Services

SERVICES

Senior expertise without building an entire internal team.

Engagements can begin with a narrow analytic question, a dashboard or application build, a research project, an institutional knowledge risk, or a broader need to rethink how data, knowledge, and analytics work inside the organization. The six practices below are deliberately designed to overlap.

Analytics Strategy & Solution Design

Organizations often know something is not working before they know what the right analytics solution should be. I help define the decision, understand stakeholder needs, assess current data and workflows, and design a realistic path forward.

Typical questions

  • What should we measure, and why?
  • Which data do we actually need?
  • Should this become an analysis, dashboard, application, pipeline, or broader system?
  • How do we design something stakeholders will trust and use?
  • What should be automated, standardized, or left intentionally manual?

Capabilities

  • Data & Analytics Strategy
  • Stakeholder Needs Assessment & Solution Design
  • Data Architecture / Analytics System Design
  • Process Improvement & Analytics Modernization

Analysis, Research & Modeling

I design and execute applied analyses intended to support real decisions—not merely produce an interesting result. That can include descriptive and inferential statistics, forecasting and projection work, survey research, program or policy analysis, and structured evaluation.

Typical questions

  • What is happening, for whom, and where?
  • Which factors are associated with the outcome we care about?
  • What should we expect under different assumptions?
  • How can we evaluate a program, policy, workforce, or market question with the data available?
  • What additional data collection would materially improve the answer?

Capabilities

  • Data Analysis & Statistical Modeling
  • Research Design & Applied Research
  • Workforce, Policy & Program Analytics
  • AI-Assisted Research & Analytics

Dashboards & Decision Support

Static reports are sometimes the right answer. Other decisions require an interactive product that lets users explore assumptions, geography, populations, benchmarks, or scenarios. I design the information architecture and build the analytic layer and interface around the decision being supported.

Typical deliverables

  • Executive and operational dashboards
  • Interactive exploratory tools
  • R/Shiny applications
  • Benchmarking tools
  • Scenario and projection explorers
  • Reproducible reports and recurring reporting systems

Capabilities

  • Dashboard & Decision-Support Development
  • R / Shiny Application Development
  • Data Visualization & Reporting

Data Systems & Automation

Many analytics problems are really workflow problems. Repeated manual cleaning, spreadsheet handoffs, inconsistent definitions, and undocumented scripts make otherwise good analysis slow and fragile. I help convert those processes into clearer, reproducible systems.

Typical questions

  • Which steps can be automated safely?
  • How should recurring datasets be stored, validated, and queried?
  • How can we reduce dependence on one person’s institutional memory?
  • What architecture is sufficient without becoming unnecessarily expensive or complex?

Capabilities

  • Data Pipeline & Workflow Automation
  • Data Architecture / Analytics System Design
  • Reproducible analytics and validation workflows
  • Reporting automation and documentation

Institutional Knowledge & AI Systems

Keep critical organizational knowledge from walking out the door.

Organizations accumulate enormous amounts of knowledge about how work actually gets done: procedures, exceptions, workarounds, relationships, decision rules, historical context, reporting requirements, data definitions, and lessons learned. Much of that knowledge is never formally documented.

It lives in the heads of experienced employees, scattered files, email threads, spreadsheets, shared drives, old procedure manuals, and systems that only a few people fully understand. When experienced staff leave, retire, change roles, or become unavailable, organizations can lose years of institutional knowledge.

I help organizations capture that knowledge, turn it into structured and maintainable documentation, and build AI-assisted knowledge systems that make it easier for staff to find and use what the organization already knows.

From undocumented work to institutional memory

This work combines process analysis, knowledge management, documentation, data architecture, and practical AI. The goal is not simply to install an AI chatbot. The goal is to create an organizational memory system that people can trust.

Depending on the organization, that can include:

  • Interviewing subject-matter experts and experienced staff
  • Documenting workflows and business processes
  • Creating and standardizing standard operating procedures (SOPs)
  • Identifying undocumented decision rules, exceptions, and workarounds
  • Inventorying manuals, policies, reports, spreadsheets, shared files, and reference materials
  • Defining terminology, data definitions, roles, responsibilities, and dependencies
  • Mapping relationships between processes, systems, people, policies, data, and information
  • Identifying knowledge concentrated in one person or team
  • Establishing processes for keeping documentation current
  • Building AI-assisted interfaces that let employees search and interact with organizational knowledge

Capabilities

  • Institutional knowledge assessment
  • Workflow discovery and process mapping
  • SOP development and standardization
  • Knowledge-risk and continuity assessment
  • Knowledge architecture and metadata design
  • Searchable internal knowledge bases
  • AI knowledge assistants and semantic search
  • Source-linked retrieval systems
  • Documentation governance and handoff
  • Retirement, succession, and transition knowledge capture

A practical engagement model

1. Knowledge discovery

We begin by understanding where important organizational knowledge currently lives. This can involve staff interviews, review of existing documentation, observation of recurring processes, analysis of shared drives or knowledge repositories, and identification of systems and data sources involved in the work.

