Appendix D — Adoption Maturity Assessment

Definition

A self-assessment instrument for determining whether an organization is positioned to attempt a Data Swimming Pool implementation — and, more often, for determining that it is not yet. The instrument is deliberately unforgiving.

D.1 Maturity Model #

flowchart LR
    M0["<b>M0 · Fragmented</b><br/>Sources siloed<br/>No shared identity<br/>Correlation ad hoc"]
    M1["<b>M1 · Consolidated</b><br/>Central platform<br/>Batch integration<br/>Query-time joins"]
    M2["<b>M2 · Streaming</b><br/>Event backbone<br/>Real-time pipelines<br/>Still per-consumer logic"]
    M3["<b>M3 · Resolved</b><br/>Enterprise entity resolution<br/>Canonical events<br/>Contracts enforced"]
    M4["<b>M4 · Correlating</b><br/>Persisted scored relationships<br/>Governed scopes<br/>Shadow-mode insights"]
    M5["<b>M5 · Acting</b><br/>Calibrated confidence<br/>Governed autonomy<br/>Compounding graph"]

    M0 --> M1 --> M2 --> M3 --> M4 --> M5

    M3 -.->|"entry point<br/>for DSP"| M4

    classDef low fill:#3d1f28,stroke:#d64550,color:#ffe8ec
    classDef mid fill:#3a2f1a,stroke:#c9a227,color:#fff6dc
    classDef high fill:#183028,stroke:#4caf7d,color:#e6fff2
    class M0,M1 low
    class M2,M3 mid
    class M4,M5 high

Figure 61. Correlation maturity model. M3 is the entry point. Organizations at M0–M2 attempting to jump directly to M4 will build a correlation engine on unresolved identities, which produces confident relationships between entities that are not the same entity.

D.2 Assessment Instrument #

Score each statement: 0 = not true, 1 = partially true, 2 = substantially true, 3 = fully true and evidenced.

Dimension 1 — Data Foundation (max 24) #

#	Statement	Score
1.1	Critical source systems emit or expose changes in near real time (CDC or events), not only nightly extracts	☐
1.2	A canonical or agreed event/record schema exists across at least three domains	☐
1.3	Data contracts are enforced at ingestion, with rejection rather than silent acceptance	☐
1.4	Source schema changes are detected before they break downstream consumers	☐
1.5	Event time is recorded distinctly from processing time in critical sources	☐
1.6	Data quality is measured continuously, with published metrics per source	☐
1.7	Late and out-of-order data is handled explicitly, not discarded	☐
1.8	Raw source data is retained in an open format, replayable for reprocessing	☐

Dimension 2 — Identity and Entity Resolution (max 18) — weighted ×2 #

#	Statement	Score
2.1	An enterprise identifier exists for your most important entity type	☐
2.2	Cross-system identity mapping is a managed service, not per-project logic	☐
2.3	Entity resolution accuracy has been measured against a labelled sample	☐
2.4	Probabilistic matches carry confidence scores that consumers can see	☐
2.5	Merge and split operations are audited and reversible	☐
2.6	Identity resolution is available at streaming latency, not only in batch	☐

Dimension 3 — Governance and Privacy (max 24) #

#	Statement	Score
3.1	A data governance body exists with authority to refuse requests, not only to advise	☐
3.2	Data is classified by sensitivity, and classification is machine-readable	☐
3.3	Purpose limitation is recorded per dataset and enforced technically	☐
3.4	Erasure requests are propagated to derived datasets, not only to source systems	☐
3.5	A DPIA process exists and is applied to new analytical uses	☐
3.6	Automated decision-making is inventoried and classified	☐
3.7	Policy is externalized and enforced at runtime, not embedded in application code	☐
3.8	The organization has previously declined a technically feasible data use on ethical grounds	☐

Dimension 4 — Platform and Engineering (max 21) #

#	Statement	Score
4.1	A durable, partitioned event log operates in production	☐
4.2	Stateful stream processing operates in production with checkpointing	☐
4.3	A lakehouse with an open table format is in production use	☐
4.4	Lineage is captured automatically at column level for critical pipelines	☐
4.5	Observability covers streaming lag, state size, and pipeline health	☐
4.6	Infrastructure is defined as code with reproducible environments	☐
4.7	The organization can run a graph database in production, or has a credible path to	☐

