Decision Intelligence Consulting That Turns Analytics Into Measured Business Decisions
MinervaDB's decision intelligence consulting practice designs, builds, and operates the systems that convert data into repeatable decisions: metrics trees with one definition per number, predictive and prescriptive models served in production, optimisation and simulation engines, and decision applications whose outcomes are captured and audited. Every layer is built on the same database-grade engineering that runs our clients' transactional and analytical platforms.
Decision Intelligence Built on Database-Grade Engineering
A dashboard describes the past. A decision intelligence system recommends or takes an action, records what happened next, and learns from it. The difference is engineering, not visualisation.
Decision intelligence at MinervaDB treats the decision as the unit of work. Each decision that matters to the business, whether it is a reorder quantity, a price change, a credit limit, a maintenance window, or a marketing spend allocation, is written down as a specification: the context it needs, the model or rule that produces a recommendation, the action it triggers, the outcome that proves it right or wrong, and the latency at which all of that has to happen. That specification is what we engineer, monitor, and report against.
The practice sits on top of our data engineering and analytics platform engineering work and alongside our Data Science & AI consulting. Most decision intelligence programmes fail below the model: a feature computed differently in training and serving, a metric with three definitions in three tools, a recommendation that arrives after the decision window has closed, or an outcome that is never joined back to the action that caused it. Our engineers have operated the databases, warehouses, and streaming platforms beneath these systems for two decades, so those failure modes are designed out rather than discovered in production.
Decision Intelligence Services, End to End
Six engineering domains, delivered as bounded consulting, embedded engineering, or managed decision operations.
Decision Inventory & Diagnostics
A structured inventory of the decisions your organisation makes repeatedly: who makes each one, on what data, at what cadence, with what latency tolerance, and what it costs when it is wrong. We measure the current state from query logs, BI usage telemetry, and operational systems, then rank decisions by value and readiness to be instrumented or automated.
Metrics Trees & the Semantic Layer
Every decision intelligence programme needs one definition per number. We decompose the business outcome into a metrics tree (contribution margin into revenue, cost, and returns; revenue into traffic, conversion, and order value) and implement it in a governed semantic layer (dbt metrics, Cube, or warehouse-native views) so that BI, notebooks, and models compute identical results.
Predictive & Prescriptive Modelling
Demand, churn, propensity, risk, and anomaly models built with point-in-time-correct features, evaluated against the decision they serve rather than against generic accuracy, and delivered with the feature pipelines and serving path required to run them in production. Delivered with our MLOps consulting practice.
Optimisation & Simulation
Linear, integer, and constraint programming for allocation, scheduling, pricing, and inventory decisions; scenario simulation and what-if engines so planners can test a decision before committing to it; and uplift and incrementality measurement so recommendations are judged on causal effect, not correlation.
Decision Applications & Real-Time Serving
Decision APIs and applications served from ClickHouse, PostgreSQL, and Valkey with p95 latency engineered to the decision window, embedded in the tools people already use (ERP, CRM, order management, ticketing) rather than in another dashboard, and instrumented so every recommendation, override, and outcome is captured.
Decision Monitoring & Governance
Decision-level observability: adoption and override rates, outcome lift against a control, feature and prediction drift, data freshness at decision time, and full lineage from source table to recommendation. Access, audit, and explainability controls that satisfy GDPR, India DPDP, HIPAA, and SOX-class obligations.
How Decision Intelligence Closes the Loop Between Data and Outcome
The same five-stage loop underlies every decision intelligence engagement we run. What changes per client is the decision window, the serving engine, and how much of the loop is automated.
Context is assembled from governed data, not exported spreadsheets. Features and metrics are computed once, in the semantic layer or feature store, with point-in-time correctness enforced at the SQL level. If the decision needs data that is 30 seconds old, the CDC and serving path are engineered to deliver it; if daily is enough, we do not pay for streaming.
The decision window is a latency SLO. A replenishment recommendation that arrives after the purchase order has been cut is worthless. Every decision carries an explicit window, and the pipeline, model inference, and serving layer are budgeted against it with the same rigour we apply to a p99 query target.
Outcomes are joined back to actions, always. The most common gap we find in decision intelligence estates is that recommendations are made but never evaluated. We instrument the action system so that every recommendation, whether accepted, overridden, or ignored, is linked to its eventual outcome and to a control group where one exists.
- Decision specifications versioned in a repository alongside the models and SQL that implement them
- Feature and metric definitions shared between training, serving, and reporting to eliminate skew
- Holdouts and control groups designed in from the start so lift is measured causally
- Override capture with reason codes, feeding both governance review and model retraining
- Decision latency budgeted across ingestion, feature computation, inference, and delivery
- Explainability and audit logs retained for every automated decision within regulated scope
The Decision Intelligence Platform We Engineer
Vendor-neutral by principle. We recommend against a platform when it is the wrong fit, including those we support commercially.
