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Merge branch 'main' of github.com:davidgasquez/handbook
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Data/Dashboards.md
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Data/Dashboards.md
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- Why this dashboard exists.
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- Who it's for.
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- When it was built, and if and when it's set to expire .
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- What features it's tracking via links to team repositories, project briefs, screenshots, or video walkthroughs.
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- Take Aways.
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- What features it's tracking via links to team repositories, project briefs, screenshots, or video walk-throughs.
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- Take-aways.
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- Metadata (owner, related OKRs, TTL, …).
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- Make them so its easy to go one layer down (X went down in Y location, or for Z new users, etc).
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- Recreate dashboard from first principles periodically.
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Data/Data Culture.md
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Data/Data Culture.md
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- The modern data team needs to have *real organizational power*—it needs to be able to say "no” and mean it. If your data team does not truly have the power to say no to stakeholders, it will get sent on all kinds of wild goose chases, be unproductive, experience employee churn, etc.
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- Data should report to the CEO. Ideally at least with some weekly metrics split into (a) notable trends, (b) watching close, and (c) business as usual.
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- If data is the most precious asset in a company, does it make sense to have only one team responsible for it?
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- [People talk about data as the new oil but for most companies it’s a lot closer uranium](https://news.ycombinator.com/item?id=27781286). Hard to find people who can to handle or process it correctly, nontrivial security/liabilities if PII is involved, expensive to store and a generally underwhelming return on effort relative to the anticipated utility.
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- [People talk about data as the new oil but for most companies it’s a lot closer uranium](https://news.ycombinator.com/item?id=27781286). Hard to find people who can to handle or process it correctly, nontrivial security/liabilities if PII is involved, expensive to store and a generally underwhelming return on effort relative to the anticipated utility.
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- [The pain in data teams come from needing to influence PMs/peers with having little control of them. Data teams need to become really great internal marketers/persuaders](https://anchor.fm/census/episodes/The-evolution-of-the-data-industry--data-jobs-w-Avo-CEO-and-Co-founder-Stefania-Olafsdottir-e16hu1l). That said, it shouldn't be the data team job to convince the organization to be data driven. That's not an effective way of spending resources.
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- People problems are orders of magnitude more difficult to solve than data problems.
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- **Integrate data where the decision is made**. E.g: Google showing restaurant scores when you're looking something for dinner.
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- 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).
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- How can we measure the data team impact?
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- Making a [[Writing a Roadmap|roadmap]] can help you telling if you are hitting milestone deadlines or letting them slip.
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- Embedded data team members need to help other teams build their roadmap too.
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- Embedded data team members need to help other teams build their roadmap too.
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- Also, having a changelog ([do releases!](https://betterprogramming.pub/great-data-platforms-use-conventional-commits-51fc22a7417c)) will help show the team impact on the data product across time.
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- [Push for a *centralization of the reporting structure*, but keeping the *work management decentralized*](https://erikbern.com/2021/07/07/the-data-team-a-short-story.html).
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- Unify resources (datasets, entities, definitions, metrics). Have one source of truth for each one and make that clear to everyone. That source of truth needs heavy curation. Poor curation leads to confusion, distrust and…. lots of wasted effort.
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- [Data ownership is a hard problem](https://www.linkedin.com/posts/chad-sanderson_heres-why-data-ownership-is-an-incredibly-activity-6904107936533114880-gw8n/). Data is fundamentally generated by services (or front-end instrumentation) which is managed by engineers. CDC and other pipelines are built by data engineers. The delineation of ownership responsibilities is very rarely established, with each group wanting to push 'ownership' onto someone else so they can do the jobs they were hired for.
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- [Becoming a data-driven organization is a journey, which unfolds over time and requires critical thinking, human judgement, and experimentation](https://hbr.org/2022/02/why-becoming-a-data-driven-organization-is-so-hard). Fail fast, learn faster.
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- [Path to create a data-driven organization](https://twitter.com/_abhisivasailam/status/1520274838450888704):
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- 1. Get a well-placed leader with influence to message, model, and demand data-driven execution .
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- 2. Hire/fire based on data aptitude and usage.
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- 3. Create mechanisms that force analytical conversations. Sometimes there is no way around spending an afternoon breaking down metrics by different segments until you find The Thing.
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- 1. Get a well-placed leader with influence to message, model, and demand data-driven execution.
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- 2. Hire/fire based on data aptitude and usage.
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- 3. Create mechanisms that force analytical conversations. Sometimes there is no way around spending an afternoon breaking down metrics by different segments until you find The Thing.
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- [Start small. Don't try to wrangle data for the entire company until you have the tools and process down for one team](https://data-columns.hightouch.io/your-first-60-days-as-a-first-data-hire-weeks-3-4/).
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- Difficulty to work with data scales exponentially with size.
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- [Rule of thumb; your first customer as a data person should be growth](https://twitter.com/josh_wills/status/1577699871335010304).
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- [Data is used largely to answer these questions](https://roundup.getdbt.com/p/bring-back-scenario-analysis):
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- What already happened? Descriptive statistics.
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- What will happen? Predictive statistics.
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- Analytics: Making sense of data to guide decisions
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- Make your [modeling technique](https://data-columns.hightouch.io/untitled-2/) explicit.
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- Have a documentation [entry-point for Data](https://github.com/mozilla/data-docs).
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- [For self-serve, aim to own as little as possible but keep in mind you can't make people do what you want but cans top them for doing what you don't want](https://youtu.be/wyW6hQGZxgY).
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Large Language Models.md
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Large Language Models.md
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- Designing prompts is an iterative process that requires a lot of experimentation to get optimal results. Start with simple prompts and keep adding more elements and context as you aim for better results.
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- Be very specific about the instruction and task you want the model to perform. The more descriptive and detailed the prompt is, the better the results.
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- Avoid saying what not to do but say what to do instead.
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- Follow [Prompt Engineering Guide](https://www.promptingguide.ai/) and [OpenAI Best Practices](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api).
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- Follow [Prompt Engineering Guide](https://www.promptingguide.ai/), [Brex's Prompt Engineering Guide](https://github.com/brexhq/prompt-engineering), and [OpenAI Best Practices](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api).
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#### Prompt Creator
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