SaaS Implementation & Integration Health

Decision: which implementations risk missing committed go-live, why, and what intervention should happen next?

Deterministic synthetic portfolio data · 60 fictional accounts · snapshot 30 Jun 2026.

Active implementations

Projects not complete at the snapshot date.

Projects not complete at the snapshot date.Source: Implementation portfolio KPIsTables: dim_account, fact_integration_run

Deterministic query executed by scripts/build_analytics_projects.py.

WITH completed AS (
  SELECT *, julianday(actual_go_live_date) - julianday(kickoff_date) AS lead_days
  FROM dim_account WHERE project_status = 'Completed'
), ordered_ttfv AS (
  SELECT time_to_first_value_days,
         ROW_NUMBER() OVER (ORDER BY time_to_first_value_days) AS row_number,
         COUNT(*) OVER () AS total_rows
  FROM dim_account WHERE time_to_first_value_days IS NOT NULL AND time_to_first_value_days <> ''
), median_ttfv AS (
  SELECT AVG(time_to_first_value_days) AS median_ttfv_days
  FROM ordered_ttfv
  WHERE row_number IN ((total_rows + 1) / 2, (total_rows + 2) / 2)
), run_rate AS (
  SELECT SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS integration_success_rate
  FROM fact_integration_run
)
SELECT SUM(CASE WHEN project_status = 'Active' THEN 1 ELSE 0 END) AS active_implementations,
       ROUND((SELECT SUM(CASE WHEN actual_go_live_date <= committed_go_live_date THEN 1 ELSE 0 END) * 1.0 / COUNT(*) FROM completed), 6) AS on_time_go_live_rate,
       ROUND((SELECT median_ttfv_days FROM median_ttfv), 2) AS median_ttfv_days,
       SUM(CASE WHEN project_status = 'Active' AND risk_status IN ('Red','Amber') THEN 1 ELSE 0 END) AS at_risk_active_accounts,
       ROUND((SELECT integration_success_rate FROM run_rate), 6) AS integration_success_rate
FROM dim_account;

On-time go-live

45.5%

Completed implementations delivered by committed date.

Completed implementations delivered by committed date.Source: Implementation portfolio KPIsTables: dim_account, fact_integration_run

Deterministic query executed by scripts/build_analytics_projects.py.

WITH completed AS (
  SELECT *, julianday(actual_go_live_date) - julianday(kickoff_date) AS lead_days
  FROM dim_account WHERE project_status = 'Completed'
), ordered_ttfv AS (
  SELECT time_to_first_value_days,
         ROW_NUMBER() OVER (ORDER BY time_to_first_value_days) AS row_number,
         COUNT(*) OVER () AS total_rows
  FROM dim_account WHERE time_to_first_value_days IS NOT NULL AND time_to_first_value_days <> ''
), median_ttfv AS (
  SELECT AVG(time_to_first_value_days) AS median_ttfv_days
  FROM ordered_ttfv
  WHERE row_number IN ((total_rows + 1) / 2, (total_rows + 2) / 2)
), run_rate AS (
  SELECT SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS integration_success_rate
  FROM fact_integration_run
)
SELECT SUM(CASE WHEN project_status = 'Active' THEN 1 ELSE 0 END) AS active_implementations,
       ROUND((SELECT SUM(CASE WHEN actual_go_live_date <= committed_go_live_date THEN 1 ELSE 0 END) * 1.0 / COUNT(*) FROM completed), 6) AS on_time_go_live_rate,
       ROUND((SELECT median_ttfv_days FROM median_ttfv), 2) AS median_ttfv_days,
       SUM(CASE WHEN project_status = 'Active' AND risk_status IN ('Red','Amber') THEN 1 ELSE 0 END) AS at_risk_active_accounts,
       ROUND((SELECT integration_success_rate FROM run_rate), 6) AS integration_success_rate
FROM dim_account;

Median TTFV days

Median kickoff-to-first-value days for eligible accounts.

Median kickoff-to-first-value days for eligible accounts.Source: Implementation portfolio KPIsTables: dim_account, fact_integration_run

Deterministic query executed by scripts/build_analytics_projects.py.