Storage and Serving: ClickHouse, PostgreSQL, Valkey
Operational decision intelligence lives or dies on read latency under concurrency. ClickHouse handles the aggregate and time-series context (sort keys, projections, and materialized views chosen from system.query_log); PostgreSQL holds decision state, entitlements, and audit trails with row-level security; Valkey or Redis serves precomputed features and recommendations at sub-millisecond latency. We size and operate all three through our ClickHouse, PostgreSQL, and in-memory practices.
Semantic Layer and Feature Store: dbt, Cube, Feast
Metrics and features are defined once and compiled to the engine in use. Point-in-time joins are written as SQL that can be inspected and tested, not hidden inside a framework. A metric that the CFO sees in the board pack, the analyst reads in a notebook, and the model consumes at inference time is the same metric, from the same definition, with the same filters.
Decision Services: Models, Rules, and Solvers
Predictive models, business rules, and optimisation solvers are deployed as versioned services behind a decision API, each with its own SLO on latency and availability. Rules remain first-class: many high-value decisions are best served by a transparent rule over a governed metric, and we say so rather than fitting a model where none is warranted.
Four Ways to Work With Our Decision Intelligence Team
From a bounded decision audit to fully managed decision operations under a 24×7 SLA.
| Model | Best for | What you receive |
|---|---|---|
| Decision Intelligence Assessment | Organisations with mature BI but stalled analytics adoption, or an AI programme that has not changed a business outcome | Decision inventory, value and readiness ranking, metric-definition conflict report, platform gap analysis, and a sequenced roadmap |
| Decision System Build | A specific high-value decision (pricing, replenishment, credit, maintenance, campaign allocation) to instrument or automate | Decision specification, semantic layer and feature pipelines, model or optimiser, decision API or application, outcome capture, and runbooks |
| Managed Decision Operations | Lean data teams that need 24×7 coverage for the platform, models, and pipelines behind live decisions | SLO-backed monitoring of data freshness, model health, decision latency, and outcome capture; incident response under our severity matrix; monthly decision performance reviews |
| Embedded Decision Engineering | Sustained programmes across many decisions, teams that want capability transfer | Senior analytics and ML engineers integrated with your cadence, with decision specifications and documentation as standing deliverables |
Decision Intelligence Measured by Decisions, Not by Models
A model with excellent offline accuracy that nobody acts on has delivered nothing. We monitor the decision, and the model is one input to it.
Every managed decision carries four SLOs: context freshness (maximum age of the data the decision was made on), decision latency (time from trigger to recommendation delivered), adoption (share of recommendations acted on, with override reasons captured), and outcome capture (share of decisions joined to a measured result). Model metrics such as drift and calibration are monitored as leading indicators of those four.
Incidents follow the same severity model as our database support: a pricing engine returning stale recommendations to a live storefront is an S1 with a 15-minute response target; a nightly replenishment run that completes late is an S2. Every incident closes with a written root-cause analysis. Quarterly, we rehearse the failures that matter to decision intelligence systems: feature pipeline stalls, model registry outages, solver timeouts, and outcome-capture gaps.
- 24×7 monitoring of feature freshness, inference latency, solver runtime, and API error rates
- Prediction and feature drift detection with automatic comparison against the champion model
- Adoption and override dashboards reviewed monthly with decision owners
- Lift measured against holdouts or synthetic controls, reported per decision per period
- Patch, upgrade, and capacity management for the serving and analytics engines
- Runbooks with verification before and validation after every model or rule promotion
Where Decision Intelligence Pays Back Fastest
Decisions that are frequent, costly when wrong, and made on data that already exists in your operational systems.
Retail & E-Commerce
Replenishment and allocation, markdown timing, promotion incrementality, and offer decisioning on a margin-aware metrics tree. See our retail data architecture practice.
Banking, Payments & FinTech
Credit limit and pricing decisions, real-time fraud scoring with human-review routing, collections prioritisation, and liquidity forecasting. See data engineering in banking and FinTech.
SaaS & Technology
Churn intervention, expansion propensity, usage-based pricing, and capacity planning driven by product telemetry. See data engineering for SaaS.
Manufacturing & Supply Chain
Predictive maintenance windows, production scheduling, supplier risk, and inventory optimisation across plants and distribution centres.
Digital Advertising & Media
Budget pacing, bid shading, audience allocation, and creative rotation decided in near real time on impression streams. See our ad-tech data engineering perspective.
Healthcare & Life Sciences
Capacity and staffing decisions, claims triage, and engagement next-best-action under HIPAA-class governance with full decision audit trails.
