+4 -3

Data/Data Culture.md

reviewed

··· 14 14 - Business Metrics Review is a 30 to 60 minutes meeting to chat and explore key metrics and teach how to think with data. 15 15 - Value of clear goals and expectations. Validate what you think your job is with your manager and stakeholders, repeatedly. 16 16 - [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. 17 17 - - [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**. 17 17 + - [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. 18 18 - Including data people in meetings causes happy accidents! 19 19 - The layout of the organization impacts time of the information to propagate and adds losses. 20 20 - 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. ··· 31 31 - Data professionals can build consensus as the company becomes more diverse. 32 32 - Data systems can establish methods for understanding the world even as it becomes more complex. 33 33 - Data literacy can create pathways for anyone to contribute equally to the organization's reality. 34 34 + - Understanding variation is the beginning of data literacy. 34 35 - Create a single space as the central place to post [[Data Request Template|data requests]]. 35 36 - On the other hand, data analysis and data science are domain level problems and cannot be centralized. 36 37 - Create a single space to [[Sharing Data Insights|share the results of analysis and decisions made based on them]]. ··· 43 44 - 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. 44 45 - **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. 45 46 - [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). 46 46 - - A big purpose of data is knowledge. Knowledge is "theories or models that allow you to predict the outcomes of your business actions". 47 47 + - 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. 47 48 - 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). 48 49 - 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. 49 50 - How can we measure the data team impact? ··· 91 92 - [Doing the fundamentals really well almost always exposes how little is actually understood about why things are happening. It’s uncomfortable for high performing people to acknowledge that your grip on the levers is slippery](https://twitter.com/gwenwindflower/status/1498822586255519744). 92 93 - [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. 93 94 - [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. 94 94 - - Data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data. 95 95 + - [Data-drivenness is about building tools, abilities, and, most crucially, a culture that acts on data](https://twitter.com/ejames_c/status/1732592768890057115). 95 96 - [Path to create a data-driven organization](https://twitter.com/_abhisivasailam/status/1520274838450888704): 96 97 - 1. Get a well-placed leader with influence to message, model, and demand data-driven execution. 97 98 - 2. Hire/fire based on data aptitude and usage.

+7 -3

Data/Metrics.md

reviewed

··· 2 2 3 3 ![[Quotes#^a5049d]] 4 4 5 5 - - Aim for a single **"North Star Metric"** alongside 3 to 5 additional supporting metrics. You may also want to consider counter-metrics (or pairing metrics) that keep you from [over-rotating on a singular metric](https://www.dataliftoff.com/wp-content/uploads/2022/10/tennis_balls-1536x2048.jpeg). 6 6 - - [Design **north star metrics that capture value to the customer** rather than value to your organization](https://roundup.getdbt.com/p/the-perfect-north-star-metric). 5 5 + - Focus on 3 to 5 metrics. You may also want to consider counter-metrics (or pairing metrics) that keep you from [over-rotating on a singular metric](https://www.dataliftoff.com/wp-content/uploads/2022/10/tennis_balls-1536x2048.jpeg). 6 6 + - [Design **north star metrics that capture value to the customer** rather than value to your organization](https://roundup.getdbt.com/p/the-perfect-north-star-metric). Beware, optimizing against at a single north star metric is like looking exclusively at the score to get insight into how to win the game. 7 7 - Common understanding of a metric matters more than the metric precision. That understanding requires some standardization (names, time spans, ...) and that needs [[Coordination]]. 8 8 - Teams need to cooperate when defining metrics. 9 9 - Rely on the SMART framework (Specific, Measurable, Achievable, Results-Oriented, Targeted). ··· 20 20 1. They can work to improve the system. 21 21 2. They can distort the system. 22 22 3. Or they can distort the data. 23 23 - 23 23 + - Every metric you use should have an Operational Definition. 24 24 + 1. A criterion — the thing you want to measure. 25 25 + 2. Test procedure — how will you measure the thing? 26 26 + 3. Decision rule — how will you decide if the thing you’re measuring should be included in the count? 24 27 ## Good Metric Checklist 25 28 26 29 - **Specific and sensitive**: Metrics should be specific to the product or feature, and need to be explicitly and quantitatively defined. The metric should also be sensitive enough to measure the impact we expect to see. ··· 42 45 - Metrics before Strategy. Your metrics are a reflection of your strategy. They help answer, is the strategy working? Metrics without strategy is looking at a bunch of random numbers. Define the strategy before you define your metrics. 43 46 - Definition Is More Important Than A Dashboard. People focus on "building a dashboard." Much more important is choosing which metrics are important and defining those metrics well. Defining is more complicated than people think... There are many ways to define a retention metric depending on your product. Your dashboard is a method to communicate your metrics, which is important, but useless if you choose and define them poorly. 44 47 - Outputs vs Inputs. Most metrics like a retention metric or revenue metric are output metrics. These are metrics you should monitor. Giving output metrics to teams as [[goals]] can be dangerous. They need to break them down into input metrics to make them actionable. 48 48 + - Output metrics represent results and input metrics represent actions. 45 49 - When output metrics are given as goals, teams can often focus on the wrong inputs or thrash between inputs. 46 50 - Focus on usage first (not revenue first). This is the most common version of outputs vs inputs. Usage creates revenue, revenue does not create usage. As a result, the most important metrics in terms of creating growth are not your revenue metrics, they are your usage metrics. 47 51 - Mixing Up Retention and Engagement. Retention and engagement are not the same things. Retention is binary. It answers the question, was this person active within my defined time period? Yes or no. Engagement is is depth. It answers the question, how active were they within the defined timed period? 0→N. Engagement is one of three major inputs into driving retention.

-1

Data/Product Analytics.md

reviewed

··· 15 15 - [Some of hard things](https://twitter.com/_MRogers/status/1511426752735760392): building a common nomenclature, knowing what exists, what's reusable/equivalent in new features, testing, aligning across different platforms... 16 16 - Don't treat all events the same. Missing some data in key events is not great. [Keep in mind that many events are ephemeral](https://twitter.com/johncutlefish/status/1511596224964534278). 17 17 - [If a stakeholder wants to know if users behave a certain way you can apply this heuristic](https://twitter.com/teej_m/status/1456719714420289536): assume they do - what decision would we make? What's the risk of just making that decision now? Risk is low? Just make it. Risk is uncertain? Let's [[Experimentation|run an experiment]]. 18 18 - - It doesn't make sense to release something if you can't tell how is performing! 19 18 - Developers should be involved in analytics. They work on building things, they will want to know the impact.

+1

Goals.md

reviewed

··· 4 4 5 5 - Minimize decision points. The main thing that consumes willpower isn't the process of doing a task, it's deciding to do the task in the first place. The goal is to get stuff done, not to decide to get stuff done. 6 6 - Shape the default. The ideal situation is for doing the right action to feel like the default. 7 7 + - You don’t hit a quantitative goal by focusing on the goal. You hit a quantitative goal by focusing on the process. 7 8 - Beware trivial inconveniences. Think of decisions in terms of activation energy. Some decisions need more energy than others depending on how are setup. Make the good decision effortless. Remove friction from the path to your goals. 8 9 9 10 ## TAPs

+1

Teamwork.md

reviewed

··· 155 155 - Diversity is represented and embraced; a broad spectrum of views are considered. 156 156 - Progress and set backs are regularly communicated to key stakeholders. 157 157 - When collaborative projects are completed, credit is shared among the contributors. 158 158 + - Management is prediction. In order to run a team well, you need to be able to predict the outcomes of your team actions. 158 159 159 160 ## Links 160 161