The objective is to distinguish between what is already documented, what is outdated, what exists only informally, and what represents a significant continuity risk.

2. Workflow mapping

Important recurring processes are mapped from beginning to end. The objective is to document how the work actually happens—not merely the ideal workflow shown in a policy manual.

Trigger → Inputs → People → Decisions → Systems → Outputs → Exceptions → Handoff

This often exposes duplicated work, undocumented dependencies, manual steps, bottlenecks, and places where only one person knows what to do.

3. SOP and documentation development

Critical workflows are converted into clear, maintainable documentation. Depending on the process, this may include standard operating procedures, process maps, checklists, decision trees, roles and responsibilities, data definitions, system instructions, exception handling, reporting procedures, business rules, source documentation, and historical context explaining why important practices exist.

Documentation is designed for actual use rather than simply satisfying a documentation requirement.

4. Knowledge architecture

The resulting information is organized so that people—and AI systems—can retrieve it reliably. Rather than accumulating another folder full of documents, we establish a structure connecting related information.

Process ↔︎ SOP ↔︎ Role ↔︎ System ↔︎ Data ↔︎ Policy ↔︎ Decision ↔︎ Source

For some organizations, a conventional document repository with strong metadata may be sufficient. More complex environments may benefit from structured databases, knowledge graphs, semantic search, or retrieval systems that preserve relationships between different kinds of organizational knowledge. The technology should fit the problem rather than the other way around.

5. AI-assisted organizational knowledge

Once the underlying knowledge is documented and structured, AI can provide a much more useful interface to it. Employees might ask:

  • How do I prepare the quarterly report?
  • Who is responsible for approving this?
  • What happens if the normal process fails?
  • Where does this number come from?
  • Why do we calculate this measure this way?
  • Which SOP applies to this situation?
  • What systems are affected if this process changes?

The system can retrieve relevant organizational knowledge and point employees back to the underlying documentation and authoritative sources. This is fundamentally different from giving employees a general-purpose AI tool and hoping it understands the organization.

Institutional knowledge at risk

One particularly valuable application is succession and continuity planning. An experienced employee who has performed a function for ten or twenty years may know hundreds of things that were never written down: which exceptions occur regularly, why a spreadsheet is structured a certain way, which data sources can be trusted, what needs to happen before an annual process begins, who needs to be consulted, which shortcuts create problems later, why a policy was interpreted a particular way, and what previous attempts did not work.

A structured knowledge-transfer engagement can capture that information before a retirement, departure, reorganization, or leadership transition. Instead of asking an employee to “write down everything you know,” I use interviews, workflow analysis, document review, and structured knowledge capture to uncover what another person would actually need to perform the work.

Possible deliverables

  • Knowledge inventory: a map of where critical organizational knowledge currently resides
  • Knowledge-risk assessment: identification of functions that depend heavily on undocumented individual knowledge
  • Workflow maps: visual representations of how important organizational processes actually operate
  • Standard operating procedures: consistent, usable documentation for recurring work
  • Knowledge architecture: a structure connecting procedures, policies, systems, data, roles, decisions, and supporting documentation
  • Searchable knowledge base: a centralized repository designed around how employees actually look for information
  • AI knowledge assistant: a secure conversational interface for finding and understanding internal organizational knowledge
  • Source-linked answers: AI responses that point employees back to authoritative internal documentation
  • Documentation governance: ownership, review, change tracking, and maintenance processes
  • Transition knowledge capture: focused documentation and interviews for retiring, departing, or transitioning employees

Where this creates value

This work is particularly useful when a long-tenured employee is preparing to retire, an organization has experienced turnover, important processes depend on one or two people, new employees take too long to learn how things work, staff repeatedly ask the same operational questions, SOPs are missing or outdated, documentation is scattered across systems, processes have evolved informally, leadership is preparing for succession or restructuring, or an organization wants to use AI internally but does not yet have its knowledge organized well enough to do so reliably.

