Analytics/Requirements Checklist

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User Story & Expectations[edit]

  • What: Captures the narrative as presented by the user (with little-to-no interpretation or cleanup)
  • Goal: Understand the user's needs, desires, and expectations.
    • The raw narrative is useful even if it doesn't really make sense! -- It helps to know where we started in the process to grok their expectations.
    • Honestly great to have a literal description of the deliverable in their own words
  • Key Questions:
    • What is their goal? Why do they want to achieve that? Gains/Pains. What are possible scale-backs?
    • Who surfaced this desire (actual person) and in what context? Who do we ultimately deliver to?
    • How clear was the user's vision of the feature, the deliverable and the reqs?
    • What sort of expectations did they have *before* coming us about the LoE of their request and/or the timeline?
    • Are there any external deadlines? Does this block anything?

User Feature Requirements & Acceptance Criterion[edit]

  • What: Cleaned and clarified description of the deliverable, broken out into features that make sense to everyone.
    • Ideally, we want both the high-level, easy-to-grok description, and the low-level, precise breakout.
    • eg. "Web dashboard w/ 2 tabs -- "Mobile Site", and "Mobile Apps". A contains 6 graphs: 1. Pageviews to Mobile Site (line, logscale, step=1mo), with metrics (all pageviews / month): total across all sites, alpha site, beta site. 2. ..."
  • Goal: Consensus around the concrete deliverable and especially its scope, including:
    • Clear and precise enumeration capabilities/limitations
    • Expectations around behavior and appearance
    • Rough expectations around a timeline, and some boxing on level-of-effort/the time commitment
  • Components:
    • Data Requirements, specified as precisely as possible.
      • We don't necessarily need to make the user sit through us hashing this out, but (imo) we do need to get them to explicitly sign off on the result.
      • I think we should consider it a hard requirement that any feature which requires new metric work have descriptions for:
        • Definitions for all metric dimensions (with datatypes) -- this means "counts" should explicitly say what dimensions are grouped and counted
        • Data output shape -- define rows/columns with types (including the step between rows); what defines a "key", and is it row-major or col-major?; cardinality/storage estimate; drill-downs/aggregations/rollups
        • Data source(s) -- both the data necessary for the computation and the origin of said data. For example, if the expectation is that we use a stream into Kraken, it is a different feature to fall back on the sampled Squid logs, as the whole implementation toolchain is different.
        • Data output format(s) -- can't just assume tsv if we aim to deliver certain visualizations (esp geo) -- is json, xml, or avro ok? how will the data be published? does it need to be public, or can we cron files into a shared space on stat1001?
        • Minimally required frequency of recomputation -- note this is different from the timestep for rows and/or rollups; this is how stale the data can be (aka, how often the job runs)
    • Presentation/Visualization Requirements, including any graphs, dashboards, domains, or other presentation features (pointing to existing examples if possible).
      • Dashboards: names, contents for each, their tabs, and the visualizations in each tab.
      • Questions for each visualization: graph type (line, bar, geo, etc); any specific formatting/appearance requests (custom labels? scale? timespan?)
      • Are any new Limn features needed?

Solution Options w/ Tasks & LoE Estimate[edit]

  • What: List several technical solutions as options for satisfying the requirements, assessing the tasks required and the level-of-effort involved.
  • Goal: Find the best way to make the user happy while conserving dev effort and advancing along the trajectory of our strategy to provide insight via a robust, automated, self-service platform.
  • If necessary, Engineering Lead coordinates team expertise and presents results.
  • Everyone works together to assess, revise, and prioritize (planning poker, etc).