WITH completed AS (
  SELECT *, julianday(actual_go_live_date) - julianday(kickoff_date) AS lead_days
  FROM dim_account WHERE project_status = 'Completed'
), ordered_ttfv AS (
  SELECT time_to_first_value_days,
         ROW_NUMBER() OVER (ORDER BY time_to_first_value_days) AS row_number,
         COUNT(*) OVER () AS total_rows
  FROM dim_account WHERE time_to_first_value_days IS NOT NULL AND time_to_first_value_days <> ''
), median_ttfv AS (
  SELECT AVG(time_to_first_value_days) AS median_ttfv_days
  FROM ordered_ttfv
  WHERE row_number IN ((total_rows + 1) / 2, (total_rows + 2) / 2)
), run_rate AS (
  SELECT SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS integration_success_rate
  FROM fact_integration_run
)
SELECT SUM(CASE WHEN project_status = 'Active' THEN 1 ELSE 0 END) AS active_implementations,
       ROUND((SELECT SUM(CASE WHEN actual_go_live_date <= committed_go_live_date THEN 1 ELSE 0 END) * 1.0 / COUNT(*) FROM completed), 6) AS on_time_go_live_rate,
       ROUND((SELECT median_ttfv_days FROM median_ttfv), 2) AS median_ttfv_days,
       SUM(CASE WHEN project_status = 'Active' AND risk_status IN ('Red','Amber') THEN 1 ELSE 0 END) AS at_risk_active_accounts,
       ROUND((SELECT integration_success_rate FROM run_rate), 6) AS integration_success_rate
FROM dim_account;

At-risk active

Active accounts classified red or amber by explicit rules.

Active accounts classified red or amber by explicit rules.Source: Implementation portfolio KPIsTables: dim_account, fact_integration_run

Deterministic query executed by scripts/build_analytics_projects.py.

WITH completed AS (
  SELECT *, julianday(actual_go_live_date) - julianday(kickoff_date) AS lead_days
  FROM dim_account WHERE project_status = 'Completed'
), ordered_ttfv AS (
  SELECT time_to_first_value_days,
         ROW_NUMBER() OVER (ORDER BY time_to_first_value_days) AS row_number,
         COUNT(*) OVER () AS total_rows
  FROM dim_account WHERE time_to_first_value_days IS NOT NULL AND time_to_first_value_days <> ''
), median_ttfv AS (
  SELECT AVG(time_to_first_value_days) AS median_ttfv_days
  FROM ordered_ttfv
  WHERE row_number IN ((total_rows + 1) / 2, (total_rows + 2) / 2)
), run_rate AS (
  SELECT SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS integration_success_rate
  FROM fact_integration_run
)
SELECT SUM(CASE WHEN project_status = 'Active' THEN 1 ELSE 0 END) AS active_implementations,
       ROUND((SELECT SUM(CASE WHEN actual_go_live_date <= committed_go_live_date THEN 1 ELSE 0 END) * 1.0 / COUNT(*) FROM completed), 6) AS on_time_go_live_rate,
       ROUND((SELECT median_ttfv_days FROM median_ttfv), 2) AS median_ttfv_days,
       SUM(CASE WHEN project_status = 'Active' AND risk_status IN ('Red','Amber') THEN 1 ELSE 0 END) AS at_risk_active_accounts,
       ROUND((SELECT integration_success_rate FROM run_rate), 6) AS integration_success_rate
FROM dim_account;

Integration success

98%

Successful sync runs divided by all attempted runs.

Successful sync runs divided by all attempted runs.Source: Implementation portfolio KPIsTables: dim_account, fact_integration_run

Deterministic query executed by scripts/build_analytics_projects.py.

