions you have made that need to be corrected.
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## Slack Template
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```md
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![Chart]()
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💡**One sentence summarizing the insights.**
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📝 Some extra information that might be useful.
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🔍 Dig deeper on link.com!
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_Questions, concerns? Thread on!_ 🧵
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```
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Documentation.md
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Documentation.md
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- Keep it simple, write in plain language.
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- [Use active voice](https://developers.google.com/style/voice): make clear who's performing the action.
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- [As you are working in a team, then you have to address the problem of shared understanding](https://surfingcomplexity.blog/2022/11/24/writing-docs-well-why-should-a-software-engineer-care/). This is where documentation come in.
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- [Use the imperative mood in descriptions and instructions](https://twitter.com/kepano/status/1751354613041872985). Use concise action-oriented sentences, written from the user's perspective.
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## Resources
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