AlterspectiveSharedo Audit

Tier 1 · Configuration

Configuration integrity & standards alignment

A Sharedo tenant accretes configuration faster than it sheds it. Fields are added for one matter type and never removed; the same concept ends up stored two ways; forms grow to ask for data no one supplies. This audit measures the whole configuration estate against Alterspective's Sharedo configuration standards library and returns a prioritised, rule-referenced clean-up backlog.

T1

1What we examine

Form-field estate & fill ratesCFG-FF

Every attribute field is measured for fill rate across the live matter population. Dead fields (0% populated) and sparse fields (under 5%) are identified — the tell-tale of forms asking for data no one supplies.

Mixed-representation defectsCFG-OS

The same concept stored two ways at once — free-text values in one field and option-set codes in another — which quietly breaks reporting and rule logic.

Single-field value driftCFG-FF

One field carrying true/false in some rows and 1/0 in others. Reports built on it silently under-count.

Duplicate field namesCFG-FN

Logical fields configured twice under keys differing only by case or punctuation (for example reserve-amount versus ReserveAmount) — data splits across both copies.

Option-set hygieneCFG-OS

Orphaned, unused, or duplicate option-set entries; values referenced by no active field.

Standards alignmentCFG-WT · CFG-PH

Configuration is measured against our standards library — 19+ active standards across work types (CFG-WT), phases (CFG-PH), key dates (CFG-KD), option sets (CFG-OS), form fields (CFG-FF / CFG-FN), participants (CFG-PT), and security roles (CFG-RS) — with every deviation cited to a specific rule identifier you can read and challenge.

Orphaned configurationCFG-WT

Work types, aspects, and rules present in the tenant but referenced by nothing live.

2How we examine it

Configuration is captured through the administration API. This tier consumes configuration only — no matter content and no personal data leave the tenant. Fill rates are computed over the live matter population; every standards deviation is cited to a specific rule identifier.

Fill rates and counts are observed measures. Every standards-deviation call cites a specific rule identifier you can read and challenge — it is a measured deviation, not an opinion.

3Example finding

Illustrative example — synthetic demonstration corpus

Across a 287-field configuration estate, 189 fields (66%) carried almost no data, 1 field's values were stored two different ways at once, and 5 logical fields were duplicated under keys differing only by case or punctuation.

  • 1 field storing values two ways at once (free-text literal and option-set code)
  • 5 logical fields duplicated under keys differing only by case (CFG-FN)
  • 95 of 287 fields never populated at all — pure configuration debt
Core fields19 fieldsModerately used79 fieldsSparse fields94 fieldsDead fields95 fields
Fill-rate buckets across 287 configured attribute fields — sparse and dead highlighted. Hover any bar for the exact count.

4The benefit

What you walk away with

A shorter, higher-signal form estate; reports you can trust; and a prioritised clean-up backlog scoped to specific rule identifiers, so remediation is defensible to your board and, increasingly, to a regulator.

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