WITH completed AS (
  SELECT *, julianday(actual_go_live_date) - julianday(kickoff_date) AS lead_days
  FROM dim_account WHERE project_status = 'Completed'
), ordered_ttfv AS (
  SELECT time_to_first_value_days,
         ROW_NUMBER() OVER (ORDER BY time_to_first_value_days) AS row_number,
         COUNT(*) OVER () AS total_rows
  FROM dim_account WHERE time_to_first_value_days IS NOT NULL AND time_to_first_value_days <> ''
), median_ttfv AS (
  SELECT AVG(time_to_first_value_days) AS median_ttfv_days
  FROM ordered_ttfv
  WHERE row_number IN ((total_rows + 1) / 2, (total_rows + 2) / 2)
), run_rate AS (
  SELECT SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS integration_success_rate
  FROM fact_integration_run
)
SELECT SUM(CASE WHEN project_status = 'Active' THEN 1 ELSE 0 END) AS active_implementations,
       ROUND((SELECT SUM(CASE WHEN actual_go_live_date <= committed_go_live_date THEN 1 ELSE 0 END) * 1.0 / COUNT(*) FROM completed), 6) AS on_time_go_live_rate,
       ROUND((SELECT median_ttfv_days FROM median_ttfv), 2) AS median_ttfv_days,
       SUM(CASE WHEN project_status = 'Active' AND risk_status IN ('Red','Amber') THEN 1 ELSE 0 END) AS at_risk_active_accounts,
       ROUND((SELECT integration_success_rate FROM run_rate), 6) AS integration_success_rate
FROM dim_account;

Implementation stage completion

Source: Implementation funnelTable: fact_milestone

Deterministic query executed by scripts/build_analytics_projects.py.

SELECT milestone, MIN(stage_order) AS stage_order,
       SUM(CASE WHEN actual_date <> '' THEN 1 ELSE 0 END) AS completed_accounts
FROM fact_milestone GROUP BY milestone ORDER BY stage_order;

Implementation stage completion
Stage	Milestone	Completed accounts
1	Discovery complete	60
2	Authentication	60
3	Mapping approved	59
4	First test sync	59
5	UAT complete	59
6	Training	56
7	Go-live	55
8	First value	55

Average implementation lead time by kickoff cohort

Source: Cohort lead timeTable: dim_account

Deterministic query executed by scripts/build_analytics_projects.py.

SELECT substr(kickoff_date, 1, 7) AS kickoff_month,
       ROUND(AVG(julianday(actual_go_live_date) - julianday(kickoff_date)), 2) AS average_lead_days,
       COUNT(*) AS completed_accounts
FROM dim_account WHERE project_status = 'Completed'
GROUP BY kickoff_month ORDER BY kickoff_month;

Average implementation lead time by kickoff cohort data
Kickoff Month	Average Lead Days
2025-08	53.67
2025-09	52.5
2025-10	51.14
2025-11	62.17
2025-12	52.67
2026-01	49.5
2026-02	50.5
2026-03	45.83
2026-04	52.4
2026-05	36

Active implementation readiness by account

Source: Intervention queueTable: dim_account

Deterministic query executed by scripts/build_analytics_projects.py.

SELECT account_id,
       account_id || ' | ' || UPPER(risk_status) || ' | '
         || printf('%+d days', days_to_committed_go_live) AS account_risk_label,
       CASE risk_status WHEN 'Red' THEN 1 WHEN 'Amber' THEN 2 ELSE 3 END AS risk_order,
       fictional_account_name, segment, days_to_committed_go_live,
       readiness_rate, mapping_coverage, uat_pass_rate,
       recent_integration_success_rate, risk_status, risk_reason, next_action
FROM dim_account WHERE project_status = 'Active'
ORDER BY risk_order, days_to_committed_go_live ASC, account_id ASC;

Active implementation readiness by account data
Account Risk Label	Readiness Rate	Risk Status
ACCT-058 \| RED \| -8 days	71.7%	Red
ACCT-059 \| RED \| -2 days	75.4%	Red
ACCT-057 \| RED \| +2 days	71.7%	Red
ACCT-055 \| AMBER \| +16 days	88.8%	Amber
ACCT-060 \| AMBER \| +40 days	26.3%	Amber

Intervention queue

Source: Intervention queueTable: dim_account

Deterministic query executed by scripts/build_analytics_projects.py.