Why Enterprises Choose MinervaDB for Decision Intelligence
We engineer the whole stack beneath the decision
Most decision intelligence vendors start at the model and stop at the dashboard. Our engineers operate the PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB systems the context comes from and the ClickHouse, Snowflake, BigQuery, and Databricks platforms it is served from, so latency, freshness, and correctness are engineered rather than hoped for.
Vendor-neutral, measurement-driven
We sell no platform licences and earn no referral fees. Every recommendation names the metric, log, or system table that justifies it, and we will tell you when a rule beats a model, when batch beats streaming, and when a decision is not worth automating.
Production posture from day one
Decision systems are treated as mission-critical. Model and rule promotions are staged and reversible, destructive changes carry confirmation gates, and every procedure states its blast radius and rollback path before it runs.
Knowledge transfer by default
Decision specifications, metric definitions, feature logic, and runbooks are documented and handed over. Your team should be able to own what we build; if they choose to have us keep operating it, that is a decision, not a dependency.
"Decision intelligence is not a better chart. It is a decision written down, served on time, acted on, and measured against what actually happened."
— The MinervaDB Analytics Engineering TeamHow a Decision Intelligence Engagement Runs
Discover
Decision inventory, metric-definition audit, measured data freshness and query patterns, latency tolerances, and compliance scope for each decision in range.
Specify & Design
Decision specifications, metrics tree and semantic layer design, feature and outcome-capture design, model or optimiser selection, and a staged build plan with rollback at every phase.
Build & Validate
Pipelines, semantic layer, models and solvers, and decision APIs delivered as code; backtests and shadow runs against production-shaped data; holdout design agreed before go-live.
Operate & Improve
SLO-governed decision operations, monthly outcome and adoption reviews, retraining and recalibration cadence, and knowledge transfer until your team is comfortable owning the system.
Decision Intelligence Consulting: Frequently Asked Questions
What is decision intelligence, and how is it different from business intelligence?
Business intelligence describes what happened through reports and dashboards. Decision intelligence engineers the full loop for a specific, recurring decision: assembling context, producing a recommendation from a model, rule, or optimiser, delivering it inside the decision window, capturing the action taken, and measuring the outcome. BI is usually one input to a decision intelligence system, never the whole of it.
Do we need machine learning to benefit from decision intelligence?
No. Many of the highest-value decisions are best served by a transparent rule or an optimisation model over a correctly defined metric. We recommend machine learning where the decision has enough volume, variance, and labelled outcomes to justify it, and we say so when it does not. The semantic layer, outcome capture, and monitoring are valuable regardless of what produces the recommendation.
Which platforms do you build decision intelligence systems on?
Context and serving: ClickHouse, PostgreSQL, Valkey and Redis, Snowflake, BigQuery, Amazon Redshift, and Databricks. Pipelines: Apache Kafka, Debezium, Apache Airflow, Apache Flink, and dbt. Modelling and optimisation: Python with scikit-learn, XGBoost, LightGBM, PyTorch, OR-Tools, and Pyomo, with MLflow and Feast for registry and features. We are vendor-neutral and work within the estate you already run.
How do you measure whether a decision intelligence system is working?
At the decision level: adoption rate, override rate with reasons, decision latency against its window, and outcome lift against a holdout or control. Model accuracy, drift, and calibration are monitored as leading indicators. We report these per decision per period, and they are the basis for the monthly review with decision owners.
Can decision intelligence run in real time on operational data?
Yes, where the decision window requires it. Log-based CDC from the transactional database into ClickHouse or a feature store, with precomputed features in Valkey, supports sub-second recommendations under high concurrency. Where a daily cadence is enough, we engineer a batch path instead, because streaming costs money and operational attention that should be spent only where the decision needs it.
How does this relate to your Data Science & AI and data engineering services?
Decision intelligence is where those practices meet the business. Data engineering delivers governed, fresh context; Data Science & AI consulting builds the models; MLOps consulting keeps them healthy in production. Decision intelligence adds the specification, the serving path, the outcome capture, and the governance that turn all of that into a measured business result.
Let's Engineer Decisions Your Business Can Measure
Talk to a MinervaDB principal consultant about the decision you most need to get right, faster. The first conversation is always with an engineer, never a salesperson.
Schedule a Consultation → Download the MinervaDB Corporate Flyer (PDF) →Decision Intelligence Resources from MinervaDB
- Data Science & AI Consulting
- Data Engineering Consulting and Managed Pipelines
- Data Analytics Platform Engineering
- Data Strategy and Analytics
- Fractional Chief Data Officer
- Vector Data Engineering
- Cloud Database Optimisation and FinOps
- Decision intelligence (Wikipedia)
- dbt Semantic Layer and metrics documentation
- Google OR-Tools documentation