Dimension 5 — Analytics and AI Practice (max 18) #

#	Statement	Score
5.1	Models are versioned, with lineage from training data to prediction	☐
5.2	Model performance is monitored in production with drift detection	☐
5.3	Model outputs are calibrated, and calibration is assessed	☐
5.4	Ground-truth outcomes are captured systematically for at least one decision process	☐
5.5	An MLOps or equivalent deployment path exists, with shadow deployment capability	☐
5.6	Generative AI outputs, where used, are grounded and citation-checked	☐

Dimension 6 — Organizational Readiness (max 21) — weighted ×1.5 #

#	Statement	Score
6.1	Executive sponsorship exists for a multi-year platform investment	☐
6.2	Funding is not contingent on twelve-month measurable return	☐
6.3	Domains accept accountability for the data they produce	☐
6.4	Cross-departmental failures can be surfaced without individual blame	☐
6.5	A platform team model exists, distinct from project delivery teams	☐
6.6	The organization can staff streaming, graph, ML, and governance expertise simultaneously	☐
6.7	There is documented demand for cross-domain questions that currently go unanswered	☐

Table 93. Assessment instrument across six dimensions. Dimension 2 is weighted double and Dimension 6 by 1.5 because entity resolution bounds technical outcomes and organizational readiness bounds everything else. Both are more commonly the cause of failure than the correlation technology itself.

D.3 Scoring #

Dimension	Raw max	Weight	Weighted max
1 — Data Foundation	24	1.0	24
2 — Identity and ER	18	2.0	36
3 — Governance and Privacy	24	1.0	24
4 — Platform and Engineering	21	1.0	21
5 — Analytics and AI	18	1.0	18
6 — Organizational Readiness	21	1.5	31.5
Total	126	—	154.5

Weighted score	Maturity	Recommendation
Below 60	M0–M1	Do not attempt. Invest in ingestion, contracts, and identity. Revisit in 18–24 months.
60–89	M2	Not ready. Address the two lowest-scoring dimensions before any correlation work.
90–114	M3	Ready for a minimum viable correlation platform (Appendix B.5) in shadow mode, one scope.
115–134	M3+	Ready for full Phase 1–2 adoption with three to five sources and one to two scopes.
135+	M4-capable	Ready for multi-scope adoption. Verify that no individual dimension scores below 70% of its weighted maximum.

Table 94. Score interpretation. A minimum threshold applies regardless of total: if Dimension 2 scores below 24 of 36, or Dimension 6 below 21 of 31.5, the organization is not ready irrespective of how strong the other dimensions are. High engineering capability does not compensate for unresolved identity or unwilling stakeholders.

D.4 Common Profiles #

Profile	Typical pattern	Diagnosis
The strong engineer	D1, D4 high; D2, D3 low	Will build an impressive platform that correlates wrong entities under unclear authority. Fix identity and governance first.
The governed laggard	D3 high; D1, D4 low	Excellent controls over data that cannot move fast enough to correlate. Invest in streaming foundations.
The AI-forward	D5 high; D1, D2 low	Sophisticated models on unreliable joins. The models are not the constraint.
The willing but under-resourced	D6 high; D2, D4 low	Genuine demand, insufficient foundations. Highest-value profile to invest in; also the most likely to attempt the jump prematurely.
The capable but unwilling	D1–D5 high; D6 low	Technically ready, organizationally blocked. Correlation exposes cross-departmental failure; without blameless culture this fails politically.

Table 95. Common assessment profiles. The capable-but-unwilling profile is the most frequent failure mode among large, well-resourced organizations, and no amount of additional engineering resolves it.

D.5 Reassessment Cadence #

Reassess at each adoption phase gate (Chapter 40) and at minimum annually. Track dimension trajectories rather than absolute scores: an organization improving Dimension 2 by six points a year is in a materially better position than one holding a higher static score.

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