SELECT account_id,
       account_id || ' | ' || UPPER(risk_status) || ' | '
         || printf('%+d days', days_to_committed_go_live) AS account_risk_label,
       CASE risk_status WHEN 'Red' THEN 1 WHEN 'Amber' THEN 2 ELSE 3 END AS risk_order,
       fictional_account_name, segment, days_to_committed_go_live,
       readiness_rate, mapping_coverage, uat_pass_rate,
       recent_integration_success_rate, risk_status, risk_reason, next_action
FROM dim_account WHERE project_status = 'Active'
ORDER BY risk_order, days_to_committed_go_live ASC, account_id ASC;

Intervention queue
Account	Fictional name	Segment	Risk	Days to committed	Readiness	Mapping	Required UAT	7-day sync success	Next action
ACCT-058	Demo Account 58	SMB	Red	-8	71.7%	66.7%	100%	50%	Run daily recovery plan with decision owner
ACCT-059	Demo Account 59	SMB	Red	-2	75.4%	66.7%	62.5%	100%	Run daily recovery plan with decision owner
ACCT-057	Demo Account 57	Mid-Market	Red	2	71.7%	66.7%	50%	100%	Run daily recovery plan with decision owner
ACCT-055	Demo Account 55	Enterprise	Amber	16	88.8%	100%	75%	87.5%	Resolve blocker and re-baseline remaining milestones
ACCT-060	Demo Account 60	Enterprise	Amber	40	26.3%	0%	0%	87.5%	Resolve blocker and re-baseline remaining milestones

Failed sync runs by error code

Source: Integration errorsTable: fact_integration_run

Deterministic query executed by scripts/build_analytics_projects.py.

SELECT CASE WHEN error_code = '' THEN 'NONE' ELSE error_code END AS error_code,
       COUNT(*) AS runs,
       SUM(records_rejected) AS rejected_records
FROM fact_integration_run WHERE status = 'FAILED'
GROUP BY error_code ORDER BY runs DESC;

Failed sync runs by error code data
Error Code	Runs
UPSTREAM_UNAVAILABLE	116
SCHEMA_FIELD_CHANGED	39
RATE_LIMITED	22
OAUTH_TOKEN_EXPIRED	3

Connector evidence

Source: Connector reliabilityTable: fact_integration_run

Deterministic query executed by scripts/build_analytics_projects.py.

SELECT connector_id,
       ROUND(SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) * 1.0 / COUNT(*), 6) AS success_rate,
       ROUND(SUM(records_accepted) * 1.0 / SUM(records_received), 6) AS data_acceptance_rate,
       ROUND(AVG(latency_ms), 0) AS average_latency_ms,
       SUM(CASE WHEN status = 'FAILED' THEN 1 ELSE 0 END) AS failed_runs
FROM fact_integration_run GROUP BY connector_id ORDER BY success_rate ASC;

Connector evidence
Connector	Success	Data acceptance	Avg latency ms	Failed runs
WAREHOUSE	96%	95.7%	2,329	26
BILLING	97.5%	97.9%	1,947	87
PRODUCT	98.6%	98.1%	1,950	22
CRM	98.7%	98.3%	1,969	45

Method and limitations

Risk is determined by visible schedule, mapping, UAT and reliability rules—not an opaque health score. Readiness combines mapping (40%), required UAT (30%) and recent sync success (30%) for chart positioning; the red/amber intervention status still follows the explicit guardrails. Template-cohort comparisons are observational synthetic patterns and do not establish causality. Account names, payloads and outcomes are fictional.

Sources

Implementation portfolio KPIssql/analytics.sql · SQLite 3 · 2026-06-30T18:00:00Z

Deterministic query executed by scripts/build_analytics_projects.py.

