Analytics that drive federal decisions.

Why federal analytics is hard

Federal analytic work looks deceptively similar to commercial BI. A SQL query is a SQL query, a dashboard is a dashboard. The gap opens once you account for the constraints that commercial teams never think about: Privacy Act records, Paperwork Reduction Act limits on data collection, FISMA authorization boundaries, Section 508 accessibility, Evidence Act evaluation requirements, and agency-specific handling rules for Controlled Unclassified Information. A dashboard that works for a consumer startup will fail an ATO the first time it tries to leave a FedRAMP-Moderate boundary or display PII to an analyst without an approved need-to-know.

Precision Federal builds analytics that pass these filters on day one. We design the data model, the authorization boundary, the access controls, and the downstream consumers together so the analytic output is not just accurate — it is legally and operationally usable. That means an auditor can trace every rendered number back to its authoritative source, a program office can defend the methodology in front of Congress, and an inspector general can reproduce the result without privileged access.

What we build for federal analytics

• Mission KPI dashboards — executive rollups over operational, financial, and performance data. Drill-downs to case, line-item, or record level with proper authorization checks.
• Program performance analytics — GPRA Modernization Act and Evidence Act-aligned measurement frameworks for grant programs, service delivery, and regulatory activity.
• Public health and epidemiological analytics — surveillance dashboards, outbreak clustering, treatment capacity analytics. Our founder shipped production SAMHSA workloads on behavioral health treatment data.
• Force readiness and logistics analytics — pursuing Army, Navy, and Air Force opportunities where sustainment, depot throughput, and personnel readiness need continuous visibility.
• Grants, awards, and contract analytics — USAspending.gov-integrated views, duplicate-detection, eligibility drift, improper payment risk scoring.
• Fraud, waste, and abuse detection — anomaly detection at scale with explainable scoring that OIG investigators can triage.
• Regulatory analytics — enforcement triage, inspection scheduling, risk-based resource allocation.
• Budget and financial execution — Treasury-aligned accounting analytics, obligation tracking, burn-rate forecasting.

The four tiers of federal analytics

Most agencies live in descriptive land — counting things that already happened, in a report that was printed last Tuesday. Moving up the stack is worth real dollars in decision quality, but each tier adds engineering, validation, and governance burden.

• Descriptive — what happened. Dashboards, scorecards, regulatory reports. The validation standard here is reconciliation to authoritative systems of record.
• Diagnostic — why it happened. Root-cause analytics, cohort drilldowns, statistical decomposition. The validation standard here is reproducibility across independent analysts.
• Predictive — what will happen. Time-series forecasts, classification of high-risk cases, demand prediction. The validation standard here is out-of-sample performance, bias testing, and model cards documenting known limits.
• Prescriptive — what should we do. Optimization, decision-support, scenario planning. The validation standard here is decision-outcome tracking and human-in-the-loop governance.

We do not sell tier-four capability when a tier-two fix will solve the mission problem. Moving prematurely up the stack is how federal analytic programs die — too much model complexity, too little trust, and the sponsor defects back to spreadsheets.

The analytics stack we actually deploy

• Warehouse & lake: Snowflake Government, Redshift, BigQuery (Assured Workloads), Synapse (Azure Gov), Databricks on GovCloud. See our data warehousing and data lakes pages for the platform-level detail.
• Semantic layer: dbt, Cube, LookML, Malloy. One version of truth per metric, versioned in Git.
• BI & visualization: Tableau (Server for Government), Power BI (GCC / GCC High), Looker, Superset, Qlik, Grafana. We meet agencies where they are rather than forcing a tool switch. See business intelligence.
• Statistical & ML: Python, R, scikit-learn, PyTorch, Prophet, statsmodels, XGBoost. For predictive tiers with full model governance.
• Notebook environments: JupyterHub on EKS, Databricks, SageMaker Studio — governed, logged, CUI-aware.
• Lineage & catalog: OpenLineage, DataHub, Atlan, Collibra. Every metric traceable to source.

Case examples and patterns

Beyond confirmed past performance, Precision Federal is pursuing analytic scopes across multiple agencies. We are targeting Army and Navy SBIR topics around readiness analytics, FBI BAA topics around investigative analytics, NSF EAGER-style exploratory data work, and cross-agency Evidence Act evaluation support. See our SAMHSA agency page and Army agency page for opportunity-specific detail.

Analytic governance, the way federal buyers need it

A dashboard is a policy artifact the moment it shows up in a Congressional testimony binder. That reality shapes how we build. Every analytic deliverable ships with:

• Data dictionaries — every field, every source, every transformation documented for non-engineers.
• Metric definitions in Git — so a change in the definition of "active case" produces a reviewable pull request, not a silent number shift.
• Lineage diagrams — automatic, column-level, auditable. When the Inspector General asks where a number came from, the answer is a clickable diagram.
• Reproducibility bundles — source data hashes, transformation version, query text, rendering timestamp. Anyone can re-run any published number.
• Access logs — who saw which slice of data, when, and for what purpose. Standard input to 800-53 AU controls.
• Accessibility conformance — Section 508 / WCAG 2.1 AA. Every chart has a keyboard path, every color has sufficient contrast, every table has screen-reader semantics.

For deeper treatment of the governance stack, see data governance.

How an analytic engagement runs

1. Mission framing (week 1-2): we interview the decision-maker, the analyst, and the data steward. We document the decision the analytics is supposed to enable, not the dashboard the stakeholder asked for. The two are often different.
2. Data discovery (week 2-4): source systems, field-level profiling, quality baseline, lineage of current reporting. We find the gap between what the agency thinks it has and what is actually in the database.
3. Semantic modeling (week 4-6): metric definitions in Git, dbt project scaffolding, test coverage for every metric, documentation generation. This is where we prevent the "five dashboards, five different answers" problem.
4. Delivery layer (week 6-10): dashboards, notebooks, or API endpoints in the target tool. Section 508 conformance testing. User acceptance with the actual decision-maker, not a proxy.
5. Governance transfer (week 10-12): runbooks, training, monitoring, handoff. The agency owns the output on day one of production.

What we will not do

We will not build a dashboard no one asked for, because no one will use it. We will not sell a predictive model when a descriptive dashboard solves the mission problem. We will not hide in notebooks when production requires a governed pipeline. We will not bolt on analytics without fixing the upstream data problem first — see data engineering and ETL / ELT for that work.

Why Precision Federal

Founder Bo Peng is a Kaggle Top 200 competitor with confirmed production federal analytics past performance at SAMHSA. Precision Delivery Federal LLC is SAM.gov-registered, NAICS 541512, UEI Y2JVCZXT9HP5. We are small, senior, and direct — the analytic work is done by the same person who scoped it, not handed to an offshore pool. Read our federal analytics playbook and recent Evidence Act analytics insight piece.