Analytics assurance & orchestration
Whether an analysis is independent, current, transportable, and still worth trusting here.
24 of 388 tools.
Audit analytical specification multiverse
Audit whether an analytical conclusion survives a prespecified multiverse of admissible windows, cohorts, metrics, and models using aligned bootstrap draws, a weighted specification curve, practical-effect support gates, and descriptive choice-influence diagnostics.
Audit analytics challenger independence
Audit whether an analytical challenger supplies genuinely independent error information: use paired temporal moving-block bootstrap bounds on error correlation, incumbent-failure catch rate and common-mode joint failure, with simultaneous Bonferroni control across every screened challenger and explicit evidence gates.
Audit analytics function calibration readiness
Gate analytical functions on paired out-of-time decision loss against a frozen baseline using temporal moving-block bootstrap, autocorrelation- and weight-adjusted effective sample size, evidence coverage, lower confidence bounds, improvement probability and recent degradation rather than declaring a model calibrated from training fit.
Audit analytics transportability
Audit whether locally calibrated analytical functions retain decision-loss improvement across target-similar operating environments using similarity-weighted random-effects meta-analysis, between-environment variance, I-squared, sign consistency and a conservative target prediction interval.
Audit forecast ensemble lineage integrity
Audit whether a claimed forecast ensemble is a complete, independently sealed and point-in-time evidence set rather than duplicated consensus, then score only mature uncontaminated outcomes.
Audit recommendation coherence
Audit whether analytical recommendations for the same decision remain comparable, current, supported and coherent after every unique evidence lineage receives one vote split across its claimants, preventing duplicated source data from manufacturing consensus.
Audit strategic assumption lineage
Audit every aggregate initiative-value claim against a versioned canonical premise, unit, validation period and independent evidence lineage; retain unknown references, detect stale, unverified, conflicting and source-reused exposure, and gate hidden portfolio concentration in assumptions shared across initiatives.
Calculate analytics calibration liability
Price the hidden financial liability of stale analytical functions from coherent joint calibration-failure scenarios, decision value at risk, loss fractions and remediation costs; calculate expected loss, VaR, CVaR, reserve breach probability, required reserve and exactly reconciled tail contributions.
Calculate analytics portfolio realized ROI
Reconcile the analytics portfolio's realized ROI from unique finance-owned incremental benefit sources, causal-evidence weights, non-overlapping function allocations, implementation/recurring/shared costs and coherent joint scenarios, with positive-value probability and CVaR loss gates.
Calculate recommendation conflict exposure
Price the expected, reserve-quantile and tail-CVaR regret of a current action when locally calibrated analytical recommendations conflict, using coherent action-loss scenarios and provenance-adjusted support that cannot be inflated by duplicate source lineage.
Calculate shared assumption risk exposure
Price coherent portfolio value loss when necessary assumptions interact multiplicatively and recur across initiatives; size reserve, breach probability and CVaR, then use exact continuous-integral Shapley attribution to reconcile nonlinear expected and tail loss to the premises creating hidden concentration.
Calculate value of independent analytics challenge
Calculate the economic value of an independent analytical challenge from coherent incumbent and challenged loss scenarios after complete challenge and decision-delay costs, with probability-of-positive-value and CVaR downside gates.
Construct calibration shared evidence graph
Discover stable candidate relationships for shared calibration design by correlating standardized held-out loss improvement across common environments, resampling whole environments, stability-selecting practical edges, controlling sign discoveries with Benjamini-Hochberg FDR and returning connected components without claiming parameter transfer.
Forecast analytics calibration survival
Forecast how long each locally calibrated analytical function remains decision-safe using right-censored calibration episodes, a discrete empirical-Bayes failure hazard, conditional survival from current calibration age, posterior uncertainty and explicit endpoint-support gates.
Forecast dependency adjusted consensus
Combine independently sealed human and model forecasts while learning context base rates and source reliability on earlier questions, discounting empirical and declared information dependence, and abstaining unless later questions beat both base rates and naive consensus.
Measure decision policy realized value
Measure candidate-versus-baseline realized net value from logged decisions with cross-fitted doubly robust policy scores, full action propensities, cluster bootstrap, importance-weight clipping, positivity mass, effective sample size, logging-policy calibration and cumulative value—so Gitrevio can substantiate decision ROI without relabeling correlation as impact.
Optimize adaptive analytics plan
Choose an exact adaptive sequence of analyses and an outcome-contingent terminal action by Bayesian belief-state dynamic programming, allowing early stopping while enforcing cost, duration, dependency, exclusion and analysis-step constraints and measuring value over the best fixed analysis sequence.
Optimize analytics challenger portfolio
Optimize a budgeted portfolio of complementary analytical challengers over coherent common-mode failure scenarios, residual losses, stochastic review demand, dependencies, exclusions and tail-risk appetite, with exact subset enumeration or a disclosed dependency-closed greedy fallback.
Optimize calibration experiment portfolio
Choose which analytical functions to calibrate next with exact Beta-binomial posterior-predictive value of sample information, result-contingent activation thresholds, false-activation loss, experiment budget/capacity, dependencies, exclusions and exact-or-disclosed portfolio search.
Optimize forecast elicitation portfolio
Choose which independent human or model forecasts to obtain next by learning chronologically validated contextual directional skill, simulating conservative entropy reduction, pricing decision relevance and removing duplicated information under budget and source-capacity constraints.
Optimize multi period calibration maintenance
Optimize a finite-horizon analytics maintenance schedule by propagating each function's healthy/degraded Markov belief under passive operation or recalibration, valuing healthy decisions and uncalibrated loss, enforcing period cash and specialist-capacity constraints, and disclosing exact state enumeration versus deterministic beam search.
Optimize shared assumption hedging portfolio
Choose a budgeted, capacity-feasible portfolio of validation, option, diversification or mitigation actions against shared business assumptions, combining repeated actions as diminishing remaining-gap closure while preserving cross-initiative reach, multi-premise complementarity, dependencies, exclusions, common scenario costs, positive-value probability and CVaR.
Solve bayesian influence diagram
Solve an exact discrete Bayesian influence diagram over actions, chance-node DAGs, action-dependent conditional probabilities, pre-decision evidence, and additive utility tables, then quantify action regret and the expected value of perfect information for observable exogenous nodes.
Value AI assistant rollout ROI
Value an aggregate AI-assistant rollout from aligned joint causal-effect draws, preserving delivery/time/defect/incident dependence while enforcing identification, out-of-time, overlap, metric-integrity, effective-sample, quality-harm, NPV, ROI, and payback gates.