Wren Data Systems · Washington state

We work with the data you have and show you what you didn't know.

Data analysis, warehousing and modernization guidance for government agencies, led by a principal with 25+ years in data.

We work with Washington state and local agencies, ports, transit and higher education, and with prime contractors on system modernizations.

What we do Contact us Capability statement (PDF)

Principal's prior results

  • $500Kin duplicate payments found in the first two days of one AP review
  • 6 statesshown which eligible children their programs were missing
  • 2 millionchildren's records analyzed inside each state's own cloud environment, so the data never left agency control
  • 100M+customers scored by a credit-offer model built at Amazon
What we do

Government data reconciliation and data migration validation.

Agencies and primes call us first for two things: finding what's wrong in the data, and making sure a migration's data is right. The rest is where that work often leads.

Core services

Where most engagements start.

01I need help with my data.

Data analysis and enrichment

Problem
Figures don't reconcile, payments may be duplicated, or the agency can't say who qualifies but isn't served.
You get
A record-level reconciliation with a documented cause for every exception, and counts of eligible people not being served.
Afterward
Recover or correct payments, answer auditors, and target outreach.
Improper payment detectionPayment reconciliation across systemsPayee and vendor matchingCase referrals for investigators and auditors
03Is this migration on track, and what does it mean for my program?

Owner's representative for system migrations

Problem
The integrator says the migration is on track, but the agency can't verify the data.
You get
Readiness reviews, independent conversion test results, reconciliation at each mock conversion, and plain-language briefs on what changes for operations and reports.
Afterward
Make go/no-go decisions on evidence, and hold the integrator to it.
Data migration validationConversion testingIndependent oversight
Featured offer · Services 01 and 03 · 3 to 4 weeks

Go-live readiness check

One Washington Phase 1A replaces AFRS with Workday for core financials, with a go-live range of October 2027 to October 2028. Agency systems that interface with the state's financials need to be ready before cutover.

We inventory a legacy system's data and interfaces, test how its records will convert to the new system, and deliver a reconciliation plan agency staff can execute.

Fixed price under $30,000, within Washington's Direct Buy Level 1 for state agencies and higher education (DES POL-DES-125-03). A discovery-only option is available under $10,000. Counties, cities and ports follow their own purchasing limits; ask us.

Deliverables
  • System and interface inventory
  • Record-level reconciliation test plan
  • Gap list with effort and owner
  • Sequenced remediation plan
What we need from you
  • Read access to the legacy system, or extracts
  • A named interface owner
  • A finance contact
  • A project sponsor who can approve access

If access takes time, we start on the documentation.

Not included
  • Fixing or converting the data
  • Configuring Workday or the new system
  • Ongoing oversight after the check (see service 03)

Where engagements grow

Government data modernization, process, AI and training work that often follows.

02Our data is everywhere. What are our options?

Data warehouse modernization and preparation

Problem
Data spread across systems with no clear path forward.
You get
A data and system inventory, a vendor-neutral options comparison, a target architecture, and migration-ready data (or, for smaller moves, the move itself with reports repointed).
Afterward
Choose an approach and support a budget request.
04This tool isn't working for us.

Workflow assessments and playbooks

Problem
A tool or process isn't working and no one can say why.
You get
A map of how work and data actually flow, a list of breakpoints and what each one costs, and a sequenced playbook with options and requirements.
Afterward
Fix things in the right order, or go to procurement with clear requirements.
05How should we be using AI?

AI readiness and safe adoption

Problem
Pressure to use AI, with no safe way to start.
You get
A playbook for AI inside the agency's own environment, such as Azure AI Foundry, and one small prototype on a real task, with the gain measured.
Afterward
Decide whether to scale it, with evidence.
06Our staff need to actually use the new system.

Technical and data training

Problem
A new system staff can't yet use.
You get
Curriculum, hands-on sessions, materials and train-the-trainer, on SQL, Snowflake, Databricks and other cloud data platforms, reporting and practical AI use.
Afterward
Staff run it themselves.
Where this applies now

Current priorities.

Three areas where agencies are asking for this work today.

  • One Washington readinessAgency systems that interface with state financials need to be ready before the Workday cutover. See the go-live readiness check →
  • Program integrity and payment accuracyImproper payment detection and reconciliation across payment systems, with a documented cause for every exception.
  • Benefits accessFinding people who are eligible for a program but aren't being served.
How we work

Four promises.

Every engagement, large or small, runs on the same terms.