SQL query

WITH completed AS (
  SELECT *, julianday(actual_go_live_date) - julianday(kickoff_date) AS lead_days
  FROM dim_account WHERE project_status = 'Completed'
), ordered_ttfv AS (
  SELECT time_to_first_value_days,
         ROW_NUMBER() OVER (ORDER BY time_to_first_value_days) AS row_number,
         COUNT(*) OVER () AS total_rows
  FROM dim_account WHERE time_to_first_value_days IS NOT NULL AND time_to_first_value_days <> ''
), median_ttfv AS (
  SELECT AVG(time_to_first_value_days) AS median_ttfv_days
  FROM ordered_ttfv
  WHERE row_number IN ((total_rows + 1) / 2, (total_rows + 2) / 2)
), run_rate AS (
  SELECT SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) * 1.0 / COUNT(*) AS integration_success_rate
  FROM fact_integration_run
)
SELECT SUM(CASE WHEN project_status = 'Active' THEN 1 ELSE 0 END) AS active_implementations,
       ROUND((SELECT SUM(CASE WHEN actual_go_live_date <= committed_go_live_date THEN 1 ELSE 0 END) * 1.0 / COUNT(*) FROM completed), 6) AS on_time_go_live_rate,
       ROUND((SELECT median_ttfv_days FROM median_ttfv), 2) AS median_ttfv_days,
       SUM(CASE WHEN project_status = 'Active' AND risk_status IN ('Red','Amber') THEN 1 ELSE 0 END) AS at_risk_active_accounts,
       ROUND((SELECT integration_success_rate FROM run_rate), 6) AS integration_success_rate
FROM dim_account;

Implementation funnelsql/analytics.sql · SQLite 3 · 2026-06-30T18:00:00Z

Deterministic query executed by scripts/build_analytics_projects.py.

SQL query

SELECT milestone, MIN(stage_order) AS stage_order,
       SUM(CASE WHEN actual_date <> '' THEN 1 ELSE 0 END) AS completed_accounts
FROM fact_milestone GROUP BY milestone ORDER BY stage_order;

Connector reliabilitysql/analytics.sql · SQLite 3 · 2026-06-30T18:00:00Z

Deterministic query executed by scripts/build_analytics_projects.py.

SQL query

SELECT connector_id,
       ROUND(SUM(CASE WHEN status = 'SUCCESS' THEN 1 ELSE 0 END) * 1.0 / COUNT(*), 6) AS success_rate,
       ROUND(SUM(records_accepted) * 1.0 / SUM(records_received), 6) AS data_acceptance_rate,
       ROUND(AVG(latency_ms), 0) AS average_latency_ms,
       SUM(CASE WHEN status = 'FAILED' THEN 1 ELSE 0 END) AS failed_runs
FROM fact_integration_run GROUP BY connector_id ORDER BY success_rate ASC;

Cohort lead timesql/analytics.sql · SQLite 3 · 2026-06-30T18:00:00Z

Deterministic query executed by scripts/build_analytics_projects.py.

SQL query

SELECT substr(kickoff_date, 1, 7) AS kickoff_month,
       ROUND(AVG(julianday(actual_go_live_date) - julianday(kickoff_date)), 2) AS average_lead_days,
       COUNT(*) AS completed_accounts
FROM dim_account WHERE project_status = 'Completed'
GROUP BY kickoff_month ORDER BY kickoff_month;

Intervention queuesql/analytics.sql · SQLite 3 · 2026-06-30T18:00:00Z

Deterministic query executed by scripts/build_analytics_projects.py.

SQL query

SELECT account_id,
       account_id || ' | ' || UPPER(risk_status) || ' | '
         || printf('%+d days', days_to_committed_go_live) AS account_risk_label,
       CASE risk_status WHEN 'Red' THEN 1 WHEN 'Amber' THEN 2 ELSE 3 END AS risk_order,
       fictional_account_name, segment, days_to_committed_go_live,
       readiness_rate, mapping_coverage, uat_pass_rate,
       recent_integration_success_rate, risk_status, risk_reason, next_action
FROM dim_account WHERE project_status = 'Active'
ORDER BY risk_order, days_to_committed_go_live ASC, account_id ASC;

Integration errorssql/analytics.sql · SQLite 3 · 2026-06-30T18:00:00Z

Deterministic query executed by scripts/build_analytics_projects.py.

SQL query

SELECT CASE WHEN error_code = '' THEN 'NONE' ELSE error_code END AS error_code,
       COUNT(*) AS runs,
       SUM(records_rejected) AS rejected_records
FROM fact_integration_run WHERE status = 'FAILED'
GROUP BY error_code ORDER BY runs DESC;