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README.md

README.md

Know Priorities

@KnowPriorities | Website | Podcast | Email Course

Prioritization engine designed to calculate a final priority list of actual value.

MIT Licensed for everyone's benefit.

Getting Started

Features

v1

  • Injest priority lists
  • Injest ranges (numeric high/low; ex. on a scale of 1-5)
  • Injest series (ex. a, b, c, d, e, f)
  • Injest behaviors (ex. systems of likes, comments, upvotes, views, heat, etc)
  • Provide feedback bridge (supporting ranges, series, and behaviors)
  • Control stakeholder volume including mute (aka assessment of how their input is valued)
  • Control group level % of say overall (ex. customers get 40% of the final say)
  • Stakeholder equality (stakeholder value does not change due to "squeaky wheel" or lack there of)
  • Population balancing (even with 1M customers, 5 employees are heard equally before % of say)
  • Global progressive system of values (uses golden ratio represented as fibonacci values)
  • Include work items' scope
  • Include work items' opportunity
  • Tagging to adjust or set stakeholder volume
  • Tagging to adjust or set item scope
  • Group level tag overrides for adjusting stakeholder volume
  • Group level tag overrides for adjusting or setting item scope
  • Depreciation of stakeholder input (ex. after 30 days person A's input is valued 33% less)
  • Group level results
  • Overall results
  • Results include distance of value
  • Support for medium-large amount of stakeholders (tested >1M; crashes at 10M)
  • Support Json injestion of data model

v2

Goals are still being reviewed for v2. However, three things are primary:

  • Raising the cap to support a theoretical unlimited number of stakeholders
  • Supporting rolling update calculations instead of full load calculations
  • Refocusing on the core with only priority lists as the injestion point (not ranges, series, or behaviors)

v2 is being built along side v1 as sub-namespacing so v2 can rollout in stages with v1 intact while also allowing v1 to support bug fixes. This also allows a relatively stable v1 to v2 comparison to occur to help ensure the results are stable or improving from version to version.