  • Trusted with sensitive dataChild, family, justice and health records handled under strict controls.
  • Patient with accessPlans assume data access takes time, so the work moves without it.
  • Vendor-neutralNo platform partnerships or resale.
  • Principal-ledThe person who scopes the work does the work.

Data reflects the system that produced it.

No agency's data is perfect, and systems do what they were configured to do. When data looks unreliable, we find the cause, whether configuration, operations or the number of systems involved, and recommend the next steps.

Understanding the data comes first.

We assess data gaps, their downstream effects and their costs, and document a plan for improving the agency's most important information.

AI stays inside the agency.

Where AI helps, we use cloud AI services set up within the agency's own environment, under its controls and agreements. Agency data is not sent to public AI tools.

  1. Follow one recordTrace a single case end to end.
  2. AssessFind the gaps, their effects and their costs.
  3. PlaybookWrite the plan, in the right order.
  4. PrototypeShow the result, then hand it to agency staff.
Working with primes

A data specialist inside larger modernizations.

Short answers to what primes ask before teaming. Turnaround times for teaming documents are on the contracting page.

Where we fit
Data analysis, conversion testing and reconciliation inside a larger modernization. We work under the prime's terms and flow-downs.
Advisory or implementation?
Both. We assess and plan, and we do hands-on data work: reconciliation, migration prep, and smaller moves end to end. We don't configure the vendor platform.
Why us instead of internal staff?
A specialist who has done record-level tracing in banking (BSA/AML, SARs) and in state early childhood data systems across six states, who can start fast and bring small business subcontracting credit.
Scale
Principal-led and deliberately bounded. We take on scopes one principal can deliver well, and say so up front when a scope is bigger than that.
After the engagement
The goal is to leave the agency or its integrator able to carry on. Ongoing oversight is available as service 03.
Signature work · Tail-end analysis

The exceptions carry the risk.

Behind service 01 is one method: we account for every record and concentrate on the exceptions, where duplicate payments, fraud, missed services and records lost between systems turn up.

Who we are

Principal-led and independent.

Wren Data Systems has no platform partnerships. The principal who scopes an engagement does the work.

Wren Data Systems is led by Jon Wren, who has spent more than 25 years in data analysis, engineering and warehousing, from bank payments to state agencies.

The first decade was in banking: Wells Fargo, then Capital One, running the data team for Capital One Payments, reviewing suspicious activity and filing SARs under BSA/AML, and building credit risk models that scored 70 million households. At Amazon, he built a credit-offer model that scored more than 100 million customers. After ad tech and SaaS data work at Cardlytics and Moz, he spent seven years at Third Sector Intelligence building integrated early childhood data systems for state agencies.

Experience at a glance
    Stacked area chart of career focus from 1997 to 2026: mergers and acquisitions at Wells Fargo first, then operations and segmentation, then payments and risk, then ad tech, warehousing and platforms alongside machine learning, then public sector data, and most recently compliance and security alongside fraud and applied AI.
    Contracting

    Buy direct (under $30,000), subcontract through a prime, or use a commercial agreement. How to contract with us →

    Capability statement (PDF)
    Contact

    Schedule a 30-minute consultation.

    Agencies and prime contractors are both welcome. Describe the data, the decisions it supports and your concerns, and we will tell you directly whether we can help.

    We reply within one business day.

    info@wrendatasystems.com
    Woodinville, Washington. Serving state and local government agencies.
    (425) 202-5164Jon Wren, Owner
    Download capability statement (PDF)
    Wrenegade LLC dba Wren Data Systems · UEI Q8HWDGVMTQH3 · WA UBI 605 644 316 · WEBS registered · Small business, self-certified · SAM.gov registration in progress
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    • 541611 Administrative and General Management Consulting
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    Tail-end analysis

    Exceptions carry the risk.

    Most data work is designed for the typical case. Typical cases rarely involve fraud, missed services or records lost between agencies; exceptions do. That is where we concentrate our work.

    A skewed distribution of records. The large bulk on the left is standard reporting territory. The thin tail on the right is where Wren Data Systems works. The majority: well served by standard reporting The tail: where we start

    Enterprise data platforms are designed to store and serve the bulk of an agency's data. Our role is analysis.

    We work within the agency's existing systems and answer three questions about exception records: what they are, why they differ, and how they should be resolved.

    • Suspicious activity
    • Fraud
    • Anomalies
    • The vendor in two systems
    • Records that fall between systems