AI comes last, not first.

A chatbot sitting on top of poorly organized information does not solve an institutional knowledge problem. The underlying work is understanding the organization: its people, processes, information, decisions, systems, and history.

Once that foundation exists, AI can make institutional knowledge dramatically easier to access.

Capture the knowledge. Structure it. Document it. Connect it. Then use AI to make it useful.

Talk about an institutional knowledge project

Institutional Knowledge Risk Assessment

For organizations that want a smaller starting point, I offer a focused assessment designed to identify where important knowledge is vulnerable before committing to a larger documentation or technology project.

A typical assessment can include interviews with 4–8 key staff, an inventory of critical workflows and existing documentation, identification of single points of knowledge failure, a risk assessment of processes that depend on undocumented expertise, and a prioritized roadmap for documentation, knowledge transfer, and potential AI-enabled access.

The assessment provides a practical path from knowledge risk → workflow documentation → SOP development → knowledge architecture → AI knowledge system.

Discuss a knowledge risk assessment

Analytics Modernization

Modernization is not simply replacing an old tool with a newer one. It means understanding the current process, identifying friction and risk, choosing the right level of technology, and designing an approach the organization can sustain.

AI can be part of that work when it has a specific job to do—research assistance, classification, summarization, extraction, coding support, knowledge retrieval, or workflow orchestration—rather than being added for its own sake.

Typical engagements

  • Current-state workflow assessment
  • Reporting and analysis process redesign
  • Migration from one-off scripts to reusable analytic frameworks
  • Practical AI integration into research, analytics, and knowledge workflows
  • Documentation, validation, and handoff design

Capabilities

  • Process Improvement & Analytics Modernization
  • AI-Assisted Research & Analytics
  • Data & Analytics Strategy
  • Stakeholder Needs Assessment & Solution Design
FULL CAPABILITY LIST

The sixteen services I offer

  1. Data & Analytics Strategy
  2. Data Analysis & Statistical Modeling
  3. Dashboard & Decision-Support Development
  4. R / Shiny Application Development
  5. Data Visualization & Reporting
  6. Research Design & Applied Research
  7. Workforce, Policy & Program Analytics
  8. Data Pipeline & Workflow Automation
  9. AI-Assisted Research & Analytics
  10. Data Architecture / Analytics System Design
  11. Process Improvement & Analytics Modernization
  12. Stakeholder Needs Assessment & Solution Design
  13. Institutional Knowledge & Continuity Assessment
  14. Workflow Documentation & SOP Development
  15. Knowledge Architecture & Internal Knowledge Bases
  16. AI Knowledge Assistants & Retrieval Systems
FOR SMALL NONPROFITS & COMMUNITY ORGANIZATIONS

Nonprofit Analytics Quick Start

A small, fixed-scope engagement for community organizations that want to get more value from the data they already collect—without beginning with a large technology project or a long consulting commitment.

Many small nonprofits are already collecting useful information through intake forms, spreadsheets, surveys, grant reports, donor or volunteer systems, and program records. The challenge is often deciding what matters, making the process repeatable, and turning those data into something useful for staff, boards, funders, and the communities they serve.

The Quick Start is designed to identify a practical first step and leave the organization with something useful even if we do nothing else together.

A typical Quick Start can include

  • A 60–90 minute stakeholder conversation
  • Review of current spreadsheets, forms, reports, and recurring workflows
  • Review of grant, board, or program reporting needs
  • Identification of 5–10 useful measures or KPIs
  • Assessment of data quality, gaps, and unnecessary manual work
  • One small immediate improvement where practical
  • A concise roadmap for the next 2–3 analytics priorities

Common problems this can address

  • Grant and annual impact reporting
  • Program participation and outcome tracking
  • Board and leadership reporting
  • Survey design and analysis
  • Volunteer or service-utilization reporting
  • Repetitive spreadsheet-based processes
  • Simple dashboards and visualizations
  • Reporting automation

Designed for: small nonprofits, community organizations, associations, and mission-driven teams that need experienced analytics help but do not need a full-time analyst.

Not sure where to start? Take the free, five-minute analytics maturity assessment first — it maps your organization’s current data, measurement, and decision-making practices and points to the right next step.

Talk about a nonprofit project Take the free analytics maturity assessment

Ways to engage

Discuss an engagement